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