How AI is Disrupting Your Business Model

Walk into any large enterprise today and ask about their AI strategy. You'll likely hear about co-pilot rollouts, productivity pilots, use-case registries, and employee training programmes. These things are real. They produce real results. And they are nowhere near sufficient.

Laura Stevens calls this the "comfortable illusion": the idea that AI is primarily a tool for making your existing operating model more efficient. The problem isn't that this is wrong. The problem is that it's a category error. Optimisation is an operational question. AI is a structural one. Applying operational logic to a structural shift means you're refining a model that may soon stop making sense entirely.

The deeper issue is that a structural shift cannot be addressed with incremental logic. If competitors are rethinking their business models from scratch, with new cost structures, new value propositions, new ways of entering markets, then being incrementally more efficient inside your current model is a path to being disrupted cleanly. The gap between "we're running pilots" and "we're rebuilding how we compete" is not a gap you close with a task force.

Markets have always been built around human buyers: impulsive, brand-influenced, limited in information processing, susceptible to marketing. Every aspect of how businesses are designed to win customers, from pricing to positioning to advertising to brand building, rests on that assumption. AI is dismantling it.

Customers increasingly begin their journeys not on brand websites but in ChatGPT, Perplexity, or AI-powered search. They ask "what's the best CRM for a mid-sized company?" or "which laptop is best under a thousand euros?" and the algorithm summarises, compares, filters, and recommends. Humans still make the final call, for now, but the consideration set and how options are framed is shaped entirely by a system that didn't exist five years ago. If you're not in that set, you're not in the race.

The disruption deepens as AI moves from shaping decisions to executing them. Agentic commerce is already live in the US, with AI assistants completing purchases inside chat interfaces. The trajectory is clear: autonomous agents that manage the full journey from discovery to checkout, followed eventually by agent-to-agent commerce where your AI negotiates with brand AIs and completes transactions with no human involvement. At that point, the interface becomes the decision maker. Brands are no longer competing for customer attention. They're competing to be selected by a system. That is a fundamentally different game.

The conventional understanding of competitive moats relies on scale: large teams, significant capital, deep expertise, long product development cycles. These things took years to accumulate and were genuinely hard to replicate. AI is compressing that accumulation dramatically. What once required 50 engineers, months of development, and millions in capital can now be built by a small team in weeks.

This is not a prediction. It's already happening. Stevens points to B44: built by a developer as a side project using AI-assisted "vibe coding." In six months, with no outside funding, the company reached 250,000 users, hit $3.5 million in annual recurring revenue, and sold for $80 million. The team? Fewer than ten people. That exit, that speed, that scale, would have required a venture-backed team of hundreds in any prior era. Basis AI tells a similar story: a company that reached a $1 billion valuation with a fraction of the headcount a traditional services business would require, built on autonomous agents that handle end-to-end accounting tasks.

The structural implication is not just that new entrants are more dangerous. It's that the cost of building alternatives to your product is falling continuously. That affects both the supply side (more competitors enter) and the demand side (customers become less willing to pay premium prices when they know alternatives are cheap to build). Coordination costs are collapsing. Production is becoming software-defined. The competitive landscape will keep compressing. The question is whether your strategy accounts for it.

Most business models are built on a simple premise: revenue scales with effort. You charge by the hour, by the seat, by the deliverable, by the project. The customer is paying for human time and expertise, and the revenue model is directly tied to how much of that time you can sell. AI doesn't just make that time cheaper to produce. It breaks the logic that made time valuable in the first place.

Stevens frames this starkly: if AI can generate a market analysis in minutes, why would a client pay five thousand euros for one? If a coaching report can be produced in seconds, what exactly is the premium attached to? The answer, increasingly, is nothing, unless you've built something more defensible than the output itself. This pressure shows up everywhere. In professional services, it hits the junior leverage model hardest: AI is automating exactly the repetitive cognitive tasks that junior analysts and associates were hired to do, compressing billable hours and shrinking the traditional pyramid. In SaaS, it hits seat-based pricing: if AI lets three people do the work of twenty, you're selling eighteen fewer seats.

The response is not to defend the old model. It's to understand what customers are actually willing to pay for as effort becomes cheap. The answer, consistently, is outcomes. Performance. Reliability. Continuous value. Stevens maps this across two categories: service businesses moving to productised, subscription-based models where the value is in the outcome delivered, not the hours billed; and asset-based businesses moving from product sales to performance contracts. Rolls-Royce doesn't sell jet engines. It sells hours of thrust. That logic is now spreading across manufacturing, construction, pharma, and agriculture.

Stevens is direct about the failure mode she sees most often: companies respond to structural disruption with operational tools. They build use-case lists. They deploy co-pilots. They run pilots. None of this is wrong, but all of it is insufficient when the disruption is structural. The question is not which processes to automate. It's whether the model those processes serve still holds.

The Board of Innovation framework organises the response into three tracks. Strategic reinvention is the first and most important: where do you play, how do you win, what makes you genuinely hard to replace when AI can replicate so much? This means understanding where your business model is most exposed, where real scarcity sits, and how you monetise in a world where performance drives value more than production does. The second track, workflow redesign, is about structural economics: which workflows are high-volume, labour-intensive, and repeatable enough that an AI-first redesign materially changes your cost base? Not incremental efficiency; structural redesign of delivery. The third track, operating model redesign, addresses the organisational consequence: as delivery changes, so must structure, governance, roles, capabilities, and culture.

Defensible positions in an AI-driven market, Stevens argues, tend to cluster around three things: proprietary data that others cannot replicate, deep integration into customer workflows that raises switching costs, and differentiated personalised experiences that make comparability harder. The Dutch entrepreneur who turned down a roughly $500 million offer from OpenAI for his gaming behaviour dataset understood this instinctively. Data is a moat. Integration is a moat. Unique experience is a moat. Generic output, no matter how well-produced, is not.