· Product Operating Model + Product Orientation  · 2 min read

Podcast Deep Dive: AI Strategy and the Operating System Mismatch (with Dave West)

A deep dive into the conversation with Dave West at Scrum.org about the 95% AI failure rate and why organizations must evolve their management operating system.

A deep dive into the conversation with Dave West at Scrum.org about the 95% AI failure rate and why organizations must evolve their management operating system.

In a recent episode of the Scrum.org podcast, I sat down with CEO Dave West to discuss why so many organizations are struggling to turn their AI investments into business impact.

While we’ve previously discussed how your Operating System can be a bottleneck for AI, this conversation focused specifically on the mechanics of how AI adoption is shifting the boundaries of traditional IT.

The High Failure Rate of AI Projects

Dave highlighted a startling figure: 95% of AI projects fail to deliver meaningful value.

But as we explored the “why” behind this number, a pattern emerged. These failures rarely stem from the technology itself. Instead, the failure happens when organizations try to “project-manage” a complex, emergent technology with a 20th-century mindset.

“The key for me, what I see where people are finding gold using AI… is when they use product techniques, whether they call it product or not.”

Beyond the IT Boundary

One of the most profound insights from our discussion was that AI adoption challenges now originate outside traditional IT. We’re seeing sales, marketing, and legal departments leading AI initiatives without the necessary operating system to support the uncertainty of high-potential developmental work.

Context is the New Training Data

We discussed the concept of “Context Development.” Just as a product defines its scope and purpose, an organization must provide AI with consistent boundaries and relevant information to be effective.

Key Takeaways for Leaders:

  1. Differentiate Work Types: Don’t treat operational optimization (stable work) the same as innovation (complex work). AI fits best into an agile, product-oriented mindset.
  2. Strategy Alignment (OKRs): Use OKRs to ground AI initiatives in real business strategy rather than technology for technology’s sake.
  3. Continuous Adaptation: Your OS must be capable of sensing, responding, inspecting, and adapting in real-time.

Listen to the full episode on Scrum.org: AI and the Implications for your Organization’s Operating System.

Frequently Asked Questions

Why did you say ‘Context is the new Code’? Because the performance of GenAI depends less on how you code the interface and more on the quality and specificity of the context (data, constraints, user persona) you provide it.

Can a traditional PMO adapt to this? Yes, but only if they shift from “schedule policing” to “enabling evidence-based governance.” They should be looking at leading indicators of value rather than just project milestones.

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    Yuval Yeret

    About Yuval Yeret

    Yuval is a rare practitioner who has shaped the agility path of dozens of organizations and influenced the frameworks used across the industry. He helps product and technology leaders move from agile theater to evidence-informed, outcome-oriented delivery that creates better value sooner, safer, and happier.

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