Transform Performance Evaluations with GenAI: Smarter Grading, Visual Insights, and Next Steps

Disclaimer: The views and opinions expressed in this article are solely those of the author and do not reflect the official policy or position of any current or former employer. Any content provided in this article is for informational purposes only and should not be taken as professional advice.

It’s That Time of Year Again… But Not the One You’re Thinking Of

No, I’m not talking about the holiday season. I’m talking about something less festive but just as familiar: performance evaluation season.

For many employees, this time of year brings feedback that feels more like an obligatory checkmark than a meaningful investment in their growth. Often, it’s just a few lines that read something like: “You met expectations. Keep doing what you’re doing, and maybe work on these areas.”

While well-intentioned, this kind of feedback can feel underwhelming. Employees are left wondering what “meeting expectations” truly means or how they can advance their careers—earning promotions, raises, and a deeper sense of fulfillment in their work. Worse, this lack of actionable insight can create a perception that the company isn’t genuinely invested in their development, potentially leading to the loss of top talent.

But imagine a different approach. What if, instead of vague platitudes, employees received a clear and actionable snapshot of their performance? What if they could see precisely how they measured up against specific criteria that determined their current level and outlined what it would take to progress?

This is what a meaningful, competency-based evaluation could look like: a tool that not only reflects where employees stand but also empowers them with the knowledge to grow. Below is an example of how this might look, offering employees an at-a-glance assessment of their performance against defined measures for their role:

The interactive dashboard, built in Tableau Public, is available for free to view (use a desktop) and download HERE. It’s a tool designed to provide employees and managers with a clear, structured view of performance against defined competencies and skills.

So, how do you read the dashboard? Let’s break it down.

At the very top, you’ll see the employee’s overall score—in this example, a 53%. At first glance, this might seem modest, but it’s important to note that this score reflects the nature of the role and the framework itself. With that in mind, expectations, or thresholds for “meets expectations” by role level are provided in the view. For this example:

• Entry-Level Data Scientist: 35–44%
• Mid-Level Data Scientist: 45–59%
• Senior Data Scientist: 60–74%
• Principal Data Scientist: 75–89%

In this case, the employee’s score of 53% places them squarely within the mid-level data scientist range, suggesting they are performing as expected for their role if they are in fact at the mid-level.

Moving down the dashboard, you’ll notice four core competencies that form the foundation of the evaluation:

1. Technical Skills and Analytical Knowledge
2. Business Acumen and Problem Solving
3. Communication and Influence
4. Innovation and Learning Agility

Each competency represents a broad area of capability. Within these competencies are specific skills, such as SQL Proficiency under the Technical Skills competency. This distinction is deliberate: skills are the building blocks of broader competencies, and understanding their relationship is key to interpreting the evaluation.

The dashboard further highlights that not all skills contribute equally to the overall score. Each skill is weighted according to its importance for the role. For example, Machine Learning and Predictive Modeling contributes 15% to the overall score, reflecting its critical role for data scientists.

This weighting ensures that evaluations focus on what truly matters, allowing for a nuanced and role-specific assessment.

To determine the score for each skill, the framework incorporates a detailed rubric accessible through the dashboard. The rubric provides specific performance criteria for each skill, making evaluations clear and consistent.

For example, the rubric for Machine Learning and Predictive Modeling includes the following performance levels (viewable within each dropdown on the dashboard by skill):

This structured approach ensures that scores reflect meaningful differences in performance, guiding both employees and managers toward actionable insights for growth.

The dashboard provides a transformative experience for employees, offering them a clear, actionable view of their performance rather than vague, generic feedback. At a glance, they can understand the following:

1. What they are being graded on, with a detailed breakdown of competencies and skills that eliminates ambiguity about expectations.
2. The relative importance of each skill, helping them prioritize areas that have the greatest impact on their overall score.
3. How they are performing in each area, with rubric scores that provide precise feedback on strengths and weaknesses.
4. Where they stand overall, with their total score contextualized against thresholds for their role level.

This detailed insight equips employees to take control of their development and advocate for promotions with confidence. For instance, in the current example, the employee is performing well within the mid-level range, scoring a total of 53%. However, the dashboard also highlights opportunities for growth. While they are already performing at a senior level in Technical Skills and Analytical Knowledge—scoring consistently above the 60% "meets expectations" threshold for a senior data scientist—they are underperforming in other areas such as Data Storytelling and Insight Communication and Innovation and Learning Agility, both of which are scored at 30%. These two skills, each contributing 12% to the overall score, represent the most significant opportunities for improvement. By focusing on these areas in the next evaluation cycle, the employee can make meaningful progress toward advancing to the senior level.

This approach isn’t just about highlighting gaps—it’s about providing a pathway to improvement. Organizations can amplify the effectiveness of these insights by offering targeted support, such as curated resources tailored to skill gaps. For example, the competency framework created with the help of ChatGPT4o includes an appendix of development resources. This appendix provides specific recommendations, direct links to learning materials, and even pricing information, ensuring employees have everything they need to take actionable steps toward growth. By providing such clear guidance, organizations not only empower their employees but also foster a culture of continuous learning and strategic alignment, driving both individual and organizational success.

Performance evaluations are most effective when supported by robust documentation that serves as a reference for employees and managers. This ensures that expectations are clear, criteria are consistently applied, and feedback is grounded in transparent guidelines. A sample of this type of documentation is available HERE, showcasing a structured approach to competency-based evaluations. To highlight repeatability, I followed the same process to make a guidebook for industrial/organizational psychologists HERE and software engineers HERE.

But how did this framework and its accompanying resources come to life? The data scientist role was selected as a notional example to demonstrate how competency frameworks can be constructed and operationalized. Generative AI, specifically ChatGPT4o, was instrumental in developing much of the content. The process involved using ChatGPT4o to iteratively refine competencies, skills, rubrics, and development pathways, creating a comprehensive and actionable framework.

The entire ChatGPT4o thread, accessible HERE, walks through every prompt used to produce the outputs necessary to build the dashboard and the guidebook. There is even a tangential discussion about the best key lime pie recipe to offset using "to" instead of "too". These touches of levity aside, the AI-assisted process highlights how technology can accelerate and enhance the development of detailed frameworks that align with real-world needs.

By combining thoughtful design with innovative tools, this example provides a blueprint for organizations to develop their own role-specific frameworks, empowering employees and ensuring evaluations are both meaningful and actionable.

Beyond the elements already discussed, several additional considerations and decisions shaped the design and development of these outputs. Each step of the process was guided by a commitment to practicality, transparency, and scalability, with ChatGPT4o serving as a valuable resource for accelerating and enhancing the work. Below is a comprehensive overview of the key aspects of the process, highlighting both strategic decisions and the technical execution behind them.

1. Prioritizing Scalability for Broader Application

One of the first considerations was ensuring that the competency framework and supporting tools could be adapted to other roles. While data scientists were the focal point, the goal was to create a replicable system that could be extended to other priority roles across the organization. ChatGPT4o was instrumental in:

• Drafting Modular Components: Competencies and skills were designed in a modular fashion, making it easy to swap or adjust them for different roles. For example, while SQL Proficiency is critical for data scientists, it could be replaced with role-specific skills like Java development or sales pipeline management for other roles.
• Testing Scalability: By prompting ChatGPT4o to generate draft competencies and skills for unrelated roles (e.g., software engineer, product manager), we validated the adaptability of the framework’s structure.

This scalability ensures the investment in developing the framework can deliver long-term value across various teams and functions.

2. Balancing Granularity with Usability

Creating detailed scoring rubrics required striking the right balance between specificity and usability. The aim was to provide enough granularity to capture meaningful differences in performance without overwhelming users or creating complexity that would hinder adoption.

• Iterative Drafting: ChatGPT4o was used to draft performance levels (e.g., novice to expert) for each skill, which were then iteratively refined to ensure clarity and alignment with real-world expectations.
• Clarity in Language: Each level was reviewed for precise, accessible language. For example, instead of saying “performs well under pressure,” criteria like “executes key deliverables within tight deadlines with minimal supervision” were used to provide actionable specificity.
• Avoiding Overlap: Particular care was taken to ensure that thresholds between levels (e.g., “meets expectations” vs. “exceeds expectations”) were distinct, avoiding ambiguity that could confuse employees or managers.

3. Designing for Equity and Consistency

One of the challenges in performance evaluation is mitigating bias and ensuring consistency across teams and managers. To address this, several measures were embedded into the process:

• Standardized Scoring: Every competency and skill was assigned explicit weights, ensuring that evaluations focused on the most critical aspects of performance.
• Clear Rubric Levels: Each skill’s rubric included detailed descriptions for each scoring tier (e.g., 10%, 30%, 50%, 70%, 90%, 100%), reducing the subjectivity of assessments.
• Cross-Functional Relevance: Competencies like Communication and Innovation were intentionally included to ensure the framework valued skills often overlooked in technical roles, promoting a more holistic view of performance.

These efforts were aimed at building trust in the framework’s fairness while ensuring evaluations were meaningful and actionable.

4. Emphasizing Development Pathways

Another priority was ensuring the framework supported not just evaluation but also growth. Employees need to understand not only where they stand but also how to progress. This focus on development was addressed through:

• Curated Resources: ChatGPT4o helped identify tailored learning opportunities for each skill, categorized by proficiency level (novice, intermediate, expert). This included free tutorials, industry-recognized certifications, and advanced workshops.
• Actionable Roadmaps: For each skill, employees are provided with specific recommendations for improvement. For example: For SQL Proficiency, resources ranged from free W3Schools tutorials for novices to advanced Udemy courses for experts. For Data Storytelling, suggestions included public speaking courses and interactive visualization workshops.
• Integrated Guidance: These resources were linked directly to the scoring rubrics, ensuring that feedback flowed naturally into development opportunities.

This focus shifted the narrative of performance evaluations from static assessments to dynamic tools for career growth.

5. Iterative Collaboration with ChatGPT4o

The role of ChatGPT4o in the process went beyond simple content generation. It served as a creative partner, facilitating rapid iteration and refinement. Key contributions included:

• Drafting and Refining Competencies: ChatGPT4o provided initial drafts for competencies and skills, which were then reviewed and adjusted for alignment with organizational goals.
• Generating Granular Rubrics: Performance levels were developed collaboratively, with ChatGPT4o suggesting nuanced criteria for each level, which were then validated for accuracy and applicability.
• Identifying Industry Trends: ChatGPT4o highlighted emerging skills and methodologies in data science, ensuring the framework remained forward-looking and relevant.

The AI-assisted process saved significant time while maintaining a high level of detail and precision.

6. Driving Engagement Through Visualization

Visualization played a critical role in making the framework actionable and engaging. While the Tableau dashboard was discussed earlier, the principles behind its design deserve further mention:

• At-a-Glance Insights: The visual layout emphasized clarity, ensuring users could quickly identify key strengths and areas for improvement.
• Interactive Filtering: Filters allowed managers and employees to tailor views to specific competencies, skills, or thresholds, making the data relevant to their unique needs.
• Progress Tracking: The dashboard enabled real-time tracking of performance improvements, motivating employees to engage with their development plans.

These visual tools transformed the framework from a static document into a dynamic, user-friendly resource.

7. Ensuring Practicality and Adoption

A critical consideration was ensuring that the framework was practical for real-world use. To this end:

• User Testing: The framework was tested with hypothetical employee profiles to ensure the scoring rubrics and thresholds worked as intended.
• Manager Enablement: Sample scoring templates and evaluation guides were created to help managers apply the framework consistently.
• Employee Accessibility: The inclusion of user-friendly resources and straightforward rubrics ensured that employees could easily understand and engage with the framework.

This focus on practicality aimed to maximize adoption and impact across teams.

8. Anticipating Future Needs

Finally, the framework was designed with adaptability in mind. Recognizing the rapid pace of change in fields like data science, the following measures were incorporated:

• Regular Updates: Competencies and rubrics will be reviewed periodically to reflect evolving industry standards and organizational priorities.
• Scalable Methodology: The framework’s modular design makes it easy to replicate for other roles, ensuring its long-term relevance and utility.

This future-focused approach ensures the framework remains a valuable resource for years to come.

In conclusion, the creation of these outputs represents a thoughtful blend of innovation, collaboration, and strategic foresight. By combining the capabilities of generative AI with human oversight, the framework provides a clear pathway for evaluating and developing high-impact roles. While the example focused on data scientists, the principles and processes behind this work offer valuable insights for organizations looking to empower their teams and drive long-term success.

Let’s keep the conversation going—feel free to share your thoughts in the comments or message me directly!

Note that the works cited label for the banner image isn't functioning. That is as follows: Abstract Visualization of Data Science Competencies." Created using DALL-E, a generative AI-powered image creation tool.