One of the early mental models for AI was that you should think of it as a highly capable intern. Give it clear instructions, check its work, and it will save you time.

It’s a useful framing when you’re just getting started, and it lowers the barrier to entry, but if you stay inside that model too long, you’ll end up building something in your organization that looks like AI adoption but functions like a more expensive version of what you already had.

The issue I have with the intern analogy is that it teaches an episodic relationship with intelligence. You assign a task. The task gets done. You move on. The intern, in this model, does not get smarter about your organization. Neither do you, really, at least not in any systematic way. Each task is its own transaction, and when the transaction closes, most of what was learned disappears.

That said, there is a much better model available, and it comes from (wait for it) software engineering.

The Value of Compounding

In May 2026, Kieran Klaassen published a guide through Every, the business and technology publication, introducing a framework called compound engineering. The core idea is that each unit of engineering work should make subsequent units easier. Rather than treating software tasks as isolated problems to be solved and filed away, compound engineering builds a loop—something like: ideate, brainstorm, plan, work, review, polish, compound, and repeat—where the output of each cycle improves the starting conditions for the next one. The work accumulates and the system gets smarter. The returns increase over time.

Klaassen was writing for engineers, but the idea underneath the framework is useful to every organization that is serious about implementing AI in a transformative way.

This is because compounding is an organizational concept, not a technical one. And most companies aren't thinking about it at all.

Every Session Should Not Start From Zero

Think about what your organization actually does with AI right now. Someone uses it to draft a proposal. Someone else uses it to summarize a report. A team uses it to prepare talking points for a client meeting.

Each of those uses probably saves time, but none of them, at least in most organizations, makes the next use of AI any smarter. The AI does not know more about your company after the proposal than it did before. It does not carry forward the assumptions embedded in that report, or the strategic context behind those talking points.

Every session starts from zero.

This is the episodic model in practice, and it is the ceiling most organizations are bumping against without quite knowing why. The alternative requires that you ask a different question.

What should this task leave behind so the next one starts from a more intelligent place?

Close the Loop and Compound

I've written before about the closed-loop organization, the idea that companies need to capture what they learn, cluster it into patterns, draw conclusions from those patterns, and codify those conclusions so the organization stops relearning the same lessons over and over.

If you’ve worked inside any corporation for almost any amount of time, you’ve probably arrived at the same conclusion I have: most organizations are leaky. Knowledge walks out the door with people who leave, or it lives in someone’s inbox, or it surfaces briefly in a meeting and then dissolves back into the noise. The closed-loop organization is designed to hold knowledge and build on it.

Compounding is the return you get when that kind of closed-loop learning gets applied to AI-assisted work. Each cycle of capture, cluster, conclude, and codify feeds the next one, and the gains accumulate rather than evaporate.

That is the shift leaders should be looking for. Not whether AI can help someone finish a task faster, but whether it can help the organization carry more intelligence into the next one.

A Summary Is Not the Same as Memory

Consider your leadership team meetings, or even your board meetings. The low-value version of AI in that context is recording the meeting and generating a summary. It's useful, but it's also, in the larger sense, nearly worthless, because a summary is a snapshot. Sure, it captures what was said, but it doesn't carry forward what it means.

The higher-value version of AI in that context is different. It allows each meeting to pass forward the decisions that were made, the assumptions those decisions rested on, the risks that were named and the ones that were only implied, the commitments that were made and by whom, and the questions that were left unresolved. In the higher value version, the next meeting doesn't start from a blank page. It starts from an accumulated record of how this organization thinks, what it has tried, what it has learned, and what it still does not know. That's not a summary; that's organizational memory—and it compounds.

A tool waits to be used.

An assistant waits to be assigned.

An intelligence layer gets trained by the way the organization works.

AI maturity, real maturity, is not measured by how many tools your company has adopted or how many hours you have saved this quarter. It is measured by something simpler and harder: whether your organization gets smarter each time it uses them.