Faster AI tools don't mean more accurate work. A year of internal use taught us about habits first, not speed.
There are plenty of essays about how to wire AI into a stack. We want to talk about what comes before that — the posture a team needs before it starts using AI.
Assume hallucination is the default
The biggest risk AI creates is "delivering a wrong answer plausibly." The field calls this hallucination. Our internal rule is simple — whenever AI answers something that should have a source, a human opens that source and checks it. No exceptions. One wrong line in a report is enough to erode client trust.
Always make it return evidence
Put "return the evidence passage alongside the summary" into your prompts from the start. Don't just take the summary; take the passage it was drawn from. That single line cuts verification time in half.
Separate what to delegate and what not to
AI is best at "repeatable, formatted work" — meeting-note summaries, interview transcription, first-pass copy options, code boilerplate. Put AI in front of "judgement work" — final naming choices, setting copy tone, creative direction — and the output flattens. The principle: humans set direction, AI handles repetition inside that direction.
Find the repetition first
Instead of asking "where do we use AI?", ask "what does our team repeat most?" Repetition = automation candidate. Brief summaries, competitor desk research, meeting notes — automate these first and team members have more time for the judgement-heavy work.
Close
AI isn't a speed tool — it's a tool for re-allocating attention. Used well, it gives back a team's "time to doubt properly." That's what using this technology at work actually means.