Hussein Hallak, serial entrepreneur and author of The Dark Art of Life Mastery, joins Peter Maddison and Dave Sharrock to talk about what really keeps entrepreneurs in the game. It's not resilience or grit, it's clarity about why you're doing this in the first place.

The conversation covers the shift from pre-COVID to post-COVID startup communities, why watching customers do their work beats asking them what they want, and the critical difference between handing off your product and handing off your purpose. Hussein also challenges the wall-poster approach to company values and explains why living your principles matters more than declaring them.

Three Key Takeaways:

• Strategy is becoming - Choose who you want to become as a founder and company, then let your thoughts, words, and actions flow from there. It's about the experience you want to have, not just the exit you want to achieve.
• Hand off the product, never the why - Founders can delegate product development once the team understands the purpose behind it. But stay close to customers. That connection informs everything and keeps you from drifting.
• Live your values, don't announce them - Stop putting principles on posters. Instead, have honest conversations with your team about what matters to them, what's missing, and how you'll work together. Build culture through behavior, not declarations.

Topics Covered:

• Why entrepreneurship is really about your relationship with uncertainty
• The founder's role: stay with the customer, hand off the product
• How 80% of features go unused (and what to do about it)
• Why watching customers work reveals more than asking questions
• Building culture through honest conversations, not corporate values posters

💬 Got feedback? Email us at feedback@definitelymaybeagile.com🌐 Visit definitelymaybeagile.com

🚨 Struggling to implement Agile because you can't get dedicated cross-functional teams? You're not alone.

In this episode, Dave Sharrock and Peter Maddison tackle one of the BIGGEST challenges facing late-adopter organizations: how to increase productivity and deliver value when dedicated teams just aren't in the cards.

In this episode, we explore:

• Why the "dedicated team first" approach often crashes in traditional organizations
• The hidden dysfunctions and perverse incentives that keep teams fragmented (spoiler: it's about promotions and funding)
• The famous "Sock Factory Parable" explains cross-functional alignment perfectly
• How context switching kills productivity with scarce specialists like DBAs
• Three essential steps to make real progress WITHOUT restructuring your entire organization

The Three Critical Steps:

1. Understand WHY dedicated teams work before forcing the structure
2. Get leadership aligned on real prioritization and trade-offs (not everything can be Priority 1)
3. Create genuine work transparency without status report theater

Whether you're an Agile Coach, Scrum Master, Product Manager, or Engineering Leader dealing with organizational resistance, this episode gives you practical strategies to move forward when structural change isn't an option.

Resources Mentioned:

• Peter's LinkedIn Learning Course on Value Stream Management

About Definitely Maybe Agile: Join Peter Maddison (XodiacInc) and Dave Sharrock (IncrementOne) as they discuss the complexities of adopting new ways of working at scale. Real conversations about digital transformation, agile, and DevOps challenges, no sugar-coating, just practical insights.

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Got a question or topic you'd like us to cover? Reach out to us!

In this episode, hosts Peter Maddison and Dave Sharrock sit down with Nick Cawthon to explore how generative AI is revolutionizing the relationship between UX, design, and agile development.

Key Topics:

• Embedding UX research into agile sprints, balancing short-term feedback loops with long-term strategic insights
• The "electric bicycle" analogy: How AI gives teams superpowers but can also accelerate you in the wrong direction
• Why Nick believes he'll never use Figma again, shifting from design tools to code-native prototyping
• Building functional prototypes using company design systems and generative AI tools
• The evolution of team size: From 6-8 person cross-functional teams to powerful 2-3 person teams leveraging AI
• The architect's mindset: Understanding the technical foundation before designing the interface

Three Key Takeaways:

1. What an incredible opportunity we have; it feels like the year 2000 again, with the excitement and disruption ahead
2. Small teams (2-3 people) with diverse perspectives can now move incredibly fast using modern tools
3. Speed is powerful, but you still need feedback loops to ensure you're building the right thing and not racing in the wrong direction

Connect with us: Website: https://definitelymaybeagile.com/ LinkedIn: https://www.linkedin.com/company/definitely-maybe-agile-podcast Email: feedback@definitelymaybeagile.com

Customer journey maps have been the standard for years. But what if they're built for a world that no longer exists?

Dave and Peter challenge the linear, step-by-step approach to understanding customer experience. From showers on Emirates flights to adaptive payment systems, they explore why our traditional mapping tools might be keeping us from seeing breakthrough opportunities.

What We Cover:

• Why traditional journey maps focus on the "critical path" and miss everything else
• The shift from cohorts and personas to individualized experiences
• The privacy paradox of hyper-personalization
• How decreasing costs make adaptive systems possible

Key Takeaways:

✅ Traditional customer journey mapping optimizes a narrow, linear experience when customers want many different paths

✅ Privacy and ethics matter more as experiences become hyper-personalized

✅ What was too costly before is now feasible, changing the game for customer experience

Perfect for product managers, CX professionals, and digital transformation leaders.

Connect: definitelymaybeagile.com | feedback@definitelymaybeagile.com

Can AI really shrink your development teams from two pizzas to one? Peter and Dave explore the promise and reality of smaller teams in the age of AI agents. While AI can handle documentation, test automation, and other "hygiene" tasks teams often skip, the real question isn't whether you can reduce team size, it's whether you should. They dig into when one-person teams make sense (startups and greenfield projects), when they don't (complex legacy systems), and why the biggest gains might come from augmenting existing teams rather than downsizing them. Plus: why most AI initiatives fail and how to find the real business problems worth solving.

This week´s Takeaways

1. AI as Capacity Booster, Not Team Replacer: AI agents excel at handling the "hygiene" work that teams often skip: documentation, test automation, release notes. Rather than shrinking teams, this gives existing teams ephemeral capacity to tackle work that improves long-term system quality and maintainability.
2. Context Determines Team Size: One-person teams work brilliantly for startups and greenfield projects where you can build from scratch. But complex legacy systems in large organizations still need the diverse knowledge and experience that comes with larger teams to navigate technical debt and organizational complexity.
3. Solve Real Business Problems First: The biggest AI failures happen when teams focus on cool technology instead of actual business needs. Before experimenting with smaller teams or AI agents, identify genuine business problems that need solving; that's where you'll see real returns and organizational support.

In this episode, Dave and Peter sit down with Radhika Dutt, author of "Radical Product Thinking: The New Mindset for Innovating Smarter," to explore why iteration-obsessed product development is failing organizations.

Radhika shares hard-learned lessons from her 25-year career across diverse industries and five acquisitions, introducing the concept of "product diseases" like hero syndrome, pivotitis, and obsessive sales disorder that plague modern product teams. She challenges conventional wisdom around OKRs and goal-setting, explaining why they often create an illusion of performance while masking real problems.

The conversation explores why goals, targets, and OKRs backfire and what actually works instead. Radhika introduces her tried-and-tested alternative: a framework for puzzle-setting and puzzle-solving called OHLs (Objectives, Hypotheses, and Learnings). This approach helps companies develop a mindset that equips teams to experiment, learn, and adapt in a disciplined way, ultimately delivering far better results than traditional goal-setting methods.

The discussion dives deep into crafting detailed, hypothesis-driven vision statements that actually help teams make decisions, rather than fluffy corporate speak that sounds inspiring but provides no guidance. Radhika explains how to balance vision debt against short-term survival needs using her three-question puzzle-solving framework.

Key Takeaways:

• The importance of writing good hypotheses and understanding customer pain points deeply before defining experiments and measurements
• Organizations need to get much closer to their target customers to truly understand their behaviors and pain points, enabling better vision statements and hypotheses that resonate
• Effective vision statements must enable decision-making; if you can't make yes/no decisions based on your vision, and understand the trade-offs between short-term survival and long-term vision, it's not valuable enough

Free Resource: Download the OHLs template and toolkit: https://www.radicalproduct.com/toolkit/#OHLToolkit

When should you let AI agents loose on your processes, and when should you keep them on a tight leash? Peter and Dave explore the messy reality of using agentic AI for process improvement.

They dig into why the processes you can easily map might not be the ones where AI agents add the most value. From recruitment pipelines that need human intuition to DevOps workflows that demand zero variation, not every process is created equal when it comes to AI intervention.

This week's takeaways:

• Categorize your processes first. Look at your processes and start sorting them. Some need to eliminate variation (like DevOps deployment pipelines), while others benefit from exploring the edges and finding creative solutions.
• Not all processes are equal when it comes to AI. There are many ways AI can help improve processes, but you need to think about whether you want to reduce variability or increase intelligent flexibility in each specific case.
• Train AI to know when to hand off. What you want AI to do is recognize when it can't handle something and pass it to the right system - whether that's a math library for calculations or a human for complex decisions.
• Understand the difference between consistency and exploration. DevOps spent years eliminating variation to create stable, repeatable deployments. Other processes might actually want that variation because it gives you something unusual and valuable.

If you're wrestling with where to apply AI in your organization without breaking what already works, this episode offers a practical framework for thinking through the trade-offs.

Resource:

• Ethan Mollick's "The Bitter Lesson versus The Garbage Can": https://substack.com/home/post/p-169199293

Questions or thoughts? Reach us at feedback@definitelymaybeagile.com

What happens when you look beyond survey data to understand what's really driving your organizational culture? James Warren, founder of Share More Stories, reveals how analyzing employee and customer stories at scale uncovers the hidden "how" and "why" that traditional data misses.

His most surprising discovery? Trust has become the single most predictive emotion across all industries. Companies with high trust create lasting loyalty, while low-trust organizations remain vulnerable no matter how well they're currently performing.

Warren shares a compelling healthcare case where well-intentioned technology actually destroyed employee experience by preventing human connections, plus insights on why leadership becomes more critical during agile transformations.

Key Takeaways:

• Trust beats everything: Trust is now the most predictive emotion across industries. High-trust cultures create sustainable advantage while low-trust organizations stay vulnerable to competitors
• Leaders must change too: In agile transformations, leadership becomes more important, not less. Leaders need to model vulnerability and change alongside their teams
• Stories reveal hidden patterns: Traditional data tells you "what" happened but stories tell you "why" it happened, uncovering emotional drivers surveys completely miss