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Transcript

AI Meets Human Insight in Hiring · The Science of Leading

• 0:00
  Okay, Edwin, so in the last few episodes we've talked a lot about data-driven
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  HR, but today feels like we're just-diving straight into the storm.
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  AI, actually changing how recruiters do their jobs.
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  I mean... when I first heard about AI-powered screening,
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  I kinda rolled my eyes.
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  Thought, sure-another fancy A T S,
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  right?
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  That's fair.
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  A lot of folks think that.
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  But real AI hiring tools?
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  Whole different beast.
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  Traditional ATS systems mostly just move applications around-they store
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  documents, follow steps, flag some keywords.
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  But these new platforms, they're analyzing patterns.
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  Past high performers, behavioral markers,
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  benchmarks across industries.
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  They're not just sorting-they're learning.
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  Surfacing people you might've never seen,
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  especially those who don't look "perfect" on paper but actually... they've
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  got the raw traits to thrive.
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  Yeah-and it's kinda spooky how good they
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  are.
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  First time my HR team used one,
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  it was for this mid-level marketing role.
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  Normally, I'd be buried under a mountain of CVs for,
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  like, a week.
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  But this time?
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  We had a shortlist by the next morning.
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  And what blew my mind-two of the top candidates had applied before... and
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  totally fell through the cracks.
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  The AI flagged them.
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  I was like-wait, what?
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  How did we miss them the first time?
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  That right there is the promise.
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  AI's not just a speed play-it's a clarity play.
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  You cut through all that early-stage noise,
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  the repetition, the biases, the fatigue.
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  And especially in high-volume hiring?
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  The benefits stack up-faster time-to-fill,
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  lower costs, smoother candidate experiences.
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  Communication gets faster.
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  Feedback loops tighten.
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  And that alone builds trust.
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  Totally.
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  Like-our applicants actually got status updates,
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  like, within hours.
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  It felt more respectful.
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  But here's what I kept wondering... If AI's scanning performance data and
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  spitting out "best fits"-aren't we just... blindly trusting the math?
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  Like, what if it misses something important?
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  That's the right question.
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  AI can absolutely reduce certain biases-like assumptions based on names,
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  gaps, degrees.
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  But if the data underneath is flawed,
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  or the success model is skewed from the start?
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  You're just making faster mistakes.
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  It's like giving bad directions in a self-driving car-it still ends up
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  in the wrong place, just quicker.
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  Right-if your "top performer" data all
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  comes from a super narrow slice of your team,
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  the model just keeps feeding you...
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  clones.
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  Which is exactly what we touched on back in Episode 7.
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  If you don't question the source of your data-you're not doing real strategy.
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  You're just automating yesterday's mistakes.
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  Exactly.
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  And that's where behavioral science comes in.
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  AI can match the numbers-job title,
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  experience, hard skills.
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  But it can't feel how someone communicates,
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  adapts, leads.
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  That's still human territory.
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  Tools like OAD help you see the underlying behavioral DNA.
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  I've seen AI rank someone low,
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  but when we actually spoke with them-structured interview,
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  behavioral insight-they were a total standout.
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  The company took the risk.
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  And it paid off.
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  We had a similar moment.
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  The resume was spotless-like, keyword-perfect.
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  But the OAD flagged a big mismatch in communication style with the hiring
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  manager.
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  We trusted it.
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  Didn't hire.
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  And a month later, the manager said,
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  "Thank god we waited." That tool saved us from a costly misfire.
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  That's
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  the key: use AI for what it's great at-scale,
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  sorting, speed.
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  But don't take your hands off the wheel.
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  Layer in interviews, behavioral insights,
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  real conversations.
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  That's where decisions get better.
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  That's where equity happens.
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  It's like-the AI sets the table.
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  But you still need humans to cook the meal.
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  So, Edwin, for a company trying to build this smart stack-what's the
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  actual playbook?
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  Like, where do they start, and what are the biggest traps?
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  First rule:
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  your inputs matter more than the algorithm.
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  If your data's messy-vague job profiles,
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  inconsistent resume formats, undefined success metrics-you'll get noise,
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  not insight.
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  Clarify what success looks like.
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  Clean up your systems.
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  That's the foundation.
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  Then the tech becomes powerful.
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  Yeah,
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  and it's not just about plugging in the AI-it's about connection.
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  If your ATS doesn't sync with your interview tools or your behavioral
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  assessments?
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  You just end up with scattered signals and confused candidates.
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  It's like-each part might work,
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  but the whole feels broken.
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  Absolutely.
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  And don't forget transparency.
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  Candidates deserve to know where AI is used,
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  what's automated, and where human judgment kicks in.
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  The smartest companies?
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  They offer opt-outs for video interviews,
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  explain their process, run audits for fairness.
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  I had a client last month-mid-sized,
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  fast-growing-they did exactly that.
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  Candidate satisfaction went up.
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  Complaints about the process feeling "robotic"?
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  Way down.
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  Can we, like, put that on a billboard?
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  There is no perfect hire.
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  No magic formula.
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  Just... thoughtful systems and real conversations.
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  And I hate when vendors sell AI as a shortcut to perfection.
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  That's just... not real life.
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  You're right.
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  AI is powerful, but it's not omniscient.
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  It's a tool.
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  And when you combine it with behavioral science-like OAD-or any robust
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  framework that helps decode how people actually work,
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  then you get something lasting.
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  You get smarter hires, better teams,
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  and fewer regrets.
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  I love that-and honestly,
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  I think that's what makes this field so exciting right now.
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  We get the power of automation,
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  but also this renewed focus on what makes people successful,
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  over the long haul.
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  So, big thanks for talking through all this with me,
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  Edwin.
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  And for everyone listening, if you want to see how you can layer behavioral
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  science into your recruitment stack,
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  you can always check out a free trial at OAD.
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  ai.
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  We'll be digging deeper into what future-ready hiring looks like in our
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  next episode, so be sure to tune in.
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  Thanks,
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  Claire.
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  Great questions as always-and thanks everyone for joining us on The Science
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  of Leading.
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  Until next time, take care.
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  Bye,
• 6:21
  Edwin!
• 6:21
  Bye everyone!