SHRM26 Debrief · Compensation & Benefits · #1750

Fair Pay, Smart Tech: Using AI + HI to Design Equitable and Transparent Compensation Systems

with Andrew Fockler

▶ Watch on the SHRM portal ~78 min, distilled

compensation designpay equityAI in HR

"AI alone is not enough. You are the contributing factor to the success."

What it was about

Compensation systems become fair, transparent, and trusted when AI's speed and objectivity in analyzing pay data is paired with human judgment (HI, "human intelligence") for context, ethics, and difficult conversations. AI alone is not enough to build trust.

By the numbers

$20 an hour vs. $22 an hour

wage differential between two competing restaurant chains in Kelowna, BC, where the lower-paying job included paid breaks and the higher-paying job did not

more than 15% of job duties

speaker's threshold for when 'other duties as assigned' should be formally added to a job description and compensated

eight to 10 times

average number of times a person changes jobs over a career lifespan (not necessarily within the same company)

Key notes

Use AI (e.g., ChatGPT) to benchmark compensation by entering role, experience, degree, and region, then always ask it to "provide source" and fact-check citations before trusting the output.
Upload anonymized pay data (names and birth dates removed) to AI tools to surface inconsistencies and flag employees who may be underpaid relative to peers with similar experience.
Conduct exit interviews consistently to identify whether departures are pay-driven, since employees often won't volunteer that information otherwise.

The contrarian takeA lower advertised hourly wage can be the objectively better-paying job once non-salary factors (like paid breaks) are accounted for — surface-level pay comparisons can mislead both employees and employers about which offer is actually more valuable.

Take this back Monday

Do this for your team

Pull anonymized pay data (strip names/birth dates), feed it to ChatGPT to flag employees underpaid vs. similar-experience peers.

Say this in your next leadership meeting

AI speeds up pay benchmarking, but keeping a human in the loop is what actually builds employees' trust in the numbers.

Watch out for

Relying on AI compensation data end-to-end without fact-checking sources, risking inaccurate or unsupported figures (e.g., Indeed listings that are inflated to attract applicants).
Over-automating pay decisions and losing the human element, which removes context, ethical judgment, and nuance from compensation calls.
Failing to update job descriptions as duties expand, so employees end up doing significantly more work than their JD and pay reflect.

Fun fact · Andrew Fockler

Dr. Drew Fockler writes "The Intuitive Series" — including The Intuitive CEO and The Intuitive Entrepreneur — and brings a part-strategist, part-stand-up style to the stage.

If this landed, see these

↳ Go deeperPay Transparency Starts Here: A Playbook for HR ProfessionalsTurns the same trust-building goal into a full nine-step compensation playbook, tools included.⇄ The counterpointFair Pay, Lean Resources: Compensation Strategies for Small & Midsize HR TeamsSays the real fix is a documented four-lens pay philosophy, not smarter AI benchmarking.✦ The unexpected oneTrust by Design: Building Credibility Across the Talent LifecycleBuilds the same employee trust through a marketing loyalty-loop lens instead of pay data.