Using GenAI for Efficient Employee Performance Reviews

By leveraging GenAI for performance reviews, companies can streamline and enhance the evaluation process with data-driven insights.

Employees’ performance review is a highly effort-intensive process that spans all layers of leadership, given that it is tied to financial compensation. The employee-focused corporations tend to conduct performance reviews professionally, which run for as long as three months of the year. A humongous amount of time is required for proper data gathering for the entire year, writing reviews, and conducting reviews with employees. This is followed by making company-wide financial compensation decisions and promotions.

To reduce that time and effort, some organizations now conduct multiple reviews at fixed intervals instead of year-end reviews. The underlying premise is that reviews would be quicker and shorter given the amount of data or period. Some organizations would rather do it any time during the year, depending upon the business results and other factors.

Performance Review is all About Data

Regardless of the frequency or interval, each performance review calls for additional overheads on managers because it still relies heavily on the “documentary” approach. Most Fortune 500 corporations use data-driven performance reviews, using a range of pointers such as balanced scorecards, performance metrics, project results, and business and operational outcomes. Thus, each review interval calls for managers to gather the data, evidence, accolades, notes, observations, and feedback about the employee’s behavior, strengths, wins, and areas of improvement. Undeniably, it requires a certain amount of preparation, analysis, 360-degree feedback, and assessment before finalizing the performance review. Throughout the performance review interval, managers continue to monitor and gather the performance data of employees based on set goals and measures. This process piles up massive datasets consisting of Excel sheets, passdown reports, project reports, email transactions, appreciation notes, 1-1 and group meeting notes, quarterly review slides, and business data.

The inferences from the broader body of qualitative and quantitative data are wrapped into human judgments and shared with the recipient in a write-up as qualitative assessment and feedback. Inarguably, performance management systems by leading vendors allow for gathering data and seeking feedback electronically in one place. However, even the most advanced performance management systems lack additional decision-making or intelligence to help managers ease that process. The managers must still make sense of diverse data from multiple sources to write and give a coherent review.

Leveraging AI for Performance Reviews

The recent AI revolution, especially GenAI like ChatGPT, has opened new doors for automating document analysis and deriving quick insights. ChatGPT has been revolutionary in a way that makes sense and makes a connection between scattered pieces of data.

A legitimate question arises: Can we leverage GenAI for performance reviews?

The answer to this question comes with several nuisances of AI since it is not perfect yet. There are limitations to the level of maturity we can expect from AI to process human-centric information. Within those constraints, there are five ways to deploy GenAI as an assistive technology to not only reduce the management overhead but also make the process more rational, data-driven, and efficient:

1. Baseline Performance Review Philosophy

The backend database can feed the chatbot all the performance review policies, criteria for rating, and corporate performance review philosophy. Previous performance reviews, written purely by humans, can be fed into the chatbot’s training data to ensure that it understands the prevailing corporate management style of performance review. This will ensure underlying rationality and an unbiased approach.

A more ambitious and futuristic approach is to use GenAI to establish a baseline by aggregating historical performance data across the organization. Analyzing patterns and trends can set benchmarks for different roles or departments. These benchmarks serve as a standard for evaluating and comparing current performance, allowing for a more accurate assessment.

Based on that, it can even create more appropriate evaluation criteria going forward. It can consider individual job roles, KPIs, and even personal development goals to establish a more nuanced baseline. This flexibility enables a more tailored approach to each employee’s performance assessment.

2. Derive Insights from Scattered or Disorganized Data

The key business case for using GenAI is its ability to analyze and summarize written text, however scattered or disorganized. With the latest advent of customized no-code GenAI chatbots, it is feasible to configure and deploy chatbots on a selective data set. The chatbots can browse the data folders, looking for specific employee names and making connections among data and text pieces in various documents across the defined repository.

GenAI’s data analytics can identify patterns and trends in an employee’s performance over time. By processing large volumes of information, it can highlight correlations between certain behaviors or actions and outcomes. This insight can guide personalized training or support strategies to enhance individual performance.

Assuming you have gathered all the data in a folder, a prompt for the AI tool to balance the objective and subjective aspects would look like this:

<prompt> “You are an expert performance evaluator. I will supply you with a set of performance objectives and goals set out for the group. This will be your reference to compare the data to. I want you to analyze all the documents placed in folder X, which relate to all the projects done by employee X during the performance interval. Analyze the results and wins, and spot the lapses or misses.”

Subsequent AI-written performance review summary is a powerful time-saver. At least this serves as a great first draft to build upon.

<prompt>: “Based on that, write a concise summary of accomplishments and lowlights. Include possible improvements to address the areas of concern. Make sure to keep the language simple, direct, and human with an encouraging tone.”

3. Unbiased Comparative Performance Analysis Across Employees

In general, employees are expected to use consistent or standard templates to report their work in the form of weekly, monthly, or quarterly passdowns. This templated data makes it a good candidate for analysis by an AI tool. GenAI can assess performance based on objective data rather than subjective impressions. It can analyze quantifiable metrics like productivity, accuracy, and consistency, removing biases or personal preferences. This ensures fair evaluations and helps recognize strengths and areas that need improvement.

AI-driven analysis of massive performance data for multiple employees allows a rational data-driven comparison among employees working on the same project.

4. Deeper and Thorough Performance Review

Several engagement survey studies pointed out that newcomers are particularly unhappy with the lack of a ‘touch of thoroughness’ in their reviews by their managers. Many of them feel that managers do not spend the deserving time on their reviews to ensure comprehensiveness. Managers own that responsibility. However, they are pressed hard against milestones, and most spend time switching from one meeting to the next and from one presentation preparation to the other. While on the one hand, their face time is reduced with direct employees, their time is also scarce to do thorough assessments considering all wins and misses throughout the year. This is where managers can leverage GenAI to make their review rigorous and thorough.

5. Continuous Monitoring and Feedback

As mentioned earlier, managers are increasingly becoming unavailable to employees to provide timely feedback. That’s where GenAI-based chatbots or automated notifiers can provide real-time or periodic feedback by analyzing ongoing performance, project, or operations metrics. This feedback can be purely based on data that allows for timely correction in direction. This powerful ability allows employees to track their progress consistently. This continuous monitoring enables timely interventions or adjustments, fostering ongoing development rather than waiting for an annual review.

Stay Human-centric

By leveraging GenAI for performance reviews, companies can streamline and enhance the evaluation process, fostering fairness, continuous improvement, and data-driven insights for better decision-making regarding employee development. However, the most critical thing to remember is that people work with their emotions, not just with their skills. While AI can be an assistant, the performance review must remain personal. AI tools have not reached the maturity level to be sensitive to the emotional nuisance of human beings. Managers have to do their part to double-check all the inferences generated by AI. They have a job to write performance reviews of their employees in the most humanly possible manner, regardless of the amount of help they seek from AI tools.

Dr. Raman K. Attri
Dr Raman K Attri is a Fortune 500 technical training leader, recognized as Chief Learning Officer of the Year, and named as one of the Brainz Global 500 leaders. He is among a handful of expert authorities on the science of speed in personal and professional performance. A holder of 2 doctorates in strategic organizational learning and a workplace performance scientist, he specializes in niche research on time-to-proficiency of the workforce. A prolific author of 50 multi-genre books, he writes on leadership, learning, performance, and workplace learning. He is also the founder of a mission-oriented academy GetThereFaster™ (, helping leaders with the frameworks to accelerate organizational learning to stay ahead.