Insight
Why Your AI Might Be Commercially Confused
A practical guide to clarifying whether AI is the product, the interaction layer, or simply table-stakes UX.
Jamie Cattell
May 2026
Every executive team is now trying to define its AI product strategy. In many cases, that effort starts with language that is too broad to be useful. The more important issue is the role AI actually plays in the offer. There are three basic possibilities: AI can be the product, AI can be the interaction layer on top of a scarce asset, or AI can be table-stakes UX. If leadership teams do not know which one they have, they are likely to misprice it, mis-sell it, and misjudge its strategic importance.
That confusion is everywhere right now. Companies are adding AI to data platforms, insights products, research tools, workflow software, and services businesses, then moving quickly into debates about separate pricing, bundling, retention, cannibalization, buyer personas, and customer willingness to pay. Those debates matter, but they often point to a more basic problem: the company has not decided what AI is in the business model.
Fernridge works with leaders on the AI, data, digital, and commercial advisory questions that determine where technology sits in the value stack.
Most AI is not the moat
A lot of leaders still behave as if "AI on top of our proprietary data" is, by itself, a meaningful strategic position. Usually, it is not. The foundation-model layer is improving too quickly and commoditizing too fast. Prompting, summarization, basic copilots, and chat interfaces are becoming standard product capabilities rather than durable advantages.
That does not mean AI is unimportant. It means AI's strategic value usually sits above the model layer and around the scarce asset, not inside the model itself. In most cases, the durable advantage comes from proprietary data, workflow position, domain context, trust, distribution, validation, governance, and feedback loops from real usage. The model matters, but the business system around the model often matters more.
The three roles AI can play
1. AI is the product
This is the cleanest case. The customer is explicitly buying the AI-driven workflow itself: a simulator, a forecasting engine, a protocol design assistant, a stakeholder modeling tool, or a decision engine. In this model, AI performs a distinct job that the customer would plausibly pay for even without the company's legacy product around it.
There are very few successful pure "AI is the product" examples, but Abridge is a cleaner example than most. Abridge sells AI-powered clinical documentation. Its product listens to patient-clinician conversations, generates structured clinical notes, maps content into the medical record workflow, and keeps clinicians in the review loop before finalization. The AI is not merely helping users search or interrogate an existing information asset. It is doing the core job the customer is paying for: reducing documentation burden and producing usable clinical documentation at the point of care.
Kantar's LINK AI also comes close. Kantar is selling a self-serve predictive ad-testing workflow. Users can upload creative, have the system analyze it, predict in-market performance, benchmark it, and get results quickly. That matters because the customer experience is fundamentally product-like. The user is not mainly interrogating Kantar's corpus manually or through a generic LLM. The system is using Kantar's proprietary data asset to do a bounded commercial job for the user: test creative and inform a go/no-go or refine-and-iterate decision.
AI as the product offers the highest upside potential. It can support standalone pricing, usage-based monetization, and software-like economics. It is also the hardest to prove. To win here, companies need a clear job to be done, repeat usage, measurable ROI, and enough workflow integration that the offer is not just a thin wrapper around frontier models. Most companies want to believe they are here, but few actually are.
2. AI is the interaction layer
In this model, the customer is still fundamentally buying the underlying asset: premium data, proprietary content, exclusive transcripts, workflow position, domain knowledge, institutional expertise, or some other scarce source of value. AI radically improves access to that asset. It makes the asset easier to interrogate, more usable by non-experts, more frequently used, and more embedded in real workflows.
A platform like AlphaSense is a useful example. The core asset is not the AI alone. It is the premium corpus: research, earnings transcripts, expert calls, company documents, and the enterprise's own internal knowledge. The AI layer makes that corpus dramatically more usable. It surfaces, compares, summarizes, monitors, and increasingly acts on behalf of the user.
This may sound like a product enhancement. At some point, it may become expected table stakes. For now, in many categories, it is a real economic lever. A strong AI interaction layer can increase usage frequency, broaden adoption, improve retention, deepen workflow embedment, expand seats, and strengthen pull-through of premium content.
The monetization logic is different from AI as the product. In this model, AI usually should not be treated primarily as a standalone SKU. Its value is often captured indirectly through the stronger performance of the underlying asset. The better commercial frame is not the price of the AI feature itself, but the additional value AI helps the company realize from the scarce asset it already controls.
3. AI is table-stakes UX
Sometimes AI is mostly a usability upgrade: a smarter search bar, a basic assistant, lightweight summarization, a more modern interface, meeting notes, or suggested action items. These features improve convenience and help the product avoid feeling dated, but they do not materially change value capture, workflow ownership, or defensibility.
Zoom is a good example. Most customers are not buying Zoom primarily for AI. They are buying meetings, communications, and collaboration. AI features such as summaries, notes, action items, and meeting assistance make the core product more useful and stickier, but they do not fundamentally redefine what Zoom is.
That is why this kind of AI should usually be bundled or lightly tiered, not treated as a major standalone product in its own right. Trying to separately monetize table-stakes AI often creates more friction than value. The danger is internal self-deception: teams overestimate the strategic importance of what is, in reality, a necessary interface upgrade.
The commercial mistakes this confusion creates
Once the three roles are clear, a lot of common AI-commercialization pain becomes easier to diagnose. Companies that treat an interaction layer as if it were the product tend to overprice it and struggle to sell it. Companies that treat an emerging workflow product as if it were just a feature tend to underinvest and leave money on the table. Companies that treat table-stakes UX as a strategic breakthrough tend to waste management time and confuse customers.
This is especially visible in data and insights businesses. A company builds an AI engine on top of a proprietary dataset and immediately gets pulled into debates about bundling, separate pricing, stickiness, cannibalization, buyer expansion, workflow change, and client readiness. Those are real commercial issues, but they are hard to resolve until the company has made a more basic decision about whether AI is the offer, the access layer to the offer, or simply modern packaging.
Without that clarity, pricing becomes reactive. Sales messaging becomes fuzzy. Product investment becomes noisy. Leadership teams end up debating packaging before they have agreed on the role AI plays in the value stack.
What actually compounds
The model layer itself is not where most compounding value lives. Durable value is more likely to come from proprietary data and context, embedded workflows, domain ontologies, governance and trust, distribution, and feedback loops from real usage.
That is why the most thoughtful companies are not trying to outbuild the frontier labs. They are building systems around the models: orchestration, workflow logic, role-based access, validation, human review, auditability, and domain-specific context. That is where AI becomes strategically meaningful. Not because the model is unique, but because the business system around the model is difficult to replicate.
A simple rule for executives
The cleanest test is straightforward. If AI performs the job itself, it is the product. If AI makes the asset usable, it is the interaction layer. If AI just makes the interface nicer, it is table stakes.
That simplicity is useful because it forces a level of commercial clarity many executive teams still lack. The companies that win will not be the ones that use the word AI most often. They will be the ones that understand exactly where AI sits in their value stack, how it changes their economics, and where the real scarcity still lives.
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