AI Adoption and Usage

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.

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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