Underperformance documentation has one job: observable facts, clear expectations, and a fair path back — written so precisely that HR, the employee, and a future reader all see the same thing. This playbook drafts improvement plans from your factual log with AI, under one hard rule: the model describes behavior, never intent.
Workflow area: Hiring & Performance · Tier: Core · Hands-on time: ~30 minutes
This is the highest-stakes writing a manager does. Done well, it gives a struggling person their clearest possible chance; done sloppily, it becomes evidence — of your bias, in a dispute. The failure mode AI amplifies is characterization: models reach for fluent explanatory prose ("shows a lack of ownership," "doesn't seem invested"), and every one of those phrases is a claim about someone's inner state that no document can prove and any employment lawyer can attack. The discipline is the same one HR has always taught — facts, expectations, support, timeline — with AI doing the structuring and a hard rule doing the protecting.
Keep the factual log before you need it. A running, dated record per concern: what was expected (with the artifact — the ticket, the agreed deadline, the design-review action), what was observed (what shipped, when, what broke), and the impact. One entry per event, written the week it happens. No adjectives, no theories: "missed the Aug 4 date agreed in sprint planning; feature shipped Aug 18 after two reviews found unhandled error paths" — not "keeps missing deadlines, work is careless."
Draft the plan from the log, with the intent rule in the prompt. Enterprise tenant only, one person per session:
Draft a performance improvement plan from my factual log. HR
will review before anything is shared.
HARD RULES:
- Describe observable behavior and work outcomes ONLY. Never
characterize intent, attitude, motivation, or personality.
Banned: "careless", "lacks ownership", "unmotivated",
"doesn't care", "bad attitude", and any synonym.
- Every stated gap must cite a dated entry from my log. If
the log doesn't support a gap, leave it out and tell me.
- Expectations must be specific, observable, and achievable
in the plan period — behaviors a reasonable person could
demonstrate and a reasonable manager could verify.
Structure: (1) purpose and support framing, (2) specific
gaps with dated examples, (3) expected standard per gap,
(4) support we will provide, (5) check-in cadence and how
progress will be assessed, (6) timeline.
Log: [PASTE FACTUAL LOG]
Role expectations: [PASTE LEVEL/ROLE STANDARD]
Audit the draft for smuggled characterization. Models leak intent-language even when told not to. Run a second pass: "List every phrase in this draft that asserts or implies anything about the person's mindset, effort, or attitude rather than observable behavior." Good output catches the subtle ones:
- "failed to prioritize the migration" — implies a choice/
mindset. Observable version: "the migration tasks agreed
on Jun 2 were not started as of Jun 30 (standup notes)."
- "did not take review feedback seriously" — intent claim.
Observable version: "the same error-handling issue was
flagged in three consecutive reviews (May 8, May 22,
Jun 5) and recurred in each subsequent PR."
Rewrite every flagged phrase. This audit is the playbook's core move — it's also worth running on documentation you wrote entirely by hand.
Check the expectations are a fair path, not a trap. For each expected standard, ask: could a person meeting the role's level genuinely do this in the time given, with the support listed? Plans with vague standards ("significantly improve quality") or impossible ones ("zero review comments") read as pretext — because they usually are. The support section is not decoration: named mentor, adjusted scope, specific training; if you can't fill it, you're documenting an exit, not an improvement plan, and HR needs to know which this is.
HR reviews before the person sees anything. No draft reaches the employee without HR sign-off — they check legal exposure, precedent consistency (is this standard the same one applied to others at this level?), and process requirements in your jurisdiction. Deliver the plan in person (pb-04 to rehearse the conversation), give the person space to respond, and log the check-ins with the same factual discipline; the plan period's record matters as much as the plan.
This is the most sensitive people-data workflow in this track — enterprise-tier AI only, one person per session, no names of other employees in any prompt, no health or personal-circumstance details ever entered into a model, and nothing shared with the employee before HR review; if your org prohibits performance content in AI tools entirely, follow that policy and use this playbook's structure by hand.
Facts with dates, expectations a fair reader calls achievable, support that actually exists, and not one sentence that claims to know what's inside the person's head — AI can structure that document, but the observations, the fairness, and the accountability for every word are yours.