S2E5 - AI That Ships: From Proof-of-Concept to Production w/ Ian P Cook
Wed Feb 04 2026
Takeaways
AI projects fail due to poor problem definition, not technology.
Clear problem specifications are crucial for AI success.
Documentation and requirements are essential for effective AI solutions.
Iterative development allows for learning from failures.
AI can significantly improve processes when applied correctly.
Understanding hallucinations in AI is vital for trust.
Regulation is necessary to ensure safe AI practices.
Leaders must embrace AI with a bottom-up approach.
Experimentation is key to finding effective AI solutions.
AI should enhance human roles, not replace them.
Summary
In this episode of the Human Protocol Podcast, host Mykel Salomon and guest Ian Cook discuss the realities of implementing AI in business. They explore the common pitfalls of AI projects, emphasizing the importance of clearly defined problems and thorough documentation. The conversation highlights the iterative nature of AI development, the significance of learning from failures, and the need for regulatory frameworks to ensure safe AI practices. Cook shares insights from his experience in the industry, advocating for a bottom-up approach to AI adoption that empowers teams and enhances human roles rather than replacing them.
Chapters
00:00 Introduction to AI in Business
03:12 Understanding the Hype Cycle of AI
11:33 Narrowing Focus for Successful AI Projects
18:09 Case Study: AI in Insurance Underwriting
21:04 Addressing Hallucinations in AI Models
29:10 Defining Success in AI Implementation
31:47 Managing Expectations and Trade-offs
35:10 The Role of Leadership in AI Adoption
37:54 Bottom-Up Approach to AI Integration
41:23 Shifting Mindsets for AI Success
46:21 Encouraging Experimentation in AI Development
48:54 The Need for Regulation in AI
54:05 Advice for Future Generations in AI
More
Takeaways AI projects fail due to poor problem definition, not technology. Clear problem specifications are crucial for AI success. Documentation and requirements are essential for effective AI solutions. Iterative development allows for learning from failures. AI can significantly improve processes when applied correctly. Understanding hallucinations in AI is vital for trust. Regulation is necessary to ensure safe AI practices. Leaders must embrace AI with a bottom-up approach. Experimentation is key to finding effective AI solutions. AI should enhance human roles, not replace them. Summary In this episode of the Human Protocol Podcast, host Mykel Salomon and guest Ian Cook discuss the realities of implementing AI in business. They explore the common pitfalls of AI projects, emphasizing the importance of clearly defined problems and thorough documentation. The conversation highlights the iterative nature of AI development, the significance of learning from failures, and the need for regulatory frameworks to ensure safe AI practices. Cook shares insights from his experience in the industry, advocating for a bottom-up approach to AI adoption that empowers teams and enhances human roles rather than replacing them. Chapters 00:00 Introduction to AI in Business 03:12 Understanding the Hype Cycle of AI 11:33 Narrowing Focus for Successful AI Projects 18:09 Case Study: AI in Insurance Underwriting 21:04 Addressing Hallucinations in AI Models 29:10 Defining Success in AI Implementation 31:47 Managing Expectations and Trade-offs 35:10 The Role of Leadership in AI Adoption 37:54 Bottom-Up Approach to AI Integration 41:23 Shifting Mindsets for AI Success 46:21 Encouraging Experimentation in AI Development 48:54 The Need for Regulation in AI 54:05 Advice for Future Generations in AI