Episode 10: Why GenAI Proof of Concepts Fall Short in Production

In this episode, Sudeep Ghatak, John Van Der Walt and Ivor Whibley reveal what's causing the gap between a proof of concept and sustained AI success and how you can potentially solve this. From data quality and process discipline to change management and leadership patience, they discuss the barriers that they've seen in their day-to-day work for customers

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Discover:

• Why success metrics for AI should differ from traditional projects
• The crucial role that data discipline and operational support play in preventing AI failures
• Practical strategies for starting small with AI use cases
• How to identify warning signs that AI programs are drifting away from core business goals
• The importance of building advocates

If you’re a leader or innovator eager to turn AI’s promise into reality, this episode offers real-world examples to guide your journey.

Perfect for managers, data teams and change agents ready to move from GenAI POC to production!

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Chapters

Introduction to Generative AI Challenges

05:59 Understanding POCs, Pilots and Production

09:10 The Reality Check: From POC to Production

11:12 Readiness and Organisational Approach to Generative AI

14:30 Technical Challenges

17:00 Understanding GenAI Implementation Challenges

17:44 Data Quality and Change Management

19:46 Outcome-Driven AI Strategies

27:00 Accountability in AI Usage

Conclusion and Key Takeaways