Is your company’s AI getting smarter every day? It should be

09.21.2026

Author - Enrique Dans

Imagine working with someone who was completely unable to learn, to gain any experience whatsoever. There’s no need to picture someone completely stupid: just think of someone who joins the company, starts to deal with a lot of customers, experiences successes and failures, attends meetings, makes decisions… but one year later, has the same skills and capabilities as on day one. Would you be able to call that person experienced?

That’s what happens with much of the corporate AI that is being deployed and implemented these days. Systems can accumulate memory, retrieve previous interactions, be updated, fine-tuned or have their surrounding logic modified. However, remembering is not the same as learning. A system can accumulate information without becoming better at deciding what to do next.

There are many streams of lessons at your company every day: from a disgruntled customer, to a discount that worked or didn’t work, a supplier that delivered late, a support operation that solved a problem or made it worse, a sales approach that failed in a segment but worked on a different one . . . All these things are specific actions, followed by specific consequences.

If we take into account what Microsoft has to say about this, the possible gains come when you learn what happened in production and you are able to feed those signals into prompts, routing, retrieval and other system decisions: everyday operations need to become lessons from which your system can learn.

There is a difference between owning the experience vs. just renting the intelligence, when different competitors can buy the same frontier models, access to these models does not constitute a sustainable competitive advantage anymore. What can become the essence of such an advantage is actually all the things you have been able to learn from your particular history: customers, decisions, campaigns, corrections, failures, exceptions, outcomes, etc. 

CEOs are currently asking questions such as “which model does it use?”, “How accurate is it?”, “How many agents can it run?” or “What does it cost per token?” But there’s another question that may matter much more: “What will this system be better at after working for us for twelve months?” Or, if you want to be more precise, “What does it learn from success and failure?”, or “If we replace the underlying model tomorrow, how much of what the system has learned about our company survives?” If the answer to the first question is “nothing unless engineers retrain or redesign it”, and the answer to the second one is “very little”, then the company may be renting intelligence without accumulating much intelligence of its own.

And I’m pretty sure that is not what you want. The next generation of corporate AI will not be defined simply by how intelligent it is on day one. It will be defined by how much better it becomes by day one-thousand. Make sure your company can capitalize on that, because others certainly will.