The Dawn of a New Age that Changes Everything – The Intelligent Enterprise Our Response

Introduction

We are entering a whole new transformative age, some of have coined this Industrial Revolution 4.0, but this could be more transformative than everything that we have seen in the past 250 years.

The driver of this new Revolution is Artificial Intelligence, which according to industry luminaries such as Mustafa Suleyman (The Coming Wave) and Ray Kurzweil (The Singularity is Near, The Singularity is Nearer when we merge with AI) will also deliver fundamental change in three fundamental forces in human life, energy (dramatically reducing the costs of Solar Energy and Battery Technologies), Robotics (significantly reducing the amount of back breaking work required of humans) and health (transforming diagnostics, treatments and dramatically extending human life).

• These changes are not happening in the next 100 years, they are happening in the next 5-10 years!!

What does that mean for your company, business, organisation?? Let’s be clear for the foreseeable future Change will be the only constant, and Business as Usual will be Change!?

I paraphrase Satya Nadella 'AI will not replace companies and economies, but companies and economies that lead with AI, will replace those that do not!'? The businesses that survive will be those who learn from Charles Darwin.? "It is not the most intellectual of the species that survives; it is not the strongest that survives; but the species that is able best to adapt and adjust to the changing environment in which it finds itself.".

We have a definition for those companies, “Intelligent Enterprises.? An organisation that uses insight, analytics and artificial intelligence 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 environment i.e. an organisation that is truly data enabled and AI Powered.

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But Haven’t we been here before?

Despite massive investments in Digital and AI technologies, the Western Economies have experienced stagnant growth in economic activity and productivity since the Global Financial Crisis of 2007-8. ??In the 1970s to 1980s a similar productivity paradox was noted by Nobel Laureate Economist?Robert Solow, in his 1987 quip, "You can see the?computer age?everywhere but in the productivity statistics." ??While the computing capacity of the U.S. increased a hundredfold in the 1970s and 1980s, labour productivity growth slowed from over 3% in the 1960s to roughly 1% in the 1980s.?Productivity growth spring into life for the next 20 years but has been largely moribund again since the Global Financial Crisis of 2007-8, despite continued significant investment in Computing Power, Digital and now Artificial Intelligence.

One critical source of Productivity Growth is Energy.?? The widespread use of Coal and then Oil to drive steam turbines and subsequently generate electricity was the fundamental enabler of productivity growth in the first Industrial Age.? Whilst in the 1950s Nuclear Power promised a further exciting step change in growth capacity, this has not delivered returns not least because of health and safety issues.? Nuclear Fusion providing ‘limitless’ energy and more recently the demands of NetZero migrating carbon fuels to more sustainable sources has again restricted the growth in energy capacity in Western Economies.? Furthermore, the exponential growth in Computing Capacity is driving ever more demand for sources of power generation.

The question therefore remains, can we produce a step change in productivity and economic growth?? My simple answer is Yes, BUT!

Meanwhile, Professor Erik Brynjolfsson who is a powerful advocate of the productivity potential that can be achieved through AI.? He has written a number of seminal books including The Second Machine Age, and?Machine, Platform, Crowd?with?Andrew McAfee.? Brynjolfsson documented a?correlation?between IT investment and productivity, going back to his earliest research in 1993 at the behest of Robert Solow. His work provides evidence that the use of Information Technology is most likely to increase productivity when it is combined with complementary business processes and human capital. A subsequent article coined the term the?Productivity J-Curve?to describe how these intangible investments might initially lead to stagnant or even lower productivity followed by a take-off. ?Brynjolffson envisions double-digit gains in economic productivity, and commenting that he foresees the potential for “massive economic disruption” leading to the creation of new occupations and new companies in the coming years.? In an interview for the Financial Times he said “I’m optimistic the technologies will affect a large number of tasks. A big percentage of the work that is done in a modern economy is amenable to being augmented by LLMs and generative AI. The effects on those tasks have been significant — double-digit productivity gains within just a few months in some of the cases I’ve studied. Multiply the large percentage of affected tasks by sizeable productivity gains for each one and you get a big total economic impact. I’m betting that productivity growth is maybe significantly higher in the 2020s than the Congressional Budget Office is projecting. They projected 1.4 per cent average per year. I think it could be twice that — closer to 3 per cent — maybe more.”

As a note of caution, with all of the excitement around AI, MIT Professor Daron Acemoglu, recently released “The Simple Macroeconomics of AI”, with the aim to review the large macroeconomic implications of new advances in AI. Acemoglu using existing estimates on exposure to AI and productivity improvements at the task level, these macroeconomic effects appear nontrivial but modest—no more than a 0.66% increase in total factor productivity (TFP) over 10 years.? starts from a task-based model of AI’s effects, working through automation and task complementarities, and further revises this down to 0.53%.

The Intelligent Enterprise – The Way to Capitalise on AI

How do we square these different research opinions.? As we said at the outset for the foreseeable future Change will be the only constant, and Business as Usual will be Change!? What that means in practice is the incremental approach to business change, leadership and strategy, which has been the norm for the past decades, is simply put, doomed to failure!

Looking at the paper from Prof Acemoglu, if we treat AI like other ‘automation’ initiatives and focus on micro-tasks then the costs could well outweigh the benefits!? Generative AI is based on very expensive compute power and the danger is we throw it any potential task in the organisation.? In my opinion, this will be the source of risk and failure for any companies working with the Big Strategy and Consulting firms… In contrast the work of Brynjolfsson, we need to rethink how work is done.? In his work on the Productivity JCurve, he describes how General-purpose technologies (GPTs) such as AI enable and require significant complementary investments, including co-invention of new processes, products, business models and human capital. These complementary investments are often intangible and poorly measured in the national accounts, even when they create valuable assets for the firm.

The result is we now need to go much further, revisiting the ‘Reengineering the Corporation’ of Michael Hammer and instead of automation processes, focus on ‘obliterating tasks and processes’. ?The Intelligent Enterprise, Designed for Change is our response.?

To consider a business that is truly data (and value) driven, we should consider the case of Amazon.? Their business strategy was famously written on a napkin by Jeff Bezos in 2001, using the ‘Flywheel’ concept from Good to Great.? Bezos picked Marketing as the critical Capability that he wanted Amazon to be world class at, and realised Data, Analytics and AI was key to that.? Bezos created a strategy that leverages on customer experience to drive traffic to the platform and third-party sellers in a cycle of continuous improvement - improve the selections of goods, and Amazon further improves its cost structure decreasing prices, which in turn increases the spin speed of the flywheel. (https://businesschronicler.com/business-strategy/amazon-flywheel-explained).

The Intelligent Enterprise model is our codified version of the Good to Great flywheel used at Amazon now standardised so it can be deployed in almost any organisation to create a Predictive by Design operating model!? It is both intrinsic and extrinsic in this definition that the organisation is uniquely able to leverage data, analytics and AI to enable and sustain this capability.? The use of Resource Based Theory and Dynamic

Capabilities (see below) help us to identify the resources and business capabilities that will form the differentiation for the business and Kotter’s Change Framework helps us to deliver the change to a business that is designed for change and can adapt continuously.

Intelligent Enterprise Strategic Frameworks

As part of a multi-year Doctoral Research programme, the author has investigated how organisations can deliver sustained and sustainable competitive performance using Data, and Analytics (including AI).? Three business and academic frameworks have been isolated as constituents:

VRIO Model of Resource-Based Theory (1)

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 (2)

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.

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Kotter’s Accelerate Change Framework (3)

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

Creating a Blueprint for Designed for Change

The starting point for Designed for Change, is whether your business is fit for 2000 or 2030.? The 2000 model is designed around Functions and Processes, which then dictate the Technology to automate them.? It works well in a ‘Business as Usual’ world where we can make incremental improvements to processes to drive cost and efficiency improvements.

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

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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 delivers transformational 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!

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1. Acemoglu, Daron, The Simple Macroeconomics of AI, MIT, May 2024?
2. Brynjolfsson, Erik (1993).?"The productivity paradox of information technology".?Communications of the ACM.?36?(12): 66–77.?doi:10.1145/163298.163309.?ISSN?0001-0782.?S2CID?15074120.
3. ?"The Second Machine Age". May 20, 2023.
4. "The long wait for a productivity resurgence".?www.ft.com. Retrieved?March 8,2024.

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