What is this meeting about?
Most organizations spend days on job grading - collecting information, aligning on methodology, and calibrating outcomes. What if that process could run in seconds, consistently, and at scale?
In this session, Mirza walks you through an AI-powered job grading prototype built using the KF/Hay methodology. He will cover the problem the tool was designed to solve, how it works under the hood, and what it takes to deploy something like this in your own organization.
The session is practical, demo-based, and designed for reward professionals who want to understand both the potential and the limitations of AI in job evaluation.
This session addresses a working prototype that is good to start with. You might elevator and shift it in your organization as is. Clearly, further enhancements are possible, It is not a finished product yet.
A discussion on how to move from good to better to best is part of the session.
Stream:
Key take-aways:
Job Grading through AI Building a Prototype | 06/01/2026
Artificial Intelligence is rapidly becoming a practical accelerator for job evaluation and rewards processes. The session demonstrated that organizations can significantly reduce the time required for job grading by combining established methodologies with AI, while maintaining human oversight, consistency, and governance. Rather than replacing reward professionals, AI was positioned as a tool that enhances expert decision-making, improves auditability, and supports emerging pay transparency requirements.
The discussion also highlighted that successful AI adoption depends less on technical expertise and more on methodology expertise, data quality, governance, and prompt engineering. Participants explored future possibilities ranging from AI-assisted grading to integrated compensation management ecosystems, while acknowledging important considerations around licensing, intellectual property, privacy, and organizational readiness.
- AI can accelerate job grading dramatically
- Evaluations that traditionally take days can be completed in minutes or seconds using AI-supported workflows. The greatest value appears in organizations with established job architectures, large role volumes, and frequent organizational changes. Human review remains an essential component of the process.
- Methodology expertise remains critical
- AI does not create a grading methodology. Organizations must translate existing frameworks, benchmark roles, scoring logic, and governance rules into clear instructions and guardrails. Success depends on the expertise of practitioners who understand the methodology and can guide AI effectively.
- Prompt engineering is becoming a key professional capability
- Reward and HR professionals may increasingly need the ability to translate organizational policies, grading logic, hierarchy structures, and evaluation principles into AI instructions. This capability was identified as a future skill requirement within the profession.
- Consistency, transparency, and auditability are major benefits
- Structured prompts, benchmark libraries, calibration processes, and audit trails can help organizations improve consistency and create defensible documentation. These capabilities may become increasingly important as pay transparency and governance requirements continue to evolve.
- Governance, licensing, and data quality require attention
- Organizations should ensure that job descriptions are of sufficient quality, align stakeholders from HR, IT, legal, and security functions, and verify contractual obligations related to licensed methodologies before implementing AI-assisted grading solutions.
AI generated by Copilot from designated meeting content only | Rumbold
