Continuous Performance Management with an AI Copilot
How to use Lattice-style AI to make performance cycles lighter and fairer — summarizing real evidence while keeping judgment human.
- Fix the inputs before the AI: performance tools can only summarize what's logged, so make lightweight continuous feedback (goals, project notes, peer shout-outs) a habit managers actually keep.
- Use AI to draft review summaries from a cycle's collected evidence, then require managers to edit from their own judgment — the draft kills the blank-page problem, the human edit is what makes the review fair and personal.
- Watch for recency bias in AI summaries: models over-weight recent, well-documented events. Managers should deliberately check the whole period and add early-cycle wins the tool missed.
- Never let AI write the rating. Keep assessment, calibration, and decisions with humans; use AI for synthesis, consistency checks, and flagging reviews that are suspiciously short or vague.
- Close the loop with growth: turn each review into 2–3 concrete development goals in the same system, so next cycle's AI summary can track follow-through instead of starting from zero.