Definitely, Maybe Agile

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 [email protected]. 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 [email protected] 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.

Most organizations are somewhere between experimenting with AI agents and quietly hoping nothing breaks in production. Logan Kelly, CEO of Waxle AI, has spent a lot of time in that gap, and he thinks governance is the piece most teams are walking past too quickly.

In this episode, Logan joins Peter and Dave to talk about what agentic governance actually looks like in practice, why a single consistent layer beats a pile of point solutions, and how to keep developers moving fast without letting things go sideways when it counts.

This week's takeaways:

• Let your teams experiment. That's how you learn what agents can actually do. Just don't skip governance on the way to production.
• Governance doesn't have to be a gate. The best version layers in without friction, and gives everyone in the organization visibility, not just the dev team.
• If a developer has to do extra work to implement a governance feature, that's a design problem. Good governance should work for the developer, not the other way around.

Most conversations about AI focus on what it can do in a controlled setting. This one doesn't. Callum Sharrock spends his days deploying AI systems in real environments, watching them succeed and fail in ways no simulation predicted, and reporting what he finds. His conclusion? The trend line is steeper than most people realize, and snapshot thinking is getting a lot of organizations into trouble.

Peter Maddison and Dave Sharrock dig into why reliability, not capability, is the real adoption bottleneck right now. They talk through what happens when non-deterministic models get applied to problems that need deterministic answers, why validation and testing are becoming more important than writing the code itself, and how the calculus around decision making is changing fast. If you can build and test something in the time it takes to debate whether to do it, the meeting starts to look like the problem.

They also get into what this means for developers, for leaders, and for anyone trying to figure out where to actually invest their energy right now. The barriers to building have never been lower. That makes the question of what to build more important than ever.

This isn't a conversation about AI hype. It's about what's actually happening at the frontier, and what it means for the way organizations make decisions.

This Week's Takeaways:

1. The barriers to building have never been lower - figuring out what's worth building is now the real work
2. Leadership is shifting toward agency and rapid decision-making, away from top-down strategy setting
3. If you can run the experiment in the time it takes to schedule the meeting about it, run the experiment

If this episode resonated, follow Definitely Maybe Agile wherever you listen to podcasts so you never miss a conversation. And if you know someone spending two hours debating whether to test an idea they could just build, send this one their way. There are plenty more episodes worth your time at definitelymaybeagile.com.

Something is shifting inside organizations right now, and it's creating a split that's hard to ignore. AI is compressing the time it takes to generate, validate, and prototype ideas. Some people inside your org are moving at a completely different speed than the systems built to support them. Peter and Dave are calling it the great decoupling, and it's already happening whether you've noticed it or not.

In this episode, they dig into why acceleration in one part of a system creates pressure everywhere else. When you map the end-to-end journey from idea to live product, you often find 30 to 40 distinct steps. AI is handling a handful of them. The rest? Still waiting on decisions, reviews, and handoffs that haven't changed in years. Development isn't the main blocker anymore. Decision latency is.

They talk through what it looks like when product managers are running parallel experiments and validating ideas in hours, then slamming into unchanged processes for security sign-off, change control, and release management. And why the smartest people on your team are quietly finding workarounds rather than waiting in line, which creates more risk, not less.

This isn't a conversation about AI hype. It's about the real organizational friction that shows up when the pace of work outgrows the systems designed to manage it. And what you can actually do about it.

If your team is moving faster but waiting longer, this one's worth your time.

This Week's Takeaways:

1. Acceleration in one part of the system creates stress everywhere else
2. Map the end-to-end flow before you optimize any single part
3. If it's happening inside your organization, you need to deal with it internally

If this episode resonated, follow Definitely Maybe Agile wherever you listen to podcasts so you never miss a conversation. And if you know someone sitting at one of those 40 steps wondering why everything feels stuck, send this one their way. There are plenty more episodes worth your time at definitelymaybeagile.com.

A few times in tech, two streams collide, and everything changes. David Kilzer has spent 50 years putting automation to work in manufacturing and distribution around the world, and he thinks we're at one of those moments right now. The convergence of AI and humanoid robotics, in his view, is the biggest shift humankind has faced since fire.

In this episode, David joins Peter and Dave to unpack where automation ends, and AI begins, why confusing the two creates brittle systems, and what organizations should actually be thinking about when making investment decisions right now. The short version: don't slap AI on everything.

This week's takeaways:

• Stay optimistic, stay connected, and participate in the change. Don't be overrun by it.
• Automation works brilliantly within its designed boundaries. But unprecedented events expose its fragility in ways we don't always anticipate.
• The shift toward flexible, adaptable robots means the environment no longer has to be built around the machine. The machine adjusts to the environment instead.

Peter Maddison and Dave Sharrock sit down with Steve Pereira, founder of Visible Flow Consulting, to talk about something most organizations get backwards: the obsession with efficiency at the expense of actual flow.

Steve works with large companies to improve operational performance through value stream mapping and continuous delivery. But the conversations he keeps having aren't about cutting costs. They're about untethering capable people from the systems that are quietly holding them back.

In this episode, the three dig into why high utilization is often the enemy of good work, how lean thinking applies to knowledge work without losing what makes knowledge work different, and why adding AI on top of a broken system just makes things break faster.

If your organization feels like it should be doing more than it is, this one's worth your time. And if you want all 4 takeaways, don't miss the last few minutes of the episode.

This week´s takeaways: 

1. Step back from the work to look at how the work works. Whether it's a value stream mapping session or a quiet moment of reflection, intentional distance helps you see not just whether the saw is dull, but whether you're sawing the right tree.
2. High utilization is not efficiency. Running people and teams at full capacity removes the slack needed to respond, adapt, and make good decisions. Optimal is closer to 80 percent. The rest needs to be budgeted, not eliminated.
3. Understand your system before adding new tools. Whether it's AI, automation, or the latest framework, bolting new capabilities onto a system you don't fully understand tends to make existing problems worse, not better. Map first. Then act.

Extra Resources:

📖 Tools of Flow by Tody Goldratt: https://www.goodreads.com/es/book/show/75304520-goldratt-s-rules-of-flow

The marketplace is full of AI noise, but what does it actually mean for how organizations innovate and learn? Dave and Peter revisit the classic pioneers-settlers-town planners model and discover something unexpected: AI has reversed the flow.

Where organizations once looked up the chain for scaling lessons, now large enterprises are watching small explorers to understand disruption, while entrepreneurs stitch together emerging technologies to solve real problems today. The old playbook doesn't quite work anymore.

We explore what this means for different types of organizations, why pretending to be a pioneer when you're not is a waste of time, and how to actually learn from what's happening in the marketplace instead of just making noise about it.

Key Takeaways:

1. The three-cohort model has flipped. In the AI era, large organizations are looking at what smaller explorers and entrepreneurs are doing, not the other way around. If you're not monitoring the marketplace to understand how others are solving problems with these technologies, start now.
2. Different organizations need different things from the AI landscape. Town planners should watch entrepreneurs for practical accelerators and explorers for early warnings about disruption. Entrepreneurs are stitching together emerging tech with real business problems to create immediate value.
3. Most established organizations aren't pioneering, and that's okay. If you have an HR department and multiple locations, you're not in the explorer space. Innovation labs aren't the same as true exploration. Understand which cohort you're actually in and learn accordingly.