The productivity gains from artificial intelligence are becoming undeniable, and the conversation for leaders has shifted from whether to encourage AI use to how to motivate employees to embrace it. One emerging answer is integrating AI fluency into performance reviews.
Some argue that AI fluency is now essential to organizational competitiveness, and that performance expectations should reflect whether employees are keeping pace. Others caution that tying AI use to performance could introduce new risks, unintended incentives, or inequities.
The riskiest stance regarding the future of performance criteria, however, is choosing no stance at all. Whether organizations decide to incorporate AI adoption into performance criteria, raise expectations for work quality, or redefine what “high performance” means in an AI-enabled workplace, they must have a point of view. Without clear direction, leaders can’t steer their people or their organizations toward meaningful, consistent progress.
Why AI Adoption Should Be a Performance Metric
As AI becomes more integrated into daily operations, the argument for including it in performance evaluations grows stronger. “AI enablement is the core skill that virtually all professionals need going forward,” said Andy Biladeau, chief transformation officer at SHRM.
But it’s not just about individual contributors using AI. Managers also need to up their game. Including AI adoption as a performance metric could be the push they need, forcing them to understand how to best blend human and AI efforts.
“Effective AI adoption is going to require managers and leaders to have a much more detailed idea of how their team's work is done, so they can direct the right work to the right people with the right AI tools,” Biladeau said.
This new reality directly impacts performance evaluation. For individual contributors, the key question becomes how effectively they can integrate AI into their tasks to improve outcomes. For managers, the metric shifts to their ability to delegate intelligently, assigning the right mix of human and AI-supported tasks.
According to EY’s Agentic AI Workplace Survey, while 84% of desk workers are eager to embrace agentic AI in their roles, 56% are concerned about their own job security working alongside AI agents. Tying AI usage to performance metrics could be a powerful motivator, encouraging employees to overcome their natural resistance to change and their fear of new technology. By making AI adoption a key performance indicator, companies can send a clear message that this technology is here to stay.
The Risks of Measuring AI Use
Despite the potential benefits, making AI use a formal performance metric is not without its risks. One of the primary concerns is that it can create misaligned incentives, leading to counterproductive behaviors.
Biladeau warned that in certain roles, such as finance, system testing, or talent acquisition, a blanket expectation to use AI could actually worsen outcomes. In other words, just because a task can be done with AI doesn’t mean it should be.
Another risk is that employees may use AI simply to "check a box" without adding real value.
Kim Billeter, global people consulting leader at EY, observed that this may already be happening. “A lot of organizations are reporting very high usage of AI, but people are generally doing less high-value work,” she said.
An alternative approach is to treat AI fluency as a normal, expected part of the job rather than a separately measured metric.
Richard Jackson, chief technology officer for EY Americas’ Assurance services and AI leader for EY Global and Americas’ Assurance services, said, “The focus of AI implementation should be setting the appropriate expectations and communications to our people around why the use of technology and AI fluency is just part of their everyday job.”
The Mindset Shift Needed When AI Factors into Performance
For organizations that choose to measure AI use in performance, a crucial mindset shift is required.
Billeter suggested that people start viewing AI as a collaborator. “We have to stop thinking about AI with a fear factor and start recognizing that these agents can be seen as AI colleagues, not just tool sets,” she explained.
This perspective is shared by many workers. Nearly three-quarters of U.S. employees (74%) said they believe AI should complement human talent, advocating for strong oversight and collaboration, according to SHRM's From Adoption to Empowerment: Shaping the AI-Driven Workforce of Tomorrow report. By adopting this mindset, organizations can foster a more collaborative human-AI workforce.
“The question becomes: How do I work better with my colleagues?” Billeter said. “As we start to recognize agents as colleagues, we’ll manage the workforce differently.”
Of course, like with any good colleague relationship, clear guardrails are critical for success.
“You need a brake and you need an accelerator for successful AI implementation. The better the braking system within an organization, the faster you can move,” Jackson said. Strong brakes, which include preventing unsafe automation, setting clear boundaries for use cases, and ensuring quality control, enable teams to innovate and adopt AI quickly without compromising standards. Without these safeguards, tying AI use to performance could incentivize irresponsible speed and unchecked automation, which is a dangerous dynamic when AI makes quantity effectively infinite.
How AI Will Change What ‘Good Work’ Looks Like
The rise of AI brings the classic debate of quality versus quantity into sharp focus. According to SHRM research, 44% of leaders said they expect future performance reviews will weigh quantity and quality equally, while 39% believe the emphasis will shift more toward quality. AI makes it easy to produce more work, but it doesn't automatically generate work that is thoughtful, contextual, or high-quality.
In this new environment, intentionality remains a uniquely human skill. “In this world where there’s no barrier to generating new ideas, the value lies in strategically choosing the ones you understand deeply enough to deliver with excellence,” Biladeau said.
What Do We Do with AI’s Gains?
AI is freeing up significant time that used to be spent on manual tasks, administrative work, or repetitive processes. But the real question for leaders isn’t simply how much time is being saved, it’s how that time should be reinvested and how performance expectations need to evolve as a result.
Jackson noted that AI’s potential goes beyond efficiencies — it offers the chance to elevate the work itself. “If people feel AI will take the toil out of their everyday experience and release time to do higher-level skills, then they can get excited about it,” he said.
This excitement has direct implications for performance evaluation. If AI removes lower-value tasks, leaders can’t simply expect “more output.” Instead, the bar must shift toward:
- Deeper analysis and judgment, rather than volume.
- Higher-quality deliverables enabled by freed-up cognitive capacity.
- More strategic thinking and problem-solving.
- Better collaboration and communication, especially in human-AI workflows.
This is the real opportunity created by AI gains: Not just doing work faster, but doing better, more intentional work that humans are uniquely positioned to deliver. Leaders who set this expectation clearly will help employees channel AI-driven time savings into skills growth, stronger outcomes, and, ultimately, higher-value contributions to the organization.
‘Fail Fast’
The decision to tie AI to performance is complex. Doing so can accelerate adoption and prepare organizations for the future, but moving too quickly without clear guardrails can lead to misaligned incentives and heightened anxiety.
As AI becomes a dominant force in the workplace, the challenge is not just integrating new tools but rethinking what constitutes valuable work and how to evaluate it. Whether you decide to formally measure AI use or not, it is crucial to pick a stance, test it, and be willing to iterate.
“Organizations have lost the ability to fail fast,” Billeter said. “We don’t celebrate failure, but maybe we should.”
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