Spin your Strategy with Data and AI

7 Habits of the Highly Effective AI Powered, Data Enabled Organisation

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!

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This model is not fit for purpose for business today, let alone 2030!

Journey to the Intelligent Enterprise

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 leverage data 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.??

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

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.

Business Strategy Frameworks for a Data Enabled, AI Powered World

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).? Two core business and academic frameworks have been considered (incorporating three different strategic models):

1. Michael Porter and the "Deductive Strategy" approach: This approach, derived from industrial economics, emphasizes identifying the industry's structures (its critical success factors) and adapting to them better than competitors.
2. The "Constructed Strategy" approach: In contrast, those following Jay Barney (2010) argue that companies with a competitive advantage possess strategic capabilities, i.e., unique resources and distinctive competencies that can reshape existing industries or create new ones.? This internally conditioned approach is generally known as the "Resource-Based View."

Porters Five Forces Framework (1)

Modern Business Strategy developed in the 1980s after Michael Porter first published ‘How Competitive Forces Shape Strategy’ in HBR (1).?? Porter draws from industrial organization (IO) economics to derive five forces that determine the competitive intensity and, therefore, the attractiveness (or lack thereof) of an industry in terms of its profitability levels.? The five forces are Power of Buyers, Power of Suppliers, Threat of Substitution, Threat of New Entry and Competitive Rivalries.

This framework remains dominant in many organisations, and yet it comes from a purist Economic theory where in industries are static and stable and competition is more likely within an industry rather than from a different industry.

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Resource-Based Theory - VRIO (2)

If a business seeks to be competitively differentiated, it cannot realistically expect to be ‘world-class’ at everything.? Resource Based Theory (2) 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.

Dynamic Capabilities - Sense, Seize, and Transform (3)

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.

There are a number of potential conflicts when considering all three:

·?????? Different assumptions about the source of competitive advantage

·?????? Porter's emphasis on industry structure vs RBT's focus on firm-specific advantages

·?????? Static analysis (Porter/RBT) vs dynamic perspective (Dynamic Capabilities)

The biggest challenge for RBT and Five Forces is the reliance on a static perspective, which in the modern highly volatile economy is challenging, albeit using Dynamic Capabilities will mitigate this challenge.? The rapid development of AI will make Dynamic Capabilities ever more important as Companies will need to focus more on building their capacity for continuous adaptation rather than maintaining static competitive advantages.

This will lead to a new synthesis of these frameworks, one that better captures the dynamic, data-driven nature of competition in the AI era.? The Intelligent Enterprise is our answer to this challenge!

?Process is the Past, Agile Decisioning the Future

These strategy frameworks were all developed in the latter part of the 20th Century.? 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.

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Intelligent Enterprises need to redefine their operating model so they are not limited by the straight jacket of legacy ‘process models’, so that they can embrace a much more Agile approach.? Processes will need to be dynamically reconfigured to meet the rapidly changing needs of customers and reconfigure what the business is delivering as Services.?? Your AI and Machine Learning Algorithms are your source of competitive advantage.?

• Sadly, these platforms which were all the rage in the 90s, 00s and 10s do your long-term business competitiveness more harm than good, by locking in legacy ways of working, and in practice companies rarely use more than 20% of what they paid for.?

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.

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BUT

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.

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ENTER THE CLOUD

?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.

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BUT 2

?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.

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.

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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.

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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.?

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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.

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3.? Data and AI Leaders stop fixating on Technology

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In the meantime, the Data, Analytics and AI Leader needs to deliver short-term value, to justify the long-term investment and they are being bombarded by software salespeople selling the latest tools.

• Do you need a Data Lake, Data Warehouse or even a DataLakehouse.? How about whether you need Data Mesh or Data Fabric?
• Which Analytical, Reporting and Visualisation tools are essential?? Should you use PowerBI, Azure AI, GCP, Tableau, Qlik, Databricks, Starburst etc
• Do you need a Data Science platform or should you let your Data Scientists just develop with Jupyter Notebooks and Python.

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For 20+ years Data Leaders were obsessed with getting to a ‘single source of the truth’, with Data that was perfectly groomed and manicured within an ivory tower environment.? To get there they were swayed by the arguments of many different vendors of which platform would be best in helping them to deliver.? The results were poor:

• It was a great way for software and hardware vendors to liberate 10s and 100s of millions of €￡$ from the business, and
• almost no companies got to the perfect manicured Data in a Data Warehouse or even in a single Data Lake.?That’s where Data Mesh and Data Fabric came in as ways to manage your data in a more distributed way.?

?In summary, whilst I am a Technology Advocate, it’s what you do with it that counts.? There is little to choose between a DAI Architecture based on Google Cloud Platform, Amazon Web Services or Microsoft Azure.? Pick one and start delivering value and organisationally embedded the capabilities – That is your source of sustained competitive advantage!

1. Michael E. Porter,?"How Competitive Forces Shape Strategy",?Harvard Business Review, May 1979 (Vol. 57, No. 2), pp. 137–145.
2. Barney, Jay (1 March 1991). "Firm Resources and Sustained Competitive Advantage".?Journal of Management.?17(1):?99–120.?doi:10.1177/014920639101700108.?S2CID?220588334.
3. ?Teece, David J.; Pisano, Gary; Shuen, Amy (August 1997). "Dynamic capabilities and strategic management".?Strategic Management Journal.?18?(7):?509–533.?CiteSeerX?10.1.1.390.9899.?doi:10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z.?S2CID?167484845.