Most Companies Do Not Have an AI Problem

Most companies do not have an AI problem.

That is not because the technology is simple, or because every model is interchangeable, or because tooling no longer matters. It is because the thing most organizations are struggling with shows up much earlier than the model.

They are struggling with context.

They do not know how knowledge should move across the business. They do not know what should be shared between teams, what should stay local, what should be structured, what should remain interpretive, or what should never be handed over to a system in the first place. They have not decided what their information means outside the function that originally produced it.

So they buy tools for a problem they have not defined.

That is why so many AI efforts feel strangely shallow after the initial excitement wears off. The first demo goes well. The system produces something impressive. It summarizes, classifies, drafts, predicts, or automates. Everyone can see the possibility. Then the real work begins. Someone tries to use it in the ordinary life of the company, and the system starts to feel thin.

Not broken, exactly. Just thin.

It can see the artifacts of the business, but not the business itself.

Marketing sees this when a system can describe the campaign but not understand the brand. It can read performance data, channel mix, and conversion rates, but it cannot feel the accumulated judgment behind why certain language works and certain language quietly cheapens the company. The team knows the difference. The system does not, because no one ever turned that knowledge into something usable.

Sales sees it when the system can summarize pipeline but cannot tell the difference between a deal that looks alive in software and a deal that is already dead in reality. The notes are there. The stage is there. The activity is there. But the system does not know that a buyer has gone cold in a very specific way, or that a certain pattern always precedes a slip, or that a rep has already tried the obvious angle and needs a different conversation entirely.

Finance sees it when the numbers look clean but the story underneath them does not. Revenue can be counted. Margin can be modeled. Cash timing can be forecast. But someone still has to know which customer always pays late, which kind of deal creates expensive downstream support, or which growth trend is technically real but operationally misleading.

Operations sees it everywhere. The documented process and the real process are rarely the same thing. Systems tend to capture the first one. People live inside the second.

That gap is where most companies actually are.

The problem is not just scattered data. It is that the organization has never built a serious way of translating its own judgment into structure.

That is the work people skip because it is less exciting than talking about models.

It is harder, slower, and much less marketable to say, “Before we decide what to automate, we need to understand how this business actually thinks.” But that is almost always the more honest sentence.

The fantasy version of enterprise AI assumes a company already knows what matters, where it lives, and how it should move. In reality, most organizations are a loose federation of partial context. Marketing knows some things. Sales knows others. Finance has a different map. Leadership has a partial synthesis. Operations is carrying knowledge no one else sees. Support is hearing things nobody put in the deck. The company does not lack intelligence. It lacks a clean way of moving that intelligence without stripping it of meaning.

That is why the phrase “shared context” matters so much to me.

Not total context. Shared context.

Those are different things.

Total context is what people imagine when they think about giving AI access to everything. Just connect the systems, dump the documents, wire up the knowledge base, and let the machine figure it out. That sounds efficient until you remember that organizations do not work that way. Not all information should move equally. Not every judgment should travel unchanged. Not every team should see everything. And not every note means the same thing outside the environment where it was written.

Shared context is more disciplined than that.

It asks harder questions. What should move across functions. At what level. Under what rules. Interpreted by whom. Remembered for how long. Reviewed by whom before it turns into action. Those questions sound procedural, but they are philosophical too. They define the relationship between the company’s knowledge and the systems now trying to use it.

That is why I do not think the winners in this next phase will simply be the people with the best technical instincts. Technical skill matters. Of course it does. But the people who become most valuable will be the ones who can see how a business actually works and translate that into design.

They will know what the brand sounds like. They will know why one team trusts a certain metric and another one does not. They will know which pieces of institutional knowledge are foundational and which ones are just noise. They will know the difference between a system that is technically impressive and a system that the organization will actually trust.

That kind of judgment is not peripheral. It is becoming the core of the work.

This is also why I have become increasingly skeptical of the way many companies talk about AI maturity. They often describe it as a stack of capabilities: models, tooling, integrations, automation. Useful categories, but incomplete. The more important measure may be whether the company has done the uncomfortable work of deciding what its knowledge means and how it should be carried forward.

If it has not, better models will mostly make the problem more expensive.

You can already see this happening. Teams build internal copilots that are technically impressive and operationally forgettable. They draft good-enough material that no one quite trusts. They summarize information without changing decisions. They automate work around the edges while the actual center of the business remains stubbornly dependent on unwritten judgment. Then the disappointment gets misdiagnosed as a tooling problem.

Sometimes it is a tooling problem.

More often, it is a context problem wearing a tooling costume.

That is the harder truth, but it is also the more useful one.

Because context can be designed.

Not perfectly. Not once and for all. But seriously. A company can decide what it knows, how it names things, what standards it protects, what boundaries matter, what should be durable, what should be local, and what has to remain human. It can build a context layer that makes AI less theatrical and more trustworthy.

That, to me, is the real opportunity.

Not just to automate more work, but to make organizational intelligence more legible, more portable, and more usable without flattening the business into generic process.

That is not a small problem. It may be the defining design problem underneath all the others.

Which is why I keep coming back to the same sentence.

Most companies do not have an AI problem.

They have a context problem.