The scrappy ways teams are using internal AI copilots
The loudest AI news still revolves around splashy launches, but the real progress hides in odd little copilots teams spin up for themselves. Last month I sat with a fintech ops lead who wired a local LLM into their Notion wiki. It doesn’t generate poetry; it just answers “Where do I file an SAR?” twenty times a day so analysts can stay in their casework. That’s the energy we need.
I keep three principles when helping teams scope these sidekicks. First, limit ambition to one verb: search, summarize, tag, compare. Anything broader turns into an internal science project that dies in quarter two. Second, make logs visible. When designers can peek at the prompt/response trail, they stop fearing the tool and start editing its behavior. Third, decide who owns the “retraining” chore. Spoiler: it’s never IT. It’s whoever screams the loudest when the bot hallucinates.
Favorite hacks from the last sprint:
- A PR crew that feeds journalist emails to a copilot trained only on their past coverage, so the bot drafts replies using the reporter’s tone.
- A CX leader who pipes Zendesk macros through an AI layer that strips apology fluff and adds concrete next steps before replies go out.
- A RevOps analyst who lets the copilot label pipeline risks every Friday based on Slack chatter, not just CRM fields.
None of these tools will make headlines. They simply hand people ten minutes back per task, and those minutes compound into calmer quarters. If you want to build your own, start in boring places with clear guardrails, document every weird edge case, and prepare to toss version one without tears.
