“We’ve gone from zero to 100, not zero to 60.” Jan Ackerman shares how Oracle built a talent market intelligence tool with ChatGPT Work to transform hiring preparation. Starting with a job description, it researches comparable roles, benchmarks compensation, and assesses talent pools across locations, giving hiring managers consistent data and insights. Read how Oracle is transforming work with ChatGPT Work: https://lnkd.in/eXQUA5kF
The jump from a job description to actual market intelligence is what makes this interesting. Turning compensation, talent pools, and role benchmarks into one workflow could save hiring teams a lot of manual research.
At Praxond, we see this as a powerful example of AI moving beyond automation to real enterprise intelligence. Oracle's use of ChatGPT Work demonstrates how AI can transform complex HR processes into faster, data-driven decisions while strengthening consistency, transparency, and business value. This is where enterprise AI delivers meaningful impact.
‘Zero to 100’ is a great soundbite. As a Data Engineer, my first thought is always: what did it take to get there? Talent data is notoriously messy—it lives in ATS systems, spreadsheets, LinkedIn, and internal databases. Building a tool that actually delivers value means first solving the data integration and quality problem. The AI gets the credit, but the data pipeline does the heavy lifting. Great use case from Oracle and OpenAI.
The useful part of this workflow is bringing role comparisons and location-specific talent signals into one preparation step. I’d keep the output traceable to its sources and use it to inform a recruiter’s judgment, especially where job titles or salary ranges differ across markets. What checks help keep the comparisons fair and current?
Consistent data helps most when a hiring manager can check it. Does the tool show the source and date behind each compensation figure, so an older benchmark is easy to spot?
Going from zero to 100 is a big claim, but turning hiring research into a repeatable workflow is where the real value is. Less manual research, better data, and faster decisions for hiring teams.
A clearly defined workflow is a useful starting point for AI adoption. I would be interested in how the team checks the freshness and comparability of the source data before managers use the benchmarks. What does that review step look like in practice?
“And, for an instant, she stared directly into those soft blue eyes and knew, with an instinctive mammalian certainty, that the exceedingly rich were no longer even remotely human.” — William Gibson, Count Zero
This is a great example of how AI can make hiring more data-driven. Better market insights can lead to smarter hiring decisions and less guesswork.
😬 ...no, you're right that you haven't gone from zero to 60 - that is actually more like going from zero to 15.