Half of Companies Could Already Be Using AI in Your Performance Reviews

Summary

This article addresses the growing integration of artificial intelligence into employee performance review processes across U.S. and U.K. organizations. Drawing on a General Assembly survey of over 500 senior leaders, the article argues that AI adoption in HR functions — including performance evaluations — is already substantial and accelerating. Key findings include that 47 percent of leaders already factor AI usage into performance evaluations, 93 percent encourage AI use at work, and 82 percent report using AI regularly. However, only 30 percent of HR professionals have received job-specific AI training. The article presents both potential benefits — such as greater consistency, reduced individual manager bias, and faster evaluations — and significant concerns, including algorithmic bias, lack of transparency for employees, and over-reliance on automation. Expert commentary highlights that companies frequently measure AI usage quantity rather than quality of impact. The article concludes that AI's role in performance management will expand, with outcomes dependent on managerial training quality and the establishment of ethical guidelines for AI use in evaluation processes.

Strengths and limitations

The primary data source is a proprietary survey conducted by General Assembly, a company that sells AI training and workforce development services. This represents a direct commercial conflict of interest: findings that highlight low AI training rates and the risks of untrained AI adoption directly support General Assembly's service offerings. The survey sample (500+ senior leaders across the U.S. and U.K.) is not described in sufficient methodological detail — sampling methodology, response rates, and sector distribution are absent, limiting academic reliability. The article presents quoted expert opinion from an HR consultant alongside the vendor CEO, but neither source is independently verified or peer-reviewed. Statistical claims (47%, 93%, 82%, 30%) are presented without confidence intervals or cross-validation. On the positive side, the article presents a balanced framing of both benefits and risks of AI in performance reviews, and includes critical expert voices. The concerns raised — algorithmic bias, transparency gaps, and legal liability — are consistent with broader academic literature on AI in HR, lending contextual credibility even if the primary data is vendor-generated.

What this implies

The data suggests a potential decoupling between AI deployment in performance management and the organizational infrastructure — training, guidelines, ethical frameworks — needed to support it. If AI usage volume becomes an evaluation criterion independent of impact, this may distort employee behavior and undermine the validity of performance data. The legal liability dimension of AI-generated discriminatory outcomes indicates a growing compliance risk area for HR functions. The training gap identified (30% of HR professionals with job-specific AI training) points to a structural lag that may intensify as AI integration in evaluation systems deepens.

Key points

• 47 percent of senior leaders surveyed already factor AI usage into employee performance evaluations, suggesting near-mainstream adoption of AI as an evaluation criterion.
• Only 30 percent of HR professionals have received job-specific AI training, creating a significant skills gap relative to the pace of AI deployment in HR functions.
• Leaders are more likely to measure how much employees use AI tools than the actual business impact of that usage, indicating a potential misalignment between AI metrics and organizational value.

What to take away

• Organizations integrating AI into performance reviews without role-specific training risk incentivizing performative AI adoption — employees using AI for visibility rather than genuine productivity gains.
• Employers using AI tools in evaluation processes carry legal liability for discriminatory outcomes, and opaque evaluation criteria are associated with employee disengagement and turnover.