New England Biz Law Update
Outlined below are three use cases for AI in the workplace and the associated pros/cons.
Employee retention
Pros: Employers can use AI-driven information obtained during exit interviews, performance reviews, or day-to-day meetings to help proactively identify the factors that contribute to resignations. By analyzing variables such as timing of pay increases, training opportunities, promotions, management changes, and employee engagement patterns, companies can better understand what drives turnover. Companies can use these predictive models to assess an employee’s risk of departure and enable timely, proactive intervention.
Cons: AI-driven retention tools present several risks, including false positives and embedded bias, and may overlook important human nuances. They also raise significant privacy and security concerns, as large volumes of sensitive employee data must be collected, stored, and often shared with third-party vendors, increasing both trust and cybersecurity risks. Additionally, litigation is cropping up when companies use AI in hiring decisions. Letting AI make a retention decision without meaningful human oversight can expose companies to lawsuits.
Performance reviews
Pros: The biggest “pro” is that a performance review or write-up exists. There is real exposure in the workplace when supervisors fail to document issues or when supervisors fail to provide meaningful feedback. Using AI for performance reviews and disciplinary write-ups can promote consistency, reduce administrative burden, and identify performance trends across teams using objective data.
Cons: Overreliance on AI may overlook context, embed bias from historical data, raise privacy concerns, and be only as good as the data that supervisors actually include. (What good is a performance review if a supervisor fails to document verbal warnings?) Like all things in AI, risk exists when automated assessments influence employment decisions without meaningful human oversight.
Job advancement
Pros: AI-driven career management can broaden internal mobility by identifying transferable skills, creating data-informed career pathways, and surfacing talent from nontraditional backgrounds who might otherwise be (by the company or themselves) overlooked.
Cons: These systems rely heavily on historical workforce data that may embed bias, misinterpret skills without proper context, and create legal or employee relations risks if predictive models influence promotion decisions without transparent standards and human oversight.
AI in the workplace holds remarkable promise — but only if approached with wisdom, guardrails, and a steady hand on the wheel. Employers who acknowledge both the wonder and the risk can harness its power thoughtfully, rather than react to it in fear.
Stephen Scott is a partner in the Portland office of Fisher Phillips, a national firm dedicated to representing employers’ interests in all aspects of workplace law. Contact him at 503-205-8094 or [email protected].
