Three structural ways AI is breaking your business model
Laura Stevens
Managing Director of Data & AI
If your AI strategy is a list of use cases, you are applying operational thinking to a structural shift. You are trying to optimize your current model, instead of questioning whether that model still holds.
And that is the core problem. Because a structural shift can never be addressed with incremental logic. Treating AI as a collection of use cases or as a toolbox upgrade misses the point entirely.
AI is not just improving how organizations operate. It is a general purpose technology, much like the internet or electricity. And we know what those technologies do.
They don’t just optimize existing processes. They reshape entire industries.
They change business models.
They alter cost structures.
They redefine what drives competitive advantage.
These are three structural ways in which AI is breaking business models.
1. AI shifts control inside markets
Most markets today are built on a simple assumption: Humans are the primary decision-makers.
- Humans compare options.
- Humans rely on brand recognition.
- Humans are influenced by marketing.
- Humans have limited information processing capacity.
And entire industries are built around that assumption. That assumption is now breaking.
Customers are no longer beginning their journey on brand websites. Increasingly, they start in systems like ChatGPT or Perplexity. They ask for the best CRM, the most suitable insurance provider, or the best laptop within a certain price range. And instead of showing a list of ads, AI systems summarize, compare, filter, and recommend.
For now, most humans still make the final decision, but the set of options they consider, and how those options are framed, is shaped by an algorithm.
As a result: competition intensifies, putting pressure on prices and compresses margins
When that happens, transparency increases and competition intensifies. It becomes easier to compare alternatives, which puts pressure on prices and compresses margins. And if you are not surfaced by the algorithm, you may not even be part of the consideration set.
The next step goes further. AI does not just shape decisions, it starts to execute them. It books flights, selects suppliers, renews subscriptions. In procurement, for example, AI can evaluate suppliers based on price, availability, and reliability, and recommend the optimal option, often with minimal human intervention.
At that point, influence shifts away from brands and toward the systems that make or guide those decisions. Suppliers become interchangeable more quickly, switching costs drop, and brand loyalty weakens. Competition is no longer about convincing people, but about being selected by systems. That is a much stronger commoditization force.
The most structural shift happens when AI removes layers entirely. Any layer that mainly generates standardized content, aggregates information, or compares options, without owning a defensible asset, becomes vulnerable. If your value is producing basic outputs, AI can now generate those directly, which means that layer may shrink or disappear.
Not all intermediaries are affected equally. Some have strong moats, such as proprietary data, control over distribution, or deep integration into workflows. But many do not.
The overall direction is clear: control is shifting, margins are coming under pressure, and certain layers in the market are disappearing.
2. AI is lowering the barriers to enter markets
The second structural shift is about who can compete.
In the past, building a successful company required scale, capital, expertise, and large teams. AI is dramatically reducing those requirements.
One of the key changes is that coordination costs are collapsing. AI enables smaller teams to coordinate their work much more efficiently. Tasks that previously required multiple layers of management and manual processes can now be handled by AI systems. As a result, these organizations can move faster and operate with less complexity.
At the same time, production is becoming increasingly software-defined. AI makes it significantly easier to build products, write code, create marketing materials, and scale expertise. What once required substantial investment and specialized talent can now be done faster and at a lower cost.
As a result: AI-native competitors are rising up
This gives rise to a new type of competitor: AI-native companies that are built from the ground up with AI at their core. These companies can operate faster, leaner, and with structurally lower cost bases. They can build and iterate quickly, often with very small teams, and still reach meaningful scale.
We are already seeing examples of companies with fewer than ten people generating significant revenue and achieving outcomes that traditionally would have required much larger organizations.
When the cost of building drops, barriers to entry fall. And when barriers to entry fall, competition increases.
This is not just about existing competitors becoming more efficient. It is about entirely new entrants entering the market with fundamentally different economics.
What do you do when AI makes your service, product or pricing obsolete?
May 21
+5,000 attendees
Virtual summit
3. AI is changing what customers are willing to pay for
The third shift is about value.
Many business models today are built on monetizing time, effort, or access. Customers pay per hour, per project, per seat, or per product. In essence, they are paying for human effort or for something that was previously scarce.
AI changes that dynamic by making many types of output dramatically cheaper to produce. Knowledge, content, code, analysis — all of these can now be generated quickly and at low cost.
When something becomes less scarce, customers are less willing to pay premium prices for it.
This creates pressure on business models that rely on time-based monetization. In service industries such as consulting, legal, or accounting, AI does not necessarily replace the entire function, but it reduces the reliance on human labor. Tasks that used to take hours can now be completed in minutes, which leads to fewer billable hours and puts pressure on traditional pricing models.
The same dynamic applies to software. AI reduces the cost of building software and makes it easier to create alternatives. At the same time, it challenges seat-based pricing models. If smaller teams can achieve the same output, fewer users need access, which reduces the number of seats that can be sold.
In both cases, the underlying issue is the same: value is no longer tied to effort or scale in the same way.
As a result: the focus shifts from selling units or hours to delivering measurable results.
As a result, customers are shifting from paying for effort to paying for outcomes. They increasingly expect performance, reliability, and continuous value. This is why we see a move toward outcome-based pricing, subscription models, and “as-a-service” offerings across both service-based and asset-heavy industries.
A structural shift cannot be addressed with incremental logic
Taken together, these three shifts — control moving to algorithms, barriers to entry falling, and value migrating away from effort — represent a fundamental reset of how markets operate.
And yet, many organizations are still responding with incremental measures. They focus on optimizing their current model instead of questioning whether that model still holds.
Understanding where your business model is most exposed is critical — which is exactly what this AI Disruption Index tool is designed to help assess.
Because incremental AI may buy time. But it will not protect you in a market that is fundamentally changing.
The real work is in reinvention, not optimization.
That reinvention requires redesigning what you sell, what you do, and how you operate.
So what do you do when AI is breaking your business model?
We are hosting AUTONOMOUS: OBSOLETE on May 21 to explore just that. For a half-day summit, we’re bringing together senior leaders from across sectors, who are in the midst of their AI transformation. Get honest perspectives and real learnings from leaders navigating this shift.
Want to know where AI is breaking your business model, and how to fix it? Drop us a note.
Managing Director of Data & AI
Laura Stevens, PhD, is the Managing Director of Data & AI, bringing a unique blend of strategic vision, analytical expertise, and leadership acumen. With a background in neuropsychology, business consulting and organizational transformation, she has successfully navigated a career spanning academia, consulting, and industry leadership. As a former VP Data & AI in an international organization, Laura has led large-scale Data & AI teams covering data science, machine learning, data engineering, data governance, and visualization. She is passionate about leading organizations through their data & AI transformation.
