AI is transforming performance management from a broken administrative burden into a human-centric engine for continuous development, enabling personalized coaching.
Maria Valero
Editorial Strategist, HR
Workday
For many HR leaders, the annual performance review cycle feels less like a strategic business process and more like an organizational nuisance that is largely made up of going through the motions. It’s a tedious exercise that consumes valuable manager time, often results in generic, compliance-driven commentary, and—most critically—fails to move the needle on employee development or engagement.
A 2025 Deloitte study found a startling reality: 61% of managers and 72% of workers could not say that they trust their organization's performance management process. When leaders and individual contributors view the primary system for measuring growth and impact with skepticism, it signals that the traditional, backward-looking model is broken.
Today, forward-thinking HR leaders recognize that the solution isn't to tweak the old process, but to fundamentally reimagine it. This is where AI becomes not a replacement for human judgment, but the very thing that enables managers to deliver what employees need to stay engaged at work: timely, personalized, and equitable feedback centered on their growth.
AI is transforming performance management from an administrative check-the-box activity into a high-impact engine for continuous human development.
72% of workers could not say that they trust their organization's performance management process.
Guide
If you ask any manager why they struggle with performance reviews, the answer often boils down to two things: time and documentation. Managers are expected to synthesize an entire year of activity into a few paragraphs, often spending an estimated 210 hours annually on the process, only to be undermined by recency bias or a lack of specific, timely examples.
This administrative burden leads to generic, low-quality feedback that fails to inspire or motivate. The shift to continuous performance management addresses the timeliness issue, but continuous feedback alone is not enough. The secret to effective performance management lies in personalization. Employees are hungry for insights that are specific to their strengths, their challenges, and their unique career trajectory.
Moving managers beyond vague summaries to specific, evidence-based coaching is one of the easiest use cases of AI—and organizations should take immediate advantage of it.
Modern AI tools work by connecting and analyzing vast, multi-source data—from goal-tracking systems and project management tools to communication patterns in the flow of work. This provides a rich, continuous tapestry of performance data that no human manager could manually synthesize.
Instead of writing, "John has shown great communication skills this quarter," AI can arm the manager with specific, timely examples, such as: "During the Q2 launch, your clear, detailed update in Slack accelerated the team's decision-making by two days. Let's discuss how we can apply that same clarity to stakeholder communications in your next project."
The secret to effective performance management lies in personalization.
This shift transforms the manager’s role from evaluator to coach, helping to crystallize growth opportunities delivered when they are most relevant. This investment into personalization directly fuels business performance and value.
For HR leaders and managers looking to leverage AI in this transformation, the change is less about the technology and more about the mindset shift it enables. Focus on these three strategic imperatives:
While personalization drives growth, fairness and bias-reduction is the foundation where trust is built.
Performance systems are inherently vulnerable to human bias. Affinity bias, recency bias, and conscious or unconscious gender bias creep into language and ratings, leading to inconsistency and inequity that can erode the entire employee-manager relationship. Research highlights the risk here: a staggering 85% of employees would consider quitting after receiving what they perceive as an unfair performance assessment.
This is where the objective, pattern-recognition power of AI offers an extraordinary advantage in safeguarding fairness. AI grounds feedback in objective data like project completion rates, goal achievement, and skills used rather than subjective perceptions, significantly reducing the impact of individual biases.
While personalization drives growth, fairness and bias-reduction is the foundation where trust is built.
It can also help flag bias by analyzing the language used in manager and peer feedback. Flagging terms that exhibit common biases—such as using “aggressive” for women or “promising” for men—and suggesting neutral alternatives in their place.
In an environment where retaining top talent hinges on fostering a culture of trust, ensuring fairness in each interaction and review is paramount. AI acts as a transparent, objective partner to managers, making sure the rules of success are clear and applied equally to all.
AI frees managers from the burdens of manual documentation and subjective assessment, turning them into high-value coaches equipped with objective, personalized, and equitable insights.
By adopting AI for performance feedback, HR leaders are doing more than just modernizing a system—they are building a human-centric workplace where every employee receives the specific support they need to grow, where trust is built on fairness, and where talent strategy directly contributes to measurable business success.
The question for every HR leader today is not if you will embrace AI in performance management, but how quickly you will use it to embody the full coaching potential in your managers and your people.
Guide
AI has been a gamechanger across nearly every industry and marketplace. But realizing the true value of AI requires buy-in between different stakeholders. In this AI Masterclass, learn how you can make the most of AI for finance, HR, and more.