Using PDSA With Your AI Use Cases

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About This Podcast Episode: Using PDSA With Your AI Use Cases

Ever felt that AI experiments seem to start with excitement, then stall when the results don’t quite land?

PMI Senior Consultant Anthony Richardson digs into why that happens, and why the real breakthrough often comes when you stop hunting for the “perfect prompt” and start treating AI like any other improvement experiment.

It’s a conversation that reframes AI work as iterative, scientific and far more familiar to improvement practitioners than they might expect. A small shift in mindset; a big shift in outcomes.

 

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Episode Highlights: Using PDSA With Your AI Use Cases

Join Anthony in the studio with Susannah Clarke as they explore how PDSA can turn frustrating AI experiments into repeatable, improvable ways of working. You’ll hear about:

  • When the Shiny Toy Stops Shining

Why teams hit hurdles and inconsistent results even when AI work starts with energy and optimism.

  • Turning a Design Review into an AI Use Case

A real client example showing where early AI experimentation breaks down, and what that reveals.

  • Separating Intelligence from Action

How breaking a problem into “thinking work” and “tasks” clears the way for better AI design.

  • Running a PDSA Cycle on Your Prompt

Using prediction, test and study to refine prompts scientifically rather than guessing your way forward.

  • Learning Faster with Structured Prompts

How frameworks like Rocco’s help teams capture each iteration’s learning and improve more quickly.

  • A New Mindset for AI Practitioners

Why prompting is always iterative, and the danger signs that you’re chasing perfection instead of experimenting.

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This, of course, is more than a suggestion box scheme. To participate effectively in improvement projects and their implementation, they’ll need to share a common language and understanding of the process and tools that are being used.

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