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 [email protected]

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

Is the Lean Startup methodology dead? Peter and Dave tackle the growing criticism around MVP approaches and explore why this foundational model still has its place in modern product development.

Drawing from George Box's famous insight that "all models are wrong, but some are useful," they discuss how tools evolve but don't necessarily become obsolete. With AI making prototyping faster and cheaper than ever, the conversation explores what's changed about experimentation and what hasn't.

The hosts dig into common misapplications of Lean Startup principles, from "MVPs" that take months to build to organizations that skip the crucial feedback loops. They also explore when incremental learning isn't enough and you need those bigger strategic pivots.

Plus, they make the case for Wardley mapping as an underutilized tool for spotting step-change opportunities that incremental approaches might miss.

Key Takeaways:

• Lean thinking remains relevant: Lean Startup is still a valuable operational tool for continuous learning and incremental improvement, but it works best when properly implemented with complete feedback loops
• You need multiple models: Organizations need both incremental change capabilities (like Lean Startup) and strategic step-change tools (like Wardley mapping) to navigate complexity effectively
• AI commoditizes development, not strategy: While AI tools make prototyping easier and faster, they don't replace the need for good product thinking, user experience design, or asking the right strategic questions

Whether you're questioning your current approach to product development or looking for ways to balance tactical execution with strategic vision, this episode offers a pragmatic perspective on using the right tools for the right challenges.

Can agile teams really move fast without breaking stuff? In this episode, Dave and Peter dig into one of the biggest tensions in modern software delivery: the push for speed versus the need to manage risk.

They unpack the idea that when agile is done right, it actually helps reduce risk, not amplify it. You’ll hear stories and analogies (yep, including a messy kitchen and airplane cockpits) that bring this idea to life. Along the way, they highlight why teams that obsess over "faster delivery" often end up with systems that are, well, kind of fragile.

Whether you're navigating compliance hurdles, trying to foster psychological safety, or just figuring out how to move fast without chaos, this conversation brings a grounded, practical take on how agile and risk can work together, not against each other.

This week’s takeaways:

• Learning continuously is more powerful than just moving quickly
• Teams should feel safe to say “something doesn’t feel right”
• Focus on what’s working, not just what’s broken

Former HSBC Canada CISO Peter Buckley shares practical cybersecurity advice for small and medium enterprises. Despite having fewer resources, SMEs face the same cyber threats as large corporations, ransomware and data breaches.

Peter breaks down how organizations can manage 80% of their cyber risk through smart planning and leveraging existing tools, without requiring massive budgets or dedicated security teams. We explore how cybersecurity extends beyond technology into HR practices, organizational culture, and the power of community collaboration.

Three Key Takeaways:

1. Have a Plan: Create and regularly test an incident response plan that outlines what to do when things go wrong, who to call, and how to communicate with stakeholders.
2. Make It Collaborative: Use tabletop exercises and casual team conversations about "what if" scenarios to build risk awareness while fostering team building.
3. Build Community: Connect with industry peers to create a network of contacts you can call during a crisis and share resources with non-competing organizations.

In this episode, Dave and Peter explore why "safe bets" in business can be the riskiest moves today. They unpack the shift from long-term plans to fast, testable experiments, and why companies must embrace uncertainty to stay competitive. Topics include digital transformation pitfalls, cultural resistance to change, and the importance of alignment over tech.

Key takeaways:

• Shorter cycles are essential
• There are no safe bets anymore
• Alignment matters more than technology

For more insights, visit definitelymaybeagile.com, subscribe, and follow us on social media. Questions or feedback? Reach out at [email protected].

In this episode of Definitely Maybe Agile, Peter Maddison and David Sharrock are joined by Derek Crager, a seasoned engineer turned AI entrepreneur who shares his journey from blue-collar work to building AI-powered knowledge management solutions. Derek discusses how AI is transforming workplace onboarding, knowledge transfer, and personal productivity, drawing parallels between today's AI revolution and the early days of the internet.

Derek brings a practical perspective on implementing AI in enterprise environments, focusing on his company's voice-powered AI assistant "Pocket Mentor" that helps organizations capture tribal knowledge and streamline employee onboarding. The conversation explores the challenges of extracting expertise from subject matter experts, the importance of having clear business outcomes when adopting AI, and advice for students navigating career choices in an AI-driven world.

Key Takeaways:

• Focus on Business Outcomes Over Buzzwords - Don't implement AI just to check a box or follow trends. Instead, identify specific business problems (like inefficient onboarding or knowledge retention) and then explore how AI can provide practical solutions to those challenges.
• AI Excels at Knowledge Augmentation and Accessibility - AI's greatest strength lies in making organizational knowledge instantly accessible 24/7, without judgment, and at a consistent quality level. This is particularly valuable for onboarding new employees and capturing tribal knowledge from subject matter experts before it walks out the door.
• Take Time to Learn and Evaluate Before Jumping In - The AI wave mirrors the early internet adoption curve and will continue evolving over the next 10-15 years. Organizations and individuals have time to thoughtfully evaluate AI solutions rather than rushing to adopt the first available option, emphasizing the importance of learning practical applications before implementation.

Talent acquisition remains stuck in the past while organizations have drastically evolved. "We still hire like it's 1999," explains Leandro Cartelli, CEO of Lana Talent, highlighting a critical disconnect between modern business needs and outdated hiring practices.

In this episode,  Dave and Peter explore with Leandro how successful teams are built through strategic cultural assessment rather than simple skill matching. The conversation reveals the difference between "cultural fit" and "cultural add," and how one retail company reduced their 80% turnover rate by half, saving millions through targeted assessment questions.

Leandro breaks down why remote team success hinges on comprehensive onboarding (which drives 80% better retention) and intentional connection building beyond work tasks. Without deliberate efforts like virtual team activities and structured check-ins, remote work becomes "just a slogan" rather than a successful strategy.

Key Takeaways:

• Culture encompasses both fit and add - assess how candidates navigate existing dynamics while bringing fresh perspectives
• Remote work requires intentional investment - comprehensive onboarding and dedicated connection-building are non-negotiable
• Treat talent acquisition strategically - connect hiring with development plans and performance expectations for real business impact