In this episode, we break down one of the most overlooked challenges in AI today: the implementation gap.

AI looks perfect in demos. Clean outputs. Instant value. Seamless automation. But that’s only 5–10% of the work.

The real challenge begins when organizations try to move from pilot to production.

In this conversation with Issac Hicks, CEO @Autonomi, technology implementer and AI operator, we unpack why most AI projects fail not because of the technology… but because of poor planning, unclear problems, and flawed execution.

We dive into:

Why companies rush into AI without defining the real problem
How assumptions early in the process lead to failed outcomes
The difference between saving time vs generating real ROI
Why scaling a broken process with AI makes things worse
The importance of testing, change management, and ownership
Build vs buy decisions and why most companies get it wrong

One of the biggest takeaways:

👉 AI doesn’t fix your business.
👉 It exposes it.

If your foundation is weak, your processes unclear, or your teams misaligned…

AI will scale those problems faster than anything else.

This episode is a practical guide for leaders, founders, and operators who want to move beyond the hype and actually implement AI in a way that delivers real business value.

Because the advantage won’t go to those who adopt AI first…

⏱️ CHAPTERS

00:00 Intro – The AI implementation gap
01:12 Meet Isaac Hicks (AI implementer & operator)
02:12 Why AI demos are misleading (only 5–10% of the work)
03:27 When companies bring in implementers (start vs rescue mode)
04:41 The #1 mistake: unclear problem definition
06:14 Solving the wrong problem with AI
06:50 Example: scaling bad outbound with AI
08:28 Why planning is everything
10:43 AI ROI explained: cost savings vs value creation
12:13 The real ROI: reallocating time to revenue
13:26 Why AI requires ongoing maintenance
16:02 Testing before go-live (UAT, anomalies, adversarial tests)
17:07 Avoiding AI failures at scale
18:51 Data challenges: production vs test data
20:39 Why change management is critical
23:14 Who owns the outcome in AI-driven processes?
26:02 Managing hallucinations in AI systems
30:35 Build vs Buy: why 95% of companies should not build
33:46 Why in-house AI projects fail
35:22 Choosing the right AI model (OpenAI, Gemini, Anthropic)
40:12 Designing flexible, model-agnostic systems
43:57 Cloud vs on-prem AI infrastructure
47:08 Contracts, liability, and data responsibility
50:43 Advice for individuals learning AI
52:37 Advice for companies implementing AI
55:36 Advice for policymakers and governments
58:30 The future of AI systems and control
01:00:28 Outro – Staying human while building the future