AI Adoption and Usage

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We add new article links daily.

The following are links to articles and posts that talk about the adoption and usage of AI and ChatGPT.  The links are in the most recent date order

AiThority - How to close the 1,600% enterprise application visibility gap – and what it could mean for productivity

Despite the integration of AI tools designed to boost productivity and streamline workflows, digital inefficiencies are costing companies a lot more than one might think. WalkMe’s recently released 2025 State of Digital Adoption Report: Special AI Edition revealed that enterprises wasted an average of $104 million on underused technology in the last year alone. The report offers a comprehensive look at how enterprises are navigating digital adoption and Gen AI integration – and where critical gaps remain.

The study surveyed close to

Information Week - Nailing the Initiative: LexisNexis Leverages Agentic AI

Quick to find uses for AI after the rise of ChatGPT, Jeff Reihl, executive vice president and CTO for the legal and professional side of LexisNexis, says his company shifted gears on certain projects. In some instances, it even meant putting some efforts on hold indefinitely in favor of a new, AI-driven path.

Forbes - Beyond The Hype: Confronting And Conquering AI Adoption Challenges

A major hurdle in AI adoption is the lack of AI and data literacy across teams. It’s not just about understanding AI—it’s about applying it effectively. Product managers may misinterpret insights, leading to poor decisions, while engineers might struggle with biases and model limitations, making AI unreliable. Many employees either distrust AI or fail to integrate it into workflows. Without training, AI remains a black box. Companies must invest in role-specific training—helping engineers fine-tune models, business leaders make data-driven decisions and teams collaborate with AI effectively. The goal isn’t AI expertise for all but ensuring confidence in AI-driven work.

High ROI Data Science Substack - How Do You Measure AI Initiative Success? Painting the Monetization Picture with Quantitative Measures

Revenue, margins, KPIs, and more granular use case metrics are essential to justify AI spending, prevent budget cuts, and demonstrate ROI. By tracking the right quantitative measures, businesses can shift the perception of their data and AI organizations from cost centers to revenue generators. In this article, I will explore quantitative measures that provide a clear picture of AI project success.

Computerworld - Analysts: Go slow on M365, Google Workspace ‘agent-ish’ AI rollouts

Microsoft Copilot Agents are good for tapping into specific data sources — such as a SharePoint site — to ensure that the right data sources are being queried, Gownder said. “It’s helpful for a sort of RAG (retrieval augmented generation) model of computing. But these aren’t complex agents,” he said.

AiThority - The Evolution of Data Engineering: Making Data AI-Ready

Gartner says that nearly one-third of GenAI projects will be abandoned after an initial proof of concept, with reasons including poor data quality, inadequate risk control, and skyrocketing costs. How do organizations navigate this in the face of mounting pressure to not only utilize AI but also excel at it to build a competitive advantage?

Network World - AI agents vs. agentic AI: What do enterprises want?

Enterprises have told me from the start that cloud-hosted generative AI based on large language models isn’t going to transform their business operation. They were very hopeful when the concept of AI agents came along, because it seemed to align with their own AI thinking. Now that this AI agent story has morphed into “agentic AI,” it seems to have taken on the same big-cloud-AI flavor that they already rejected. What do enterprises want from AI agents, why is “agentic” thinking wrong, and where is this all headed?

Forbes - Enterprise AI Is Headed Toward Autonomy, Says NTT Data’s AI Chief

Collins was, however, candid about the current limits. In industries like insurance and financial services, agentic AI is already reducing cost and latency in call centers and procurement. These domains work because the processes are predictable and well-documented. If a task can be reduced to rules and data flows, it can be delegated to an agent.

Pascal's Substack - Many AI projects failed not because the tech didn't work, but because the problem didn’t require AI in the first place. Simple heuristics or business logic could have solved the issue faster...

The failure rate for AI projects is estimated at 80%, double that of traditional IT projects—a staggering statistic that underscores systemic issues in AI implementation​.

Information Week - Balancing AI’s Promise and Complexities: 6 Takeaways for Tech Leaders

The challenge lies not only in adopting AI but also in fostering an environment where innovation thrives. This requires rethinking organizational structures, embracing cross-functional collaboration, and cultivating a culture of continuous improvement.

Computerworld - Discover Financial Services exec: GenAI isn’t yet ready for prime time

“It’s going to be some time before we get AI to the place where we’re making our existing employees so much more efficient. We’re attacking the backlog right now; adding more features and capabilities and other things to our products and services before we go to the place where we’re just looking to cut costs and reduce jobs. I just think that’s the reality of it.

Information Week - The End of Business as Usual: How AI-Native Companies Win

An executive mindset shift -- or mindshift -- is needed to not only reimagine businesses forward, but to also prepare workers for roles that don’t yet exist. Seismic shifts lie ahead: artificial intelligence will reshape 86% of businesses by 2030, according to a new World Economic Forum (WEF) report. That same report also predicts that AI and automation will create 170 million jobs, while displacing 92 million roles as companies adapt to technological change; 39% of existing skill sets will become outdated between 2025-2030.

ZDNet - How Salesforce's 5-level framework for AI agents finally cuts through the hype

"While agents can be deployed quickly, scaling them effectively across the business requires a thoughtful, phased approach," says Shibani Ahuja, SVP of Enterprise IT Strategy at Salesforce. "Understanding the progression of Al agent capabilities is crucial for long-term success, and this framework provides a clear roadmap to help organizations move toward higher levels of AI maturity."

Diginomica - Adding a journey builder for marketers - how HubSpot is building depth into its AI agents

Holland says HubSpot has customers reporting a 90% resolution rate with the Customer Agent, and wondering how they did it. What these companies did was spend time capturing all the answers and knowledge needed to train the agent. So, HubSpot built a Knowledge Base Agent that does the same thing.

Computerworld - Atlassian gathers its apps into collections to bolster productivity

The company also announced the Rovo Studio App, which is “a single place where customers can create agents, automations, assets, hubs, and other building tools that are accelerated through AI,”

ZDNet - AI will change the trades too - and field service technicians can't wait

Eighty percent of technicians reported that AI agents would allow them to focus on the more fulfilling aspects of their jobs that led them to their professions in the first place. Technicians estimate that AI agents could take 35% of their administrative tasks off their plate entirely -- a savings of over two hours per employee, per standard 40-hour work week.

InfoWorld - Why is cloud-based AI so hard?

The statistics tell a sobering story: Gartner estimates that 85% of AI implementations fail to meet expectations or aren’t completed. I consistently witness projects begin with great fanfare, only to fade into obscurity quietly. Companies excel at spending money but struggle to build and deploy AI effectively.

AiThority - The Next AI Gold Rush? Revenue Leaders Are Betting Big on Predictability

One of the biggest challenges to successful AI implementation is disparate data sources and fragmented data. For too long, revenue teams have operated in disconnected systems, where CRM, ERP, demand and marketing automation systems, cloud data warehouses, and custom applications create blind spots. Without a unified data strategy and foundation, businesses struggle with incomplete insights which inevitably lead to missed opportunities and unpredictable revenue performance.

VentureBeat - Wells Fargo’s AI assistant just crossed 245 million interactions – no human handoffs, no sensitive data exposed

The system works through a privacy-first pipeline. A customer interacts via the app, where speech is transcribed locally with a speech-to-text model. That text is then scrubbed and tokenized by Wells Fargo’s internal systems, including a small language model (SLM) for personally identifiable information (PII) detection. Only then is a call made to Google’s Flash 2.0 model to extract the user’s intent and relevant entities. No sensitive data ever reaches the model.

Praescivi Substack - Racing Into the AI Future: The Hidden Cost of Superficial Strategies

From most of my discussions on AI strategy with executives and senior leadership, I have often come out underwhelmed. There has been a curious sense of unbridled enthusiasm combined with a bizarre complacency when it comes to AI development, adoption, and integration into their respective businesses. It’s difficult to blame CEOs and leaders of AI startups for being excessive hype sellers if their environments (investors, clients, the public, and stakeholders/counterparts) are all vying for the hype, even demanding it.

Computerworld - How enterprise IT can protect itself from genAI unreliability

The AI-watching-AI approach is scarier, although a lot of enterprises are giving it a go. Some are looking to push any liability down the road by partnering with others to do their genAI calculations for them. Still others are looking to pay third-parties to come in and try and improve their genAI accuracy. The phrase “throwing good money after bad” immediately comes to mind.

VentureBeat - From AI agent hype to practicality: Why enterprises must consider fit over flash

If every employee leaves the workday with a long list of to-dos for the next day and another list of to-dos to deprioritize altogether — items that would have created value if they could have been prioritized — there is an imbalance of value, time and effort, leaving value on the table.

Diginomica - Trust me on this - Cisco's Privacy Benchmark Study exposes alarming contradictions around AI adoption and data privacy practices

But here’s another interesting seeming contradiction - while compliance with regulation is seen as a burden by enterprises, it’s also seen as an enabler of trust, and that’s a crucial driver in AI adoption as has been acknowledged by every vendor worth its salt. There is uniform agreement across the 12 countries polled for the Cisco study that privacy laws do have an overwhelmingly positive impact - 94% agreement vs three percent against in India, 88% vs one percent in China, 95% v one percent in Brazil and so on.

SiliconANGLE - Stanford HAI’s annual report highlights rapid adoption and growing accessibility of powerful AI systems

Globally, there remains a significant gap in terms of education. While two-thirds of the world’s nations now offer, or plan to offer computer science education to K-12 students, access remains limited in areas like Africa because of a lack of basic infrastructure such as electricity. Moreover, there are still challenges in terms of teaching basic AI skills In the U.S., 81% of teachers say they believe AI should be a foundational element of computer science education, but less than half say they’re able to teach it

VentureBeat - AI lie detector: How HallOumi’s open-source approach to hallucination could unlock enterprise AI adoption

“What HallOumi does is analyze every single sentence independently,” Koukoumidis explained. “For each sentence it analyzes, it tells you the specific sentences in the input document that you should check, so you don’t need to read the whole document to verify if what the [large language model] LLM said is accurate or not.”

Forbes - Why This CEO Believes AI Failures Will Dominate The Headlines In 2025

“There’s a difference between failed implementations and use cases that never even go live,” he said. “We hear about it when a live AI agent makes a critical mistake, but what about the thousands of enterprises that are left with nothing after vendors fail to deliver even a working solution?”

CIO - From concept to reality: A practical guide to agentic AI deployment

AI teams face many complex challenges as they work to develop and scale their agentic AI systems. The traditional approaches for implementing AI have focused on AI chatbots, single declarative agents or synchronous processes. As the industry moves to take advantage of more complex solutions, we are seeing multi-agent architectures and dynamic workflows orchestrating agents to perform tasks. These more complex solutions require more focus and thought on the activities needed for deployment and operations.

The Hacker News - AI Adoption in the Enterprise: Breaking Through the Security and Compliance Gridlock

Imagine this all-too-familiar scenario: A CISO wants to deploy an AI-driven SOC to handle the overwhelming volume of security alerts and potential attacks. Before the project can begin, it must pass through layers of GRC (governance, risk, and compliance) approval, legal reviews, and funding hurdles. This gridlock delays innovation, leaving organizations without the benefits of an AI-powered SOC while cybercriminals keep advancing.

AiThority - Language I/O Report Finds that Half of All Organizations Expect AI to Improve Global Customer Communication

According to new research from Language I/O, a global leader in AI translation solutions, most business leaders now see real-time translation as their most urgent AI need. The 2025 global study reveals that 54% of enterprises rank translation technology as their top AI priority, as companies struggle with accuracy, security and growing demand for seamless communication across languages.

Diginomica - Why Accenture expects agentic AI to reveal business value of gen AI

Many organizations still view gen AI as a technology to deploy rather than a catalyst to think differently about talent. Only 35% of execs in Accenture’s research reported having a roadmap for how gen AI will reshape their workforce. Yet, this is already a technology where adoption is known to be a people issue – the public perception is that AI can both improve productivity and also displace human jobs.

VentureBeat - How Amex uses AI to increase efficiency: 40% fewer IT escalations, 85% travel assistance boost

The IT chatbot is just one of Amex’s many AI successes. The company has no shortage of opportunities: In fact, a dedicated council initially identified 500 potential use cases across the business, whittling that down to 70 now in various stages of implementation.

AiThority - 84% of Tech Leaders Say AI Won’t Replace Low-Code and No-Code Tools

“It’s clear low-code and no-code tools are here to stay. As companies incorporate AI and other new technologies into low-code and no-code development, tech leaders must not only leverage the benefits of automating more app development processes faster, improving collaboration and increasing productivity, but ensure their organizations and development teams continue to evolve and upskill with their technology,”

Diginomica - Qualtrics Product Chief, Brad Anderson - 'The good old fashioned survey' is a smart place to start with agentic AI

The key point here is how Qualtrics sees that it has an ideal entry point for agentic AI via its survey tooling, which could be used to 'close the loop' for customers. Qualtrics believes that a strategy of making incremental yet highly impactful agentic add-ons, at least initially, could be a quicker path to proving value for customers - rather than promising that agentic AI will be the solution to everything.

Forethought Substack - The AI Adoption Gap: Preparing the US Government for Advanced AI

The US federal government is falling behind the private sector on AI adoption. As AI improves, a growing gap would leave the government unable to effectively respond to AI-driven existential challenges and threaten the legitimacy of its democratic institutions.

Forbes - What AI Product Managers Can Learn From Upwork — And Beyond

The lesson: Product managers need to architect AI products for adaptability, not permanence. Your competitive edge won’t come from locking in today’s solution but from how quickly you can swap it out when something better emerges.

Network World - Linux Foundation Networking shares new AI projects, milestone releases

“Salus is a framework that brings in AI guardrails on top of the data and models, which ensures enhanced security, data privacy and traceability and prevents sensitive issues like biases,” explained Joshipura. “For networks, this is relevant because it’s becoming clearer that AI for networks needs centralized and uniform data and model strategy.”

Forbes - CIO-CFO Tensions Jeopardize AI Success, Study Finds

A central finding that should top board and executive agendas is an untenable AI leadership rhetoric-reality gap. Despite espousing unified vision, tech and finance chiefs stake nearly identical claims to AI plans. Specifically, 59% of CFOs and 61% of CIOs assert they're in charge — with only about a third of each group characterizing rollouts as shared.

VentureBeat - Gartner forecasts gen AI spending to hit $644B in 2025: What it means for enterprise IT leaders

Gartner’s report highlights a sobering reality: many internal gen AI proof-of-concept (PoC) projects have failed to deliver expected results. This has created what Lovelock calls a “paradox” where expectations are declining despite massive investment.

ZDNet - Home Innovation Artificial Intelligence 4 ways you can start using gen AI to its full potential

Personal productivity is a ripe area for AI, both as co-pilot and co-thinker, Farri and Rosani explain. In its co-pilot role, AI can assist with routine tasks such as email or summarizing documents. As co-thinker, though, it opens up new avenues, "to improve the depth of your self-reflection and thinking," they state. "It can help you review feedback you receive, analyze areas for improvement, and act on them for your growth."

Information Week - What You Should Know About Agentic AI

Initial agentic Ai adopters will likely be enterprises looking to maximize their AI investments, boost productivity, and tackle complex business challenges, predicts Lan Guan, chief AI officer at business advisory firm Accenture. "These organizations are particularly interested in solutions that can scale across multiple functions and operate with minimal human oversight," she notes via email.

VentureBeat - Credit where credit’s due: Inside Experian’s AI framework that’s changing financial access

Experian has developed its own internal processes, frameworks and governance models that have helped it test out generative AI, deploy it at scale and have an impact. The company’s journey has helped to transform operations from a traditional credit bureau into a sophisticated AI-powered platform company. Its approach—blending advanced machine learning (ML), agentic AI architectures and grassroots innovation—has improved business operations and expanded financial access to an estimated 26 million Americans.

Forbes - Predictive AI Too Hard To Use? GenAI Makes It Easy

But to date, predictive AI has achieved only a fraction of its great potential because it is hard to use. Anyone can use genAI, since it’s trained to respond to human-language prompts, but predictive AI isn't readily accessible to business users in general. To use it, a business professional needs the assistance of data scientists as well as a semi-technical understanding of how ML models improve operations. Since this understanding is generally lacking, most predictive AI projects fail to deploy – even when there are data scientists on hand.

VentureBeat - Why businesses judge AI like humans — and what that means for adoption

When software begins to look and act human, users stop evaluating it as a tool and begin judging it as a human being. This phenomenon — judging non-human entities by human standards — is anthropomorphism, which has been well-studied in human-pet relationships, and is now emerging in the human-AI relationship.

Forbes - Appian CEO: Defining Value From AI In Business Processes

“The key to unlocking AI’s full potential is embedding it inside a business process. The process zone is where business happens. It’s where companies make decisions, save and spend money, serve customers and scale business operations,” said Calkins. “When AI operates within processes, it gains purpose, governance and accountability - all factors that are essential to delivering business value from AI.”

AiThority - Applause 2025 AI Survey: Failure to Prioritize Testing and Embed Gen AI in Development at Odds With Increasing AI Investments

Yet, 23% of software professionals say their integrated development environment (IDE) lacks embedded Gen AI tools (e.g., GitHub Copilot, OpenAI Codex), 16% aren’t sure if the tools are integrated with their IDE, and 5% have no IDE.

The AI Strategy Navigator Substack - Measuring AI's True Value

Even more striking, this week's Exponential View column reported that teams using AI as a team member are three times more likely to produce top-tier solutions than traditional specialists working without AI.

Forbes - The Other Side Of AI: AI At The Edge Is Quietly Powering The Future

From oil rigs to outer space, a new frontier of edge-based AI is enabling organizations to operate smarter, safer, and with fewer resources, in order to detect gas leaks, prevent equipment failure, monitor hazardous zones, and ensure worker safety—without ever needing to send data back to the cloud.

Diginomica - Are we entering a new AI winter? And if so, why might the public mood be cooling?

That hardly breeds trust, and trust is what the AI sector needs to succeed in the long term. Sure, countless ‘me too’ enterprise users – 95% of them by some estimates – are buying the AI hammer then looking for the nail, desperate to find ways to cut internal costs and boost productivity. But that is not evidence of a sustained AI Summer. Survey after survey over the past three years has said the same thing: users want AI to cut costs, not make the organization smarter. And if it doesn’t?

Fast Company - 31% of employees are actively ‘sabotaging’ AI efforts. Here’s why

According to a new study by generative AI platform Writer, 31% of employees—including 41% of Gen Z workers—admit to “sabotaging” their company’s AI strategy by refusing to adopt AI tools and applications. As a result, roughly two-third of executives say Generative AI adoption has led to tension and division within their organization, with 42% suggesting it’s “tearing their company apart.”

VentureBeat - From alerts to autonomy: How leading SOCs use AI copilots to fight signal overload and staffing shortfalls

The latest generation of copilots has moved far beyond chat interfaces. These agentic AI systems are capable of real-time remediation, automated policy enforcement and integrated triage across cloud, endpoint and network domains. Purpose-built to integrate within SIEM, SOAR and XDR pipelines, they’re making solid contributions to improving SOC accuracy, efficiency and speed of response.

ZDNet - Home Business Retailers say agentic AI is the best way to boost customer sales

AI agents greatly expand AI's impact by independently responding to customer inquiries, managing inventory, and more. Seventy-five percent of retailers say AI agents will be essential for a competitive edge by 2026. Retailers view leveraging AI as their number one opportunity.

Forbes - How Predictive AI Will Solve GenAI's Deadly Reliability Problem

In such a scenario, the system employs an (expensive) human 15% of the time but achieves 85% of genAI’s promise of autonomy. That is, 85% of the time, genAI does its thing with no human in the loop – a vast improvement over an unworkable genAI system that cannot be deployed.

VentureBeat - ‘Gradually then suddenly’: Is AI job displacement following this pattern?

While employment impacts from AI are (so far) nascent, that is not true of AI adoption. In a new survey by McKinsey, 78% of respondents said their organizations use AI in at least one business function, up more than 40% from 2023. Other research found that 74% of enterprise C-suite executives are now more confident in AI for business advice than colleagues or friends. The research also revealed that 38% trust AI to make business decisions for them, while 44% defer to AI reasoning over their own insights.

Forbes - AI Writing Is Now Widespread Online — New Research Explores Its Impact

This growing occurrence is indicative of both the potential and danger of AI writing. On the upside, LLMs bring efficiency as professionals are able to produce content more quickly. On the downside, overuse of AI would make communication homogenous and destroy confidence in authenticity.

Computerworld - Evaluating AI agents? Early adopters outline practical challenges

“There’s a point at which we have to trust the model works,” Daniels said. “It generates novel insights and we’ve researched and we trust that and we’re using it with the human to make the final decision. That’s a wonderful advancement.”

Forbes - AI In Healthcare: Why Your Organization Is Not Ready (And How To Fix It)

1. Things You Can Implement On Your Own (But Haven’t): Simple, low-risk AI tools, like automated reporting dashboards or internal chatbots, can often be deployed without waiting for leadership approval. The question is: Have you started? Basic training in granular but powerful topics like these can make a big difference.

ZDNet - Most US workers don't use AI at work yet. This study suggests a reason why

According to a Pew Research study released this week, around 80% of Americans don't generally use AI at work, while those who do use AI seem unenthusiastic about its benefits. Moreover, fewer than one-third of those surveyed said they're "excited" about using AI in future workplaces. Only 6% of workers say workplace AI use will lead to more job opportunities in the long run.

Forbes - State Of GenAI – Only 1% – ShopTalk 2025 In Numbers

AI is #1 – it's the most mentioned technology: 256 out of 683 vendors — roughly 40% — claim to offer AI capabilities. Well, AI has been around since the 1960s, thus... yes – 40% was less than I expected. Analytics, workflow automation, and data management still dominate.

Diginomica - Adobe Summit 2025 - a week when agentic AI took center stage. Here's why

As companies start to dive into using agents and tech providers build agents on top of their products and platforms, it’s the interoperability that I haven’t seen much of yet. That’s partly because the tech providers are still trying to figure out how to create agents that work within their own systems. And some are creating multiple agents that don’t talk to each other. Trying to figure out how to design them to talk with agents outside their ecosystem is a bigger, broader initiative.

Forbes - The Commodification Of AI: How Reduced LLM Costs Are Reshaping Healthcare And Beyond

AI is now at this same inflection point. The true competitive advantage no longer lies in merely developing AI models but in applying them effectively—mirroring the transformative shifts seen in electricity, global logistics, cloud computing and other foundational technologies.

Microsoft - AI for everyone: Transforming business at every stage of the journey

Accenture is one example, its developers now retain over 88% of the code suggested by GitHub Copilot as part of their solutions. They recognized an 84% increase in successful builds with Copilot’s help, and 67% used the tool at least five days a week. This external study contains the Accenture stat: randomized controlled study with Accenture, developers retained 88% of the code suggested by Copilot, recognized an 84% increase in successful builds with Copilot’s help, and 67% used the tool at least five days a week.

AI Simplified for Leaders Substack - #29 AI Use in the Boardroom: Bridging Hesitation and Opportunity

As many of you are business leaders and board directors, I've decided to focus on AI use in the boardroom this time, a very popular topic in my recent conversations. While legal advisors and compliance teams often tell directors "not yet" regarding AI use in board work, I thought it's time to explore what's actually happening and why some remain hesitant. And even more interestingly, what it means for the art of governance. It's remarkable how quickly perspectives have shifted—just six months ago, many directors told me AI wasn't relevant to their roles!

VentureBeat - Small models as paralegals: LexisNexis distills models to build AI assistant

When a user asks Protégé a question about a specific case, the first model it pings is a fine-tuned Mistral “for assessing the query, then determining what the purpose and intent of that query is” before switching to the model best suited to complete the task. Riehl said the next model could be an LLM that generates new queries for the search engine or another model that summarizes results.

ZDNet - 60% of C-suite execs are actively seeking new roles at AI-forward companies

Around 93% of the executives and 90% of the employees said that they're optimistic about their company's approach to generative AI. The survey respondents also pointed to several reasons why their employer has adopted AI. Revenue opportunity was the top reason, followed by employee interest, and then competitive pressure. Customer demand, a solution to a pain point, and executive ultimatum also made the list.

AiThority - How Small, Specialized Language Models Can Outperform the AI Giants

For example, conversations with your department heads could reveal that customer service teams struggle with error-prone tasks, such as managing high volumes of repetitive inquiries or pulling data from incoming orders. In this scenario, an LLM might generate inconsistent outputs or require extensive training to perform effectively, negating potential efficiency gains.

Forbes - The AI Graveyard: 7 Deadly Mistakes That Kill Most Enterprise AI Projects

Think of AI projects like icebergs. What executives see in vendor presentations and tech magazines is the gleaming tip above water – the finished, polished success stories. What remains hidden is the massive underlying structure of data preparation, infrastructure requirements, talent needs, and organizational change management that makes those successes possible.

AiThority - Qualtrics Report: Executives are Hesitant to Lead in AI Transformation, Putting Up to $1.3 Trillion at Risk

Findings from the Qualtrics report reveal organizations across a range of industries stand to gain an estimated $860 billion in annual revenue and cost savings – a figure which could rise to $1.3 trillion – by using AI to improve the experiences they deliver to customers. While three-quarters (72%) of executives say AI will significantly change how they approach customer experience over the next three years, few executives are willing to lead the charge. Only 15% of executives aspire to be at the forefront of how AI changes the business landscape.

Forbes - AI Agents Need To Be Managed As If They are Human

"Empowering AI agents means ensuring they have access to the key elements they need to operate effectively," Franceschini explained. "Common examples are data and analytics. Are your data and analytics accessible programmatically? Is your data well governed, clean and managed scalably? Ensuring that data, analytics and APIs are clean and accessible is one of the first challenges that companies must overcome to effectively enter the agentic workflows era."

VentureBeat - Successful AI adoption comes down to one thing: Smarter, right-size compute

“It’s a tall order. Organizations are struggling to stay up-to-date with AI compute demands, scale AI workloads efficiently and optimize their infrastructure,” says Mahesh Balasubramanian, director, datacenter GPU product marketing at AMD. “Every company we talk to wants to be at the forefront of AI adoption and business transformation. The challenge is, they’ve never been faced before with such a massive, era-defining technology.”

SiliconANGLE - Enterprise AI adoption jumps 30-fold as organizations face growing cybersecurity risks

The over 3,000% year-on-year growth in enterprise AI and machine learning usage highlights the rapid adoption of AI technologies across industries to unlock new levels of productivity, efficiency and innovation. Through Zscaler’s platform, enterprises were found to be sending significant volumes of data to AI tools, coming in at 4,500 terabytes. At the same time, enterprises were also found to block almost 60% of all AI and machine learning transactions, signaling awareness around the potential risks associated with AI and machine learning tools, including data leakage, unauthorized access and compliance violations.

AiThority - Choosing the Right Agentic AI Framework: Improving Efficiency and Innovation

Businesses are increasingly integrating Agentic AI frameworks to automate complex workflows, with over 50% already leveraging AI-driven agents and 78% planning to produce them. The leading use case for AI agents is research and summarization, utilized by 58% of users, followed by task automation for personal productivity and assistance, adopted by 53.5%. These frameworks establish a structured foundation for developing autonomous AI systems, defining how agents communicate, coordinate, reason, and make decisions to enhance operational efficiency and scalability.

Diginomica - How IBM is enabling back to front gen AI transformation in financial services

IBM’s challenge in promoting its ability to apply AI to banking transformation projects is that customers are being coy about the technology’s use in their organizations and this shyness is exacerbated by the fact that most projects are still works in flight. However, it is interesting that where other sectors are starting by applying gen AI to customer-facing processes, financial services is getting started with gen AI in internally-facing processes. This, of course, is one of the reasons IBM sees code generation as the big growth area for AI in financial services

AiThority - 68% of C-suite Say AI Adoption Has Caused Division at Their Company, Reveals Writer AI Report

Despite these challenges, the vast majority of workers and executives actively using AI say they’ve benefited from generative AI tools, and at least 9 out of 10 are optimistic about their company’s approach to generative AI. Additionally, among employees using AI, 77% are AI champions or have the potential to become AI champions. With this in mind, the survey also explored strategies to strengthen the AI adoption process, which 95% of the C-suite admit their company needs to improve.

Diginomica - AI in the enterprise? It's not a silver bullet, not yet at least, says Google DeepMind's CEO

A useful reality check from one of AI's leading figures on the challenges still ahead as AI continues to evolve. AI is only as good as the data it has to hand, and if it doesn't have enough reliable data to give it the full context of what it needs to do, the scope for things to go wrong rapidly spirals out of control. In the often unpredictable business landscape, that's a massive challenge. But it's nevertheless clear that the technology is already delivering business value, and that the ongoing pace of innovation looks set to deliver much more in the future.

Forbes - Marketing Leaders Are Chasing GenAI ROI, But Most Are Missing The Mark

Effectively applying the principle of MVD = MVD to GenAI entails leadership first bringing all the revenue chain’s stakeholders together in a planning-stage work group, with the goal of answering an initial question, “What is the minimum viable data — and therefore also the most valuable data — for this proposed solution?”

VentureBeat - Launching your first AI project with a grain of RICE: Weighing reach, impact, confidence and effort to create your roadmap

Generative AI works best as a collaborator, not a replacement. Whether it’s drafting emails, summarizing reports or refining code, AI can lighten the load and unlock productivity. The key is to start small, solve real problems and build from there.

VentureBeat - Visa’s AI edge: How RAG-as-a-service and deep learning are strengthening security and speeding up data retrieval

To meet employee demand while balancing these concerns, Visa introduced what it calls ‘Secure ChatGPT,’ which sits behind a firewall and runs internally on Microsoft Azure. The company can control input and output via data loss prevention (DLP) screening to ensure no sensitive data is leaving Visa’s systems.

Diginomica - Deloitte Digital weighs up agentic AI and gen AI for marketing

Creation of trust with employees is something that Deloitte helps with by selecting a group to participate in the pilot as part of the transformation process. These employees provide a lot of valuable feedback and moving forward they can act as evangelists. We also help run ‘promptathons’ to promote prompt engineering internally as part of the change management roll out.

Diginomica - Does AI bridge the digital skills gap, or make it wider? A look behind Workday's Global State of Skills Report

As expected, concern levels vary by industry and geography - but the problem statement is clear. What gets more interesting is defining where skills gaps are the biggest. Workday includes a breakdown of the most common skills groups in today's organizations, and the most important skills in the future. No surprise: the biggest gap was in "digital skills," with 60 percent in the present, but 65 percent needed in the future

Information Week - AI’s Next Frontier Is Applications: How to Stay Ahead

Just as having a mobile app is now table stakes for most businesses, AI-powered features will soon be expected rather than optional. Integrating AI for efficiency will be commonplace, not a differentiator. The real winners will be those applying AI in ways that make entirely new experiences possible. This is why the application layer of AI will create the most long-term value.

Forbes - Agentic AI Enters Management: Taco Bell's Byte-Sized Approach To Virtual Restaurant Leadership

Building on its Byte By Yum AI platform, which already uses AI to take customer orders at drive-through windows, Yum plans to deploy virtual restaurant managers. However, it also says that it doesn’t believe they will replace human management jobs.

VentureBeat - How Yelp reviewed competing LLMs for correctness, relevance and tone to develop its user-friendly AI assistant

Yelp Assistant helps Yelp users find the right “Pro” to work with. People can tap the chatbox and either use the prompts or type out the task they need done. The assistant then asks follow-up questions to narrow down potential service providers before drafting a message to Pros who might want to bid for the job.

Forbes - GenAI Exceeds Expectations, But Only If You Can Scale It

More than 80% of business leaders say generative AI has exceeded their initial expectations, new research out of Accenture finds. However, scaling this success to something bigger is a challenge – only 13% are seeing significant “enterprise level-value," the study, involving 3,400 executives and Accenture’s 2,000-plus gen AI projects, finds.

Techno Sapien Substack - 7 Secrets of AI Success From High-Performing Innovators

But just as a camera captures what it’s aimed at, using AI is an artistic endeavor. AI can summarize documents, suggest better email phrasing, and make statistical predictions, but it can only be helpful if humans apply creativity, experience, and instinct.

VentureBeat - Operational excellence with AI: How companies are boosting success with process intelligence everyone can access

But, in a business world still generally defined by process immaturity, rushing to roll out emerging technologies doesn’t necessarily equate to faster time-to-value. Sorry to say! So where should enterprises be focusing their use of AI to boost operational efficiency? Without doubt, some of the most meaningful operational changes are happening at the interface between intelligence and its application — the human behind the keyboard.

Pascal's Substack - GPT-4o: Only 10% of companies have "very advanced" AI maturity. Given AI’s rapid adoption, a higher number was expected. Only 49% of companies measure AI ROI.

Larger companies ($1B+) are the least likely to have an AI council. While expected to be leaders, many still lack AI governance structures.

BigDataWire - Qlik-Backed IDC Study Reveals AI Ambition Outpacing Execution

The study highlights a significant gap between ambition and execution: while 89% of organizations have revamped data strategies to embrace Generative AI, only 26% have deployed solutions at scale. These results underscore the urgent need for improved data governance, scalable infrastructure, and analytics readiness to fully unlock AI’s transformative potential.

AiThority - Customer Service Trends in the Age of AI

All of this is for the sole purpose of creating differentiated customer experiences while simultaneously eliminating substantial operating expenses from the business. The winners of this race will be the companies that get started quickly. Those that adopt a “wait and see” approach will inevitably get left behind.

AI Snack Byte Substack - Digital Co-Worker To Automate Your Boring Tasks

Instead of a one-size-fits-all per-user fee, AgentExchange allows businesses to choose from a range of specialized agents designed to automate tasks—anything from IT help-desk support to appointment scheduling and even complex tasks in healthcare administration. Companies like ZoomInfo and Remarkable are already reaping the benefits of using these agents in Slack, significantly boosting productivity.

AiThority - Not all AI is equal: why specialized models are the real ROI driver in 2025

I believe that 2025 will be a moment we will point to as the tipping point at which for many businesses, AI moved from abstract ideas to real-world results. But while off-the-shelf AI solutions can make valuable and lasting commercial contributions, decision-makers need to be aware of both their potential and their limitations. It’s the “precision over versatility” of specialized AI that promises to deliver seismic gains for businesses. We’re already seeing this in the world of Language AI, and I can’t wait to see where else its impact will soon be felt.

Harvard Business Review - Is Incrementalism Holding Back Your AI Strategy?

Hotel chains digitized operations and customer interfaces but ceded value to entrants with digital-first business models like online travel agents and peer-to-peer accommodation and experience platforms. Legacy media companies digitized content but lost eyeballs and advertising revenue to social media companies that transformed how content is created, shared, engaged with, and monetized. The lesson is clear: thriving during disruption means reimagining your future, not simply improving your present.

High ROI Data Science Substack - Are The Agents Coming? Big Bang AI Products Are Hitting A Wall

I will use Microsoft as the case study in this article, but I could just as easily use Apple, Salesforce, or several others. I will explain the problems Copilot faces and why many of those problems were created by deciding to go big rather than going incrementally.

natalabs Substack - What Most People Get Wrong About AI Success Metrics

Companies waste millions on wrong AI metrics. Most track model accuracy & features shipped, but miss what matters: actual business impact. Time to measure what drives real results.

Pascal's Substack - GPT-4o: Artificial Intelligence (AI) is no longer an experimental technology—it is a fundamental driver of business transformation.

AI is not just a tool—it is a business imperative. Companies that strategically integrate AI will gain a significant competitive advantage, while those that hesitate risk falling behind. Leaders must take a proactive approach to AI adoption, ensuring that AI aligns with business priorities, enhances productivity, and adheres to ethical and regulatory standards.

Information Week - How to Create a Winning AI Strategy

“When applied correctly, AI is a powerful tool that can accelerate your organization’s ability to solve customer problems and streamline operations and therefore drive revenue growth. This offensive approach will organically lead to cost optimization as efficiencies emerge from streamlined processes and improved outcomes.”

Forbes - Wendy’s Embraces AI And Innovation Under CIO Matt Spessard

“We initially asked whether we could automate the order-taking process with a conversational AI model,” said Spessard. “At first, rule-based solutions seemed viable, but we quickly realized the complexity required a more advanced approach. That’s when we partnered with Google Cloud to develop a generative AI model that could ‘speak Wendy’s.’”

ZDNet - How businesses are accelerating time to agentic AI value

Only 29% of enterprise apps are integrated and share information across the business. To prepare for the expanded use of AI, enterprise CIOs allocate 20% of their budgets to data infrastructure and management, four times more than their spend on AI (5%).

Computerworld - Enterprise mobility 2025: Automation lightens the load

Among the areas where AI and genAI could enhance UEM, Cipolla says, are genAI-infused chatbots to simplify product usage, AI-generated actionable insights to improve endpoint management and digital employee experience (DEX), and improved script generation through genAI.

ZDNet - 4 ways to get your business ready for the agentic AI revolution

Raymond Boyle, vice president of data and analytics at Hyatt Hotels, said his organization takes a tried-and-trusted approach to emerging technologies like agents: let line-of-business departments decide how innovations are exploited.

AiThority - From Time-Saving to Decision-Making: What’s Generative AI’s Next Leap for 2025?

A Boston Consulting Group experiment found that 90% of participants improved their performance when using genAI for creative ideation; however, when applied to business problem-solving there was a 23% decline in performance. This was primarily because participants trusted misleading outputs, highlighting the necessity of proper training and understanding of genAI limitations to harness its full potential, especially in more strategic areas.

The Hacker News - 89% of Enterprise GenAI Usage Is Invisible to Organizations Exposing Critical Security Risks, New Report Reveals

While the GenAI hype may make it seem like the entire workforce has transitioned their office operations to GenAI, LayerX finds the actual use a tad more lukewarm. Approximately 15% of users access GenAI tools on a daily basis. This is not a percentage to be ignored, but it is not the majority.

AiThority - 94% of Businesses Are Investing More in AI—Yet Only 21% Have Successfully Operationalized It

ESG’s research report, “Data Readiness for Impactful Generative AI,” reveals that businesses are moving aggressively to scale AI, but many lack a structured plan to build the data foundations necessary for long-term success. While 94% are increasing spending on products and services supporting data readiness for AI, only 21% have fully embedded AI into their operations. While most organizations recognize that data quality is crucial, governance, compliance, and bias detection remain key gaps, preventing organizations from fully realizing AI’s potential.

VentureBeat - How big U.S. bank BNY manages armies of AI agents

Pattanaik added that its agents have reduced the number of people many of its client-facing employees must speak to in order to determine a good recommendation for customers. So, “instead of the salespeople talking to 10 different product managers, 10 different client people, 10 different segment people, all of that is done now through this agent.”

Diginomica - Sage has had to build its own LLMs because 'GPT doesn't recognize accounting terms'

“The first rule to building safe AI is to not use AI when you can use traditional technology instead. When you can revert to rules, they will always produce a more reliable, more trusted answer than AI will. So we are not ever going to give up traditional technology for the sake of AI when that traditional technology gives us tried and true and highly trusted results.”

natalabs Substack - How to Stop Wasting Resources on AI Projects?

A company wanted to use AI for inventory management. After analysis, they found that simple statistical forecasting worked just as well, saving them months of development time and thousands of dollars.

Diginomica - "Our goal is not to displace people" - Planful CEO Grant Halloran takes a stand on the future of AI for finance leaders

I think the hype will, if it's not already subsiding, it's going to subside pretty quickly. It's like, 'What are we actually getting from this? To what extent are massively compressing our cycle times for these processes? To what extent are we getting better decisions in the business? How do we measure that?' How do we poll our constituents in the organization to say, 'Are we helping you make faster decisions with better information?'

Buy the Rumor; Sell the News Substack - Begun, the AI retrenchment has

But enterprise adoption doesn’t work that way. It’s governed by long procurement cycles, compliance hurdles, security concerns, and the inertia of legacy IT systems. No CIO is going to rip out mission-critical workflows overnight just because a new AI model is slightly better than the previous one. Unlike consumers, who experiment with new tools on a whim, enterprises require pilot programs, regulatory approvals, risk assessments, and internal buy-in before making big changes.

CognitivePath Substack - Transformation: The Key to Winning the AI Race

While an overwhelming 98% of business leaders express eagerness to adopt AI and have conducted initial experiments, still only 17% can demonstrate concrete operational adoption that has created benefits in their profit and loss statements, according to Accenture (see above chart). This gap between ambition and results hints at a crucial truth: the challenge extends far beyond the actual AI technology.

Diginomica - Three crucial AI projects all of us should complete

There may be several projects here – one for every affected process area or workflow in your firm. Each one will have different opportunities to use different kinds of AI. If I had to pick an area to start with, I’d choose HR, specifically talent acquisition as there is no shortage of AI tools being deployed in this space. Some Finance processes like procure-to-pay and order-to-cash could be solid candidates, too. Where will your firm focus first?

VentureBeat - Medical training’s AI leap: How agentic RAG, open-weight LLMs and real-time case insights are shaping a new generation of doctors at NYU Langone

Every night, the model processes electronic health records (EHR), matching them with relevant research, diagnosis tips and essential background information that it then delivers in concise, tailored emails to residents the following morning. This is an elemental part of NYU Langone’s pioneering approach to medical schooling — what it calls “precision medical education” that uses AI and data to provide highly customized student journeys.

CIO - Prioritizing AI investments: Balancing short-term gains with long-term vision

In a study by O’Reilly, 48% of businesses utilize machine learning, data analysis and AI tools to maintain data accuracy, underscoring the importance of solid data foundations for AI initiatives. A contrasting study by Qlik indicates that 21% of enterprises face real challenges with AI due to lack of trusted data for AI applications, highlighting the need for reliable data platforms.

AiThority - Data Teams Are Quickly Adopting GenAI — And Expecting Big Things

Adoption is moving fast. Overall, 64% of data teams are already using GenAI, and every organization surveyed has, at minimum, plans to use GenAI in the future. A remarkable 23% say their GenAI efforts are already “fully scaled.” The most common use case is automatic data curation (58%), with conversational analytics (51%) and data tests and quality (51%) running a close second and third.

Diginomica - Getting the AI balance right when trusted content is a business imperative - Yelp's Jeremy Stoppelman sets out his priorities

We're using that to begin a transformation of the search experience, have something more conversational. We're in the very early innings of that transformation, but I think it's a powerful one. It should allow us to create a very unique experience, especially within local. And then, of course, we can take that and wrap it in an API and provide that to any other AI agent out there that might want to tap into useful local content or send through a Request to Quote, working with our Yelp Assistant technology

Information Week - Key Ways to Measure AI Project ROI

"These forward-looking metrics offer insights into the initiative’s promise and help leaders determine if they align with the business goals." Additionally, for current ROI, leaders should consider using metrics that look at realized outcomes, such as actual cost savings, revenue increases tied directly to AI initiatives, and improvements in key performance indicators like customer satisfaction or throughput.

AiThority - Making AI a True Partner in Human-Driven Innovation

A recent survey found that nearly half of IT leaders reported that their AI projects have yet to generate profit, with 14% recording losses. These underwhelming results often stem less from the technology itself and more from how companies implement it. When AI operates in isolation, it struggles with ambiguity, adapts too slowly, and delivers inconsistent results. AI works best when paired with human intelligence.

Information Week - The Cost of AI: How Can We Adopt and Deliver AI Efficiently?

What can companies do to stay on budget when pursuing AI? How can they determine a rational budget for the scope of their plans? What happens if they realize they cannot achieve their goals within that budget?

BigDATAwire - NTT DATA Highlights AI Responsibility Gap as Leadership Fails to Keep Pace

“AI’s trajectory is clear—its impact will only grow. But without decisive leadership, we risk a future where innovation outpaces responsibility, creating security gaps, ethical blind spots, and missed opportunities,” closed Dubey. “The business community must act now. By embedding responsibility into AI’s foundation—through design, governance, workforce readiness, and ethical frameworks—we unlock AI’s full potential while ensuring it serves businesses, employees, and society at large equally.”

AiThority - The AI Revolution in Fintech – Funding Trends and Industry Developments in 2024

AI is upgrading fintech with notable advancements, particularly in the areas of transaction categorization, fraud detection, and risk assessment. AI makes it possible to rapidly classify financial transactions through automated pattern recognition, the use of Natural Language Processing for text recognition and analysis, context-aware categorization, and personalized transaction classification.

Forbes - 111 Ways AI Drives Return On Investment

Some business experts also point out that you need to have these systems focused on how to support people. Human-centered design is important, because so many of these types of AI innovations are assistive to business people. They’re not replacing people. They’re helping people to work smarter, not harder. They’re decision support tools, not automatons that will build their own companies.

AiThority - Real-World Implementations of Agentless AI in IT Monitoring

Agentless AI in this context refers to AI-powered IT monitoring and automation solutions that do not require installing dedicated software agents on endpoints, servers, or network devices. Instead, these solutions gather data using APIs, logs, network traffic analysis, and other non-intrusive methods. AI algorithms then analyze this data to detect anomalies, predict failures, and automate responses.

Pascal's Substack - GPT-4o: I agree with George Lee’s optimism that AI’s real value lies in specific, high-impact use cases. Claudia Harris is spot on about adoption barriers.

The transcript of Tech Tonic podcast episode titled “Making money from AI — Searching for a ‘killer app’” by the Financial Times (hosted by Madhumita Murgia and produced by Josh Gabert-Doyon) covers various perspectives on how companies are using AI and whether it is delivering profitability

Diginomica - How to win at AI? Here's the three point plan, according to HubSpot CEO Yamini Rangan

Our AI support bot now handles over 35% of support tickets while maintaining high customer satisfaction and we're working to get this to over 50% in 2025. This has enabled us to grow our customer base without adding more support staff, freeing our team to focus on solving more complex issues. Similarly, our AI sales bot is resolving over 80% of website chat inquiries, making our chat teams more efficient while delivering great customer experiences.

Forbes - 5 AI Mistakes That Could Kill Your Business In 2025

This approach – putting technology before business strategy – is probably the number one driver of failed AI initiatives and – worse still – disillusionment and giving up on AI altogether.

Pascal's Substack - How organizations can integrate AI and data at the core of their business operations. This talk is structured around three case studies from the speaker's personal experience.

The speaker discusses how organizations can integrate AI and data at the core of their business operations. It is structured around three case studies from the speaker's personal experience with Volkswagen Group, Volkswagen Financial Services, and Zalando, offering insights into scaling AI and data transformation.

ITPro Today - Turns out AI isn't that popular at work – just 4% of workers use the technology in the majority of daily tasks, but developers are among the top early adopters

"Interestingly, both low-paying and very-high-paying jobs had very low rates of AI use (these were generally jobs involving a large degree of manual dexterity, such as shampooers and obstetricians)," the blog post noted. "It was specific occupations in the mid-to-high median salary ranges, like computer programmers and copywriters, who were—in our data—among the heaviest users of AI."

AiThority - More Than Half of Companies Adopting AI are Worried About the Reliability and Quality of Their Data, According to New Dun & Bradstreet Survey

Other top concerns cited by survey respondents related to AI implementation include data security (46%), data privacy violations (43%), sensitive or proprietary information disclosure (42%) and data’s amplification of bias (26%). In line with these concerns, only half (52%) of organizations believe they have a good data foundation that will set them up for success with generative AI.

Information Week - How Enterprise Leaders Can Shape AI’s Future in 2025 and Beyond

The years ahead likely will be defined by how adeptly businesses can navigate this duality. The immense promise of transformative AI innovation is counterbalanced by the equally critical need to mitigate risks through robust data validation, human-in-the-loop systems, and proactive ethical safeguards. As we head into 2025, these three themes will drive the future of AI.

Forbes - Anthropic Economic Index — 10 AI Workplace Trends Business Leaders Must Know

Interestingly, the highest and lowest-paid positions show relatively limited AI adoption, indicating that highly specialized expertise and hands-on manual work remain primarily human domains. This pattern helps organizations understand where to focus their AI investment for maximum impact.

VentureBeat - Who’s using AI the most? The Anthropic Economic Index breaks down the data

The data suggests that AI is playing a significant role as a collaborative tool rather than simply serving as an automation engine. In fact, 57% of AI usage in the dataset involved “augmentation,” meaning AI was assisting workers rather than replacing them. This includes tasks such as brainstorming, refining ideas and checking work for accuracy. The remaining 43% of usage fell into the category of direct automation, where AI performed tasks with minimal human involvement.

VentureBeat - Inside Monday’s AI pivot: Building digital workforces through modular AI

The initial deployment of gen AI at Monday didn’t quite generate the return on investment users wanted, however. That realization led to a bit of a rethink and pivot as the company looked to give its users AI-powered tools that actually help to improve enterprise workflows. That pivot has now manifested itself with the company’s “AI blocks” technology and the preview of its agentic AI technology that it calls “digital workforce.”

Forbes - AI At Work: Revolutionary Agriculture In Africa And Elsewhere

“AI success does not just mean depleting diseases,” he said. “It means (the world) can tackle the unique agricultural challenges in Africa … with these tools, smallholder farmers can basically increase food security and decrease the impact of climate change, and globally, it just means more food quality for everybody.”

VentureBeat - Begin with problems, sandbox, identify trustworth vendors — a quick guide to getting started with AI

“KPIs are essential in gen AI deployments for a number of reasons: Objectively assessing performance, aligning with business goals, enabling data-driven adjustments, enhancing adaptability, facilitating clear stakeholder communication and demonstrating the AI project’s ROI. They are critical for measuring success and guiding improvements in AI initiatives.”

eWeek - AI Usage By Researchers Varies Depending on Their Career Phase, Field & Region

While interest in AI remains high, concerns about the models persist. About 81% of researchers expressed one or more concerns about AI, with ethical issues (54%), lack of transparency in AI training and facility (46%), accuracy (51%), and data security or privacy (47%) ranking as the most common. This highlights the significant obstacles researchers face in increasing their use of AI, the report said.

AiThority - Diffusion Models: A Game-Changer for the AEC Industry

Diffusion models, a class of generative AI techniques, are proving to be a powerful tool in reimagining architectural design, engineering workflows, and construction management. By refining complex patterns, generating high-fidelity simulations, and enhancing predictive analytics, these models are enabling faster, more efficient, and sustainable project execution. Their ability to generate detailed design variations, optimize structural efficiency, and streamline project workflows is making them a game-changer for the AEC industry.

Jing Hu's AI Breakdown Substack - Want It or Not, $2 Extra Please.

Again, the precise number of businesses using generative AI is unknown. Most data you can find varies wildly depending on who’s running the survey, who’s being surveyed, and what “adopting” even means.

Forbes - Here Comes The Big, Strong Agentic AI Wave

One report by Deloitte predicts that “25% of companies that use generative AI will launch agentic AI pilots or proofs of concept in 2025, growing up to 50% by 2027.” The report further notes that some agentic AI applications, in some industries, and for some use cases, could see actual adoption into existing workflows in 2025, especially by the back half of the year.

Information Week - Digital Mindset: The Secret to Bottom-Up GenAI Productivity

Third, GenAI tools are probabilistic rather than deterministic. Having employees attend structured training makes sense for a deterministic system, one that will always generate predictable outputs from a given set of inputs. Conversely, GenAI tools rely on statistical methods and have inherent variability in their outputs. Enter the same prompt in your favorite large language model (LLM) twice and you will get two different responses.

CIO Influence - Global CIO Study Reveals ROI Remains Greatest AI Adoption Barrier, Despite Three-Fold Spend Increase

Despite the forecasted surge in AI spending, business decision-makers are not unanimous in their optimism of its impact. The CIO Playbook revealed that 37% of management remain skeptical or have reservations toward AI, while approximately 9 out of 10 AI-adopting respondents, mostly made up of IT professionals, said that AI has met their expectations. This highlights a significant divide between the unbound potential of AI and business confidence.

AiThority - Rapid GenAI Application Adoption Drives New Era of Application and Infrastructure Modernization

As GenAI application adoption and implementation move at a blazing pace, the ECI uncovered that while the majority of organizations have already implemented a GenAI strategy, implementation targets vary significantly. Organizations are eager to leverage GenAI for productivity, automation, and innovation, but they also face critical hurdles in the form of data security, compliance, and IT infrastructure modernization. Further, 90% of respondents expect their IT costs to rise due to GenAI and modern application implementation. But promisingly, 70% of organizations expect to make a return on their investment from GenAI projects over the next two to three years.

Forbes - Half A Million Students Given ChatGPT As CSU System Makes AI History

We are proud to announce this innovative, highly collaborative public-private initiative that will position the CSU as a global leader among higher education systems in the impactful, responsible and equitable adoption of artificial intelligence. The comprehensive strategy will elevate our students’ educational experience across all fields of study, empower our faculty’s teaching and research, and help provide the highly educated workforce that will drive California’s future AI-driven economy.

Information Week - AI’s Hidden Cost: Will Data Preparation Break Your Budget?

Preparing data for AI is a tricky and potentially costly task. IT leaders must consider several factors, including quality, volume, complexity of data, along with preparing for costs associated with data collection, cleaning, labeling, and conversion suitable for an AI model. When added on top of needs for new hardware, software, and labor costs associated with GenAI adoption, and the bills add up quickly.

AI Policy Perspectives Substack - An agents economy

At this point, organisations could achieve agents that are quasi-substitutable for human employees, providing almost equivalent value. This might happen either because an organisation has reinvented itself to gradually shape agents capable of understanding and absorbing tacit knowledge as effectively as humans, or because the task has been outsourced to an ‘AI-first’ start-up unencumbered by operating with fewer legacy constraints.

The AI Memo Substack - Prioritizing What Matters Amid AI Hype And News Cycles

First-order concerns about dominant AI models, players, and countries should be replaced by second-order concerns, such as identifying and prioritizing promising AI capabilities that realize tangible results, and third-order concerns, such as the impact on individuals, industries, and societies. The higher the order of the concern, the less important the minutiae of this week’s news cycle become.

AiThority - AI’s Modern-day Gold Rush: Seizing Opportunities in an Era of Rapid Innovation

Businesses everywhere are racing to integrate AI into their operations, eager to stake their claim in what feels like a modern-day California Gold Rush. But just like those early prospectors of the mid-19th century, many are still unsure where the real value lies—or worse, they’re sifting through sand hoping for a quick windfall.

Information Week - The Real Cost of AI: An InformationWeek Special Report

How many pennies does it take to run efficient, effective enterprise AI? Even if CIOs are willing to spend freely, will they get the return on investment they're looking for? And while they're emptying the coffers, what hidden costs are racking up for their business and for society at large? We investigate the thorny issues in this three-week deep dive.

Network World - AI investments face integration, compliance, and skills challenges: survey

An overwhelming majority (94%) of CIOs and CSOs have started implementing AI for network management, and 58% of those executives report the implementation is complete. Yet just 42% of network engineers reported their businesses have fully implemented AI for network management. Nearly two-thirds (63%) of network engineers report that AI has been integrated into their cybersecurity systems to some degree, and 28% say it has been fully integrated. Still, 70% of network engineers said they believe AI will enhance their organization’s ability to respond to cybersecurity efforts, with only 27% saying it will significantly reduce response times.

Forbes - What Is the Cost of AI: Examining the Cost of AI-Enabled Apps

As we try to answer the core question, what is the cost of AI, let’s start small. Let’s look at some of the costs organizations may face when they seek to develop AI-enabled apps in-house. This is often a way for enterprises to make their first inroads into leveraging AI for their operations.

ZDNet - How Gen AI means better customer experiences - see one bank's approach

"We let our colleagues explore this technology within a safe space," she said. "That initial exploration suggested some areas we might look into. We collected about 100 use cases. Personalization was an obvious place to focus for our customers."

Dr Phil's Newsletter Substack - The AI Illusion in L&D

According to the Jagged Frontier research published by Harvard Business School in 2023, while AI tools like ChatGPT improved performance on functional tasks requiring little domain knowledge (like content summarisation and email writing), they actually decreased performance quality by 19% for more complex, domain-specific tasks that were poorly represented in AI's training data.

Information Week - Speech-to-Speech AI: Empowering a More Connected World

The process begins by capturing voice input, which is then transformed into mel-spectrograms -- a visual representation of the sound’s frequency content over time. Advanced neural networks, such as those used in models like OpenAI’s Whisper, apply deep learning techniques to these spectrograms, enabling automatic speech recognition (ASR).

Aithority - Why Every Employee Should Have Sanctioned Secure Access to New AI Thinking Models

The continuing emergence of new AI models is not about replacing humans and automating intelligence. Rather, it’s about how employees and companies can use these tools as vital resources to become more efficient. Providing employees with secure and sanctioned access to the latest AI assistants with advanced reasoning capabilities empowers them to reshape how they work, think and solve problems in a safe and managed environment.

Pascal's Substack - 14 AI experts in Publishing discuss 14 use cases. The AI Special by InPublishing.

These interfaces will merge content with chat capabilities, allowing users to read, listen, or watch seamlessly. The prediction emphasized a rapid transition where AI and human collaboration redefine workflows and content delivery, heralding a dominant conversational and multimedia-driven approach within just a year.

AiThority - The Impact of AI on IT Operations: A New Era in Enterprise Tech Management

The rise of AI in IT operations has been swift, with organizations adopting AI-driven tools for automation, data analysis, and issue resolution. Machine learning and natural language processing (NLP) are enabling smarter interactions with systems and data, creating a more seamless IT environment. This swift evolution is no longer optional for businesses aiming to stay competitive—it’s essential for optimizing service delivery, improving response times, and effectively managing increasingly complex digital landscapes.

Forbes - Why AI Agents—Not ChatGPT—Will Dominate 2025

From a business perspective, Stearn says AI agents are only as useful as the training they are programmed with as well as the quality of the data and resource inputs. Basically, AI agents need to be trained like employees—they require structure, documentation, and a clear set of instructions.

AiThority - Unlocking AI Potential: What the 2025 State of the Data Lakehouse Survey Taught Us

Aside from empowering models, AI-ready data fosters greater collaboration across teams. By providing a unified source of truth, it ensures that data scientists, analysts, and business stakeholders can work together more effectively. This alignment drives better outcomes, from more accurate predictions to faster decision-making cycles. Organizations that prioritize AI-ready data are building a strong foundation for sustained advantage.

VentureBeat - 93% of IT leaders see value in AI agents but struggle to deliver, Salesforce finds

“Integration is incredibly foundational to making AI agents work because AI agent outputs depend on connected data that enables a comprehensive understanding of the context and nuances within user queries.”

The AI Agent Architect Substack - The 24.69% Productivity Leap: Proof Implementing GenAI Can Deliver

GenAI isn’t a plug-and-play solution. It’s about weaving AI into the fabric of your business - your processes, your data, your workflows. And honestly? That’s where most companies fall flat on their faces. They treat GenAI as a tech initiative instead of the transformation project it needs to be.

Forbes - Why AI Agents Are The Next Big Thing In Sales

Here, AI agents can be a valuable asset. For example, a website chatbot that engages with prospective customers while gathering data about their needs, preferences, and timelines. As these conversations take place, the AI agent updates sales records, qualifies leads, and refines forecasts—all while your team focuses on other priorities.

EdSurge - Smarter Schools: Artificial Intelligence Enters the District Office

One of the most immediate impacts of AI in school administration is in the realm of recordkeeping. Intelligent Document Processing (IDP) is revolutionizing how schools handle paperwork by drastically reducing the amount of manual entry needed for information to flow from forms into systems. From student registration to health records, IDP transforms time-consuming tasks into swift, efficient processes.

AiThority - UiPath Report Reveals Agentic AI is Driving Investment to Tackle More Complex Business Workflows

In fact, respondents identified IT security issues (56%) and integration with existing systems (35%) as their top concerns with agentic AI, along with cost of implementation (37%). When asked what capabilities would be most critical to effective implementation of agentic AI workflows, the top-ranked response was to “safety and privacy,” followed by “seamless integration with existing systems.”

Information Week - AI Projects at the Edge: How To Plan for Success

While edge will never be able to compete with the cloud in terms of sheer processing power, a new class of system-on-chip (SoC) processors has emerged, which is designed for AI inference. Many of the vendors in this space have also designed chipsets that are dedicated to specific use cases that allow further cost optimization.

Enterprise Data Science Substack - Starting with the Business Process: A Foundation for Effective AI Solutions

Consider a translation workflow in an organization that depends on SMEs to analyze foreign-language documents. By understanding the process, you may identify repetitive tasks (e.g., document categorization or keyword extraction) where AI could help. However, unless the AI-generated translations are accurate and reduce SME workloads, the solution will not succeed. The AI must enhance the speed and quality of the process while reducing reliance on costly external services.

eWeek - Small Measures for Big Impact: GenAI Era for Businesses

Having been responsible for many “firsts” during the transition to digital, I know that leaders who fail to take action fall behind to early adopters—and it’s more difficult than ever for organizations to claw their way back. Being an early adopter not only positions your company as innovative, but also creates a critical advantage in building customer trust and attracting top talent. Early movers often establish benchmarks, shape industry trends, and set the pace for competitors to follow.

Forbes - Is Gen AI Adoption Inevitable?

For most organizations I engage with, however, adoption is not widespread. One sees productivity "microbursts" primarily driven by gains from specific repeatable tasks. But 10 hours per employee per month is not a game-changer for anyone. This is not to say that specific organizational roles are not impacted significantly

eWeek - 78% of Executives Plan to Invest More in AI

AI’s capabilities may be transformative, but its success depends on people. Companies that excel at AI adoption recognize the importance of collaboration between technology and human insight. Managers translate the organization’s AI vision into actionable goals, ensuring their teams are equipped to execute them. Employees are also integral to the process, participating in pilot projects and providing feedback to refine AI solutions.

Forbes - 5 Ways To Set Up Your Company For AI Success

Work with your IT, data, and legal teams to ensure that governance policies are updated to account for these new scenarios and that the guidance is communicated and understood. Focus on areas such as AI ethics (making AI free from bias and aligning it with your company values), appropriate data access, and internal and external transparency regarding your AI usage.

B2B GTM teams have a lot to consider before successfull

Information Week - Why Enterprises Struggle to Drive Value with AI

“If the work I’m doing takes three hours and now it takes a half an hour, that’s easily quantifiable, [but] human performance is variable,” says Rao. “The second way is having a baseline. We don't [understand] human performance, but we are saying AI is 95% better than a human, but which human? The top-most performer, an average performer, or the new employee?”

Salesforce - The World’s Largest Agentic AI Deployment? Salesforce Is Running It on Itself

By piloting its own technology, it gains immediate benefits from tools like Agentforce while gathering valuable insights to refine its offerings and showcase their potential for broader adoption. In fact, by rolling out Agentforce to support its own massive customer base, Salesforce may be in the midst of the world’s largest agentic AI rollout to date.

World Economic Forum - What is a small language model and how can businesses leverage this AI tool?

The emergence of SLMs signals a significant paradigm shift in enterprise AI strategies. Organizations are transitioning from experimental approaches to strategic, purpose-driven implementations, which are more targeted and can be more cost-effective.

Network World - Cisco CEO Robbins on AI: Pressure to deploy is real

AI creates a massive demand for infrastructure, he said. “Not only GPUs, CPUs, LPUs, but networking infrastructure,” Robbins said. “Customers want to know how they are going to be monitoring applications, thinking about data… I think most everyone in this industry would say that sometime in the next 12 months, or 24 months, we’re going to exhaust all the publicly available information [for AI systems to learn].”

AiThority - Workers Embrace Agentic AI Despite Concerns About Trust and Reliability, Says Research

When asked to rate agentic AI concerns, the top ones were clear: 47% believe AI lacks human intuition and emotional intelligence, 40% are uncomfortable submitting AI-generated work, and 34% worry that AI-produced work isn’t as good as their own, tied with concerns over accuracy and reliability.

Information Week - Why Every Employee Will Need to Use AI in 2025

For example, a company’s solutions will only ever be as good as their best contributors. Organizations must do everything they can to maximize the abilities of their Center of Excellence employees, because they set the bar for the rest of the organization. At one software company, I saw leaders transfer an expert in clean coding to a team struggling with code quality; improvements were evident across the organization within weeks, demonstrating the contagious nature of expertise.

Diginomica - Davos 2025 - Successful AI adoption demands 'show, not tell', strong change management, and HR re-invention, advise AWS and Accenture CEOs

"The hardest bit is getting people's heads around what's possible and, and not even what's possible now, but what's possible 24 months from now, or 48 months, or two, three, four years out. I think people have a hard enough time realizing what's possible today, but as you're implementing it 12 months later, the technology is going to be much further along, so you really have to think about what's going to be possible and unlocking it."

InfoWorld - The bitter lesson for generative AI adoption

If organizations constantly need to fine-tune new models for specific tasks, they might be in a costly cycle of catching up with new technology. In contrast, prompt engineering and retrieval-augmented generation (RAG) focus on improving the retrieval and integration of information, a process that can continuously benefit from advances in generative technology. This is a more sustainable short-term adoption strategy.

VentureBeat - Deloitte: 74% of enterprises have already met or exceeded gen AI initiatives (but challenges remain)

Despite longer-than-expected time to value, nearly three-quarters (74%) of respondents reported that their most advanced gen AI initiatives are meeting or exceeding ROI expectations. Cybersecurity and IT functions are leading the way in terms of ROI and successful scaling.

Data Science Central - What’s behind the AI adoption and hiring challenge in organizations?

Companies for a decade now have been thinking they have to adopt AI, or they’ll get left behind. But most still don’t acknowledge the nature or the depth of the real challenge they face. Ultimately, it’s not an algorithm or machine learning challenge. Instead, it’s a people, process, data resource and app rationalization challenge.

Forbes - Innovation Management And AI: Embracing Change In A Dynamic Era

To maximize the potential of AI, company leaders must adopt a thoughtful and strategic approach. A critical first step is cultivating an AI-ready workforce. Investing in training programs ensures employees understand AI’s potential and feel confident using it. By fostering a culture of curiosity and experimentation, organizations can empower teams to test AI tools in low-risk environments, which often leads to unexpected and transformative applications.

Diginomica - Will AI roboticize enterprise procurement?

McFarlane says the question of data quality comes up frequently when the procurement technology company talks to CFOs and prospective customers. Organizations are also worried that their contract data is placed into a large language model (LLM) and is no longer part of the intellectual property of that business.

The AI Agent Architect Substack - Is Your Business REALLY Ready to Deploy AI Agents in 2025?

Investing in high-performance storage systems like Redis, MongoDB Atlas, and SQL databases ensures your AI Agents have the backbone needed to handle vast datasets and deliver low-latency responses. Without these foundational systems, achieving scalability and reliability will be impossible.

Diginomica - Is AI really the silver bullet to transform government or another tech pothole to fall into?

Meanwhile new multi-country data from Salesforce, based on 11,750 political constituents, is angled more towards how successful public sector adoption of AI could improve their engagement with public services. In the current Agentforce-centric era, there’s an big agentic angle at play here, of course, with 90% of global respondents saying they’d use an AI agent to interact with the public sector. The percentages vary by country - 88% of US respondents in this case vs 84% in the UK, but 100% in India.

The Product AI Agent Substack - The AI trap most organisations fall Into: How to blend human wisdom with AI power

I'll share a trade secret. The best AI projects treat the system like a brilliant but inexperienced team member. You wouldn't let a fresh grad rewrite your core infrastructure, would you?

Buy the Rumor; Sell the News Substack - Enterprise adoption of AI agents will be slower than many expect

However, within enterprises, the practical realities of integrating such technologies tell a different story. Businesses face challenges rooted in legacy systems, compliance requirements, constrained budgets, and risk-averse cultures. This essay explores the disconnect between the visionary roadmaps of tech leaders and the complex realities organizations face when adopting autonomous AI agents.

VentureBeat - Early days for AI: Only 25% of enterprises have deployed, few reap rewards

According to the report, the majority of companies still in production are in various stages of their AI journeys — building out and evaluating strategies and proofs of concept (PoC) (53%), beta testing (14%) and, at the lowest level, talking to users and gathering requirements (7.9%).

In Pursuit of Good Tech Substack - Watching the 2025 Horizon

On one end, companies can look for solutions that target specific task sets with relatively low implementation needs. These solutions are typically designed to automate pre-existing tasks, with the heaviest lift being to train the humans how to use it. The challenge on this end of the spectrum is that unless the AI shows immediate return on investment and recurring use, the chances of it sticking quickly decline as the ‘cool new tool’ factor fades.

CIO - 10 AI strategy questions every CIO must answer

“The time for experimentation and seeing what it can do was in 2023 and early 2024. I don’t think anyone has any excuses going into 2025 not knowing broadly what these tools can do for them,” Mason adds. “So the organization as a whole has to have a clear way of measuring ROI, creating KPIs and OKRs or whatever framework they’re using. And the tech side of the house should push to make sure there’s clarity on this.”

VentureBeat - The AI gold rush: Why risks and rewards remain a balancing act

A recent study from global SaaS solutions provider Stibo Systems, “AI: The High-Stakes Gamble for Enterprises,” found that a full 49% of business leaders admit they are not prepared to use AI responsibly, 79% of organizations do not have bias mitigation policies and practices in place, and 54% of organizations have not implemented new security measures to keep up with AI integration — but only 32% of business leaders admit they’ve rushed AI adoption.

CIO Influence - New Research Reveals the Role of AI in Revolutionizing Network Operations for Service Providers

New industry analysis reveals that the adoption of artificial intelligence (AI) is accelerating across the telecoms industry, with 88% of fixed broadband service providers now investigating or trialling AI automation to enhance their fixed broadband services. Key use cases such as network monitoring, predictive maintenance, and resource optimization are at the forefront, driving significant cost savings and improving customer experiences.

Information Week - Are We Ready for Artificial General Intelligence?

“One underappreciated reason there are so few generative AI use cases at scale in the enterprise is fear -- but it’s fear of job displacement, loss of control, privacy erosion and cultural adjustments -- not the end of mankind,” Habib notes. “The biggest ethical concerns right now are data privacy, transparency and algorithmic bias.”

Computerworld - The next AI wave — agents — should come with warning labels

“These agents leverage the strengths of multiple paradigms while mitigating risk by using more deterministic techniques when appropriate,” Coatney said. “I am particularly excited about the potential of integrating systems and data both within and beyond the enterprise. I see great potential in unlocking value still largely isolated in departmental and vendor silos.”

NextGov/FCW - HHS AI plan looks to the private sector for collaboration

“Increasing collaborative partnerships between stakeholders … and intentional public engagement throughout the innovation pipeline could enhance the potential of AI being equitably adopted across medical research and discovery by sharing ideas, approaches, best practices, example applications and key risks to mitigate between groups,” the document said.

VentureBeat - AI is set to transform education — what enterprise leaders can learn from this development

After six decades of dreaming and experimenting, we might be on the cusp of a technology-enabled revolution in education. The Arizona State Board for Charter Schools recently approved the application by Unbound Academy for a new online school that will replace traditional teachers with AI teaching assistants, promising to deliver 2.4 times the academic growth for students compared to results from conventional schools.

Harvard Business Review - What Companies Succeeding with AI Do Differently

What surprised us is that the kind of partnerships changed significantly. In our 2021 survey, academia and startups were the most common partners; two years later, respondents named a maturing ecosystem of consultants, vendors, and industry partners. The implication is that AI has matured enough that practical approaches are valued the most.

CIO - AI will transform the enterprise ‘in a generation,’ say CDOs

There is, he said, a real divergence in organizations as to whether an AI initiative “sits on the business side or the technology side. I mean, historically, it sat on the technology side, but there’s been a progression, a migration to more and more data and AI leaders sitting on the business side of it. Personally, I believe it should sit on the business side.”

ZDNet - AI agents may soon surpass people as primary application users

About half of executives responding to Accenture's survey, 48%, report they soon expect agents to be able to upgrade and modernize functions and integrations. At least 46% said agents will soon be able to assure the quality of digital functions and systems, and 45% anticipate agents will access functions from internal systems. 

Diginomica - The top five mistakes vendors make with enterprise AI

In vendor meetings, I heard my analyst colleagues push vendors on industry-specific AI roadmaps for target verticals. Most vendors didn't go nearly far enough. Either they aren't ready, or didn't want to show their cards. But customers aren't going to wait for transparency; they'll find an AI vendor that is. Industry apps, like the legal LLM I referred to above, are potent for customers. When you show customers you're on top of how their markets are changing, you're a step closer to earning the AI trust.

The AI Strategy Navigator Substack - The Hidden Costs of AI Implementation

If you do not begin with a diverse group of cross-organization folks understanding the common vision, the project will go off the rails pretty quick. This will occur when one or more groups misinterprets their goals, risks are not identified and realized until just before or :gasp: after launch, or the shiny new (expensive) investment collects dust on a shelf because no one wants to use it.

TechBullion - TechnologyInnovative Artificial Intelligence Companies Transforming Industries

Artificial intelligence is no longer confined to the realm of science fiction. Today, AI is embedded in everything from enhancing decision-making skills in finance to automating customer service in tech companies, while its power is being leveraged to drive efficiency, improve accuracy and enhance customer experiences.

AiThority - Most Companies Are Unprepared to Deploy AI, New Section Research Finds

Most AI Experts use AI daily (67%), save more than 20% of their time each week by using it (57%), and trust its contributions (100%). They also have strong buy-in from their employers and managers (93%), and access to company deployed AI tools (60%). But they’re only 1% of the knowledge workforce.

VentureBeat - Cohere just launched ‘North’, its biggest AI bet yet for privacy-focused enterprises

The platform lets employees build and customize AI tools for their specific needs without requiring technical expertise. Early testers include companies in finance, healthcare, manufacturing, and infrastructure — sectors where data security has traditionally limited AI adoption.

ZDNet - Home Innovation Artificial Intelligence Autonomous businesses will be powered by AI agents

The focus of this article will be on the binary big bang and the agentic AI revolution in the enterprise. Accenture notes that AI will drive new levels of autonomy throughout business, evolving the ability to reinvent with tech, data, and AI -- a limitless opportunity for innovation and growth. The key disruption here is "AI cognitive digital brains."

WSJ - How Are Companies Using AI Agents? Here’s a Look at Five Early Users of the Bots

If these AI agents work as promised, they could also provide businesses with the return on investment they have been looking for out of generative AI. According to some corporate technology leaders, that means the ability to tie the technology to a reduction in the number of hours employees work, or even how many new people they need to hire.

Forbes - Multimodal AI In 2025: From Healthcare To eCommerce And Beyond

At r2decide, a company a few Cornellians and I started, we’re using multimodality to merge Search, Browse, and Chat into one seamless flow. Our customers are eCommerce companies tired of losing revenue because their users couldn’t find what they needed. At the core of our solution is multimodal AI.

Computerworld - Apple Intelligence: Is AI an opportunity or a curse?

With that in mind, it seems a slow and steady approach to AI deployment could end up being the King’s Gambit in the game. Rather than chasing the evangelists, the industry should focus on putting solutions together that deliver genuine benefit, rather than simply looking good in headlines, (whoever writes them). We need to see true and tangible improvements to foster trust, and if the people behind them genuinely believe AI will drive future hardware sales, they’ll make sure their AI solutions do just that.

Forbes - 3 Alternatives To Microsoft Copilot For Secure AI Employee Chatbots

Companies cannot afford to lag in AI adoption in 2025. Investors expect executives to articulate AI's role in their strategies, and top talent gravitates toward forward-thinking organizations. By the end of the year, at least 10% of your employees should have access to secure, AI-driven employee chatbots at work to remain competitive in attracting and retaining talent.

NextGov/FCW - HHS’ 2024 AI use case inventory shows move toward internal chatbots

The most recent AI use cases that HHS reported include an initiated effort to create a chatbot that can help with applying for grants, an initiated chatbot to help Division of Global Migration Health personnel “with developing an initial draft response to inquiries where the response could have been found on our website” and a chatbot to help researchers find data sets for environmental health research efforts, which is in the acquisition and/or development phase.

AiThority - Predictive GenAI: Redefining ROI in the AI Revolution

As we look to 2025 and beyond, the narrative around AI is evolving. No longer is the question “if” businesses should adopt AI but “how effectively” they can leverage it. Predictive GenAI, with its ability to deliver actionable insights, represents a seismic shift in how organizations approach decision-making and ROI.

Computerworld - Health AI: How Apple can boost public health

The Health app is a major component of all of this. Think of it as a digital hub. Not only does it gather information from all your devices, but it also sucks in data from some third-party services and has the capacity to share and ingest information with health professionals. All those insights are private and personal to you, and Apple wants to keep it that way.

AiThority - The Buy vs. Build Dilemma; What the Enterprise Needs to Consider

The International Data Corporation (IDC) recommends a “buy” approach for companies aiming to quickly implement AI and achieve faster results. Purchasing a proven AI solution delivers immediate value without the extensive time commitment required for in-house development, making it a strategic choice not only for organizations with limited AI expertise but also for those seeking to configure and expand on an established foundation. While building a custom solution offers tailored functionality, it demands significant machine learning expertise, specialized data science resources, and incurs high costs for data acquisition and infrastructure.

AiThority - AI vs. UI: Getting a Handle on the Real Revolution

Look at voice assistants, for instance. The technology underlying contemporary voice assistants like Siri and Alexa—natural learning processing, voice recognition, machine learning—has existed for a long time. But these disparate technological innovations, however impressive, simply didn’t matter to the average consumer, who had no way to access them. It was only when they were brought together into an affordable, fun, user-friendly package that the real revolution could begin.

Diginomica - From governance to collaboration - the AI trends defining 2025

The best planned AI initiatives are shifting from proof-of-concept to proof-of-value, focusing on solving real problems – and these are set to become more than just buzzwords in 2025. Instead of merely demonstrating feasibility, projects should start with a concrete challenge and use real data to address it.

Information Week - Building an AI Council to Drive the 2025 Tech Revolution

Acting as central hubs, these councils streamline efforts by unifying AI investments, helping enterprises move beyond experimental projects to scalable strategies that deliver measurable outcomes. For example, AI councils bridge insights across departments, from pre-sales to customer support, while establishing governance and literacy. 

ZDNet - Why ethics is becoming AI's biggest challenge

More than half (56%) of businesses are delaying major investments in generative AI until there is clarity on AI standards and regulations, according to a recent survey from the IBM Institute for Business Value. At least 72% say they are willing to forgo generative AI benefits due to ethical concerns.

eWeek - AI Disruption: BCG Study Reports 70% of Global Economies Not Ready for the Shift

As AI continues to redefine industries, the divide between readiness and exposure grows more pronounced. Bridging this gap requires bold initiatives, long-term planning, and investments in education, R&D, and infrastructure. Economies that adapt swiftly to AI technology will secure sustainable growth, while those that delay risk falling behind in an increasingly AI-driven world.

Diginomica - Using AI to support people and services during natural disasters

The system aggregates real-time weather pattern data from a range of sources, which is combined with historical data on rainfall and flooding. It’s then all run through an AI agent to compare the old and new data and make customized reports.

InfoWorld - The AI backlash couldn’t have come at a better time

Developers, engineers, operations personnel, enterprise architects, IT managers, and others need AI to be as boring for them as it has become for consumers. They need it not to be a “thing,” but rather something that is managed and integrated seamlessly into — and supported by — the infrastructure stack and the tools they use to do their jobs. They don’t want to endlessly hear about AI; they just want AI to seamlessly work for them so it just works for customers.

Diginomica - AI for ROI, not FOMO - three lenses for adoption in a post-hype world

If an AI project is an experiment - there’s likely to be uncertainty of how big it could grow. Both at the trial phase and looking further down the line to full scale implementation. Being able to grow from POC, through deployment, and then support a successful project is vital. Organizations need to be able to scale without having to throw away computing resources and start again.

AI EduPathways - Balancing Productivity and Purpose: The Corporate Risk of AI Adoption

“Why? Because AI isn't just augmenting human creativity – it's replacing it. The study found that artificial intelligence now handles 57% of "idea generation" tasks, traditionally the most intellectually rewarding part of scientific work. Instead of dreaming up new possibilities, scientists may find themselves relegated to testing AI's ideas in the lab, reduced to what one might grimly call highly educated lab technicians.”

FedScoop - Federal government discloses more than 1,700 AI use cases

According to the CIO Council post, OMB granted year-long extensions to be in compliance with risk management practices to 206 use cases. The most common risk management practices that agencies cited needing an extension for were “the requirement to conduct independent evaluations, mitigate emerging risks to rights and safety, and complete an AI impact assessment for their rights- and safety-impacting use cases,” the post said.

BigDataWire - Survey: 86% of Enterprises Require Tech Stack Upgrades to Properly Deploy AI Agents

The survey reveals plans to make significant investments in AI agents, with 42% of enterprises planning to build over 100 AI agent prototypes and 68% budgeting $500,000 or more annually on AI agent initiatives. However, this scale of deployment faces serious hurdles without a unified integration platform, as evidenced by nearly half of respondents (48%) reporting their existing integration platform as a service (iPaaS) products are only “somewhat ready” for AI’s data demands.

eWeek - 53% Surge in AI Recruitment Adoption: Key Findings from HR Research Institute

Although artificial intelligence is being rapidly integrated into HR software and enterprise technology solutions, satisfaction with these tools is growing far more gradually. Just 45 percent of HR professionals rated their recruitment tech stacks as good or excellent, a mere six percent increase from 39 percent in 2023. This is a mild improvement compared to the vast increase in AI recruitment adoption, suggesting that while many HR professionals are using AI tools for hiring, a disproportionately lesser number are seeing its benefits.

Diginomica - The Enterprise Tech Year - AI in action

So did we learn nothing in 2024? I think we learned quite a bit. Taking generative AI live at scale isn't easy for most companies, but on the positive side, the lessons for getting AI projects right are piling up. When AI vendors can speak directly to perceptions of risk - and earn AI trust through data transparency - there are usually good projects to be had on the other side.

Forbes - Google Breaks Out Veo 2, To Significant Fanfare

"The future of video creation will be defined by AI as a co-creator, democratizing access to professional-grade tools and enabling anyone to turn ideas into compelling visual stories,” says Fei-Fei Li, Co-Director of the Stanford Institute for Human-Centered AI, and someone who we have quoted extensively in plumbing the depths of AI’s advent. “This shift will not only revolutionize how content is made, but also who gets to create it."

Diginomica - UK Government’s adoption of AI could help save public finances - ‘the numbers are big’

He said the public sector must go faster and deeper with emerging technology. The report suggests the Department for Science, Innovation and Technology (DSIT) can be a one-stop shop for spreading best practices in AI. The research also suggests creating a Data Academy to upskill civil servants

AiThority - Qlik Identifies Key Trends to Shape the AI Economy That Will Separate Leaders From Laggards

“We are not yet using AI to its full potential, but through awareness, education, and careful stewardship, we will work toward that in the year ahead.” Organizations embedding AI within real-world contexts while balancing cost and value will lead in driving tangible business outcomes.

Forbes - How GE HealthCare Is Using AI To Revolutionize Every Aspect Of Patient Care

One of the most innovative projects is the Health Companion research initiative, which explores how multiple AI agents can mimic the collective expertise of a multidisciplinary cancer care team. Each AI agent specializes in a different medical domain - radiology, oncology, genetics, pathology, and more - analyzing patient data independently before a supervisory agent synthesizes their insights

Forbes - 16 Essential Generative AI Tools Transforming HR In 2025

While the technology is impressive, the real value lies in how these platforms free up HR professionals to focus on what matters most – the human aspect of their role. Organizations that successfully integrate these AI solutions while maintaining their focus on employee experience and workplace culture will be best positioned to thrive in this new era of HR technology.

Aithority - The Rise of AI in Contact Centers – No Longer Just a Trend

With AI technology continuing to advance, contact centers’ futures appear bright. Virtual assistants and chatbots will manage routine tasks, allowing human agents to address more complex customer issues. Business strategies and decision-making will be aided by deep AI-insights into customer behavior and preferences.

Diginomica - The right way to think about AI - Twilio’s Chief Product Officer Inbal Shani on the difference between successful and not successful companies

It’s the difference, she says, between successful and less successful companies. If you are grounded by the customer problem you're trying to solve and figuring out how AI can help solve that problem, you will realize ROI and see successful adoption. Companies that sprinkle a little generative AI on something that doesn't materialize into a product or feature that customers want to adopt and buy will struggle.

ZDNet - We're not ready to support autonomous AI agents, survey suggests

The survey found that 42% of respondents expect to build or prototype more than 100 AI agents over the coming year, and 36% expect to see more than 100 put into production. The survey, commissioned by Tray.ai, found a similar percentage (41%) expect to address more than 20 distinct business problems.

I Read AI - How to Turn AI Project Costs Into Measurable Business Value

Many AI projects stumble not because of the technology itself but due to a misalignment between technical capabilities and business objectives.

Computerworld - Making AI popular is a marathon, not a sprint

It’s almost as if people purchasing these products are a little turned off by a technology that threatens to destroy their employment, exacerbate wealth inequalities, and supercharge surveillance advertising in exchange for email summaries and a search engine powerful enough to help you file your next welfare benefit claim.

eWeek - Defeating Task Paralysis: 88% of Gen Z Trust AI to Handle Overwhelming Projects

AI is reshaping more than workflows—it’s shaping careers. Over 50 percent of survey respondents actively share insights about AI tools with colleagues, emphasizing the role of generative AI in fostering collaboration and innovation. With the ability to automate routine tasks, young leaders can redirect focus toward strategic and creative endeavors.

AiThority - When AI becomes a commodity, how can your business differentiate?

The majority of enterprises are using OpenAI’s GPT-4 as their foundational model, mostly through Microsoft’s Azure AI service. Anthropic, Mistral and Cohere are also popular but less widely used. The performance of these foundational large language models doesn’t differ widely, which means companies are using basically the same technology. You could compare it to using an Oracle or IBM database – the technology is broadly similar and doesn’t offer significant differentiation by itself.

FedScoop - Watchdog says DOE needs to improve data analytics, take steps to adopt AI

Concerning the implementation of the statutorily required actions for implementing AI, OIG recommended that the director of DOE’s Office of Intelligence and Counterintelligence expedite the creation and submission of a report to Congress on the adoption of AI to enhance inner-agency workflows. Additionally, OIG asked the director to “continue focus on funding needs, including those related to human capital” as well as highlighting those needs to implement the Intelligence Authorization Act of Fiscal Year 2023 and “full implementation of AI into intelligence operations.”

Forbes - Beyond Bureaucracy: How AI Is Shaping The Future Of Public Service

AI systems are keeping an eye on data to increase efficiency, reduce errors, and speed up or even eliminate legacy processes. While there’s still way too many paper-based processes in government, AI-powered computer vision and natural language processing (NLP) systems are rapidly increasing the digitization of documents, and automatically analyzing and updating records without requiring human rekeying of data.

FedScoop - From translation to email drafting, State Department turns to AI to assist workforce

At this very moment, State Department workers stationed across the globe could use the internal AI chatbot, StateChat, to help them draft an email, translate a document or brainstorm policy. They can turn to Northstar to summarize and analyze news stories from multiple countries. And they might even query another chatbot, FAM Search, to look up department practices for booking a flight or cybersecurity protocols.

AiThority - Deloitte’s 16th Annual ‘Tech Trends’ Report Reveals AI is Quickly Becoming Foundational to the Modern Enterprise

But a new kind of thin IT may emerge after that, as AI democratizes development, and IT orchestrates large portions of today’s manual workload. It’s possible AI may eventually recast IT into an “outcome-a-S” in its own right, delivered to the enterprise by a combination of carbon and silicon.

CognitivePath - AI Agents, Consolidation and Widespread Enterprise Adoption? Oh My!

Many of the experts see parallels to previous tech revolutions like Web 2.0, with expectations of improved infrastructure, governance, and integration into existing software platforms. They also predict a move from individual use to enterprise-level implementation, even as leaders grapple with the organizational factors that stand between failure and success.

AiThority - Allvue Survey Reveals Key Insights on AI Adoption and Technology Challenges for GPs

“The survey findings reveal an industry at a crossroads, where the potential of advanced technologies like AI and robust data solutions remains out of reach for many firms,”

BigDataWire -AI-Based Innovations Propel Retail Profitability and Sustainability, Research Finds

The research looked at the impact of AI in all major areas of retail, including merchandizing and marketing, demand performance, workforce productivity, store and space and sustainability and waste management. It found that the world’s 200 largest retailers have boosted pre-tax profits by 39.4% and revenue by 31.3% over the past five years while also reducing their carbon intensity (CO₂e produced per $m of sales) by 20.9%

AiThority - 61 Percent of Global Businesses are Scaling Back AI Investment as a Result of Trust Issues

A lack of AI skills, governance issues and insufficient resources are all hindering successful AI deployment, causing many projects to get stuck in the planning stages. Ready-made solutions are a preferred way for global businesses to start working with AI solutions, and see return on investment in the technology.

CSO Gen AI use cases rising rapidly for cybersecurity — but concerns remain

Top among these use cases, cited by 56% of respondents, is employing gen AI to augment common operational tasks, such as automating administrative processes, accelerating case management, and translating natural language into policy.

CognitivePath - Part II ChatGPT Impact: The Next Two Years in AI

Our experts highlighted several key themes: start with clear goals, define specific use cases, upskill your teams, emphasize ethics, and think more in terms of augmentation than replacement. They also emphasized the importance of AI readiness, change management, and accepting that failure is part of any business transformation.

AiThority - The Power of Local AI: A New Path to Accessible, Practical AI for Business

Local AI solutions offer a powerful alternative to cloud AI services, enabling companies to experiment, test, and use AI securely within their own environments. By running AI models on company infrastructure or user devices, all usage of the AI system and data sent to the system can be monitored and secured. This local-first approach bridges the gap between advanced AI capabilities and practical, day-to-day implementation, empowering businesses to integrate AI into operations without compromising security or data control.

eWeek - New Study Identifies Major Roadblocks to AI Adoption for Businesses — Are You Aware of Them?

For example, most organizations use large language models (LLMs) like those owned by OpenAI, Google, Microsoft, and others. Relatively few have developed their own small language models (SLMs), yet the report found that three out of four organizations using GenAI have noticed that SLMs outperform LLMs in speed, cost, ROI, and accuracy. SLMs can be finely tuned to organizational needs, enabling them to understand context, jargon, and nuances relevant to particular industries. This specialization leads to faster processing times and improved outcomes, allowing businesses to automate repetitive tasks such as data extraction, categorization, and summarization.

AiThority - Navigating the Landscape of AI: Insights from the 2024 State of AI Report

Generative AI is experiencing rapid growth in 2024, with adoption rates up 17 percentage points from last year. This surge is driven by advancements in natural language processing (NLP) and its increasing use in chatbots and automation tools. Companies are primarily adopting generative AI to boost productivity and efficiency in internal operations, IT operations support is the second most common use, while its role in research and development has grown by 9 percentage points year-over-year.

GeekWire - Hidden AI revolution: Why leaders must address covert adoption of new tech

My own research (a non-scientific survey posted to my LinkedIn page) found that 3 in 4 leaders have either “experimented with ChatGPT a few times” or “use AI tools sporadically but not systematically.” Leaders want to better understand how generative AI can help them with better information organization, improved team collaboration, and enhanced decision-making.

eWeek - Can AI Be Trusted? 7% Rise In AI Optimism Challenges Traditional Skepticism

The appropriate processes and guardrails should be in place to assure GenAI users that they can trust the reliability of outputs and that they are not inadvertently engaging in theft, plagiarism, or misuse of intellectual property (IP). Additionally, processes should be in place so that humans don’t blindly accept AI’s conclusions and responses as being 100 percent true.

CognitivePath - What’s the biggest impact ChatGPT has made on business over the past two years?

While ChatGPT offers opportunities and challenges by shifting views on human capital—raising questions about task automation and even role replacement—many professionals face a dilemma about embracing AI tools or risking their career relevance. Experts note a surge in innovation and startups fueled by AI’s hype, yet some caution that the true transformative impact may still lie ahead, and there have already been missteps and flawed investments among both startups and established companies.

Platforms, AI, and the Economics of BigTech - From Charlie Munger to ChatGPT - The 'productivity gains' paradox

It’s still possible to enhance profits and secure a stronger competitive advantage with generative AI in your core product- but only if you leverage it to build a proprietary product advantage within your business. The key is ensuring that this advantage is developed internally, rather than relying on a third-party supplier of AI.

Computerworld - What are AI agents and why are they now so pervasive?

In 2025, 25% of companies that use genAI will launch agentic AI pilots or proofs of concept, according to report by professional services firm Deloitte. In 2027, that number will grow to half of all companies. “Some agentic AI applications…could see actual adoption into existing workflows in 2025, especially by the back half of the year,” Deloitte said. “Agentic AI could increase the productivity of knowledge workers and make workflows of all kinds more efficient. But the ‘autonomous’ part may take time for wide adoption.”

Forbes - Businesses are going all in on AI for the holidays, but will it really make a difference?

Brands continue to keep AI and social commerce front and center of their ecommerce strategy, with 69% planning to ramp up their investments in both technologies. But AI can be an expensive proposition, especially when fully committing. To prioritize spend, Jones advises that brands hone in on the customer journey, from ideation and discovery, to selection, checkout and delivery, and look at how AI can transform key customer touchpoints.

ZDNET - Your AI transformation depends on these 5 business tactics

"You can't do stuff without your data being right," he said. "You can go much deeper -- and be much more solid on your outcomes and what you're trying to achieve -- as your data gets better."

Diginomica - Using AI to improve proposal processes and boost the bottom line at Microsoft

Embrace the power and efficiency that AI will drive, especially for proposals. This technology accelerates what proposal professionals can do and allows them to use their brains for strategic relationship-building exercises and oral presentations. If you can increase the quality of proposals by ensuring your teams can leverage past content, you’ll increase your win rates, and your teams will be less burned out.

AI Supremacy - State of AI Report 2024 Summary

The buzziest Gen AI startups don’t have much longer to prove their worth in actual revenue generation compared to the investment in them. Revenue can ramp up even substantially for OpenAI and Anthropic in 2025 and they might even begin to see profitability on the horizon until early next decade! It’s daunting future for any business, nevermind the Gen AI duopoly players.

Diginomica - How to survive the looming AI avalanche of enterprise automation

These are automations that always were needed, and could have been delivered earlier if they'd been prioritized, but were denied the resources, budget or motivation. AI simply lowered the bar to getting them done. The ROI comes not because of AI per se, but as a result of the automation of previously manual processes. Nevertheless, AI swoops in at the last moment and gets all the credit.

eWeek - Appen Report Highlights 9.4% Decline in ROI for Deployed AI Projects

Appen’s study points out the main reasons for this downward trend: the importance of data quality, data management, and the expertise of data partners. Companies value diversity, bias reduction, and scalability, emphasizing that “human insight is key to refining AI systems,” 80 percent of the survey respondents said.

Axios - How ChatGPT changed the future

Two years of living with ChatGPT still haven't shown us the perfect use case for generative AI. But they have proven the technology's allure — and that will drive the industry to keep looking till it finds a killer app.

Forbes - HP Study: Why Work Isn’t Working And What Can Fix It

Regardless of the specific leadership or technology developments that unfold in the future, it is clear that employer and employee expectations have evolved, and that leveraging both smarter management practices and smarter technology is essential to meeting the needs of today’s workforce. In particular, AI will shape the future of work by creating solutions and experiences that foster business growth while supporting personal and professional fulfillment.

Diginomica - The enterprise stories we need for 2025

Mainframe product owners wanted their customers to move to newer client server products. It took seemingly forever and few vendors could build migration paths that were easy, fast and cost effective. We saw the same thing a few years later when client-server solutions yielded to newer cloud apps. And now, those cloud apps are ceding ground to AI-powered, public-cloud solutions. In each migration cycle, vendors have to incent, cajole and/or threaten customers to move to more technically current and, hopefully, more functionally powerful solutions.

VentureBeat - AI that clicks for you: Microsoft’s research points to the future of GUI automation

Industry experts predict that by 2025, at least 60% of large enterprises will be piloting some form of GUI automation agents, potentially leading to massive efficiency gains but also raising important questions about data privacy and job displacement.

Forbes - AI Is The New UI. 3 Steps Business Leaders Must Take Now

S&P Global, the financial intelligence giant, has embraced AI interfaces across its product suite. The company is augmenting or replacing traditional financial terminals and data dashboards with conversational interfaces that allow analysts to query complex datasets, generate reports and analyze market trends through natural language interactions.

eWeek - Microsoft Outshines Amazon and Google in Cloud AI Engagement

According to ABI Research, the AI-driven cloud infrastructure expansion will focus on large and mega-sized colocation facilities. The firm predicts that 43 percent of data centers will be large-scale by 2030, up from the current 28 percent, as companies build out facilities capable of handling AI and other data-intensive applications.

Network Computing - Cisco Report: Enterprises Ill-prepared to Realize AI’s Potential

Companies are experiencing a real urgency from their leadership to make the most of AI within the next 18 months. Close to 85% of companies surveyed believed they only have 18 months to begin showing how AI is impacting their business. Meanwhile, close to half, or 59%, give themselves only 12 months to show AI’s impact on their organizations.

GeekWire - Beyond the Buzz: Making AI Work for Large-Scale Web Data Collection

This leads to the second issue for businesses and investors – AI-washing. Instead of investing in AI-related research and development, many businesses simply say or imply that AI is at work even when it plays no meaningful role in achieving results. Sometimes, companies will advertise AI capabilities that are seemingly available now. Still, on further investigation, you will learn that it is merely a projection for the future, or, at best, you can contact sales to try an underdeveloped version of a feature that might someday be launched publicly.

Redmond PIe - Key Trends Shaping The Future Of AI In 2025

Artificial intelligence (AI) will be more accessible with lower-cost GPUs. Smaller and even medium-sized businesses can be enabled to build complex AI models without having to break the bank. Artificial intelligence (AI) will be democratized.

ZDNET - 88% of workers would use AI to overcome task paralysis, Google study says

Some 47% believe AI can help improve communications to resolve problems and foster better working relationships. Specifically, they believe AI can better coordinate tasks across different business teams and enhance communications for the hybrid workforce.

Yahoo!Tech - As AI gets real, slow and steady wins the race

Sutton explained that AI's inconsistency remains a challenge: "One contract I can put in and the AI kicks it out perfectly. Another one will be 40 percent right. That lack of certainty means lawyers still have to verify everything."

Forbes - As ChatGPT Turns Two, AI Innovation Is Thriving

The reality is organizations needn’t spend millions of dollars building or licensing large language models (LLMs). Rather, small language models (SLMs) running in hybrid IT environments provide more than enough AI firepower to satisfy most targeted business use cases.

Diginomica - Celonis - British Government could calm tax concerns by supporting AI adoption

But there's a whole series of end to end ideas, that needs to be a package of work, to really deploy - not just AI, but any technology - to take us to a productivity level that is completely different to where we are today. We've got to have something that's legislative to help get ourselves ahead of everyone else.

EDSource - How are college students using AI tools like ChatGPT?

In a 2023 survey, 56% of college students said they’d used AI tools for assignments or exams. However, opinions on its use vary widely between students. Some view AI as a revolutionary tool that can enhance learning and working, while others see it as a threat to creative fields that encourages and enables bad academic habits.

Forbes - The Top AI Use Cases Of 2024 And What You Should Know About Them

As we approach the end of 2024, an analysis of the 'Top 15 AI Use Cases' reveals a noteworthy trend: the use cases that are the most popular are focused on task-oriented activities that are traditionally time-intensive, rather than complex problem to solve. They act as force multipliers, allowing us to allocate time more efficiently towards higher -value activities that require judgment and creativity. These use cases are also characterized by their low-risk nature.

SiliconANGLE - New research report shows rapid, wide adoption of generative AI

One interesting dynamic of this is how AI changes work. If AI saves workers time and enables tasks to be completed sooner, should companies change people’s goals? For example, if salespeople are no longer required to put information in customer relationship management services because AI can automate that, should more meetings and closed business be expected? I’ve asked CEOs, heads of human resources and line-of-business managers this, and there is no consensus opinion, but it does appear workers’ goals will need to be adjusted.

Network World - Cisco: Pressure to deploy AI is up, but only 13% feel ready

“This is not deterring them, as leaders say they will not only continue to invest in AI, but actually increase their spend,” Cisco stated. The Index showed that 50% of those surveyed have between 10% and 30% of their current IT budget dedicated to AI.

Diginomica - AI in healthcare – how to support a workforce of health experts but tech novices?

Roughly half of respondents in the Avanade survey of nine industries, including healthcare, were stuck on these basics. Yet they remain confident of the rapid returns promised by evangelists, such as Microsoft. How can such confidence be justified if you have no idea what need AI addresses?

ZDNET - Nearly half of Gen AI adopters want it open source - here's why

On average, 41% of those using Gen AI report that their organization's code infrastructure supporting AI is open source. This percentage rises to 47% for high adopters of Gen AI. It's far higher than that when you look behind the curtain and see how machine learning generates large language models (LLM) in the first place.

AiThority - 95 Percent of Retail Leaders Prioritize AI, but Only 40 Percent Feel Ready Due to Data Gaps

Currently, 54% of business and IT decision makers in retail say the primary reason for using AI is to drive operational efficiencies versus growth (46%). However, during the next three years, when AI is anticipated to mature, those numbers flip, with 56% of organizations saying AI will primarily be a growth driver versus driving efficiencies (44%).

ITPro. - IT leaders are less AI-ready than they were a year ago, says Cisco report

There are plenty of reasons. The biggest is infrastructure readiness, with gaps in compute, data center network performance, and cybersecurity. Only 21% of organizations have the necessary GPUs to meet current and future AI demands, and just three in ten have the capabilities to protect data in AI models with end--to--end encryption, security audits, continuous monitoring, and instant threat response.

ZDNET - AI transformation is the new digital transformation. Here's why that change matters

Almost two-thirds (64%) of CEOs believed 2023 was a breakthrough year for the power of AI. That was the year when many CIOs were busy telling me Gen AI remained at the exploratory stage and nowhere near production. It's a similar story now. Gen AI production stories are the rarity, not the rule.

The AI Memo - Making AI Adoption Less Uncomfortable—For All

In conversations with CIOs and VPs of IT, I hear that their companies already use AI—often developed and implemented by their IT teams. These are real business scenarios in which AI adds value or helps the business team do a task they were unable to do before, from supply chain optimization to product descriptions and document processing. But it’s not without struggles. Organizational dynamics, politics, and resistance to change come up more often as adoption barriers.

ITPro Today - The 4 Checkboxes of Scalable AI in the Enterprise

Boiling down the attributes of enterprises most capable of highly effective generative AI scaling, these businesses embed the talent, the efficiency, the infrastructure, and the cohesion required to launch and maintain generative AI initiatives beyond their initial phases. Here are your four non-negotiables.

TechBullion - Getting AI Right the First Time – How to Deploy the Right Tech Fit for Your Business

“Many organizations jump into AI without a clear objective,” he shares. “They risk investing in projects that never move from concept to production.” For Intelygenz, the mission is clear: design custom AI solutions that integrate effectively into a company’s workflow, delivering value from day one. This approach positions Intelygenz as an agile partner, ensuring clients don’t lose valuable time on ineffective solutions while their competitors advance. Chris believes this proactive, customized approach is key to unlocking tangible ROI from AI investments.

VentureBeat - How Sema4.ai is empowering business users to deploy AI agents in minutes

Six Fortune 2000 companies are piloting the platform in early proof-of-concept (PoC). Bearden explained that these partners are using agents to automate invoice processing, payment reconciliation, employee onboarding and regulatory compliance. In two of the PoCs, Sema4.ai’s platform is autonomously performing more than 80% of knowledge work tasks.

Diginomica - Josh Bersin on the ROI of AI in HR Read later

When the HR leader takes a step back and looks at all these changes, they say, ‘How are we going to reorganize the HR department to deal with this big technology change?’ We took all of this information we had about HR job titles and HR skills, and we put it into an AI system, and built a career planning tool for HR people.

AiThority - Team-GPT Raises $4.5Million to Discover and Deploy AI Use Cases in Enterprises

Team-GPT allows users within enterprises to interact with any AI model, organize prompts, and discover actionable AI use cases. This facilitates an easy integration of AI into business processes, empowering companies to decrease hiring needs and dramatically enhance productivity.

Data Science Central - Boosting government efficiency with valuable AI knowledge

For instance, a valuable use of AI is it can analyze spending patterns in procurement processes that can help decision-makers identify unnecessary expenditures. This can help governments save money. A study estimates that automating government processes could save between 96.7 million and 1.2 billion labor hours annually, translating to potential savings of $3.3 billion to $41.1 billion.

ZDNET - Sticker shock: Are enterprises growing disillusioned with AI?

Hitting a "data wall": The main issue enterprises are running up against is "not because the generative AI technology is bad, but because their data's bad," he explained. The challenge is "there's no easy fix for this, you're going to have to stop what you're doing, loop back, and fix your data. For many of these organizations, that particular problem hasn't been addressed for the last 20 or 30 years.

TechRepublic - AI Market Trends: Key Insights & How Enterprises Should Adapt

Smith said one dangerous path is for enterprises to test every use case for AI. Instead, he said organisations should prioritise only those use cases that are most important for the business to pursue today. While some business use cases might be effective, they may also be much cheaper to do when the cost of an AI token decreases, so organisations would be better off waiting in some cases.

ZDNET - Organizations face mounting pressure to accelerate AI plans, despite lack of ROI

Across the Asia-Pacific, 98% of respondents expressed an increased urgency to deploy AI over the past year, with 49% pointing to their CEO and leadership team as the main source of pressure. Another 40% said their middle management was feeling pushed to adopt AI, while 36% cited their board of directors.

BigDATAWire - ‘Playtime is Over’ for GenAI: NTT DATA Research Shows Organizations Shifting From Experiments to Investments

Ninety-six percent of respondents are considering how GenAI can streamline future employee workflows and support processes. However, 67% of respondents said their employees lack the necessary skills to work with GenAI. About half are planning employee education and training to increase GenAI adoption.

eWeek - AI Agents Set to Transform Customer Experience Landscapes

It’s difficult to estimate the number of ways AI agents can transform a business, and more are being developed almost daily. To date, some of the most common applications include contact centers, financial applications, data collection and analysis, task and project management, personal assistance, and more.

Diginomica - Are you over the AI hype yet? Desk workers are. Slack global research exposes harsh realities of workforce adoption

Well, up to a point perhaps, but there are still complications and not-spoken-enough realities to be factored in, not least a(nother) gap between management and worker attitudes. While desk workers on the frontline have cooled towards AI, their bosses - and budget holders - have not. Some 99% of execs polled say they plan to invest in AI this year, while 97% still feel some urgency to incorporate generative AI into business operations.

Microsoft - How real-world businesses are transforming with AI

Generative AI is revolutionizing innovation by speeding up creative processes and product development. It’s helping companies come up with new ideas, design prototypes, and iterate quickly, cutting down the time it takes to get to market. In the automotive industry, it’s designing more efficient vehicles, while in pharmaceuticals, it’s crafting new drug molecules, slashing years off R&D times. In education, it transforms how students learn and achieve their goals. Here are more examples of how companies are embracing generative AI to shape the future of innovation.

ZDNET - Businesses must reinvent themselves in the age of agentic AI

The three engineering principles for building a modern, adaptable digital core are Architect with intent, Connect the dots, and Thrive with ecosystems. Leading companies building a reinvention-ready digital core adopt these ACT principles two times more than others.

TechTalks - How to choose the first machine learning project for your organization

Start with tasks where you already have a good amount of data, preferably clean and structured in a data warehouse. If not, look for areas where you have a lot of unstructured documents that hold a lot of value. In many cases, you can get a lot of value from raw documents or get them ready with minimal annotation.

SiliconANGLE - Agentic AI: Exploring its scope, applicable use cases and current state

“We have those kinds of applications that are emerging today, many of which will be AI agent-based,” he said. “Industrial automation, smart manufacturing, predictive maintenance, supply chain optimization — I use that personally myself. Anything that can benefit from the utilization of an intelligent agent that’s already able to carry out a certain narrow set of duties, that’s really what it’s good at.”

AiThority - AI Integration in SMBs: Balancing Fear and FOMO

SMBs are navigating challenges with AI. In the U.K., top concerns are cost (35%), lack of skilled staff (32%), and integration issues (29%), while in the U.S., tech failure (29%), employee resistance (28%), and high costs (27%) lead. Sectors like retail, healthcare, and manufacturing are especially wary, with 42% of retail SMBs expressing fear over AI’s “unknown future.”

Evidently AI - ML and LLM system design: 500 case studies to learn from

How do companies like Netflix, Airbnb, and Doordash apply AI to improve their products and processes? We put together a database of 500 case studies from 100+ companies that share practical ML use cases, including applications built with LLMs and Generative AI, and learnings from designing ML and LLM systems.

The Augmented Advantage - Why You Should Stop Looking for AI Use Cases

Even if their AI system worked flawlessly (it didn't), they'd need ~2.5 years just to break even. Meanwhile, they had other processes leaking $50,000 per month that could have been fixed with simple AI augmentation.

Here's a simple rule I use with my clients: If a business problem isn't worth at least $10,000 per month in value (either cost savings or additional revenue), it's probably not worth building an AI solution for.

Forbes - 8 Ways To Generate ROI From AI (By Entrepreneurs Actually Doing It)

I talked to entrepreneurs using AI right now. Not theorizing about it, not planning to use it someday, but getting real ROI today. Their results paint a clear picture. AI tools aren't just cutting costs, they're fundamentally changing how businesses operate and grow.

CognitivePath - Understanding AI Agent Hype: Promises versus Potential

The potential for AI agents is excellent. In reviewing some of the initial new AI agent packages that are out there, most initial offerings are domain-specific, acting within very confined task paths such as customer response agents or sales development assistants embedded in CRMs. Narrow AI scoping is expected as enterprise software platforms will create somewhat customizable agents that serve the broadest swath of their customers possible.

VentureBeat - Knowledge workers are leaning on generative AI as their workloads mount

A State of AI at Work report from Asana found that only 31% of companies have a formal AI strategy in place, and that “dangerous divides exist between executives and individual contributors in terms of AI enthusiasm, adoption and perceived benefits”.

MIT Technology Review - How ChatGPT search paves the way for AI agents

In the next year, Godemont says, he expects the adoption of AI for customer support and other assistant-based tasks to grow. However, he says that it can be hard to predict how people will adopt and use OpenAI’s technology.

Diginomica - Accenture - companies need to think about generative AI for revenue growth, not just cost reduction

The theory being put forward by Accenture is that the companies that are performing the best when it comes to productivity are not just using technology and knowledge within their organization to drive out costs - viewing all productivity endeavors through the lens of cost - but are rather are thinking about how technology and knowledge can be used to drive revenue growth. This thinking, the research argues, becomes even more critical with the adoption of generative AI, which can have a multiplier effect.

GeekWire - Column: To navigate the promise and peril of AI, resist the urge to fall into extreme narratives

It’s okay not to have everything figured out. This technology is still new, and it’s evolving fast. But if we stay open-minded, set smart guardrails, and ensure fair treatment for everyone involved, I think we can unlock AI’s potential without losing what makes us human.

InfoWorld - Cloud providers make bank with genAI while projects fail

You don’t need to look far for the disappointing stats. Gartner estimates that 85% of AI implementations fall short of expectations or aren’t completed. I see the same thing in my practice: Projects start and then stop, many never to be resurrected. You can google all the other reports of AI bad news; the general trend is that companies are good at spending money but bad at building and deploying AI.

Forbes - How To Get Started In AI Even If You Don't Have Technical Skills

Based on recent headlines, you might be convinced you’re light-years behind if your company hasn’t already integrated AI into its processes. According to a recent survey from EY, the speed of AI adoption is one of the biggest triggers for AI anxiety. In that same survey, polling 1,000 Americans with desk jobs, 90% said that their organization uses at least one AI technology, with Gen AI topping the list.

TechRepublic - Thoughtworks Reports Rapid Growth in AI Tools for Software Dev

According to the report, rapid adoption of AI tools is starting to create antipatterns — or bad patterns throughout the industry that are leading to poor outcomes for organisations. In the case of coding-assistance tools, a key antipattern that has emerged is a reliance on coding-assistance suggestions by AI tools.

Forbes - AI In Healthcare—Delivering Value Today And In The Future

Diagnostics: AI algorithms are enhancing diagnostic accuracy and efficiency. For instance, Google Cloud Healthcare is enhancing diagnostic accuracy and speed to identify potential treatments and improve patient outcomes. Butterfly Network's handheld ultrasound device, powered by AI, enables point-of-care imaging, making early diagnosis more accessible.

FedScoop - OpenAI further expands its generative AI work with the federal government

On the defense side, the company is entering a limited ChatGPT Enterprise partnership with the Air Force Research Laboratory, which does research and development work for the military service. Similar to USAID’s application of the technology, the partnership will focus on using generative AI to reduce administrative burdens and increase efficiency, experimenting with using the technology to improve access to internal resources and basic coding, for instance.

The FuturAI - Adapt or Perish: Why AI Agents Are No Longer Optional for Business Survival

As the underlying AI continues to improve at breathtaking speed, the set of human activities that can be handed off to agents will rapidly grow. How long will it be before an agentic system can fully automate the work of a lawyer? An investigative journalist? A policymaker? A venture capitalist? An AI researcher?

Diginomica - What should enterprises build with agentic AI?

This does sound like an advance, and tech vendors who believe they've got there first with this new class of AI-fueled agents are very excited to see their customers take them on board. But what will enterprises actually do with agentic AI that moves their organizations forward? What are the unmentioned gotchas they need to be wary of? And how soon will agentic AI be superseded in turn by the next new, new thing?

aiThority - The AI Landscape: Technology Stack and Challenges

As we stand in the early stages of the AI revolution, it’s clear that the challenges we face today are just the beginning. The second and third-order effects of widespread AI adoption are yet to be fully understood or experienced. However, this uncertainty also allows the global community of practitioners, researchers, and policymakers to unite.

BigDataWire - ServiceNow Unveils New Research on Government Organizations Setting the Pace for AI-Driven Transformation

“This report reinforces that leading governments are already reaping the benefits of AI through accelerated service delivery, citizen experience, and employee productivity. Whether you’re a Pacesetter or still in the beginning stages of a digital journey, these findings show that the future is promising for those willing to embrace AI.”

AiThority - Bust through the Hype and Build Practical AI Solutions that Drive Rapid, Measurable Productivity for Business

While not every AI solution is ready for immediate enterprise-wide use, especially in complex IT environments with numerous data sources, most businesses can find targeted, low-risk high-reward applications where AI adds immediate value.

Diginomica - Prepare for talking AI documents and the AI factory

Organizations are awash with content, and much of it only acts as a repository of information - loan terms or health records, which, in turn, need storage and archiving technology as well as applications for managing content. Bates believes AI within documents is bringing the enterprise content management (ECM) sector back to life as AI relies on extracting and acting on metadata, the very essence of ECM.

Information Week - It Takes a (C-Suite) Village to Implement AI

The stakes are high: globally, poor customer experiences cost organizations $3.7 trillion annually — an increase of $600 billion from last year. According to our 2024 AI and Customer Service Index, only 50% of people believe AI has improved service in recent years. As we look ahead to 2025, it’s clear that customer service is still broken, and the traditional approaches aren’t delivering. New answers are needed, and those answers lie in the powerful fusion of data, AI and humans.

VentureBeat - 5 ways to overcome the barriers of AI infrastructure deployments

The Harvard Business Review estimates the failure rate is as high as 80% — about twice the rate of other corporate IT project failures. One of the top barriers preventing successful AI deployments is limited AI skills and expertise. In fact, 9 out of 10 organizations suffer from a shortage of IT skills, which exposes execution gaps in AI system-design, deployment and ongoing cluster management.

Computerworld - Meta, Apple say the quiet part out loud: The genAI emperor has no clothes

Amidst the mountains of vendor cheerleading for generative AI efforts, often amplified by enterprise board members, skeptical CIOs tend to feel outnumbered. But their cynical worries may now have some company, in the form of a report from Apple and an interview from Meta — both of which raise serious questions about whether genAI can actually do much of what its backers claim.

Tom's Hardware - Linus Torvalds reckons AI is ‘90% marketing and 10% reality’

On a more positive note, Torvalds reckons there is change afoot. “In five years, things will change, and at that point we’ll see what AI is getting used every day for real workloads.” But it now seems fitting to remind readers that this isn’t the first instance of an IT industry heavyweight asking about the validity of the AI industry. Just a week ago, we reported on the CEO of Baidu voicing an even more pessimistic opinion – that the AI bubble would burst and that just 1% of companies would continue to pick up the pieces after the predicted ‘pop.’

Fortune - Researchers disagree about the speed of gen AI adoption. But one thing’s clear: The tech is increasingly everywhere

The 40% figure, he pointed out, would include someone who simply asked ChatGPT to write a limerick once in the last month. The paper actually said only 0.5%-3.5% of work hours involved generative AI assistance—and only 24% of workers used it once in the last week prior to being surveyed, and only one in nine used it every workday.

The Globe and Mail - What companies must consider when implementing AI solutions

Potential enterprise AI benefits include increased efficiency, reduced ‘grunt work’ so employees can focus on higher-order activities, and enabling data processing at scale. Pitfalls are also a factor. How do you manage risk? How do you handle governance? How do you use AI as a tool to maximize human potential rather than replace it?

Information Week - 7 Things That Need to Happen to Make GenAI Useful to Business

Losing interest in more models and features sounds counterintuitive but it actually isn’t. Organizations need time to learn the models and features that exist now to see clearly how they can be leveraged. The rush to constantly upgrade before you get your bearings borders on insanity from a purely business, capture- the-ROI perspective.

Blocks & Files - Wharton Business School study claims enterprises buying into Gen AI

The surveyed businesses are now actively using Gen AI across multiple functions, such as coding, data analysis, idea generation, brainstorming, content creation, and legal contract generation. Nearly half of organizations are hiring Chief AI Officers (CAIOs) to lead strategic initiatives. Such CAIOs are now in 46 percent of companies. Challenges around accuracy, privacy, team integration, and ethics persist, though these concerns have slightly eased compared to last year.

VentureBeat - Gartner predicts AI agents will transform work, but disillusionment is growing

There are a few fundamental reasons for this, he explained. First, VCs have funded “an enormous amount of startups” — but they have still grossly underestimated the amount of money startups need to be successful. Also, many startups have “very flimsy competitive moats,” essentially serving as a wrapper on top of a model that doesn’t offer much differentiation.

SiliconANGLE - Generative AI adoption sets the table for AI ROI

Moreover, 97% of leading gen AI adopters report that they’re achieving tangible benefits from their deployments. Adoption of gen AI is relentlessly on the rise and nearly two years in, is poised to begin throwing off enough value that it will heighten a mandate to apply AI to drive business results. As such, we believe that as we exit Q4 into 2025, the demand for AI solutions will continue to occupy the headspace of business technology pros and AI momentum will maintain its accelerated pace.

VentureBeat - Enterprise AI moves from ‘experiment’ to ‘essential,’ spending jumps 130%

The remaining investment is distributed across training and upskilling the existing workforce, onboarding new employees and consulting services. While much of the hype and news in generative AI in 2024 has been about the technology, that’s not the differentiator for many enterprises at this point.

CDO Trends - From Sandbox to Center Stage: AI Moves Towards Broad Adoption in Financial Services

On the buy-side, AI is the top tech priority in 2024, moving beyond the hype and into broader adoption. 64% of respondents plan to launch customer-facing services using GenAI in 2024 before shifting toward externally focused use cases. Capital markets firms are now uncovering the most salient use cases to embed in business and operational models while continuing to navigate their own internal risk postures and concerns. The buy-side strategically invests to increase efficiency, reduce costs, and enhance accuracy and compliance.

AiThority - Wealth Management Firms Expected to More Than Double AI Budgets: Wipro Survey

All surveyed firms indicate that they have started adopting AI in different parts of their operations. However, less than half (44 percent) say they are using AI extensively. That said, these extensive users report tangible benefits, with 73 percent experiencing significant competitive advantage because of AI adoption. These extensive users also lead the pack in leveraging AI to enhance client engagement, with 65 percent expecting significant AI-driven changes in client relationship management over the next 1-2 years.

Diginomica - Four CDOs explain how they make Artificial Intelligence work for their business

JP Morgan Chase is focused on building cloud-based foundations that help the firm develop AI-enabled products safely and securely. Jain recognizes the excitement around generative AI and says prioritization around use cases is crucial. The firm has a “quad model”, where product, data, technology and design work hand in hand to deliver high-quality solution

Forbes - Tech Budget Pressures Highlight Growing AI Hype Gap

AI is all the rage, and it’s driven up excitement in the tech markets. But if you are a tech executive, it’s also causing headaches. There might be pressure on you to allocate money to unproven generative AI projects, and that means squeezing money out of the bread-and-butter tech budget.

VentureBeat - LinkedIn founder Reid Hoffman unveils ‘super agency’ vision at TED AI conference, takes subtle shot at Elon Musk

“Humans not using AI will be replaced by humans using AI,” Hoffman predicted, arguing that the real divide won’t be between humans and machines, but between those who embrace AI’s capabilities and those who don’t.

CIO - IT pros: One-third of AI projects just for show

“This can lead to a dangerous cycle where decision-makers become skeptical of AI’s potential, reducing future investment,” Nagaswamy says. “The long-term impact is even more worrying — companies risk falling behind competitors who are implementing AI strategically. Their teams miss out on crucial learning experiences, leaving them ill-equipped to handle genuine AI deployments down the road.”

Forbes - In 2025, There Are No Shortcuts To AI Success

The next big piece of emerging tech in the world of AI is agentic AI, but enterprises with ambitions to build advanced agentic architectures themselves will meet significant hurdles. The challenge is that these architectures are convoluted, requiring diverse and multiple models, sophisticated retrieval-augmented generation stacks, advanced data architectures, and niche expertise. Mature companies will recognize these limitations and opt to collaborate with AI service providers and systems integrators, leveraging their expertise to build cutting-edge agentic solutions.

VentureBeat - AI agents fed by process intelligence power the next gen of enterprise AI performance

According to Gartner, the global market for process mining software grew 40% in 2023. Worldwide sales for process automation are expected to reach $26 billion by 2027. Nearly 90% of corporate leaders surveyed by HFS Research plan to increase investments in process intelligence. A big part of the appeal, Gartner concludes: “Generative AI helps organizations use process mining to uncover hidden patterns, optimize operations and make informed decisions.”

FedScoop - National Archives getting a big boost from AI to transform its search capabilities

NARA’s CTO says the agency has gone all in on the technology, with pilots on auto-filling metadata, PII redaction, FOIA processing and more.

TechTarget - Computer Weekly - Dell CTO: Enterprise AI poised to take off in 2025

One key difference, according to Roese, is that enterprises are primarily focused on inference – running data through existing models – rather than training their own models. This will require a different infrastructure approach: one that’s distributed, optimised for inference and potentially extending to the edge. “We’ve seen a maturing of the enterprise market in the last six months,” Roese observed, pointing to the rise of off-the-shelf AI tools and capabilities aimed at enterprises, which are generally better at consuming than producing technology.

ZDNET - The secret to successful digital initiatives is pretty simple, according to Gartner

"They define a well-scoped, impactful problem and use gen AI to solve [it], and it's easy to measure success and ROI. The most successful business cases identify how to solve a problem that the business already cares deeply about and [will] deliver additional value to customers."

VentureBeat - Generative AI grows 17% in 2024, but data quality plummets: Key findings from Appen’s State of AI Report

“Generative AI outputs are more diverse, unpredictable, and subjective, making it harder to define and measure success,” Chen told VentureBeat. “To achieve enterprise-ready AI, models must be customized with high-quality data tailored to specific use cases.”

Computerworld - GenAI surges in law firms: Will it spell the end of the billable hour?

For more than a decade, law firms have been using machine learning and artificial intelligence tools to aid the discovery process, helping them hunt down paper trails and digital content alike. But it wasn’t until the arrival two years ago of OpenAI’s generative AI (genAI) conversational chatbot, ChatGPT, that the technology became common and easy enough to use that even first-year associates straight out of law school could rely on it for electronic discovery (eDiscovery).

ZDNET - Generative AI doesn't have to be a power hog after all

But increasingly, businesses are finding that building small custom models doesn't require massive banks of powerful systems (with some companies I've heard from building their models on a single engineer's laptop) and that power consumption can be kept to a minimum. In fact, when we asked what technologies businesses were purchasing to support their AI initiatives, GPUs had dropped to 4th place while increased storage capacity and hybrid cloud capabilities topped the list.

Information Week - Gartner Keynote Bites into AI ‘Sandwich’

They also talked about the different races going on to adopt GenAI. It’s important to understand, they said, that the vendor race happening should be separated from their own race to implement AI technologies. CIOs are bearing most of the burden of rapid AI innovation expectations: A Gartner survey showed 57% of CIOs were tasked with creating an AI strategy.

Big Data Wire - Riverbed Finds Financial Services Leading AI Readiness Despite Gaps

At the same time, Financial Services decision-makers are also more assured in the practical benefits of AI than most, with 96% of respondents believing it provides their business a competitive advantage. Financial organizations are vying for an edge against digital-native startups – which demands a strategic and practical approach to AI that reduces costs, increases efficiency, offers bespoke services, and mitigates customer risk.

InfoWorld - AI stagnation: The gap between AI investment and AI adoption

This significant talent shortage means that enterprises cannot implement AI technologies. This stifles innovation. This disconnect between high levels of investment and the slowing pace of AI adoption underscores the need for a more strategic approach, bridging the gap between technological advancements and practical applications. This is what it will take to get us out of the AI stagnation intersection.

CIO - CIOs under pressure to deliver AI outcomes faster

The enterprise landscape is littered with Version 1.0 generative AI proof of concept projects that did not materialize into business value and have been dumped. While the industry remains in the early stages of uncovering and implementing AI use cases into business workflows, the blueprint for success remains elusive for many CIOs.

Big Data Wire - Hammerspace Report Reveals New Enterprise Uses for GPUs Beyond AI

“The next wave of innovation is being driven by how companies activate their unstructured data,” said David Flynn, founder and CEO of Hammerspace. “Our research shows that the GPUs many enterprises originally purchased for AI projects are becoming the Swiss Army knife of data processing. This infrastructure is unlocking value in ways we never expected across various sectors.”

Diginomica - AI dystopia? Not if I can help it, says self-confessed AI optimist Zack Kass

Kass says a recent study he took part in found there's currently a 12% profit margin improvement by companies meaningfully adopting AI. He predicts the beginning of an incredible boom cycle as companies introduce AI into their systems, both on a cost basis and a revenue basis, as the most important trend he is currently tracking is the rapid decline in cost of this technology.

AiThority - Why Is AI Washing Harmful to the AI Industry?

Similarly, companies may claim that their onboarding process uses AI, but only a small element, like document recognition, might be powered by AI. In reality, most of the process relies on traditional methods with minimal AI integration. Also, AI’s potential in credit scoring seems excellent — it can help with more detailed and sophisticated big data analysis. However, it is again often a conventional data-based automation with minimal ML usage.

Forbes - How You Can Make Money With AI: Real Opportunities You Can Tap Into

Creating an app sounds like a big task, but platforms like Microsoft Azure and IBM Watson allow small teams—or individuals—to build AI-powered apps. Entrepreneurs are developing everything from chatbots for customer service to tools that help with scheduling and task management. If you spot a problem that an app can solve, there’s a good chance you can build a solution with AI.

FedScoop - For Customs and Border Protection, AI has been a ‘game-changer’

Alalasundaram pointed specifically to Google’s Vertex AI system, which allows CBP staffers to “search across disparate data sources” and integrate that data into one entity. “It’s an absolute game-changer,” he said.

ZDNET - AI is a $9-trillion market, and enterprises have barely begun to touch it

"Traditional robotic process automation, embodied by UiPath, has struggled with high set-up costs, brittle execution, and burdensome maintenance," they said. "Two novel approaches, FlowMind (JP Morgan) and ECLAIR (Stanford), use foundation models to address these limitations."

Dell - AI Projects Only Matter If They Move the Needle

Business and IT stakeholders also examine the implementation feasibility of each use case. Some of the key categories of feasibility are data availability and readiness, degree of AI model tuning, processes and people and a capable platform.

Diginomica - Grounding AI in reality - Qlik isn't looking through rose-tinted glasses

Qlik's study found that 58% of leaders are trying to secure investment in AI technologies, but the rush to embrace AI can be fraught with missteps. As some organizations have discovered to their public embarrassment, launching AI projects without a clear roadmap or understanding of their long-term impact can lead to costly failures. More than 30% of early AI adopters have seen over 50 AI projects fail after the development stage.

FedScoop - VA staffers are piloting two chatbots for internal use

“The reception has been extremely positive, especially for the basic chat interface,” Yuen said. “I think people are just super excited to have something that makes their daily life a little bit easier. … For the [smaller] tool, I think there’s a little bit more feedback about how they wish that it had a bit more features and we are working toward building some of those features.”

Computerworld - Here’s how Cleary Gottlieb law firm uses genAI for pre-trial discovery and more

For more than a decade, law firms have been using machine learning and artificial intelligence tools to help them hunt down paper trails and digital documents. But it wasn’t until the arrival two years ago of OpenAI’s generative AI (genAI) conversational chatbot, ChatGPT, that the technology became easy enough to use that even first-year associates straight out of law school could rely on it for electronic discovery (eDiscovery).

Diginomica - digibyte - (welcome) CIO candor around AI enterprise expectations

CIOs also appear aware that the AI hype cycle has washed over everyone in the business, thanks mainly to the impact of generative AI at a consumer level, and as such, expectation management is needed, with CIOs citing a mismatch between departments when it comes to AI. Functional areas such as customer service may well be among the most frequently cited as having most potential AI use cases today, but they are also potentially among the least prepared for the rollout of the technology in practice.

Computerworld - How Ernst & Young’s AI platform is ‘radically’ reshaping operations

Even so, the company’s executive leadership insists it’s not handing off all of its business functions and operations to an AI proxy and that humans remain at the center of innovation and development. Looking to the future, EY sees the next evolution as artificial general intelligence (AGI) — a neural network that will be able to think for itself and capable of performing any intellectual task a human can at that point it will become a “strategic partner shifting the focus from task automation to true collaboration between humans and machines,” according to Beatriz Sanz Saiz, EY global consulting data and AI leader.

Forbes - The Increasing Use Of AI In Education

AI is particularly useful in “hyperpersonalizing” education, in which we can treat each individual as an individual and provide a deeply personalized learning experience. This personalized approach is helping students learn at their own pace, addressing their individual strengths and weaknesses, rephrasing or reframing content so that it can be better understood, and helping learners focus on areas where they need or want to dive deeper.

FedScoop - Data, talent, funding among top barriers for federal agency AI implementation

A FedScoop analysis of 29 of those documents found that data readiness and access to quality data, a dearth of knowledge about AI and talent with specific expertise, and finite funding levels were among the most common challenges that agencies reported. Agencies also disclosed obstacles when it comes to their IT infrastructure, limitations in government-ready tools, and testing and evaluation challenges, among other issues.

The Wall Street Journal - Companies Had Fun Experimenting With AI. Now They Have to Show the Returns.

“This is a year where you have to be expecting business results,” Brynjolfsson said, adding that the technology is mature enough to deliver them. “This is a time when you should be getting benefits, and hope that your competitors are just playing around and experimenting.”

ZDNET - Why agentic AI is the new electricity, and nearly 80% of business leaders are afraid of the dark

The survey revealed that 71% of respondents said AI agents would increase workflow automation, 64% said they'd improve customer service and satisfaction, and 57% said the potential productivity improvements outweighed the risks. The biggest use case (75% of respondents) for agentic artificial intelligence (AI) was in software development -- to generate, evaluate, and rewrite code.

Diginomica - FORWARD 2024 - the future of enterprise AI and agentic automation

Those businesses that capture these advantages with AI and automation now are best positioned to harness the next evolution in AI with AI agents, also known as agentic automation. Agentic automation is an evolutionary leap from robotic process automation that combines robots, agents, and humans to deliver AI transformation enterprise wide. But for many enterprises, agentic AI is a new buzzword entirely – and a new set of questions are on the table.

ITPro Today - Roulette or Rigor? Don’t Rely on Luck With Generative AI

As the generative AI hype subsides, some early adopters have already seen compelling business value in process automation, customer support, and IT operations. However, as with any groundbreaking technology, the implementation of generative AI is fraught with challenges. So, what have successful organizations done differently? Instead of playing roulette with AI, they managed their deployments thoughtfully and thoroughly, steering clear of costly errors and missed opportunities.

Information Week - Avoiding GenAI Disillusionment to Make Magic in the Cloud

Some believe cloud-based GenAI is not cost-effective because it’s less expensive to deploy the necessary high-end processing and networking on-premises. However, operating GenAI on-premises requires GPUs, which are both hard to find and expensive, and you need to run workloads 24x7 at a 90% resource utilization. This isn’t effective for organizations that want, and financially need, to develop incrementally

SiliconANGLE - Desperately seeking AI ROI as IT budgets tighten

The implication is that AI investments are going to have to start throwing off positive cash flow or business line managers will be under pressure. That pressure will ripple through the organization and cause a potential backlash. The reality is the super-high-value AI projects will take many more months or even years to pay super large dividends at most companies. Though technology progresses quickly, organizations’ ability to absorb it broadly is not trivial. As such, the macro picture won’t see the effects of AI for 12 to 18 months at least and there could be some pain in the meantime.

Information Week - The Great Accelerator: Why Generative AI Is Primed for Long-Term Impact

What’s further, the modernization of legacy applications and platforms has garnered elevated interest in generative AI. More and more enterprises are seeking generative AI-assisted modernization that is more secure than traditional methods and are looking to accelerate those initiatives. Because of reduced human dependency, these generative AI models have the vast ability to identify vulnerabilities and process threat incidents faster, thus enhancing the overall accuracy of organizational operations.

VentureBeat - Skeptical about AI? It’s normal (and healthy)

Conversely, though, a recent study predicts that by the end of 2025, at least 30% of generative AI projects will be abandoned after the proof-of-concept stage, and in another report “by some estimates more than 80% of AI projects fail — twice the rate of IT projects that do not involve AI”.

Forbes - The Game-Changing Impact Of Generative AI On The Enterprise

First and foremost is platform enablement. Organizations need to carefully consider their technology choices and how they'll build their overall AI environment. This isn't just about selecting the right software; it's about creating an ecosystem that can support and scale AI initiatives across the enterprise. Brier emphasizes that this decision is more complex than simply choosing between vendors. Companies must consider factors such as integration capabilities, scalability, and alignment with existing IT infrastructure.

AiThority - AI and Its Biggest Myths: What the Future Holds

It’s worth taking a moment to acknowledge the significant impact AI is having across various industries. In e-learning, for example, companies like Coursera and Duolingo are using it to create more personalized learning experiences. AI algorithms can analyze how users interact with content and then tailor future lessons to their specific needs and interests. This kind of personalization is something that traditional education methods often struggle to achieve with outdated one-size-fits-all content.

eWeek - 13 Generative AI Examples (2024): Transforming Work and Play

One industry that seems nearly synonymous with AI is advertising and marketing, especially when it comes to digital marketing. Many marketers feel AI can reduce the amount of time spent on manual tasks to make room for enhanced creativity. As a result, the advertising and marketing sectors are experiencing a paradigm shift with the integration of generative AI. They are seeing unprecedented levels of personalization, content creation, and customer engagement.

Diginomica - Workday Rising 2024 - customers reveal early lessons from gen AI in action

Sanchez acknowledged that AI/automation projects can stoke internal fears of job loss. DataStax has an instructive way to handle this: address the future of work at head-on, and: give employees a flavor for new roles they could take on. Some companies are doing a poor job of communicating what AI means to them

eWeek - 6 Generative AI Use Cases (2024): Real-World Industry Solutions

Generative AI technologies are proving invaluable in healthcare, aiding in everything from administrative tasks to drug discovery. By using GenAI, healthcare professionals can improve daily operations, enhance patient care, and accelerate research. Some of the most common GenAI tools for healthcare include Paige, Insilico Medicine, and Iambic.

AIThority - TestRail Releases Landmark Report on AI’s Role in Quality Assurance

Based on the QA industry’s first AI-focused research survey, this comprehensive report draws on insights from over 1,000 QA professionals and aims to cut through the hype surrounding artificial intelligence, offering a clear and accurate picture of how QA teams are adopting, planning for, and responding to AI technology.

ZDNET - Bank of America survey predicts massive AI lift to corporate profits

Software is the industry that may see the greatest product margin expansion (5.2%) due to enterprise Gen AI, followed by semiconductors, and the energy sector. The least likely sectors to benefit are healthcare equipment and services, and telecommunications, which may see a deterioration of profit margins, according to the bank.

Computerworld - Checkr ditches GPT-4 for a smaller genAI model, streamlines background checks

That move did the trick. The accuracy rate for the bulk of the data inched up to 97% — and for the messy data it jumped to 85%. Query response times also dropped to just half a second. Additionally, the cost to fine-tune an SLM based on Llama-3 with about 8 billion parameters was one-fifth of that for a 1.8 billion-parameter GPT-4 model.

Computerworld - Microsoft 365 Copilot rollouts slowed by data security, ROI concerns

A Gartner survey of 132 IT leaders at companies of a variety sizes in June — around half with 10,000 or more employees — showed that 60% of respondents have started pilot projects to deploy Microsoft 365 Copilot. But just 6% had finished their pilots at that point and were actively planning large-scale deployments. And only 1% had completed a Copilot deployment to all eligible office workers in their organization.

Forbes - How Executives Should Think About AI In 23 Questions

For example, in some 2023-2024 data we recently collected at Villanova University, we discovered that despite all of the excitement around AI, only 20% of the companies surveyed defined AI initiatives as high priority, and over 47% defined them as insufficient or unknown. Consistent with this finding, only 25% adequately fund their AI initiatives and 37% believe they do “sometimes.” 37% report that their AI initiatives are not adequately funded or they just don’t know!

Diginomica - AI fast becoming a young male preserve, suggests workplace research

Companies are buying the hammer then looking for a nail, yet in the meantime are throwing that hammer around the workplace – and in some cases using it to help re-erect employment walls that have been slowly dismantled this century.

AiThority - Identifying and Overcoming AI Challenges with Strategic Solutions

Compounding these infrastructure challenges is a widespread shortage of skilled IT professionals, which makes it harder to meet evolving needs. Organizations lacking specialized expertise might struggle to deliver seamless digital experiences, leading to dissatisfied customers and a weakened competitive edge. Additionally, communication gaps between tech teams and the C-suite can exacerbate these issues, as executives may not fully understand the scope of the challenges.

Forbes - AI Agents Will Be The Key To Achieving ROI From AI

For example, an AI agent will have both programmatic or deterministic qualities as well as holistic or unstructured qualities (such as LLM prompts and outputs). It might, for example, initiate a credit check when a user asks questions about a loan; in that case, the LLM would craft a loan proposal based upon the credit score and the natural language inputs of the user, taking into account what terms other banks are offering. By contrast, trying to do the same thing using a chatbot would require multiple requests, and the user might or might not understand how to craft the prompts to get the answer they need.

InfoWorld - How to get LLM-driven applications into production

Many organizations are building generative AI applications driven by large language models (LLMs), but few are transitioning successfully from prototypes to production. According to an October 2023 Gartner survey, 45% of organizations are currently piloting generative AI, while only 10 % have fully deployed it. The lack of AI success is similar for enterprises, product companies, and even some startups focused on LLM-based applications. Some estimates place the failure rate as high as 80 %.

VentureBeat - Generative AI adoption surpasses early PC and internet usage, study finds

“Generative AI has been adopted at a faster pace than PCs or the internet,” the researchers write. “This is driven by faster adoption of generative AI at home compared with the PC, likely because of differences in portability and cost.” The ease of access to tools like ChatGPT and Google Gemini has played a crucial role in this faster uptake.

ZDNET - 98% of small firms are using AI tools to 'punch above their weight'

According to the report, "technology use is linked to growth among small businesses." Many small businesses that deployed technology platforms (such as productivity tools, digital payment, and accounting software) are "more likely to have experienced growth in sales and profits over the past year as well as an increase in their workforce."

InfoWorld - Too much assembly required for AI

I could go on. The point is that in these early days of AI, we keep expecting mainstream users to be able to do all the work of understanding and manipulating still-janky LLMs. That’s not their job, just as it wasn’t the “job” of mainstream enterprises to get under the hood and compile Linux for their servers. Red Hat and others came along to package distributions of Linux for mass-market use. We need the same thing for genAI and soon. Once we get that, we’ll see adoption (and the productivity it can generate) soar.

AiThority - An AI Use Case that Every Company Needs – Fixing Revenue and Margin Leakage

Anomaly detection: One of the strengths of AI is its ability to identify outliers and anomalies in data that may be indicative of leakage. For example, AI can analyze sales data to detect unusual discounting patterns, or even changes in revenue or order frequency, that may be correlated with past account churn or defections.

Diginomica - Workday Rising 24 - three CIOs on modernizing tech and improving business processes

Three stories that demonstrate that the role of the CIO, and the platform they chose to implement, are about so much more than technology; these are stories of organizations working in new and more efficient ways.

AiThority - The Promises, Pitfalls & Personalization of AI in Healthcare

Beyond more time with patients, AI can provide real-time insights from patients and clinical teams that traditional surveys or feedback systems simply cannot. New, human-centric AI solutions may be built with a moral framework that ensures the technology closely aligns with societal values to deliver compassionate feedback, relevant coaching and the next best actions to frontline teams, leaders and clinicians.

Forbes - Achieving AI Success In 2024: A Blueprint For IT Leaders

The first step is choosing the right use case for your AI initiative. The most promising use cases are often simply alleviating nagging challenges that have long troubled an organization. These are the problems that have persisted for years despite previous attempts to solve them, and they are typically aligned with overarching company priorities.

Government Technology - Building an Enterprise AI Model

The cloud is an essential part of an enterprise strategy for data access. A flexible and secure cloud computing environment lets organizations easily retrieve data and use it with different AI models. Moreover, this environment provides the compliance guardrails required for sensitive government data, along with a range of AI tools to help organizations take advantage of AI technology in new ways.

Digital Insurance - How insurers use AI to improve their practices

AI can help detect fraud in insurance, but this same technology can be used against insurers to submit fraudulent claims. Rather than using Gen AI to manipulate damage photos for claims, a bigger issue is its use for false identification of policyholders making claims. Synthetic IDs, which are fabricated identities that are not based on real people, are being used to make claims, Karen Jennings, a special investigations unit manager at American Family Insurance, told Digital Insurance's Michael Shashoua.

Data Science Central - How to transform your business digitally with AI

What are the potential downsides of AI in online businesses?

There are multiple drawbacks such as upfront cost, professional staff needed, a small mistake that can lead to the generation of inaccurate results, and many more.

VentureBeat - Grounding LLMs in reality: How one company achieved 70% productivity boost with gen AI

Drip Capital’s approach to AI implementation is notable for its pragmatism. Rather than attempting to build their own LLMs, sophisticated Retrieval Augmented Generation (RAG), or engage in complex fine-tuning, the company has focused on optimizing their use of existing models through careful prompt engineering.

Forbes - Most Investment Banks Are Applying AI According To This Study

Here’s a number that stood out to me – the assertion that the study found AI can potentially generate up to $1 million annually in value, per employee, in many investment banks.

VentureBeat - DataStax CEO: 2025 will be the year we see true AI transformation

Kapoor strongly advocates for open-source solutions in the GenAI stack, and that companies align themselves around this as they consider ramping up with AI next year. “If the problem is not being solved in open source, it’s probably not worth solving,” he asserts, highlighting the importance of transparency and community-driven innovation for enterprise AI projects.

Forbes - AI’s Role In Saving Teachers Time And Revolutionizing Education

Grading assignments, preparing lesson plans, managing student feedback, emailing parents, and attending professional development meetings all pile up behind the scenes. Teachers often spend more time on these tasks than they do teaching in the classroom. The result? High levels of stress and an increasing number of teachers leaving the profession.

Computerworld - AI to create better products and services, add $19.9T to global economy — IDC

A survey of CFOs in June by Duke University and the Atlanta and Richmond Federal Reserve banks found that 32% of organizations plan to use AI in the next year to complete tasks once done by humans. And in the first six months of 2024, nearly 60% of companies (and 84% of large companies) said they had deployed software, equipment, or technology to automate tasks previously done by employees.

Fast Company - Beyond the hype: The hard truth about AI and data

According to an IBM study, the financial impact of poor data quality on the U.S. economy is estimated to be $3.1 trillion annually, and only 53% of companies surveyed can leverage big data for a competitive advantage. AI will highlight any decencies in the overall data model and schema. AI won’t function as promised if the data isn’t accurate.

Forbes - AI Overlords To AI Overload: Why The AI Hype Needs A Reality Check

AI will undoubtedly remain a major topic of discussion, but there’s an increasing trend of "AI-washing," where marketing hype often eclipses tangible benefits. However, genuine advancements are still happening in fields like medical imaging, surveillance, cybersecurity, industrial production, autonomous driving, amongst others. As AI continues to permeate industries, the real challenge lies in distinguishing meaningful innovation from mere buzzwords.

SmartR AI - Decoding AI Adoption

While embracing AI is crucial for staying competitive to keep up or leapfrog its competitors, overzealous adoption without proper experience can lead to detrimental outcomes. These companies must prioritize gaining experience and proficiency in implementing and managing AI solutions effectively before attempting widespread integration.

AiThority - Nearly 70 Percent of Leaders Prioritize GenAI for Data, with Almost Half Expecting to Double ROI in Three Years, Study Reveals

According to the report, the early adopters find that the technology’s ability to accelerate data-driven decision-making is a key benefit of implementation (44%), alongside its ability to improve products and services (44%), closely followed by how the technology can improve the quality of business insights (42%).

Datanami - Riverbed Global Survey Shows AI Adoption Accelerating, But Gaps Remain

However, the next three years are anticipated to be a period of rapid expansion as enterprises seek practical AI approaches and solutions, and by 2027, 86% of leaders expect their organization to be fully prepared to implement their AI strategy and projects.

Salesforce - New Research Identifies 5 Types of People Defining the AI-Powered Future of Work

Slack’s new Workforce Lab research explores what motivates workers to use AI and how they feel about using it at work. Through in-depth interviews and a survey of 5,000 full-time desk workers, the research uncovered five distinct AI personas that employers need to understand as they implement AI and bring workers onboard “The AI Team” — a workplace where humans and AI agents work successfully side-by-side

Building - Top 150 Consultants 2024: What artificial intelligence and machine learning tools are you using?

“We also have an AI-powered automated tool for checking standard design documents. We have an AI supported knowledge-sharing internal tool. We are in development of a number of other AI applications across the business and we have developed a number of machine learning applications for clients.”

Forbes - Corporate Gen AI Projects Should Plan For Failure - And That’s Okay

As we close in on the two-year anniversary of the launch of ChatGPT-3, it’s become clear that there’s a very high chance your organization will “fail” at building a generative AI assistant. There’s a high likelihood your firm will make the wrong choices, requiring a significant rebuild of the AI assistant sometime within the next three years.

Information Week - Forrester CEO: Lessons for Executives to Implement AI Successfully

“We looked at it and realized, oh, this is only running at 65% accuracy -- not good enough for our clients,” Colony said. “So, the early part is euphoria, but then the hard work sets in. It took us about 12 months to move from 65% accuracy to 85% ... So, when you develop, it’s going to look easy at first, and then it’s going to get really, really, hard.”

AiThority - Kyndryl Survey Reveals 86 Percent of Enterprises Are Moving Fast to Adopt AI to Accelerate Mainframe Modernization

According to the survey respondents, IT modernization projects and patterns are yielding substantial business results, including triple-digit one-year return on investment (ROI) of 114% to 225% and collective savings of $11.9 billion annually. Almost all organizations have opted for a hybrid IT strategy — a combination of modernizing on the mainframe, integrating with public/private cloud, and moving applications and data off the platform. Furthermore, 96% of respondents are migrating some workloads — on average 36% — to the cloud.

ZDNET - Early adopters are deploying AI agents in the enterprise now, with scaled adoption in 2025

What we describe as the six levels of autonomous work refer to the maturity levels of AI assistants versus AI agents. To better understand the adoption forecasts and the impact of AI assistants and agents in the workplace, AI agents are made possible through the emergence of large language models (LLMs) that enable deep language understanding, reasoning, and decision-making.

SiliconANGLE - Salesforce unleashes an army of artificial intelligence bots with Industries AI

“Organizations of every size and every budget can now easily get started with AI capabilities that were purposefully designed to solve their specific challenges, whether it’s helping banks resolve transaction disputes faster, helping retailers better manage their inventories and more,” he said.

Information Week - Designing for the ‘Human in the Loop’

The practical application of AI can often fall short due to a failure to integrate it thoughtfully. Leaders recognize this, but many are still trying to understand what working harmoniously with AI really looks like in their organization. Deloitte research shows that 73% of leaders believe in the importance of ensuring human imagination keeps pace with tech innovation, but a mere 9% are making progress toward achieving that balance.

Forbes - The State Of The AI Super Cycle - NVIDIA, Apple, And The Overall Demand For AI

Then there is a collection of companies just trying to find their place in the AI race. We know the AI device (handsets and PCs) cycle has been a bit slower and that incremental growth of SaaS from AI is showing signs of life from the likes of ServiceNow Inc., Salesforce Inc., and CrowdStrike Inc., who are proving AI can provide stickiness and drive growth on the top and bottom lines. Things have only just begun here.

ZDNET - 1 in 3 workers are using AI multiple times a week - and they're shouting about it

The Observer (16%): Observers have yet to integrate AI into their work. They are watching with interest and caution. This population is mostly indifferent (66%) about AI in the workplace. One-third are interested in learning or further developing AI skills. Companies can work proactively to inspire one out of five workers to be AI advocates. The true enemy of progress is indifference.

TechRepublic - Generative AI Projects Fail Amid High Costs and Risks

The average AI investment of global IT leaders was $879,000 in the last year, according to a report by automation software provider ABBYY. Almost all (96%) of respondents to that survey said they would increase these investments in the next year, despite a third claiming they have concerns about these high costs.

Diginomica - A new enterprise gap to bridge - employers buy into gen AI for productivity gains, their workers aren't so sure

Some of this is because they have to spend time learning how to use the new systems (23%). But two in five also complain of needing to spend more time reviewing or moderating AI content. One in five even say they are finding themselves being asked to do more as a direct result of the technology being introduced.

AiThority - What is Return on AI – and How Do Companies Measure It

This article aims to demystify the concept of RoAI and provide you with a blueprint to measure the true impact of GenAI beyond the hype. We’ll explore why understanding and quantifying RoAI is crucial – not just for tech teams, but for anyone in a leadership role looking to make informed, strategic decisions about AI investments. After all, shouldn’t the adoption of new technology be as smart as the technology itself?

ZDNET - A third of all generative AI projects will be abandoned, says Gartner

For example, at the low end of the scale, using a Gen AI API, which allows a user to consume the publicly-hosted Gen AI model, for things such as coding assistance, means a company might spend around $100,000 to $200,000 upfront, and up to an additional $550 per user per year, Gartner estimates.

Network World - Businesses struggle to balance AI tools and employee skills

The majority of respondents also indicated they would be purchasing business tools with AI features as well as investing in AI tools from vendors. Fewer respondents said they’ll be developing their own tools for internal use. Among IT staff, 71% said they would be buying business tools with AI features, while 67% of business staff said the same. Thirty-five percent of IT staff said they would purchase AI tools from vendors, while 32% of business staff said they would do the same.

VentureBeat - Why we need to check the gen AI hype and get back to reality

We are still in AI’s toddler phase, where popular AI tools like ChatGPT are fun and somewhat useful, but they cannot be relied upon to do whole work. Their answers are inextricable from the inaccuracies and biases of the humans who created them and the sources they trained on, however dubiously obtained. The “hallucinations” look a lot more like projections from our own psyche than legitimate, nascent intelligence.

Forbes - Deja Vu All Over Again? Smoothing The Ups-And-Downs Of AI Hype Cycles

Data quality and availability are also currently the major issues that are slowing down or inhibiting AI from living up to its promise. “AI systems are fundamentally reliant on the quality of the data they are trained on,” said McDonagh-Smith. “I see many organizations struggling with data silos, inconsistent data formats, and complex privacy concerns that span geographies and jurisdictions.”

VentureBeat - Introducing AI’s long-lost twin: Engineered intelligence

When a breakthrough is made in AI, however, there is no distinct discipline for applied artificial intelligence, leading to organizations investing in hiring data scientists who earned their PhD with the aspiration of making scientific breakthroughs in the field of AI to instead try to engineer real-world solutions.

The result? 87% of AI projects fail.

AiThority - Kong’s 2024 Report: 83 Percent of Developers See AI Investments Creating New Product Opportunities

Of course, strengthening security should remain a priority as the report finds an expected 548% growth in the forecasted annual number of API attacks by 2030. Understandably, data privacy and security/governance were a top concern for nearly 60% of developers surveyed when it comes to integrating AI services with existing microservice infrastructure.

Diginomica - Going slow and knowing when not to use generative AI - ServiceNow’s VP of Employee Workflows shares thoughts

Speaking with Alarcon, it’s clear that in her experience buyers are taking a practical and measured approach to how they think about generative AI. Whilst often the impression from vendors can be that generative AI is the key to solving all of an organization’s problems, the reality is that there will be applications where it is useful and situations where it is not. Companies will likely start small, test the appetite amongst employees and consider where they see value. In particular, this in many ways can be seen as an extension of the AI/ML work that enterprises were already implementing.

Blocks & Files - Starburst research highlights key strategies driving AI success

However, in terms of technical obstacles, 52 percent of organizations said they faced “significant hurdles” in organizing structured data for machine learning with AI applications, and 50 percent cited difficulty in preparing unstructured data for retrieval-augmented generation (RAG) in AI deployments.

Computerworld - BCG execs: AI across the company increased productivity, ‘employee joy’

BCG allows its consultants to build their own GPTs for specific customer interactions, which has fostered an atmosphere of innovation. To date, more than 6,000 GPTs have been created by BCG’s employees to perform tasks such as summarizing documents and video meetings, and automatically generating email responses to clients.

FedScoop - Unlocking the potential of AI with a modern data strategy

Iqbal adds that federal agencies also face similar challenges with data fragmentation and governance. “There needs to be a cloud-based, centralized data lake architecture to store and manage all the structured, semi-structured and unstructured data across the organization,” he says. Iqbal outlines five elements crucial for effective AI applications: a unified data platform, seamless data integration, scalable analytics, secure data governance and access to diverse large language models.

Forbes - When Will AI’s Rewards Surpass Its Risks?

The database of 700-plus AI risks finds more attributed to AI systems (51%) than humans (34%), and were more likely to be seem after AI was deployed (65%) rather than during its development (10%). However, even the most thorough AI frameworks overlook approximately 30% of the risks identified across the factors surfaced in the database.

ZDNET - Six levels of autonomous work: How AI augments, then replaces

New companies very soon will be AI natives, meaning that they simply will not hire humans in the first place except when they have to. These companies will probably show the rest of us where humans are still valuable and where they're not, and we'll follow suit (some faster than others).

Forbes - Why Artificial Intelligence Hype Isn't Living Up To Expectations

You might assume, given the fervor around AI, that industries across the board are rapidly integrating these technologies. Despite the excitement surrounding AI technologies, actual use remains surprisingly low. By February 2024, only 5.4% of firms were utilizing AI. Even with optimistic projections, this number is only expected to reach 6.6% by Fall 2024. This limited adoption suggests that AI’s overall impact is still confined to a narrow slice of the business world.

HRD - Is the hype surrounding generative AI declining?

The survey found that 41% of respondents have struggled to define and measure the impact of their GenAI efforts, with only 16% regularly reporting on the value created by these initiatives.

Rand - The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed

Industry leaders should ensure that technical staff understand the project purpose and domain context: Misunderstandings and miscommunications about the intent and purpose of the project are the most common reasons for AI project failure.

PR Week Magazine - AI and communications: Rising above the hype

“It's best for those companies that can adopt it in a way that's natural for their business, brand and culture,” notes HPE’s Paula Berg. “There's a real benefit to companies who allow their employees to use these tools and start to make the cultural changes that will be needed to see the ROI in the coming years.”

Forbes - Hype And Hope Don’t Hide AI’s Hurdles

On one hand, organizational, technical and socio-technical challenges related to the evident need for human oversight, transparency, explainability, technical robustness, security, privacy, data governance, non-discrimination, and fairness are nothing less than business performance requirements for which the hope has not yet fully met its potential.

Computerworld - Generative AI is sliding into the ‘trough of disillusionment’

AI-assisted code generation tools are increasingly prevalent in software engineering, and somewhat unexpectedly have become low-hanging fruit for most organizations experimenting with genAI. Adoption rates are skyrocketing. That’s because even if they only suggest a baseline of code for a new application, automation tools can eliminate hours that otherwise would have been devoted to manual code creation and updating.

Teckedin - AI Adoption and Usage

Every one of us has data in some system, at some company, or in a government agency. I want my data completely purged after it is used for whatever purpose it was collected for. You would think an AI could take care of this. However, our data appears more at risk than it has been with all the scraping of information for AI training purposes.

VentureBeat - Deloitte survey reveals enterprise generative AI production deployment challenges

The new report paints a picture of organizations striving to capitalize on gen AI’s potential while grappling with issues of scalability, data management, risk mitigation and value measurement. It highlights a critical juncture where early successes are driving increased investments, but the path to widespread implementation remains fraught with obstacles.

Information Week - Enterprises Are Racing to Leverage GenAI, But Can They Scale It?

“And, board members often are leading from, how do we know this isn’t going to get us in trouble more than how do we know this is a way to really accelerate the value of the organization? So, I think tech companies are getting pulled in on the trust side, because they’re going to be asked, ‘How do you protect this?’”

SmartR AI - The Reality Check on AI Progress: Challenges and Opportunities

The pace of AI innovation is outstripping the market's ability to adapt and find practical applications; the market is still trying to figure out how and what they should use this new technology for. This has led to overinvestment in promising ideas without sufficient market demand to support them.

Pascal's Substack - GPT-4o: Nearly all companies (98%) indicated they are willing to forgo being the first to use AI in their sector if it means ensuring AI is delivered safely and securely.

Low Adoption Despite Hype: Despite the extensive hype surrounding AI, only 5.4% of U.S. businesses were using AI to produce a product or service in 2024. This is surprisingly low given the massive attention AI has received, indicating that many companies are still in the early stages of AI adoption.

Forbes - Calming Down Nervous Business Leaders As AI Proliferates

More deeply understanding the capabilities of AI, along with its shortcomings, is essential. “Businesses need to put forth the effort to understand AI and identify the appropriate AI models to use before adoption,” said Villanustre. “Like any tool, the adequate use of AI can deliver significant returns on investment, but the incorrect use has the potential of risk and loss.”

VentureBeat - 73% of organizations are embracing gen AI, but far fewer are assessing risks

She said it’s important to ensure accountability and ownership for responsible AI use and deployment be traced to a single executive. This means thinking of AI safety as something beyond technology and having either a chief AI officer or a responsible AI leader who works with different stakeholders within the company to understand business processes.

ZDNET - AI governance and clear roadmap lacking across enterprise adoption

Many also lack a comprehensive AI strategy and are acquiring products primarily for their bells and whistles, according to IBM's AI Readiness Barometer Study released this week. Just 17% of companies assessed in the report have a well-defined AI strategy, with the majority, 38%, still in the midst of developing an AI strategy. Another 30% have an AI strategy that is focused on specific use cases, while 7% admitted to having an AI strategy they eventually discarded or were unable to implement effectively.

CIO - Is the gen AI bubble due to burst? CIOs face rethink ahead

Gen AI projects can cost millions of dollars to implement and incur huge ongoing costs, Gartner notes. For example, a gen AI virtual assistant can cost $5 million to $6.5 million to roll out, with a recurring annual budget hit of $8,000 to $11,000 per user.

Datanami - Is the GenAI Bubble Finally Popping?

The lack of a “killer app” besides coding co-pilots and chatbots is the most pressing concern, critics in a Goldman Sachs Research letter say, while data availability, chip shortages, and power concerns also provide headwinds. However, many remain bullish on the long-term prospects of GenAI for business and society.

Forbes - Go Small To Go Big: Small Wins Prove AI Isn’t A Bubble

Our data is also showing triple-digit percent increases in multimillion-dollar proof of concepts (POCs) in 2024 and mid- to high- double-digit percentage growth of AI use in key industries like financial services, healthcare, and telco growing near 40% CAGR over the next five years. Use cases like object detection and conversational AI are also seeing more than 35% growth (five-year CAGR), which means the silicon investment and infrastructure build out will have to convert to software and industry use cases.

VoxEU - The effect of AI adoption on jobs: Evidence from US commuting zones

Our results also show that the negative effect of AI adoption is not limited to the service sector but also extends to employment in manufacturing, where the use of these technologies is still limited. Specifically, the manufacturing sector accounts for almost 45% of the overall impact of AI adoption on employment, against 60% for the service sector

Diginomica - Getting the business problem horse before the AI cart - MIT finds organizations taking a cautious approach

Shifting through the AI hype and having begun to understand the complexity and cost of implementing LLMs and gen AI from scratch, many companies surveyed have begun to work with partners, rather than adopting a go-it-alone approach, on the basis that if you don't have the necessary expertise and resources to make a significant investment, it’s better to fine-tune and optimize off-the-shelf models.

Forbes - Getting AI Past The Finish Line, Responsibly And Ethically

AI ambitions are substantial, but few have scaled beyond pilots, the survey showed. Fully 95% of companies surveyed are already using AI and 99% expect to in the future. But few organizations have graduated beyond pilot projects, the survey finds. The great majority, 76%, have only deployed AI in one to three use cases.

VentureBeat - LLM progress is slowing — what will it mean for AI?

Over time, we could see some level of commoditization set in, similar to what we’ve seen elsewhere in the technology world. Think of, say, databases and cloud service providers. While there are substantial differences between the various options in the market, and some developers will have clear preferences, most would consider them broadly interchangeable. There is no clear and absolute “winner” in terms of which is the most powerful and capable.

CNBC - The gap between AI expectations and outcomes in the workplace are wide

Meanwhile, companies are also increasing their spending on new AI tools. Forty-four percent of companies said artificial intelligence is the single-largest technology spending budget item for them in the next year and 60% described their new AI investments as “accelerating” in a recent CNBC Technology Executive Council survey.

ZDNET - AI will change all businesses and most leaders are not ready

The report notes that using real-time data and AI in change initiatives can help leaders understand what changes are happening, which areas of the company are most affected, and what actions are best to maximize their investments.

VentureBeat - 86% of enterprises see 6% revenue growth with gen AI use, according to Google Cloud survey

At 63%, more than half credited AI as a business growth driver. The survey noted that, on average, companies saw improved customer leads and acquisitions directly stemming from AI tools. While other verticals like retail and manufacturing also ranked AI-powered lead generation high, 82% of respondents in the financial services said it found the most growth in that area thanks to AI.

Computerworld - How to train an AI-enabled workforce — and why you need to

IT leaders in all regions and sectors are struggling to get skilled people into the right roles and/or upskilling current employees. IDC research reveals that more than half of organizations worldwide are seeing product delays, quality problems and lost revenues because of the skills crisis, according to reserach firm IDC. IDC predicts that by 2026, more than 90% of organizations will similarly suffer, amounting to more than $5.5T in losses relating to product delays, impaired product quality and missed revenue goals.​

Forbes - Is The AI Bubble About To Burst?

Unlike many dot-com era startups, these AI-centric companies benefit from the support and partnerships of larger tech firms. Moreover, the AI startup landscape is less crowded than the dot-com boom, with fewer companies gaining significant traction. This more concentrated field of serious contenders might lead to a different outcome than we saw in the early 2000s.

Diginomica - Twelve percent of marketers can't live without AI? The State of Marketing AI study holds some surprises

When asked at what stage of AI transformation they are at, nearly half (49%) said they are still understanding (ie learning how AI works and exploring use cases and technologies), 41% are piloting (ie running small pilots with narrow use cases), and 10% are scaling (ie wide-scale adoption that is leading to efficiencies and increased performance). Again, Roetzer said the scaling number is probably lower, depending on how you define scaling.

Forbes - Points Of Adoption On The Gen AI Continuum

But, data work alone is not sufficient; they also needed to integrate the Gen AI technology into their existing technology. In addition, achieving the disruptive impact and improving the business function required a significant investment in operations. The audit firms had to retool the way they conduct audits, restructure the processes, and train the audit teams to use these new processes and new techniques.

Data Science Central - Small Language Models: A Strategic Opportunity for the Masses

While LLMs play a pivotal role in generative analytics, only a few companies have the capabilities and resources to develop and maintain them. Additionally, approaches such as Retrieval-Augmented Generation (RAG) and fine-tuning public LLMs do not fully leverage an organization’s proprietary knowledge while safeguarding sensitive data and intellectual property. Small language models (SLMs) tailored to specific domains provide a more effective solution, offering enhanced precision, relevance, and security.

Information Week - Are Enterprises Investing Too Much or Too Little in AI Now?

New technologies can rise or stumble depending on the monetary investment put into its development. OpenAI introduced an enterprise API to go after more of the business market. Yet, there are reports the company might lose $5 billion this year and potentially run out of money in 12 months.

VentureBeat - In the age of gen AI upskilling, learn and let learn

Generative AI’s rapid introduction across industries has led to a significant skills gap among not only leaders, but employees too, placing heightened pressure on all of us to rapidly uplevel our knowledge base and evolve with the times. However, this evolution isn’t happening organically: 62% of employees report that they lack the skills to effectively and safely use gen AI — and only one in 10 workers globally feel they possess in-demand AI skills.

Digit News - Gartner: 30% of GenAI Projects Will Be Abandoned by 2026

“What you spend, the use cases you invest in and the deployment approaches you take, all determine the costs. Whether you’re a market disruptor and want to infuse AI everywhere, or you have a more conservative focus on productivity gains or extending existing processes, each has different levels of cost, risk, variability and strategic impact.”

ZDNet - Intel sees AI in enterprise on a 'three to five-year path'

The data show "43% of enterprises are exploring proof of concepts on generative AI, but 0% of them had brought generative AI to production in terms of use cases," said Evers, summarizing the findings.

InfoWorld - The other shoe drops on generative AI

Reality has hit the AI hype machine. On Alphabet’s recent earnings call, CEO Sundar Pichai touted widespread adoption of Google Cloud’s generative AI solutions, but with a caveat—and a big one. “We are driving deeper progress on unlocking value, which I’m very bullish will happen. But these things take time.” The TL;DR? There’s a lot of generative AI tire-kicking, and not much adoption for serious applications that generate revenue.

Futurism - Investors Are Suddenly Getting Very Concerned That AI Isn't Making Any Serious Money

"Despite its expensive price tag, the technology is nowhere near where it needs to be in order to be useful," Goldman Sach's most senior stock analyst Jim Covello wrote in a report last month. "Overbuilding things the world doesn’t have use for, or is not ready for, typically ends badly."

Computerworld - Nearly one in three genAI projects will be scrapped

“After last year’s hype, executives are impatient to see returns on genAI investments, yet organizations are struggling to prove and realize value,” said Rita Sallam, a Gartner distinguished vice president analyst. “As the scope of initiatives widen, the financial burden of developing and deploying genAI models is increasingly felt.”

VentureBeat - Unifying gen X, Y, Z and boomers: The overlooked secret to AI success

One critical (yet oftentimes overlooked) facet to gen AI success is the people behind the technology in these projects and the dynamics that exist between them. To derive maximum value from the technology, organizations should form teams that combine the domain-specific knowledge of AI-native talent with the practical, hands-on experience of IT veterans. By nature, these teams often span different generations, disparate skill sets, and varying levels of business understanding.

eWeek - Generative AI for Business: A New Frontier for Efficiency

For instance, if you’re in the financial sector, GenAI can create synthetic transaction data that retains the patterns of real customer behavior, allowing your organization to train AI models for fraud detection without divulging customer information. Synthetic data generation from GenAI solutions is also ideal for the healthcare industry, where data confidentiality is a priority.

The Wall Street Journal - A Clamor for Generative AI (Even If Something Else Works Better)

“The hype is taking all the oxygen out of the conversation and not allowing the appropriate attention on other types of AI models that can really generate value,” said Robert Blumofe, chief technology officer at Akamai Technologies. “The goal is not to solve the business problem. The goal is to adopt AI.”

VentureBeat - How Microsoft is turning AI skeptics into AI power users

“In 2024, we’ve pivoted to that goal, which starts with understanding the business problems that AI is best positioned to solve,” Stallbaumer says. “We are working on it now, across every function — like sales, finance, HR, marketing — looking at all of the processes, hundreds of processes, and then [determining] the KPIs within those processes where AI can actually make a difference. That’s how we’re thinking about measurement now.”

Diginomica - Is generative AI project success in 2024 a realistic goal? A mid-year reassessment

The AI dissonance factor kicks in - we're now running into a troubling issue I call AI dissonance, where research on generative AI adoption boomerangs from optimistic to cautious (the cautions trace back to concerns on security, data privacy, and trust). Therefore, you can cherry pick an AI study to bolster any AI narrative you want, from 'immature technology' to 'enterprise game changer,' so that doesn't help us much.

Forbes - From Pilot To Production in Generative AI: 3 Lessons Learned

In fact, many companies are well down their data platform journey with key decisions behind them on infrastructure and modern data stacks, but data cleansing continues to be “a journey - not a destination”. In reality, this is actually a two-part journey - first cleaning up current data sets, and then designing the new data build – to be first-time right.

Information Week - How to Avoid a GenAI Proof of Concept Graveyard

While the entire GenAI tech stack might seem complex, don’t get stuck over-analyzing the selection of every technology component. Most cloud providers offer similar LLM options, and the technologies are constantly evolving. Pick the most reasonable, popular LLM without too much emphasis on scientific evaluation. Bigger and better models are being released every week. Ensure the architecture is flexible enough for leveraging APIs and swapping out key components with new ones.

Forbes - A New Study Shows That AI Adds To The Workload And Stress Of Employees

“In some cases, AI tools are expected to solve complex problems instantly, leading to unrealistic expectations from management. When AI fails to deliver immediate results, the burden falls back on employees, who must compensate for the gap, increasing their stress levels. While AI has the potential to revolutionize various aspects of our business, it is crucial to recognize and address the challenges it brings,” Alston pointed out.

Forbes - Business Leaders Still Aren’t Prepared For The AI Revolution

Even tech giants like Google or Amazon, I would argue, have found themselves caught off-guard. Sure, both have developed chatbots and integrated them into their services in a number of ways. But have they captured the potential of AI to radically shift the paradigm when it comes to online search or shopping?

VentureBeat - Betting on AI? You must first consider product-market fit

The AI boom isn’t going to plan. Organizations are struggling to turn AI investments into reliable revenue streams. Enterprises are finding generative AI harder to deploy than they’d hoped. AI startups are overvalued, and consumers are losing interest. Even McKinsey, after forecasting $25.6 trillion in economic benefits from AI, now admits that companies need “organizational surgery” to unlock the technology’s full value.

InfoWorld - Messy data is holding enterprises back from AI

AI systems need gigabytes and gigabytes of clear and accurate data to be effective. They find patterns within data and respond to your requests of what those patterns likely mean and how to leverage the insights for strategic business purposes. Suppose the data lacks hygiene or accuracy or is dysfunctional? You’ll get incorrect inferences for your AI system or perhaps answers you won’t know are wrong until it’s too late.

VentureBeat - Moving beyond AI paralysis

AI, of course, relies on data, and given that data volume is expanding at a wild speed, questions around quality, lineage and long-term storage are more critical than ever. Complicating the issue is the number of disparate tools and technologies used to access and manage that data, causing bottlenecks that hamper AI tools.

Android Headlines - Companies are losing faith in AI, and AI is losing money

The fact of the matter is that the companies propping up this technology (the ones injecting billions of dollars into AI companies) are starting to shy away. They’re not as likely to invest so much money in it. Sure, you can’t go online without seeing an ad for some new AI service. You can’t go on social media without seeing some new AI-generated video that makes you fear for the film industry. But, the people making that possible might be stepping back a bit.

Computerworld - Want ROI from genAI? Rethink what both terms mean

That meant that 2024 has become the year of AI postmortems and recriminations about why projects went sour and who was to blame. What can IT leaders do now to make sure that genAI projects launched later this year and throughout 2025 fare better? Experts are suggesting a radical rethinking of how ROI should be measured in genAI deployments, as well as the kinds of projects where generative AI belongs at all.

yahoo!tech - More than 40% of Japanese companies have no plan to make use of AI: Reuters poll

Asked for objectives when adopting AI in a question allowing multiple answers, 60% of respondents said they were trying to cope with a shortage of workers, while 53% aimed to cut labour costs and 36% cited acceleration in research and development.

VentureBeat - Capgemini digs into the real reasons that gen AI proof of concepts rarely take off

He went on to explain that a big chunk of the reason that data is often referred to as the new oil is because oil’s only useful after refinement. In a world where 50% of business decisions will be made by AI by 2030 — that’s to say, primarily in autonomous supply chain applications — that’s unacceptable from a risk perspective. And it poses a profound risk from a data perspective.

AiThority - New HFS Research and Tech Mahindra Report on Generative AI Adoption Finds Most Significant Risk is Doing Nothing

The report emphasizes that adopting a proactive “doer” mindset can propel enterprises beyond proof-of-concept (POC) stages and pilot projects, enabling full operational deployment of GenAI. Compared to those still in planning stages, enterprises embracing this approach are five times more likely to achieve functional GenAI deployment, showcasing tangible benefits of practical strategies.

AiThority - New EY Research Finds AI Investment Is Surging, With Senior Leaders Seeing More Positive ROI as Hype Continues to Become Reality

“Business leaders are beginning to shape their future by raising strategic AI investments,” said Traci Gusher, EY Americas AI, Data and Automation Leader. “But the survey uncovered significant risks on the path to enterprise-wide AI adoption, including data infrastructure, ethical frameworks and talent acquisition. These are key to fully maximizing AI’s abilities and will allow organizations to differentiate themselves in the marketplace.”

VentureBeat - Top ten ways Intuit is revolutionizing personalization with generative AI

The company launched Intuit Assist in September 2023. It is its first gen AI-powered financial assistant for small businesses and consumers. The tool is integrated with and capitalizes on Intuit’s proprietary Generative AI Operating System (GenOS). It’s designed to deliver personalized financial insights and recommendations across products, including TurboTax, QuickBooks, Credit Karma and Mailchimp.

Computerworld - Renegade business units trying out genAI will destroy the enterprise before they help

One of the more tired cliches in IT circles refers to “Cowboy IT” or “Wild West IT,” but it’s the most appropriate way to describe enterprise generative AI (genAI) efforts these days. As much as IT is struggling to keep on top of internal genAI efforts, the biggest danger today involves various business units globally creating or purchasing their very own experimental AI efforts.

ZDNet - Time for businesses to move past generative AI hype and find real value

The report found that 27% of organizations run generative AI pilots, with 11% tapping the technology in their software operations. About 75% of large enterprises, with an annual revenue of at least $20 billion, have adopted the technology, compared to 23% of organizations with an annual revenue of between $1 billion and $5 billion.

CIO Dive - CIOs resist vendor-led AI hype, seeking out transparency

While early trends indicate enterprise interest in AI is at an all-time high, CIOs can keep the upper hand in vendor conversations by developing a clear understanding of their organization’s risk appetite, auditing provider claims and leveling unnecessary hype.

VentureBeat - Enterprises embrace generative AI, but challenges remain

It is not clear how pouring money into generative AI is affecting departments that could have otherwise benefitted from the budget, and the return on investment (ROI) for these expenditures remains unclear. But there’s optimism that the added value will eventually justify the costs as there seems to be no slowing in the advances of large language models (LLMs) and other generative models.

AiThority - Survey Reveals Only 20 Percent of Senior IT Leaders Are Using Generative AI in Production

Nearly half of respondents (44%) indicated that their current data tools do not fit their analytics and AI needs, and 43% reported that their current data analytics stack does not meet modern infrastructure standards. Another 88% do not have specific tools or processes for managing LLMs.

Diginomica - AI FOMO - a reality check on AI adoption from Freshworks global study

So here’s a thing - four in ten employees believe they use AI at work, but they aren’t actually sure about that or to what extent! Not that this is preventing a lot of them from claiming to be AI experts. In fact, three quarters of IT people think that, as well as nearly two-thirds (65%) of marketing professionals and just over half (52%) of finance and accounting.

SiliconANGLE - AI’s uncertain path: Companies weigh strategy and implementation in 2024

“Every CIO tells me, their bosses are screaming, ‘We need an AI strategy,’ so they’re investing in the technology,” Kerravala said. “But are we going to wind up in a situation where, without a lack of a roadmap of what to do with AI, you bring the technology, and you spend the money, and then it looks like a bit of a failure, because you didn’t know how to implement it properly?”

Forbes - The Promise And Perils Of Building AI Into Your Business Applications

No matter where you may sit on the AI adoption spectrum, it’s clear that the businesses that are embracing AI are winning a competitive edge. But it’s not as easy as plugging an AI model into your existing infrastructure stack and calling it a win. You’re adding a whole new AI stack, including the model, supply chain, plug-ins and agents—and then giving it access to sensitive internal data for both training and inference. This brings a whole new set of complexities to the security game.

yahoo!finance - Ken Griffin is hitting pause on the AI hype, saying he’s unconvinced the tech will start replacing jobs in the next 3 years

“I’ll ask the question, ‘So tell me how you use AI at your firm today,’ and you get these big smiles, and you get these really enthusiastic answers,” he said, “that have almost nothing to do with AI.”

Network World - AI success: Real or hallucination?

The AI deployments that appeal to enterprise IT teams are those with real, measurable gains – such as AI-driven customer support chatbots, using AI to automate network operations, and self-hosted AI models for business analytics.

Diginomica - While some white collar workers may be worried, logistics and transport sectors are optimistic on AI

And it’s easy to see why. According to the report, every single organization using AI reported benefits in terms of improved safety (45%) and employee productivity (42%). Even at this relatively early stage in the life cycle of AI, it’s a pretty impressive thumbs up for the technology.

Diginomica - Accenture clocks up $2 billion in generative AI sales so far this year - with more to come, according to CEO Julie Sweet

Two important caveats here - that’s impressive growth, but gen AI revenues are still a drop in the ocean when placed against Accenture’s quarterly total of $16.47 billion. The other point to note in terms of shaping future growth expectations is that those numbers are coming from “smaller projects as our clients primarily are in experimentation mode”.

Datanami - CalypsoAI Data Powers Everest Group Report That Reveals Generative AI Adoption Trends

Across business functions, IT (51%) and security (15%) exhibit high GenAI adoption rates, followed by legal (14%) and HR (11%).

The top use cases within IT are automated code generation and debugging (35%), enhanced cybersecurity (30%), virtual assistance and support (20%) and software testing and quality assurance (15%).

ZDNet - Enterprises are preparing to build their own LLMs - why that's a smart move

Constructing these foundational models "is complex and expensive," said Vin, who pointed out that internal enterprise models would build upon the capabilities of these models. "These models will leverage the basic skills of foundational models -- such as language understanding and generation, reasoning, and general knowledge. But they need to extend and specialize them to the industry, enterprise and activity context."

InfoWorld - We need a Red Hat for AI

Everyone is doing AI, but no one knows why. That’s an overstatement, of course, but it feels like the market has hit peak hype without peak productivity. As Monte Carlo CEO Barr Moses highlights from a recent Wakefield survey, 91% of data leaders are building AI applications, but two-thirds of that same group said they don’t trust their data to large language models (LLMs). In other words, they’re building AI on sand.

Forbes - Hype To Harmony: Integrating Generative AI Into Complete AI Approach

Some cracks are certainly starting to show. In the months following ChatGPT’s launch, a slew of new GenAI offerings emerged with many eliciting ante-raising amazement that was certainly earned. But together, they also exposed some technical limitations and adoption challenges, leading more companies to adopt a wait-and-see approach, according to BCG.

AiThority - Taking Generative AI from Proof of Concept to Production

Data readiness and the maturity of the technology used are critical factors in the successful transition from POC to production. POCs are frequently developed using datasets that are not representative of real-world conditions, leading to unrealistic expectations about performance in a production environment. Additionally, the technology used in the POC phase may not be mature enough to support scalable deployment.

eWeek - 10 Most Impactful AI Trends in 2024

As we power through 2024, staying ahead of today’s key AI trends is essential for IT professionals and businesses aiming to leverage cutting-edge technologies to drive growth and innovation. Here’s a quick glance at the top AI trends of the year and their potential impacts:

VentureBeat - McKinsey: Gen AI adoption rockets, generates value for enterprises

The biggest increase in adoption is in professional services, and gen AI is (today at least) most often being used in marketing and sales (for content, personalization and sales leads); product and service development (for design development, scientific literature and research review); and IT (for help desk chatbots, data management, real-time assistance and script suggestions). Also, organizations are seeing the greatest cost reduction in human resources.

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Reuters Institute - What does the public in six countries think of generative AI in news?

ChatGPT is by far the most widely recognised generative AI product – around 50% of the online population in the six countries surveyed have heard of it. It is also by far the most widely used generative AI tool in the six countries surveyed. That being said, frequent use of ChatGPT is rare, with just 1% using it on a daily basis in Japan, rising to 2% in France and the UK, and 7% in the USA. Many of those who say they have used generative AI have used it just once or twice, and it is yet to become part of people’s routine internet use.

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TechBullion - Impact Of AI Chat Technology Across Key Industries

AI chat technology is transforming various industries by enhancing customer service, streamlining operations, and improving overall efficiency. From retail and healthcare to financial services, travel and hospitality, e-commerce, and education, AI chatbots and virtual assistants are driving innovation and delivering significant benefits. As these technologies continue to evolve, their impact will likely grow, further revolutionizing the way businesses operate and interact with customers. By embracing AI chat technology, industries can enhance their service delivery, optimize processes, and stay competitive in an increasingly digital world.

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Fast Company – Instagram is training AI on your data. It’s nearly impossible to opt out 

“To help bring these experiences to you, we’ll now rely on the legal basis called legitimate interests for using your information to develop and improve Al at Meta. This means you have the right to object to how your information is used for these purposes. If your objection is honored, it will be applied going forward.”

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Computerworld - Public opinion on AI divided

Age and gender made a big difference: Males aged 18-34 were by far the biggest users at 33%, while only 16% of females in that age group regularly use LLM chatbots at work. Almost half (48%) of workers using LLMs said they had figured out how to use the tools on their own, although this, too, varied by age. Workers under 55 preferred to explore the technology on their own, while those aged 55 or over expressed a desire for formal AI training.

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Diginomica - Clarity may be emerging in AI capabilities pricing. Here's how

Software vendors have poured on the communications blitz to inform customers and buyers that they have started incorporating artificial intelligence into their products. But, they have simultaneously, conspicuously and quietly avoided discussions about what these new capabilities will cost. Hang onto your checkbooks folks as software vendors might be gearing up to wallet-frack your company’s bank account again.

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Information Week - Addressing Trending Questions About Generative AI

The most forward-thinking organizations are creating an ongoing GenAI educational curriculum to build awareness, increase knowledge and foster creation among their staff. This approach feeds a dynamic, iterative process of collecting ideas and use cases in a methodical manner.  

Techradar - Hardly any of us are using AI tools like ChatGPT, study says – here’s why

Even among the people who have used generative AI tools like ChatGPT, Google Gemini or Microsoft Copilot, a large proportion said they'd only used them "once or twice". Only a tiny minority (7% in the US, 2% in the UK) said they use the most well-known AI tool, ChatGPT, on a daily basis.

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Computerworld - Job seekers and hiring managers depend on AI — at what cost to truth and fairness?

The darker side to using AI in hiring is that it can bypass potential candidates based on predetermined criteria that don’t necessarily take all of a candidate’s skills into account or they can contain hidden biases based on how they were trained by their creators. And for job seekers, the technology can generate great-looking resumes, but often they’re not completely truthful when it comes to skill sets.

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VentureBeat - OpenAI is devouring the media industry

The most obvious answer is that in so doing, it gains access to licensed training data that it can use to build powerful new AI models that can write as well as your average Wall Street Journal reporter.

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 ZDNet - I tested Opera's new Gemini-powered AI capabilities and came away impressed

This new integration isn't just about being able to respond more quickly and accurately to queries. Users will also find Opera's Aria AI now includes new features, such as the ability to read responses out loud. It's also capable of rendering images based on queries, thanks to the Imagen 2 model on Vertex AI.

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ComputerWeekly - Why reliable data is essential for trustworthy AI

“Governance is even more critical when delivering AI-infused data products,” Gartner’s Alys Woodward told the firm’s Data and Analytics Summit. “With AI, unintended consequences can emerge rapidly. We’ve already seen some examples of successful implementations of GenAI. These organisations deploy the technology with appropriate guardrails and targeted use cases, but we never know when our AI-infused data products will lead us into trouble.”

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Forbes - Three Change Management Priorities For Seamless GenAI Deployment

It’s also important for employees and customers to be assured that their data privacy will continue to be top of mind as the technology becomes further scaled to be used within day-to-day workstreams. Be sure to communicate that protocols and guidelines will evolve in the spirit of fostering an ethical and socially responsible AI culture. All of this translates to more support and buy-in across the enterprise.

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