What does it mean to work with AI rather than just use it?

The difference, briefly

Using AI is transactional: you ask, you get, you move on. The output is a one-off response to a one-off need.

Working with AI is iterative and directional. You bring context the model doesn't have — your expertise, your judgement, your organisation's constraints — and you shape the output through that. You catch errors. You push back. You refine. You make decisions on the basis of what you've seen, not just what the model produced.

The distinction matters because the second approach produces something different in kind, not just in quality. When you work with AI deliberately, you're not just getting better outputs. You're surfacing knowledge — decisions, constraints, reasoning — that often didn't exist in an explicit form before the conversation started.

What good collaboration actually produces

A skilled person working with AI typically produces things like this:

A constraint that was implicit becomes explicit — articulated clearly enough to brief an agent on.

A decision that was fuzzy becomes recorded — the reasoning captured in a form that survives the session.

A goal that was vague becomes specific — defined precisely enough that someone else, or another agent, could act on it.

These are not just better outputs from the model. They are often the raw material for knowledge the organisation didn't have before — not yet stable, but worth capturing. And that's where most accounts of "working with AI" stop — at the individual's improved output — without asking what happens to that knowledge next.

The evaporation problem

The output of good AI collaboration lives, by default, in a chat window. When the session ends, the context that was built — the constraint that was articulated, the decision that was reasoned through, the reasoning that connected them — evaporates. The next agent starts from scratch. The next colleague starts from scratch. The next session starts from scratch.

This is why teams that invest heavily in AI fluency without investing in where the output goes keep hitting the same ceiling. Individual productivity improves. Organisational memory doesn't. Each person is getting better at working with AI; the organisation's agents remain as uninformed as they were on day one.

The question underneath "how do I work with AI?" is therefore: what am I supposed to do with what we produced?

When the output has somewhere to land

The value of working with AI compounds when the knowledge it surfaces gets captured — authored into a shared record rather than left in a chat window.

The constraint that was articulated becomes a Constraint an agent can operate under. The decision that was reasoned through becomes a Decision with its reasoning attached. The goal that was made specific becomes a Goal an agent can work toward. Instead of individual fluency producing individual outputs, it produces organisational knowledge — the kind agents can draw on the next time they're deployed.

This is what connects AI literacy to AI governance. Literacy is why the knowledge gets surfaced in the first place. Where it lands determines whether it stays.

A practical starting point

The 4D AI Fluency Framework — Delegation, Description, Discernment, and Diligence — developed by Rick Dakan and Joseph Feller in partnership with Anthropic, is one of the most useful practical entry points for developing the habits that make collaboration productive. It's free, model-agnostic, and designed for exactly this: helping people develop the judgement to work with AI deliberately, not just efficiently. Available at Anthropic Academy.

Fluency in those four areas is what makes the output of AI collaboration worth capturing. Without it, you're not surfacing knowledge — you're just getting faster at producing first drafts.