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A review cycle usually starts the same way. A manager has scattered evidence across one-on-ones, project notes, customer feedback, and dashboard metrics, then needs to turn it into a coherent narrative that supports a rating and will hold up in a calibration session. The work is rarely the rating itself. The time sink is reconstructing the year and writing in a consistent format across a cohort.
What follows maps the manual workflow most organizations still run during annual or semiannual reviews.
Manual drafting breaks down at predictable points, especially when managers are writing under time pressure.
The highest-judgment step is rating and calibration. That is where you separate performance from likability, discount recency effects, and align to role expectations and level definitions that vary by organization.
In an AI-assisted review workflow, the instrument produces drafts at specific steps where structure and consistency matter. At the drafting stage, it can generate a first-cut performance narrative from manager-provided evidence, organize feedback into strengths and growth areas, and propose behavior-linked examples that match your review template. In a 15-person manager cohort, this changes the cycle from days of writing to hours of targeted editing because every review starts from the same structured baseline.
When the tool generates a draft narrative, you review it against the factual record before anything is delivered or filed. You confirm dates, metrics, project names, and the employee’s scope, and you remove any confident errors that would weaken defensibility. When the tool proposes rating language, the manager still decides the rating and owns the rationale used in calibration, because the rating is a judgment against role expectations and documented standards, not a writing task.
Explore the AI-assisted workflow and the decision points it creates.