Most organizations treat prompt engineering as something individuals pick up on their own — a video here, a cheat sheet there. It's the same mistake companies made with spreadsheets: assuming a skill will spread through the org by osmosis.
It doesn't spread. What spreads is inconsistency. One person gets useful, structured output. The person next to them gets vague summaries and quietly gives up. Both are using the "same" tool. Neither knows why the results are different.
The skill nobody's teaching
Individually, prompt engineering usually means: know a few tricks, iterate by trial and error, keep what works in a personal notes file nobody else sees. That's a hobby, not a capability.
Treated as a team skill, it means something different:
- Shared prompt patterns for recurring tasks — the team's actual weekly report, not a generic example.
- A common vocabulary for describing what went wrong when output is bad, so feedback is specific instead of "this isn't very good."
- Review, the same way code gets reviewed. A prompt that produces inconsistent results on real inputs is a bug, not a personal quirk.
Why this compounds
When one person gets better at prompting, you get one better set of outputs. When a team gets better together, the good patterns spread immediately — because they're written down, shared, and refined by more than one person's trial and error.
We've watched this play out inside client teams directly: two people doing the same job, one producing usable first drafts and one requiring three redos every time, and the entire difference was that nobody had ever compared notes.
What this looks like in practice
It isn't a policy document. It's closer to a shared, living reference: three or four solid prompt patterns for the tasks your team actually repeats, updated whenever someone finds something that works better, reviewed the way you'd review any other piece of internal tooling.
That's a two-hour conversation to set up, and it outperforms months of individual trial and error.
If your team's AI output still varies wildly from person to person, that's not an AI skills gap. It's a knowledge-sharing gap that happens to be about AI.