“Use AI to challenge all of your assumptions about every hiring process before you use it to improve those existing processes.”
This is part four in a continuing story – based on true facts – how one company used “Moneyball for HR!”, data analytics and financial analysis to cut turnover by 50% in combination with Performance-based Hiring. In Part 1 of the story Johan and Alice discovered that the company’s job board advertising was its most expensive sourcing channel when turnover and candidate quality were considered. In Part 2 they started doing something about it. In Part 3 they found a simple way to stop making offers to candidates who would likely quit in the first year. In this segment, Part 4, they decide to use AI for benchmarking best practices.
Claude AI conducted a separate objective assessment of the whole idea and thought the numbers were conservative.
Join our “Moneyball for HR!” discussion group to find out how this story ends (or maybe it’s just beginning.)
“Johan, we need to talk,” Alice said, settling into her chair with a fresh cup of coffee. “I’ve been working with Alex Quan from our O/D team, and what he’s uncovered about our personality assessments is… well, it’s concerning.”
Johan looked up from his laptop, where he’d been reviewing logistics department hiring data. “Interesting timing. I was just analyzing Miles Brady’s hiring results. Go ahead – what did Alex find?”
Alice shared Alex’s research findings about the variable nature of personality traits’ predictive value. “We’ve been using these assessments as if they’re universal truth-tellers, but Alex’s analysis shows the relationships between personality traits and job performance are highly context-dependent. We might be screening out great candidates based on factors that don’t even matter in their specific roles.”
Learning from Success Stories
Johan nodded thoughtfully. “That actually aligns with what I’m seeing in Miles’s department. His approach is completely different – he focuses on performance expectations and asks candidates to describe comparable accomplishments. No personality tests, just real-world evidence of capability.”
“And the results?” Alice leaned forward, intrigued.
“Lowest turnover and highest engagement scores for professional staff in the company,” Johan replied. “He even includes the challenges right in the job postings and asks candidates to address them in their cover letters. It’s like he’s been doing Performance-based Hiring naturally. Even better, fewer candidates apply and most of those that do are stronger people we can follow-up with for other roles.”
Alice’s eyes lit up. “Johan, I think I know what our next step should be. Instead of just applying AI to our existing processes, we need to use it to benchmark what actually works. We need to study our success stories.”
A Four-Pronged Benchmarking Best Practices Strategy
Alice grabbed a marker and walked to the whiteboard. “Here’s what I’m thinking. We need to benchmark four things:”
She wrote as she spoke:
- How our best performers actually changed jobs and found success
- How our most effective managers, like Miles, consistently hire and develop great people
- How our top recruiters find, recruit, and close outstanding candidates
- What sourcing channels consistently produce the best results
“I already have a list of 6-8 internal recruiters we should talk to,” Alice continued, “plus a few external ones with impressive track records.”
Johan was already typing notes on his laptop. “And we can use AI to analyze all this data, identify patterns, and help us develop a hiring process that combines the best elements of what’s working.”
The Validation Challenge
“You know what’s fascinating?” Johan looked up from his screen. “Performance-based Hiring essentially followed this same benchmarking approach in its development. What if we run A/B tests comparing it to our current process starting right away? We could validate the methodology and then work on embedding it into our ATS.”
Alice smiled. “And this time, we’ll have AI helping us measure and analyze the results. Plus, we now know to look at the context-specific factors Alex identified. We’re not just copying a process – we’re validating and adapting it to our specific environment.”
What Johan and Alice were discovering went beyond just improving their existing processes. Performance-based Hiring had emerged as a comprehensive expert business process precisely because it started by studying how the best managers like Miles Brady actually hired, how top performers chose their jobs, and how elite recruiters brought the two together.
Rather than starting with traditional HR practices and trying to optimize them, it began with understanding the real decisions that led to long-term success. The methodology crystallized these best practices into a systematic approach that could be taught, measured, and scaled. The key insight was clear: before you can use AI to improve hiring, you need to understand the fundamental human decisions that drive successful outcomes.
The key insight was clear: before you can use AI to improve hiring, you need to understand the fundamental human decisions that drive successful outcomes.