By Gleb Tsipursky, PhD
Pace University’s September 25 Actionable AI Conference brought professionals and business leaders to Westchester for practical guidance on using AI responsibly and effectively. The natural next question for local employers is what happens when employees leave the conference, open an AI tool at work, and receive an answer that looks convincing but is wrong.
That moment exposes a gap in many workplace AI programs. Companies spend heavily on tools and training while leaving review standards vague. Employees hear that they must use human judgment, but they may not know which facts require independent checking, when an output can be approved, who owns the final decision, or what kind of error requires escalation.
IBM’s September 21 global CHRO study shows why those details matter. IBM surveyed 1,500 CHROs and senior workforce executives along with 8,800 employees. Seventy-one percent of the executives identified the ability to supervise, validate, and override AI output as the most essential workforce skill. IBM also found that 41 percent of CHROs believe employees may not feel safe challenging or overriding AI outputs.
Westchester employers should turn the phrase “human review” into specific operating rules. Start with the consequence of the task. A brainstorming exercise may require little review. A customer communication, financial analysis, hiring recommendation, safety-related decision, or public-facing claim deserves a more explicit standard. The greater the consequence of an error, the clearer the verification and escalation requirements should be.
Those requirements should answer ordinary questions. Which sources must the employee check? What information may the AI use? Who signs off before the output reaches a customer or colleague? What happens when the employee and the model disagree? When should the task return to a primarily human process? A rule that simply says “review AI output” leaves too much room for inconsistent judgment.
Managers should also measure review work. IBM found that 80 percent of CHROs believe AI creates invisible labor through validation, correction, context, and exception handling. If employees spend substantial time repairing AI output, leaders need to see that time when evaluating productivity. Otherwise, an AI workflow can appear efficient because the organization measures generation and ignores cleanup.
Clear review rules can also improve adoption. Employees who distrust AI accuracy have a reasonable concern, especially when they remain accountable for the final result. Giving them defined checkpoints and explicit authority to override the system turns skepticism into a useful control rather than treating it as resistance. Employees who already use AI enthusiastically benefit from the same boundaries because they know where experimentation ends and accountability begins.
The rules should evolve from experience. Managers can review samples of AI-assisted work each month, identify the errors employees catch most often, and adjust checklists or approval gates accordingly. Recurring mistakes may point to weak source data, poor prompts, an overly broad use case, or a task that should not be automated further.
Pace’s conference emphasized practical AI rather than hype. Westchester employers can honor that principle by defining what practical review looks like in everyday work. Better tools will keep arriving. The organizations that benefit most will be the ones that make human judgment visible, teachable, and operational before asking employees to trust the next system.
Supervisors need training too. An employee cannot exercise meaningful override authority if a manager treats every challenge as delay or disloyalty. Leaders should reward people for catching consequential mistakes early and make clear that declining to use AI on a poorly suited task can be a sign of competence. That culture gives review rules practical force instead of leaving them as language in a policy document.
Gleb Tsipursky, PhD, a behavioral scientist, CEO of Disaster Avoidance Experts, and author of The Psychology of AI Adoption at Work: From Resistance to Results (Georgetown University Press, 2026). https://disasteravoidanceexperts.com/aibook