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The AI Transformation 100: 100 Concrete Ideas for Fixing How We Work
Conductor, an answer engine optimization platform based in New York City, requires that every employee have a 'high level of AI competence' and has made it a core part of performance reviews.
The company's 250 employees are rated on a scale from "role model" to "does not demonstrate" based on how effectively they use, innovate and champion artificial intelligence, said head of revenue development Joe Taylor.
"Then we have criteria for each of those categories to help people understand and guide them towards what they should be doing in order to achieve the highest level of the competency," Taylor explained, adding that the AI competency metric is also incorporated into its hiring framework.
AI is increasingly being factored into performance reviews at companies such as Google, Meta and others as they mandate that employees use the technology.
As a result, AI use is playing a role in promotion decisions, "especially where leadership wants to signal that AI adoption is a priority," said Shuhua Sun, an associate professor of management and entrepreneurship at Tulane University. "Whether that is the right move depends on the company's broader AI strategy."
If AI Does the Work, Should Employees Be Held Accountable?
"If AI is central to the role, then it becomes central to the performance evaluation," said Richard Landers, a professor of industrial-organization psychology at the University of Minnesota. However, it's up to organizations to set expectations for how AI should be used and how employees will be evaluated for it.
Even if employees use AI to conduct much of their work, they still need to be held accountable, Sun said, "but the nature of accountability changes."
Accountability shifts from "execution to judgment," Sun explained. People should be held accountable for functions, such as whether AI was an appropriate tool for the task, they evaluated its outputs critically, identified risks and intervened when a problem arose.
If someone accepts AI output without review, for instance, it's a performance issue, said Sun. If they used reasonable oversight, but the AI tools still failed, it's a system design or governance issue, and employees shouldn't be held accountable.
Ultimately, "you're still responsible for whatever work you produce, whether you used AI to help you produce it or any other tool," Taylor said.
Along with weighing work output, companies using AI should consider employees' capacity and willingness to use AI tools in their jobs in performance reviews, said Weichun Zhu, an associate professor of management at Kean University.
How Companies Should Weigh AI Use
How companies factor AI use into employee performance reviews varies by organization and industry. But here's what experts say this process should involve:
Focus on the Broad AI Strategy
"Strategic alignment," or how employees' AI use supports an organization's overall AI strategy, is crucial, Sun said. "Managers are in a position to assess whether the way AI is being used advances the company's objectives."
Often, companies may rush to adopt AI without considering the risks (or benefits) to their employees or customers, said Lindsay Mastrogiovanni, founder and CEO of ConsciousHR.
Before creating a process to evaluate an employee's AI use, organizations "really should be asking questions like, what are they using it for? Is it driving revenue, and is that through efficiency or sales growth?" she said. Then, create a performance policy that's aligned.
Organizations must also ensure that AI tools they use are successful before evaluating employees' performance using them, Landers added.
Defining Success
Organizations must define what "good AI use actually means," Sun said. For instance, is it based on frequency of use, measurable productivity gains, quality improvement, innovation outcomes, responsible oversight or skills development?
Setting usage targets isn't enough, Sun said. "The real challenge is not whether people use AI, but whether they know when to rely on it, when to question it and when to override it."
Success should "shift from less of a productivity conversation to more of a judgement conversation," agreed Mastrogiovanni. For instance, if innovation is an organization's core value, AI use should be weighed against it.
"Instead of adding a checkbox for AI use, companies should rethink what strong performance looks like in a workplace where AI is part of everyday work," Sun said.
Changing Performance Metrics
Many companies change performance metrics to account for AI use, but approaches vary, Sun said.
For example, among the companies Sun has studied, an e-commerce organization tied about 20% of performance to the effectiveness of AI use based on a supervisor's assessment. A media company encouraged AI use, but didn't evaluate employees on whether an AI project succeeded or failed, but instead, assessed them on whether the chosen AI direction was reasonable, assumptions were tested and the individual demonstrated that AI advanced innovation.
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Companies should adjust performance metrics to reflect their industry context and strategic priorities, Sun added. "The common thread, though, is a shift from measuring simple usage to evaluating effectiveness, judgment and capability development in working with AI."
At Conductor, metrics include how employees present new ways of using AI in their roles and demonstrate an understanding of AI's value, Taylor said. The company doesn't focus on a scorecard of how many times someone uses a specific AI tool a day, he said.
Being Transparent
Employees need job descriptions and tasks, and performance should be "aligned with those expectations," Landers said.
"It's the drift that we're having right now that's the problem, where we hired a person with an expectation that they would be doing a certain kind of work," Landers said. "Then, suddenly, some manager somewhere said, 'Oh, but if they use AI, they could be faster or better or whatever.' That's creating ambiguity."
Being transparent about how an individual's AI use will be evaluated, what performance metrics are changing and why and which algorithms or tools are used boosts trust in the system, Zhu said. Plus, give employees a chance to offer feedback, he added.
"Employees need confidence that they will not be unfairly penalized for system-level failures," Sun said.
Offering Support
Pair any AI-related evaluation changes with support, such as offering training, time for experimenting or career pathways that incorporate AI-related capabilities, Sun said.
"When employees see that the organization is investing in their development rather than simply adding or tightening evaluation standards, changes are far more likely to be accepted and effective," he said.
Keeping Humans in the Loop
This is a given, Mastrogiovanni said, adding that managers should review the information included in performance evaluations and distribute and discuss it with employees.
Managers at Taylor's organization are expected to be hands-on when evaluating employees and giving feedback, he said. Employees also conduct self-evaluations and are encouraged to speak up if they disagree with something.
While there's no one formula for evaluating AI competency, managers need to assess the nuance. "A system can tell you how often someone used a tool, but it cannot easily tell whether that use improved judgment, strengthened the work or prevented an expensive mistake," Sun said.
Editor's Note: How else is AI changing people strategy?
- Performance Metrics Are Obsolete. AI Is About to Prove It — Why outdated performance metrics and legacy work systems are now organizational risks when AI is a digital collaborator.
- How AI Is Rewiring People Strategy and What HR Can Do to Adjust — HR leaders see AI transforming work beyond automation — reshaping teams, culture and people strategy. The future is “human-engaged” work, not human-replaced.
- Why AI Hiring Discrimination Lawsuits Are About to Explode — AI is reshaping hiring — and the courtroom. Job seekers are suing over biased screening tools, and experts say a wave of lawsuits is just beginning.