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February 17, 2026

How AI Caught What Bonuses, Training, and Pay Raises Couldn't Fix

Proof of Work — Episode 097
Why Most AI Spending Never Reaches the P&L
Jimmy Bijlani, AI Momentum Partners
00:00
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A healthcare network serving children with autism and intellectual disabilities had tried everything to improve care quality — bonuses, training programs, gift cards, higher pay. Nothing worked.

Ryan Kurt, CEO of The AI Lab and a 13-year veteran of generative AI, helped them build something different: an AI system using 300 existing cameras to identify exceptional caregivers in real time — and pay them more for it. With 99% accuracy. No punishment. No surveillance. Just rewarding people for doing what they should already be doing.

That case study took two and a half years, a lot of failure, and a breakthrough in computer vision. And it started with one CEO who was committed enough to own the problem himself.

In this episode of Just Curious, Stu Willson sits down with Ryan to unpack what separates AI initiatives that actually transform a business from ones that quietly die. Ryan is direct: the technology is not the hard part. Leadership readiness, data hygiene, and total ownership are.

If you're a CEO who's been handed an AI strategy deck and felt something was missing, this episode is for you.

Chapters:

00:00 – Intro
01:00 – How a CIA-backed AI startup in 2013 became a 9-year career
03:30 – Why "we need an AI strategy" is the wrong starting point
05:50 – The Magic Wand Exercise: rebuild your company from scratch
08:00 – Who AI Lab works with — and who they turn away
10:00 – Why the CEO has to own AI transformation personally
12:00 – What "total ownership" actually means (and what it isn't)
15:00 – Getting your data house in order before anything else
18:00 – Why the competitive gap is about to become exponential
21:30 – The healthcare case study: improving care with incentive-based AI
25:00 – From failed pilots to 99% accuracy — what the breakthrough looked like
27:00 – Human-in-the-loop: compliance, accuracy, and data labeling explained
29:45 – Cultural and operational outcomes on campus
31:30 – How to slow down and sequence AI correctly
33:30 – Peer-based learning and The AI Lab community model
35:00 – Who should reach out to The AI Lab

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chapters
19 marks
00:00
Intro
01:00
How a CIA-backed AI startup in 2013 became a 9-year career
03:30
Why "we need an AI strategy" is the wrong starting point
05:50
The Magic Wand Exercise: rebuild your company from scratch
08:00
Who AI Lab works with — and who they turn away
10:00
Why the CEO has to own AI transformation personally
12:00
What "total ownership" actually means (and what it isn't)
15:00
Getting your data house in order before anything else
18:00
Why the competitive gap is about to become exponential
21:30
The healthcare case study: improving care with incentive-based AI
25:00
From failed pilots to 99% accuracy — what the breakthrough looked like
27:00
Human-in-the-loop: compliance, accuracy, and data labeling explained
29:45
Cultural and operational outcomes on campus
31:30
How to slow down and sequence AI correctly
33:30
Peer-based learning and The AI Lab community model
35:00
Who should reach out to The AI Lab
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