Model capability is improving faster than most companies can absorb it. The remaining constraint is increasingly local: what the company knows about its own work.
A capable model still needs context, guardrails, examples, and the small operational decisions that make a workflow reliable. Teams discover these through trial and error. They learn which prompt is too broad, which data source cannot be trusted, which exception breaks the process, and when a person must review the result.
Most of that learning stays with the person who did the work. The output remains—a document, a query, a deployment—but the reasoning behind it disappears. The next person sees the result without the path that produced it and repeats part of the learning cost.
Data is not the same as memory.
A company can store every file and still forget why a decision was made. Useful memory connects an outcome to its evidence, rejected paths, operating conditions, and human judgment. It also makes clear what is trusted enough to reuse.
This matters more as AI does more of the work. Without company memory, better models simply help each person move faster in isolation. With it, every solved problem can improve the next decision across the team.
This is the question behind Wheatfield: how can companies keep the reasoning, failed attempts, and judgment they already paid to create—and make them useful again?