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.

Product manager. Product owner. Project manager. Three roles that often exist in the same organization, sometimes in the same meeting, and frequently stepping on each other's toes. In this episode, Dave and Peter break down what actually separates these roles, why the confusion happens, and what it costs when the lines blur in the wrong ways.

They dig into the difference between a project-centric operating model and a product operating model, and why that distinction matters more than most organizations realize. They also get into a concept Peter uses with clients: product owners reduce decision latency, project managers reduce reporting latency. It sounds simple, but the implications reach into how teams are funded, how authority is distributed, and why some transformations stall halfway.

The conversation covers real patterns from the field, including what happens when a technical project manager spends most of his time coordinating 14 dependency groups just so a product owner can get a decision made, and what it looks like when a project-centric funding model quietly undermines a product operating model that was never quite finished.

They also touch on where AI fits into all of this, and where it currently falls short as a bridge between these two worlds.

Three key takeaways from this episode:

1. It's not either-or. Both project management and product management are necessary. The goal is to use each skill set in the right place, not to eliminate one in favor of the other.
2. The relationship between product managers and project managers works best as a true peer-to-peer dynamic. Hierarchy between the two tends to break things down quickly.
3. Be clear about decision-making authority. If your product owners don't actually have the autonomy to make decisions, the role isn't working. And if your project managers exist primarily to satisfy a funding model that doesn't match your operating model, that's a signal to look at finishing what you started.

If this is a conversation your team needs to have, share this episode with them. And if you're finding value in Definitely Maybe Agile, follow the show on your favorite podcast platform so you never miss an episode. New conversations drop every week.