AI Performance
The credible use of AI in performance management is not scoring people. It is removing the writing and summarizing load that makes reviews late and shallow, while the judgement stays human. Spark.work is explicit about that line, and this page describes exactly where it sits.
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Review cycles fail on effort, not intent. These are the tasks that consume the effort and that assistance can absorb.
Being specific about the limits matters more here than in any other part of the platform, because the decisions affect people's careers.
Every agent recommends or drafts; a person reviews and approves before anything takes effect. That approval step is part of the product, not a policy statement.
Assistance is only specific enough to be useful when it works on your own review, feedback and objective data - which is why this belongs in the same platform as performance itself.
Performance work draws on several agents rather than one general assistant.
HR leaders evaluating AI in performance processes, and managers who want review cycles to take less time without becoming less considered.
AI Performance
AI assistance sits on top of the performance module, not beside it.
FAQ
No. Spark.work AI does not produce performance scores, rankings, promotion or termination decisions. It assists with drafting and summarizing, and a person reviews and approves the output.
It reduces the writing and reading load: drafting text, summarizing multi-source feedback, and surfacing themes across free-text responses. The evaluation itself stays with the manager.
The review, feedback, competency and objective data already held in your Spark.work platform. See the data privacy page for how that is handled.
Through the human-in-the-loop review and approval workflows, which are part of how the agents work rather than an optional setting.
