The one thing AI feedback tools can’t account for

Workplace culture shapes this too, Grandey notes. In a culture where mistakes are penalized or one bad review ends in termination, employees are more likely to be fearful and reactive when feedback arrives, regardless of how it’s delivered.

The behavioral data current platforms don’t capture

Belluccia’s concern is less with AI-generated feedback itself than with what current tools leave out. In his view, most platforms are optimizing for the message while the person receiving it goes unconsidered. Most analyze communication patterns, goal progress, and historical performance data. What they don’t capture is the behavioral profile that predicts how a specific person will respond when that feedback lands.

That gap matters more than ever. Gallup data from early 2025 found that just 31% of U.S. employees feel engaged at work, a 10-year low. Belluccia connects that directly to people not feeling seen. In an era of AI-generated content, he says, feedback that feels generic or impersonal is making the problem worse.

Belluccia points to a 2025 randomized controlled study of over 20,000 peer reviews at the International Conference on Learning Representations (ICLR) as an analogy for what happens when AI enters any feedback process. Reviewers who received AI assistance wrote longer, more comprehensive reviews. But the AI couldn’t replicate the human judgment that comes from actually knowing the subject matter, the person, or the stakes involved. In a workplace context, he argues, employees can feel that difference.

Research consistently shows a disconnect between why managers think employees leave and why they actually do. Managers tend to assume it’s about pay, while employees more often cite relational reasons: not feeling recognized or seen. Feedback tools built on productivity data, Belluccia says, do nothing to address it.