Balancing Human Judgment and AI in Performance Reviews

Performance reviews are the backbone of employee development, yet they’re often met with groans and eye rolls.

We don’t need to tell you why: the traditional approach can be time consuming, uncomfortable, and demoralizing. That’s where artificial intelligence (AI) is coming in. It can make performance conversations more data-driven, objective, and — if you can believe it — a little bit exciting.

It’s no secret that AI is quickly becoming a staple in performance management systems. A recent survey from the Society for Human Resources Management (SHRM) found that 57% of HR professionals already use it to provide more comprehensive feedback and facilitate employee goal setting.

AI promises to uncover hidden insights, reduce bias, and streamline performance conversations for all parties, which is great. But before we get carried away with the hype, there are some questions to ask, like: How do we ensure that AI doesn’t dehumanize the process? After all, performance reviews are ultimately about people —their growth, challenges, personalities, and potential.

Here, we’ll show you how to find that sweet spot where AI’s capabilities enhance, not overshadow, the human touch. We’ll discuss the benefits, address the risks, and offer practical tips for integrating AI in a way that empowers both managers and employees — and keeps the heart and soul of performance conversations intact.

Using AI in performance reviews without letting it take over

McKinsey research suggests that AI will vastly improve individual and workforce productivity and may automate up to 30% of business activities across occupations by 2030.

For instance, when prepping for performance reviews, AI can revolutionize how you gather data, offer feedback, and identify growth opportunities for your employees. Because of its ability to sift through mountains of information — like emails, project timelines, and peer feedback — AI can spot trends that might escape your human eye.

Source: Zensai

Did a team member consistently exceed sales targets? Has their coding efficiency improved over time? AI helps managers mine these gold nuggets, thus freeing them to focus on high-value human interactions and master the art of performance conversations.

Human judgment and insight in performance conversations: the heart of the matter

Performance conversations should be based on more than metrics and serve as opportunities to motivate and connect with your direct reports.

This is yet another reason why it’s so important to maintain a personal touch during performance reviews.

While AI can certainly help tell a story, it doesn’t always capture the whole picture. A living, breathing human manager can pick up on subtle cues — like maybe one team member has been juggling a family emergency, or another is part of a new mentorship program that’s paying off.

These nuances are necessary for contextual understanding, not to mention crafting a personalized employee development plan. AI simply can’t replicate that level of empathy (and we probably shouldn’t want it to).

There are major benefits of using AI in performance reviews

Let’s say you’re part of a giant corporation struggling with inconsistent performance reviews across departments. AI can analyze thousands of past reviews, scoping out biases and pinpointing best practices. That way, you’re equipped with a standardized framework, making things more fair and transparent across the board. 

Or, maybe you’re at a scrappy startup where time is of the essence (because isn’t it always?). AI-driven performance management tools can collect feedback and whip up insightful reports, giving you hours of your life back.

A recent University of Cambridge study found that people “inherently trust feedback from machines.” Moreover, employees perceived AI-powered feedback as more fair than human managers alone, especially in large organizations. With the right approach, AI can be your new best friend (for performance reviews, at least).

Source: Zensai

AI-powered performance management means:

• Better objectivity: AI doesn’t play favorites or get swayed by emotions. It looks at the facts, reducing the risk of unconscious bias creeping into reviews and ensuring fairer evaluations for everyone.
• More efficiency: AI can break down performance data in no time. Automating this process gives managers more time and energy to do pretty much literally anything else.
• Data-driven insights: AI can find patterns that help you understand what makes your teams tick. It can tailor feedback to individual employees and suggest development opportunities based on their unique strengths and challenges, helping them feel more valued.
• Early warnings: Because AI can detect early signs of performance dips or burnout, it allows you to intervene proactively and provide support before issues blow up.

But we can’t rely solely on AI for performance evaluations

AI is powerful, but it’s not infallible. Algorithms can inherit biases from the data they’re trained on, leading to unfair feedback and reviews. Again, context is key — a missed deadline might seem like a red flag, but maybe there were unforeseen technical hurdles or personal issues that AI wouldn’t grasp. Overreliance on technology can also erode the manager-employee relationship and make your team members feel like cogs in a machine, like they don’t really matter.

We can’t emphasize this enough: AI is not a replacement for human judgment. It’s a tool designed to complement managers’ expertise, helping them make more informed decisions, have more meaningful conversations, and drive better performance outcomes for their people.

And sure, perhaps you’re still wondering: why can’t we just let AI do its thing? Well, because nothing is perfect, and there are some things machines just don’t understand, like:

• Nuance: AI might flag an employee for not hitting the mark, but a human manager understands the circumstances behind their change in performance.
• Motivation: Sure, AI can give solid feedback, but only humans can inspire and coach employees to be their absolute best.
• Putting the “human” in human resources: At the end of the day, people want to connect with other people. An AI-generated review just feels cold and impersonal compared to a face-to-face conversation with another human.

Striking the balance: strategies for blending human and AI-powered insights

In a recent PwC survey, 44% of business leaders said that their companies are planning to implement data modernization efforts in 2024 to take better advantage of generative AI. ​​But the real magic happens when we combine that AI-generated data with actual human context.

So, how do you strike that balance? For starters, you let AI do all the heavy lifting (data analysis, identifying performance patterns). Then, you have a human manager pop in to interpret the findings and add that nuance and motivation we mentioned. 

While AI can provide a great starting point for feedback, managers should always be encouraged to personalize it and have genuine, non-robotic conversations. This shows that you’re looking at the numbers and understanding the stories behind them.

Source: Zensai

Lastly, be open with your employees about where AI is being used and where it’s not being used (i.e., not to make final hiring or firing decisions). This hybrid approach combines data-driven objectivity with good old-fashioned human intuition, creating a more holistic view of employee performance.

4 tips for integrating AI in performance reviews

Before you unleash the algorithms, you must ensure the transition is seamless and stress-free for your organization. Integrating new tech can be tricky, and like any big shift, it requires a thoughtful, forward-thinking approach.

Here are our top tips for taking the leap (and making the transition as smooth as possible):

1. Choose wisely, not wildly: The AI market is booming, but not all tools are created equal. Before you jump on the bandwagon, research and compare different options. Look for tools that fit your company’s goals, culture, and values. Consider factors like scalability, ease of integration with existing systems, and customization options.
2. Keep data diversity in mind: As we said, even AI can be biased. The algorithms are only as good as the data they’re fed. To mitigate the risk of algorithmic bias, ensure your training data is diverse and actually representative of your workforce. Regularly audit the AI’s outputs for fairness and accuracy, and be prepared to adjust your methods if needed (more on that later).
3. Be the change management you want to see: Don’t spring AI tools on your team overnight. Instead, introduce them gradually, with plenty of training and support for managers. Be quick to address any concerns or resistance, and make sure to highlight the benefits for both employees and the business.
4. Communicate openly: Transparency builds trust. Talk with your team about how AI is used in the performance review process. Explain how it enhances human judgment, not replaces it. Encourage questions and feedback from employees, and more importantly, be open to making tweaks based on their input.

Making AI work for your company and your people

Right, so you’ve got this fancy AI tool all set to shake up your performance reviews. Now it’s time to take it from shiny new tech toy to prized team player.

First things first, align AI with your company’s values. Take a step back and ask, “What are we trying to achieve?”.

If your organization prioritizes innovation, AI should help you spot those creative sparks within your team. If collaboration is the goal, AI should analyze communication patterns and suggest ways to strengthen teamwork. The point is that the metrics AI tracks are in sync with your company’s overall vision.

Next, establish trust. According to Microsoft and LinkedIn’s 2024 Work Trend Index report, only 39% of people globally who use AI at work have received training from their company. That means most employees are left to figure it out on their own, which can lead to confusion and mistrust — not exactly the outcome you want.

Invest in comprehensive training programs to combat this so everyone understands how AI is used and why. Maintain an open, ongoing dialogue about AI’s role in performance reviews and allow employees to share feedback. Transparency will go a long way in building trust and positioning AI as a helpful tool instead of a threat.

AI can generate a ton of data, so don’t let it bury your managers in information overload. Focus on the most actionable insights — the ones that can genuinely help employees grow and improve.

Use AI to create personalized development plans, spotlight strengths to celebrate, and find areas for improvement. Then, use those AI-generated insights to initiate meaningful conversations about their performance, career goals, and learning and development opportunities. Encourage them to ask questions and offer their perspectives. This puts everyone on the same page and helps make AI feel like a partner in your team’s success.

Best practices for keeping AI accountable

Ensuring AI consistently performs at its best (and upholds ethical standards) is an ongoing journey. Flemming Blåbjerg, head of digital transformation and compliance at Zensai, emphasizes the importance of monitoring and evaluating each AI service you use, thus building and maintaining trust with stakeholders. Blåbjerg adds that Zensai “leans very, very heavily on the framework of trustworthy AI that Microsoft provides” and sticks to the same three pillars for all AI usage:

1. Lawful: Adherence to all applicable laws and regulations, including the upcoming EU AI Act, is paramount.
2. Ethical: AI should be free from bias and maintain ethical standards in its decision-making processes.
3. Robust: AI should be developed with safety and security in mind, ensuring it doesn’t cause intentional harm and performs as expected in various scenarios.

To implement these principles, conduct a “thorough risk assessment” for each AI service you’re considering. This involves evaluating potential risks in relation to those three pillars — where does your AI tool currently stand in terms of lawfulness, ethics, and robustness? It’s a proactive approach that keeps a watchful eye on AI and ensures that it’s being used responsibly.

In addition to Zensai’s method, there are some broader best practices to consider.

Establishing clear metrics and KPIs for AI-driven performance reviews

Define specific, measurable, attainable, relevant, and time-bound (SMART) goals for your AI the same way you would an employee. Are you aiming to reduce bias in performance reviews? Or improving employee development plans? Track progress towards these goals and adjust your AI strategy accordingly.

Monitoring and adjusting AI model performance for continuous improvement

Human oversight is everything. Review the data inputs and algorithms used by your AI models, and do it regularly. Look for any sources of bias or error, and make timely tweaks to keep your AI-driven performance insights fair, accurate, and consistent. Involve diverse stakeholders in these audits so that various perspectives are included, and always make sure you test any adjustments before implementing them.

Addressing employee concerns and building trust in AI systems

Encourage your employees to share their experiences with a new AI-powered platform and address any concerns, misconceptions, or trust issues they may have. Their feedback can help you assess areas where AI could be improved.

As AI technology evolves, so should your understanding of its capabilities (and limitations). Investing in ongoing training for your HR team and managers is vital so they can effectively interpret and utilize all the new insights coming at them.

The future of AI in performance management

AI is becoming more sophisticated by the day, and we’re on the cusp of some cutting-edge developments, including:

• Nuanced chatbots. We’re talking about AI that can understand the nuances of language like never before. Think of chatbots that can interpret tone and sentiment, giving managers real-time feedback on employee engagement.
• Advanced predictive analytics. AI promises to predict which employees are likely to excel in leadership roles or who might be on the verge of burnout — and it’s getting closer to making this a reality. By analyzing patterns in performance review data through predictive analytics, AI can recognize high-potential employees and flag issues before they escalate.
• Hyperpersonalized feedback. The future of AI is all about tailoring insights to each individual’s learning style and career goals. AI-powered platforms will suggest specific courses, mentors, or projects based on an employee’s strengths and aspirations. Research consistently shows that employees who receive regular, personalized performance feedback are more engaged at work and likely to stick around longer at their company.

AI is not a substitute for human judgment

AI has incredible possibilities, yet one thing remains constant: human judgment is irreplaceable. It’s up to us to interpret the data, make educated decisions, and build authentic relationships with our people.

After all, the most successful companies will be those that find the perfect synergy between human intuition and AI’s analytical prowess. By discovering that sweet spot, we can create a performance review process that’s fairer, more efficient, and — dare we say it again — enjoyable.

Discover the key strategies for impactful employee performance check-ins and boost team success!

Edited by Jigmee Bhutia