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AI continues to influence how organizations implement various talent practices, including performance management (PM). However, a new HBR article by Boston University professor Chris Dellarocas suggests many may be scaling the wrong application of AI in PM. While companies like Citi, JPMorgan, and BCG have deployed AI to streamline performance reviews (with some reportedly cutting review-writing time by 40%), the author argues these are writing-efficiency tools that produce faster versions of the same flawed evaluations. The better use of AI is to surface direct evidence of performance, including decisions, problems solved, collaborators influenced, and contributions to team outcomes. The article offers three suggestions for making this shift. As HR practitioners think through practical implementation and implications, a few questions to consider: Has our organization developed a clear strategy for how, if at all, AI will be used in PM? What traditional PM problems would an AI-based solution help solve? Where will AI not play a role in PM? What evidence are we using to make these decisions? What risks could emerge, and how will we mitigate them? These are just a few of many questions that will need to be considered. For those of you in my private community for internal HR practitioners, Talent Edge Circle, you can catch the replay of our recent discussion with Dr. Anna Tavis, Clinical Professor and Chair of the Human Capital Management Department at NYU School of Professional Studies, and author of The Digital Coaching Revolution, where we discussed how organizations are using AI-enabled digital coaching to develop employees at scale.
