I tried to automate myself away. Here is what I learned.

The problem: lost context

I started for a simple reason: I was losing too much time trying to find my way back into my own work.

A subject would come up again after a few days. Before I could continue, I had to search through emails, notes, tasks and earlier decisions. Only then did I remember what had been promised, what was still missing and why I had chosen a particular direction.

So I built a work system with AI. When I returned to a conversation after a few days, it should find the history, show the open questions and prepare a first draft. I was not trying to automate conversations or decisions. I wanted to stop searching for the same context every time.

The early versions often sounded more certain than they should have. An old note appeared to be the current state. Missing information was filled with a plausible assumption. A draft looked more complete than it was. The problem was not that the AI knew nothing. The problem was that I could not always tell what it really knew.

Reliable preparation, not false certainty

That changed my goal. I did not need as many answers as possible. I needed reliable preparation. Every claim had to come from a visible source. Missing information had to remain visible as a gap. A draft could never be mistaken for a sent message. A recommendation remained a recommendation until I made the decision.

This taught me that trust in AI is not granted once. It develops through daily use. I review a result, correct errors and change my rules only after an improvement has proved useful. The system becomes a little better with every correction. Responsibility still remains with me.

The same challenge at company scale

Companies face the same problem on a larger scale. Important knowledge is scattered across specialist systems, emails, customer histories and the minds of experienced employees. When a key person is unavailable or leaves, others have to piece the context together again.

AI can help find that knowledge and prepare it for a decision. It cannot decide which source is authoritative, which exception should become a new rule or who may approve an action. That requires clear ownership and people who review results, correct mistakes and adopt new rules only after a deliberate decision.

The useful role of AI

Today my system does not do the real work for me. It gets me to the point where I can decide, write or speak. I arrive prepared for a conversation, remember commitments and see open questions before they become problems.

I did not automate myself away. I built a system that keeps knowledge and context from getting lost. That, to me, is the useful role of AI: not replacing people, but giving them room to do the work that still needs them.