AI is changing how products get built. That part isn't news. But it's also changing who needs to do what - and that's a conversation most organizations haven't had yet.

In this episode, Peter and Dave dig into one of the more interesting tensions emerging in 2026: as coding agents take on more of the actual development work, the thing that drives quality output isn't just better tooling. It's better context. Clear, structured, well-owned context that tells agents what you're actually trying to build, who it's for, and what can't be compromised.

Which raises a real question. Who owns that? Where does it live? And what happens when it's missing - which, let's be honest, it usually is?

They get into the rise of "context engineering" as a role, why the name creates its own problems, and what this shift means for product owners, product managers, and the long-standing gap between business and technology teams.

Key takeaways from this episode:

• Most organizations have never truly written down their product intent in a structured, usable way. AI is making that gap impossible to ignore.
• Good context drives better outcomes from agents - and the work of capturing, structuring, and maintaining that context needs a clear owner.
• Start asking: what context exists to guide your products? Where is it stored? Who creates it? Who picks it up and moves it through the system?
• The business and technology divide matters more now, not less. You can't afford to throw things over the wall anymore. The two groups need to work closely together, not in parallel.
• What's new here isn't the idea. It's the urgency. These are transformations organizations have been attempting for years. AI is just forcing the issue.

Want to continue the conversation?

If this episode brought up questions about how your teams are navigating the shift to agentic development - or where context ownership actually sits in your organization - reach out at feedback@definitelymaybeagile.com. We'd love to hear what you're seeing.

A lot of organizations are betting that AI will make their teams faster. Some of them are right. Most are solving the wrong problem.

AJ Bubb, founder of MxP Studio and host of Facing Disruption, joins Peter and Dave to talk about what actually happens when AI lands in a development team without fixing the system around it. If engineers can't get approvals, can't get access, and spend half their day in meetings, AI just means they produce more output the organization still can't handle. That's not a tooling problem. It's a structural one.

They also get into velocity without direction, what ownership really looks like when a ticket gets blocked, and why synthetic user testing might be the most polite way to avoid talking to actual customers.

This Week's Takeaways

• Own the problem from the customer all the way down. When something is blocked, it's still yours until it moves.
• When an outcome surprises you in either direction, ask whether your model was wrong. Most teams take the win and move on. The ones that improve don't.
• Before reaching for a technical solution, ask why five times. The problem someone walks in with is usually the invitation to a conversation, not the actual problem.

If this episode got you thinking, we'd love to hear from you. Drop us a note at feedback@definitelymaybeagile.com or leave a review on your podcast app. And if you know someone navigating AI adoption right now, send this one their way.

Most organizations are running some version of a project operating model or a product operating model - or, more honestly, an uncomfortable mix of both. In this episode, Peter Maddison and Dave Sharrock get into what actually separates these two approaches, where the tensions show up, and why copying what works somewhere else rarely lands the way you expect.

They dig into how the nature of your work - ordered versus unordered, stable versus volatile - should shape how you plan, who holds decision rights, and how closely your experts need to stay involved. They also talk honestly about the hybrid trap: why trying to be all things to all teams usually ends up serving nobody, and what a smarter version of "borrowing from both" can actually look like.

Real examples from large organizations, including a couple of banks, show just how messy it gets when the model is mandated from the top without enough room for context.

Key takeaways from this episode:

• There is no universal operating model. The right fit depends on your context right now, not what worked somewhere else.
• If your plan is constantly changing, lean toward the product side. If it's stable and predictable, the project side probably serves you better.
• Be intentional about your choices. Ask why you're organizing work the way you are, and how you'll know if it's working.
• Getting an outside perspective matters. It's easy to stay stuck in familiar patterns without someone who can see the system clearly and name what isn't working.
• Get your operating model working before you add AI into the mix. Throwing new tools at a system that isn't working yet just breaks things faster.

Which end of the spectrum does your organization sit on right now - and is it actually working for you? Leave a comment below. We read everything.