The Intelligent Enterprise - No More Process Please!
  • 22 May 2024
  • 13 Minutes to read
  • Dark

The Intelligent Enterprise - No More Process Please!

  • Dark

Article summary

Thank you to Eddie Short for sharing his expertise and content in our knowledge base.

Executive Summary

The advent of artificial intelligence (AI), and in particular so-called Generative AI has precipitated an era of unprecedented transformation in the business landscape. Processes - 19th Century model for automating and scaling Manufacturing are increasingly irrelevant in a C21 Digital Service economy.

What is key is being able to dynamically orchestrate activities or tasks along customer 'journeys'. eg Whilst in car manufacturing you still need a chassis to attach wheels, but in the service world things can be done in many (sometimes practically infinite) different ways. Traditionally, ERP, CRM, CRM, P2P/R2R type report structures lock organisations into legacy and minor improvement when 'process obliteration' is increasingly key.

Ultimately an omni channel decisioning model where AI orchestrates Next Best Offer/Action is at the core of a modern agile business.

1.Journey to the Intelligent Enterprise

The current business landscape, is characterised by unique levels volatility, uncertainty, complexity, and ambiguity (VUCA), complicated by global mandates for sustainability and NetZero, presents unprecedented challenges. Amidst this, the rise of AI, Generative AI, Quantum Computing, and other technologies opens vast opportunities for transformative value creation and yet today most businesses are built around the Consulting principles of People, Process, Technology from the 1990s – which is best characterised Process, drives Technology and People (reduction) is the business case!

Charles Darwin noted ‘It is not the strongest or fittest that survive, but those most adapted to change’.  That is the ethos for the Intelligent Enterprise, a business operating model built around a Capability Flywheel, where Human in the Loop Data and AI powered decisioning enables the optimum next best offer and action for the business.  The Intelligent Enterprise is a data enabled, AI powered business that has the capability to leveragedata and insight to dynamically reconfigure itself in response to the expected needs of its customers, and simultaneously anticipate and respond to changes and events in the external market environment.

The pre-2000 Process centric model is designed around Functions and Processes, which then dictate the Technology to automate them.  It worked well in a ‘Business as Usual’ world where we can make incremental improvements to processes to drive cost and efficiency improvements.  It is singularly unfit for the future.

2. Process is the Past, Decisioning the Future

In the 19th Century, it was important to be able to document how things were done in order to build interchangeable parts manufacture, as pioneered by Samuel Colt for the Colt 45 revolver, and likewise the first cradle to grave automotive manufacturing and Chemical (Process) Industries, relied on industrialised processes.  This culminated in Kanban and Total Quality Management/6 Sigma starting in post World War II Japanese manufacturing to produce high quality manufactured goods.

Michael Hammer in his book ‘Reengineering the Corporation’ kicked off the world of IT powered Transformation, and the Enterprise Resource Planning/Customer Relationship Management systems revolutions of the 1990s and 2000s.  In this world, vendors such as SAP, Oracle and Salesforce provided hugely complex systems as a platform that helped companies to industrialise their key processes.


ERP and CRM programmes were typical of the 70% Failures in Transformation Programmes, noted by John Kotter, which does not seem to have improved from the late 1990s until today!  In the first round, it was clear that these complex systems needed substantial customisation to fit the needs of clients.  The result were massive business transformation programmes, running to many years where the only real winners were Technology and Management Consultants.  The mantra was always ‘out of the box’ – the problem was it was always in someone’s interest to make changes to that ‘out of the box’ solution, as everyone could come up with a reason why ‘one size fits all’ did not work for them!  Consequently, systems were loaded into ‘on premise’ data centres after massive customisation, making them highly expensive and inefficient.


Salesforce were the pioneers of Cloud based Computing, but the reality is that Cloud is merely an evolution of the Mainframe computing of the 1970s to 1990s, where third parties could ‘time share’ access to compute power.  Amazon took this to the max, with Amazon Web Services and others including Microsoft and Google followed.  The result for the Enterprise Systems providers was to run their platforms ‘out of the box’ in the cloud!  This promised to dramatically reduce the end user configuration of previous generation of ‘on premise’ systems.


Running ‘out of the box’ in the Cloud is great, until you realise you genuinely are getting a ‘one size fits all’ solution, whatever the vendor has promised.  Major competitors have the same system, with the same processes… losing any source of differentiation or competitive advantage, as the reality is we are all working with the same lowest common denominator solution.

3.Enter Data and the Intelligent Enterprise

The problem with all those Enterprise Systems models, based on Process – Data… Data is always treated as the ‘exhaust’ of Process.  Post their ERP and CRM programmes, on premise or in the Cloud, companies spend large amounts of money collecting that exhaust data and putting it back together to tell them what happened…  It’s backward looking by design.

Instead, the Intelligent Enterprise, Data is in the input (as well as the output) of Process.  Data drives the business capability flywheel and AI starts to become the brain of the business and instead of automating process, instead obliterates it as we start to orchestrate the underlying activities in real or near real time to meet the optimum needs of the customer, business and the market.

This is not rocket science, and in fact basic computing 1-0-1 would tell you that the heart of computational models is Input/Process/Output, where Data is the Input and yet Process based business models largely ignore this  Instead we need to think of a Capability Flywheel, as originally postulated by Jim Collins in Good to Great, and made famous for Digital Companies as the basis for how Jeff Bezos redesigned Amazon after the DOT.COM crash.

In the Capability Flywheel, we focus on those capabilities that we want to differentiate ourselves (called the Hedgehog by Jim Collins).  In the case of Amazon, Bezos chose Marketing and in the manner of Data being the input to process, focused his business leaders on Input KPIs (such as Customer Experience, Page Views and Conversion Rates) as the focus to help drive the future revenue of the business, and far less time ‘wasted’ focusing on where they had hit or missed the output KPIs (Revenue, Cost etc albeit they of course remained vital).  With that as a baseline he set about using Data, Analytics and AI to spin the Amazon faster than the competition.

There is much more about this elsewhere in the Blog.

In the Intelligent Enterprise we use Data as the Fuel of ‘process’ and we take the exhaust of that data to drive insights, but increasingly create a ‘machine learning’ feedback loop that takes the output and feeds it back to the input to drive continual and continuous improvement.  We then create a ‘Decisioning’ framework and models to help the business identify optimum next best actions and offers.  These can be totally automated using AI or used to supercharge human in the loop decision making!  Over time, the logic is sucked out of those ‘process’ systems and more and more of it sits in a Data and AI ‘brain’ for the business.  From there we can escape the process straight jacket and dynamically orchestrate activities to deliver step change performance for the business.

4.Building and Implementing your Capability Flywheel

I’m not going to provide all the answers here, suffice to say at Intelligent Enterprise Partners we focus on three main ways to help you, Resource Based Theory, Dynamic Capabilities and Change Management (enabled by Kotter’s change framework).  The goal is delivering an Intelligent Enterprise that is Designed for Change, making  your business fit for 2030 (instead of 2000).

VRIO Model of Resource-Based Theory

If a business seeks to be competitively differentiated, it cannot realistically expect to be ‘world-class’ at everything.  Resource Based Theory helps firms to identify and focus on internal resources that are best aligned with opportunities and threats from the external environment, to identify key combinations of internal capabilities that uniquely differentiate.   The VRIO framework is a strategic analysis tool designed to evaluate an organization's resources and capabilities to discover competitive advantages. The acronym VRIO stands for four questions to ask about a resource or capability to determine its potential: Value, Rarity, Imitability, and Organisation. If a resource or capability is valuable, rare, costly to imitate, and the firm is organised to capture its value, it can sustain a competitive advantage.

Sense, Seize, and Transform from Dynamic Capabilities

Dynamic capabilities refer to a company's ability to continuously renew its competitive advantages in response to rapidly changing business environments.  The VUCA world we live within, seems to be here to stay, so being able to drive continual renewal will be critical to maintaining competitive advantage.  The Dynamic capabilities framework is built around three key actions: Sense, Seize, and Transform. 'Sense' involves identifying and assessing opportunities and threats in the external environment. 'Seize' is about mobilizing resources to capture value from those opportunities and mitigate threats. 'Transform' requires the firm to continuously renew and reconfigure its assets and organizational structure to maintain alignment with the changing business landscape. This approach empowers organizations to be proactive and adaptive

Kotter’s Accelerate Change Framework

Building an Intelligent Enterprise is a fundamental rearchitecting of the operating model of the business, and as we have noted the 70% Failure rate of most major change programmes is unacceptable when the business is at stake.  As Kotter first highlighted this challenge in 1996, we are happy to leverage his approach and frameworks to support the Design for Change transformation.

Developed by John Kotter, over 25 years, the Accelerate Change framework provides a methodology for leading and managing change in organizations. It outlines eight steps to create a sense of urgency around the need for change, build a guiding coalition, form a strategic vision, and enlist a volunteer army to implement the vision. The process also includes enabling action by removing barriers, generating short-term wins, sustaining acceleration, and instituting change. Unlike traditional change management models, Kotter emphasizes the importance of operating with a dual system—one that operates under traditional hierarchies and another that uses a network-like structure to facilitate rapid change and innovation. This model is particularly effective in today’s fast-paced, complex business environments.

Individually, these models are utilised by organizations globally to inform their strategic decision-making and adapt to internal and external pressures, ensuring sustainability and competitive positioning in the market.  We bring them together to deal with the challenges and embrace the opportunities of the VUCA world and AI.

Kotter’s Accelerate Change Framework

5. Creating a Blueprint for Designed for Change

The 2030 Intelligent Enterprise model is Predictive by Design i.e. it is Designed for Change.

·      NB Predictive by Design does not merely mean that you have sophisticated analytics for reporting and insight, but in addition that those analytics and AI drive continuous improvement in your core business processes.  AI in the form of Machine Learning can be used to drive continuous process improvement and orchestration – over time the business logic in many enterprise systems will be ‘sucked out’ of the enterprise systems such as ERP and CRM, and become resident in a Data, Analytics and AI brain that will power the Intelligent Enterprise!

The Volatile, Uncertain, Complex and Ambiguous world looks to be with us for the foreseeable future.  To address this the Intelligent Enterprise is one that can dynamically reconfigure itself in weeks and months, rather than multi-year change.  It requires a fundamental move from the traditional ‘Process’ centric approach to business operating model and organisational design.  We are not driving efficiency by automating processes, rather to quite Michael Hammer, we are obliterating processes.  Using our Intelligent Enterprise model enables the opportunity of 50-80% cost savings, whilst driving 50-100% performance improvement in some functions and capabilities, not the traditional 20% savings that efficiency initiatives offer.

The driving success is that Data is both an Input and an Output for the processes and operating model of the business.  You can’t dynamically reconfigure your business unless you have the capability to change processes and ways of working in a truly agile way taking weeks, not months or years!

The leading businesses, look at Input Metrics as well as Output Metrics, after all Data is the fuel of the business and therefore drives the processes that operate the business.  Input Metrics could include Page Views, Stock Availability, Price, Discounts, Convenience.  The key for Amazon was that Input Metrics driven by working backwards from the Customer Experience were (and are) used to help Amazon drive its business forwards.

1.    Better Customer Experience leads to more traffic

2.    More traffic attracts more sellers seeking those buyers

3.    More sellers leads to wider selection

4.    Wider selection enhances customer experience, completing the circle

5.    The cycle drives growth, which in turn lowers cost structure

6.    Lower costs lead to lower prices, improving customer experience, and the flywheel spins faster

Pulling together the Flywheel and how Data and AI, we created the Intelligent Enterprise, Predictive by Design model, which enables the creation of sustained performance using Resource Based theory and Dynamic Capabilities as the essence of our approach to create a Designed for Change Business and Technology architecture!

Underpinning the Intelligent Enterprise are seven habits for a highly effective Data Enabled, AI Powered organisation.

We will not describe all of the seven habits here, but as an example to be an Intelligent Enterprise, requires the most formidable Servant Leadership capabilities.  When talking about Servant leadership and the people we serve, there are three key communities that need to be supported and enabled:

1. Customers of the organisation You must be using Data, Analytics & AI to develop products and services that make a difference to the end customer.  You must similarly be using that same Data & AI, to drive the marketing, sales and service to those same customers.  Your mission is to ensure that those customers are delighted!

2. People within your business they are your immediate customers, and typically they are the people you must serve, to ensure the end Customers are delighted.  The whole concept of the Intelligent Enterprise is to ensure that the Data in your business serves the people in your business to help them make better decisions, which improve service to the end customer and allow the organisation to deliver better revenues, lower costs and improved ESG outcomes.  This brings to life the Resource Based Theory principle of Organisationally embedded.  To make Data, Analytics and AI a source of sustained competitive performance, means upskilling the whole business and developing amongst other things ‘citizen’ data scientists as well as ownership of data amongst the whole employee population.

3. Transformation Technology and Business team (s), and the job of the effective Transformation Leader is to create, grow and nurture a great team and provide them with exciting work that deliverstransformational products and services for the other two populations.   I include in here also the machine learning/AI that your team develop which will make an ever-increasing number of decisions on behalf of the business.

In other words, we are engaging the whole organisation in the change, which is where the Kotter Accelerate Change framework comes in.  Without a clear process and principles change will fail!  Even with a process and principles, when we are transforming the operating model of the business without a network of change agents who continually develop and renew the change things will fail!

6. Conclusion

We face a future with unique levels of both Opportunity and Uncertainty.  The only certainty is that Change will be continuous and therefore for a business to be future fit for 2030 and beyond, its Operating Model and People must be Designed for Change.  A model based around Process can be automated and incrementally improved.  BUT in the world we find ourselves we need to obliterate the Process straight jacket!

The Intelligent Enterprise, which is fully data enabled and AI Powered, is a business that is Designed for Change, especially when the biggest change opportunity in the next few years will be driven from AI.  To build an Intelligent Enterprise™, risk averse Investors, Boards and CEOs need confidence of a much better than 30% success rate, which is why we have developed the Designed for Change ™ methodology which embodies over 100 years of Executive level practitioner experience and three proven strategy and change frameworks (Resource Based Theory, Dynamic Capabilities and Kotter’s Accelerate Change Model).


1. VRIO Framework

- Barney, J.B. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99-120.

2. Dynamic Capabilities

- Teece, D.J. (2007). Explicating Dynamic Capabilities: The Nature and Microfoundations of (Sustainable) Enterprise Performance. Strategic Management Journal, 28(13), 1319-1350.

3. Kotter’s Change Framework

- Kotter, J.P. (2014). Accelerate: Building Strategic Agility for a Faster-Moving World. Harvard Business Review Press.

Was this article helpful?