One of the themes I keep coming back to in my workshops on AI, emerging technology and managing change is the risk of looking at the new through the lens of the old. Of getting stuck in optimising existing systems or ways of thinking, and neglecting the opportunity that comes from reinventing and rethinking at a more fundamental level. Or as Clay Shirky once put it: ‘institutions will try to preserve the problem to which they are the solution’ (The Shirky Principle).
One of my favourite examples of this is what happened (or rather didn’t happen) with British tanks in World War One and what has been called (by Brian Holden Reid) ‘the most famous unused plan in military history’. ‘Plan 1919’ was an ambitious strategy originated by Major General J F C Fuller to use a huge combined force of the new British tanks to deliver a sledgehammer blow to the German Army and end the war. Fuller was the Chief Staff Officer of the nascent Tank Corps and he originated a plan which involved 5,000 heavy and medium British tanks opening up German defences along a 90-mile front supported from the air. Another 800 faster-moving medium tanks would then be used to rapidly attack the line of German command structures miles behind the front line, followed up by a another force of faster tanks and truck-mounted infantry to penetrate far behind enemy lines.
This ‘lightning thrust’ strategy was revolutionary. ‘Tactical success in war’ wrote Fuller, ‘is generally gained by pitting an organised force against a disorganised one.’ It was a significant shift away from the attritional trench warfare that had characterised the war almost since it had begun. Until that point tanks had only ever been used to open up small gaps in the enemy lines through which infantry could advance a few miles. The strategy was so radical in fact, that when it failed to come to fruition, many nations (including the British) continued in the belief that the best use of tanks was in small pockets to support infantry. The Army even stopped the publication of Fuller’s book on the topic. But this new form of mechanised warfare was studied by the German General Heinz Guderian who implemented it to devastating effect in the Second World War, often against the British. Fuller had in fact invented Blitzkreig.
This failure to capitalise on a radical new approach is a good analogy for how difficult it is for transformational ideas to take root in organisations, and AI is bringing this into sharp focus right now. So much of what we see in AI application is currently focused on efficiency and productivity gains. It’s a natural place to start. It’s a faster route to initial gains. But as Sangeet Paul Choudray has elegantly put it, AI doesn’t just change tasks, it reshapes the systems in which work happens, and offers the opportunity to challenge the existing logic of your industry by changing how the work is organised within it:
‘The most consequential impact of AI is not that it makes existing tasks faster, but that it makes different architectures of work possible by shrinking the unit of work and enabling new forms of coordination.’
This distinction between evolving tasks and changing systems is echoed in a 1990 paper from the MIT and Harvard researchers Rebecca Henderson and Kim Clark that distinguished between four different types of innovation, and the degree to which they change not just the product, but the system in which the product operates:
- Incremental innovation: often focused on improving core product components whilst maintaining the existing linkages between them. An example of incremental product innovation might be improving one component of a car. An example of incremental innovation for AI is the current focus on AI co-pilots that can augment existing workflows (without fundamentally changing the workflow itself).
- Modular innovation: this may change the fundamental technology of a component but still retains the same system architecture. In our car example, this might be creating an automatic, rather than manual, transmission. For AI, this might be an insurance business swapping out a traditional rules-based underwriting component for a sophisticated ML model that assesses risk in fundamentally different ways (pattern recognition across vast datasets rather than predetermined decision trees). Like the automatic transmission example, the technology of the component may change radically but the overall system architecture (how applications flow through the business, how policies are issued, how the organisation is structured around the process) remains essentially the same.
- Architectural innovation: here the components may not change significantly but the way in which they are organised or link together does. For our car example this might be something like front-wheel drive transmissions. But for AI it might be how functional components are reorganised to work in a new way. For example, in an agency or client marketing function the same components still exist (strategy, copywriting, design, production, media) but AI can facilitate fundamentally different relationships between them enabling a shift from a rigidly sequential waterfall (brief, strategy, creative, production, media) towards parallel, iterative loops where strategy, creative and media inform each other simultaneously. The components are recognisable, but the architecture is completely reconfigured.
- Radical innovation: As the most extreme form of innovation this involves changing both the technology of the components and also the way in which they are organised as a system. For our car example, this would be the origination of electric vehicles. A good example of this for AI is the pioneering drug discovery business Recursion. They’ve replaced traditional ‘wet-lab’ experimentation as the primary discovery method with computational biology and AI-driven simulation. But they’ve also restructured the entire system so that drug discovery, preclinical testing, and candidate selection operate as a continuous computational loop rather than discrete sequential stages with handoffs between siloed teams. This creates a system which is unrecognisable from the traditional pharmaceutical model.
Henderson and Clark make the case that architectural and radical forms of innovation require significant changes in the existing organisational structures and processes, which is much harder for incumbents. It requires underlying changes to the system, not just the components within the system, and familiar (or optimised) components can fool organisations into thinking the system hasn’t fundamentally changed. Most businesses are currently clustered in the incremental and modular innovation quadrants. They’re using AI to make existing components faster within unchanged systems. They’re sticking tanks in front of infantry when they should be implementing a new form of mechanised warfare.
Conway’s Law tells us that organisations inevitably design systems that mirror their own communication structures. Existing organisational architecture (hierarchies, information flows, team boundaries, decision-making pathways) that have evolved over time become embedded in the products and processes a company creates. Whilst this is fine for incremental and modular AI innovation (since the system architecture remains unchanged) it’s a problem for architectural and radical innovation where mirroring becomes a trap and the organisation’s structure actively constrains its ability to reimagine how the pieces fit together. In 1918, the British Army’s entire command structure had been designed around infantry warfare, profoundly limiting their ability to see the potential impact of what Fuller was proposing. Incumbents effectively become prisoners of their own design.
Escaping Conway’s Law requires redesigning how work happens, not just layering AI onto existing structures and processes. The answer isn’t to abandon existing structures entirely but to create deliberate space outside them. I’m a big believer that small, cross-functional teams with real autonomy can drive big change. An empowered multidisciplinary team of less than 10 people can move fast and not be encumbered by entrenched hand offs or dependencies. Given the right support they can work back from the problem, and redesign workflows at the system level, not just at the task or functional level. They can break out of the org chart.
But this also requires us to think bigger. To ask first principles questions like ‘what would this look like if we were to design it from scratch today?’. To actively invest in shared context and decision transparency so that new architectures can emerge. We need to stop preserving the problem to which we are the solution. And that takes bravery, a willingness to question fundamental assumptions and the realisation that what got us here is unlikely to get us there.
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