🚀 NEW: What AI fluency actually looks like in a GTM hire right now ➔
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with Guest Name, Guest Title at Company
Kyle Norton –
CRO, Owner.com
Most companies say they want AI-native hires. Almost none of them can tell the real thing from a polished talking point.
You're writing "AI fluency required" into every job description, but when you sit across from a candidate, you don't actually know what good looks like. And your interview process? It's probably optimized for charisma, not competence.
Kyle Norton is CRO at Owner.com, one of the fastest-growing companies in restaurant tech B2B Tech. He leads a GTM org spanning sales, partnerships, onboarding, demand gen, rev ops, and enablement and he's built a dedicated Applied AI function inside his revenue team that most companies haven't even considered yet.
This conversation gives you a concrete framework for assessing AI fluency in interviews, a clear case for centralizing AI capability instead of spreading it thin, and a structured hiring process that replaces gut feel with repeatable data. You'll walk away knowing exactly where to invest and where to stop guessing.
Kyle breaks down the AI sophistication ladder for GTM professionals, explains why early-career hires are outperforming tenured reps at Owner.com, and makes the case for killing vibe-based interviews for good.
This one's for revenue leaders, founders, and hiring managers building teams that need to be competitive in the next two to three years.
>> Centralized AI teams produce orders-of-magnitude better output than reps tinkering side-of-desk. Kyle explains why he made Applied AI the first hire after Owner.com's Series C.
>> AI fluency matters most at senior and leverage roles like rev ops, but frontline managers and reps don't necessarily need it if the right infrastructure exists around them.
>> The fastest way to spot genuine AI fluency in an interview is to be deeply fluent yourself or have someone who is sitting in the process. Buzzwords fall apart fast under real scrutiny.
>> Structured interviews with identical questions, weighted scorecards, and separated criteria consistently outperform "vibe hiring" and they're the only way to build a data set you can actually learn from when a hire doesn't work out.
00:00 - Kyle Norton on AI fluency and hiring on vibes
01:56 - The internet comparison: why AI is already table stakes
04:48 - Where to actually learn about AI: Twitter, YouTube, and the feed algorithm
08:58 - Interviewing for AI fluency: what genuine answers look like
13:39 - Building a centralized Applied AI function inside GTM
16:45 - How the Applied AI team expanded beyond sales
19:04 - Why Owner.com shifted to early-career hiring
23:33 - Finding top talent at lesser-known companies
26:15 - The structured interview process: scorecards, mock calls, and bar raisers
32:32 - Go-to-market org structures for the next 2–3 years
36:25 - One concrete step: kill vibe hiring this week
40:09 - Danielle's closing thoughts
When did you last audit your interview process against real performance data? Drop your take in the comments or share the one hiring change that made the biggest difference for your team.
If this episode made you rethink how you're hiring for AI fluency or structuring your interviews, share it with a founder or revenue leader who needs to hear it. And if you're building a GTM team that needs to be competitive in the next few years, visit captivatetalent.com.
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