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.

You know that expensive software system your company bought that everyone... stopped using? Deborah Kaminetzky sees this pattern constantly. Projects delivered on time and on budget that still fail because nobody wants to touch them.

Deb brings a unique lens to technology implementation. She's a former attorney turned project management consultant who specializes in what she calls the "messy middle" – the space between buying software and actually getting value from it. Her secret? Translation. Not just between tech teams and business teams, but between what's being sold and what people actually need to do their jobs.

In this episode, we dig into why user involvement isn't just a nice-to-have (spoiler: shadow IT is alive and well), the difference between being heard and influencing outcomes, and why your C-suite needs to stop treating technology teams like the organizational stepchild.

This Week's Takeaways:

1. Solve the problem before you buy the solution – Understanding what you're actually trying to fix has to come before you start shopping for software. This seems obvious, but most organizations skip this step entirely.
2. Mediation matters more than metrics – When users are involved in gathering information and partially in decision-making, adoption happens. When they're just told what to do, they find workarounds. The question is: how much of that involvement is just making people feel heard versus actually changing what gets built?
3. Outcomes over outputs – On-time, on-budget means nothing if the software gathers dust. Find ways to measure whether you're getting the value you expected, not just whether you hit the deadline.

Want to reach out? Email us at [email protected] or visit definitelymaybeagile.com.

What happens when you need to ship software in environments where failure isn't just expensive, it's catastrophic? Ivan Gekht, CEO of Gehtsoft, joins Peter and Dave to challenge how we think about agile delivery in high-stakes, regulated systems.

Forget the innovation lab. Ivan argues that real innovation happens 10 minutes at a time, every day, at your desk. He shares why learning without outcomes is just an expensive distraction, why retrospectives reveal more than sprint planning ever will, and how the biggest transformation killer isn't resistance, it's apathy.

Plus, the dinner party analogy that will change how you negotiate scope vs. time, and why organizations that obsess over features are asking the wrong questions entirely.

Key Topics:

• Why "nobody cares" is the hardest transformation problem to solve
• The reversed iron triangle: hitting dates by flexing scope
• Theory of Inventive Problem Solving (TRIZ) and structured innovation
• Goals vs. features: reframing conversations with leadership
• Why agile fails when it becomes anarchy

This Week's Takeaways:

• Language and framing matter more than we think. Finding the right words and the right way to present ideas can genuinely shift how change happens in an organization.
• The Theory of Inventive Problem Solving deserves a deeper look. Innovation isn't the big thing happening in the cool kids area; it's the thing that happens every day at your desk.
• The dinner party analogy is going straight into my next executive presentation. When sales want locked dates and fixed scope, this framework shows why that's wishful thinking, and what actually works instead.

We love to hear feedback! If you have questions, would like to propose a topic, or even join us for a conversation, contact us here: [email protected]

As we close out 2025, Peter and Dave are making predictions about what's coming in 2026, especially around AI, organizational change, and how teams actually work.

They cover five key predictions:

1. AI moves from tools to organizational capability: Organizations that invest in literacy, governance, and data foundations will pull ahead of those just sprinkling AI on top and hoping for the best.
2. Critical thinking beats prompt engineering: The real competitive advantage won't be writing clever prompts. It'll be knowing when to pause, think through the problem, and decide if you even need the AI in the first place.
3. Product delivery becomes non-negotiable: After 20 years of pushing Agile principles, AI might finally force organizations to actually adopt them (even if they're reluctant to call it "Agile").
4. Businesses return to fundamentals: Just like the dot-com bubble, we're heading toward a moment where the market will care more about revenue, customers, and sustainability than hype.
5. Reskilling becomes a structural investment: Organizations will need to figure out what roles actually look like in an AI-enabled world and invest in growing their people, not just replacing them.

At the end, Peter and Dave pick which prediction is hardest to measure (spoiler: it's critical thinking) and commit to revisiting these in March to see how wrong they were.

If you've been wondering where all this AI stuff is actually heading, this episode cuts through the noise with grounded, practical predictions you can actually use.

Related episodes:

• AI and Knowledge Management with Derek Crager: https://www.buzzsprout.com/1643821/episodes/17360635
• Product vs. Process Innovation: https://www.buzzsprout.com/1643821/episodes/7953100
• There Are No Safe Bets in Business Anymore: https://www.buzzsprout.com/1643821/episodes/17433034

Reach out: [email protected]

What happens when your greatest strength becomes your biggest blind spot? In this episode, Peter and Dave sit down with Jill Macauley, COO of Behavioral Essentials, to explore how self-awareness shapes better leadership. They dig into why even talented leaders struggle with identity shifts, how generational expectations are changing the workplace, and why the best coaches focus on small tweaks rather than complete overhauls. From the reluctant engineer-turned-manager to the chef who can't slow down, this conversation gets real about the grief of letting go of old identities and the messy work of looking in the mirror.

This week´s takeaway: 

1. Your gifts become your blind spots when overused. That strength that got you promoted? It might be working against you now. The key is recognizing when speed becomes recklessness, when confidence becomes rigidity, or when expertise becomes tunnel vision.
2. Identity shifts are a grieving process. Moving from individual contributor to leader to leader of leaders isn't just a promotion. It requires letting go of the identity you've built your career on, and that loss is real. Give yourself (and others) permission to struggle with it.
3. Skip the woo-woo, ask "why" instead. Self-reflection doesn't have to feel soft or abstract. Simple questions like "Why did I react that way?" or "What role am I playing in this?" are pragmatic tools that work in any meeting, with any team.

In this episode, Peter Maddison and Dave Sharrock sit down with Josh Hill, an HR innovator who's challenging the traditional transactional approach to people management. Josh shares his unconventional journey from the Australian military to progressive HR, where he's pioneering the concept of "work as a product" at marketing agency Tier 11 and through his recruiting venture, Super Hired.

Josh explains how HR teams can shift from rushing to solutions toward discovery-led approaches that treat employees as customers. He walks through real examples of iterative onboarding improvements, the importance of understanding jobs to be done in hiring, and why talent density matters more than filling seats quickly.

The conversation explores compensation dynamics, the value of matchmaking over recruiting, and how small experiments can build momentum for broader HR transformation. Whether you're leading people operations or navigating organizational change, this episode offers practical insights on making HR less transactional and more intentional.

Key Takeaways:

1. Start with discovery, not solutions – Before building HR processes or solutions, take time to interview employees, understand their stories and experiences, and map out what's actually obstructing outcomes. Even 10 minutes of discovery beats rushing to a result.
2. HR as a matching exercise, not a numbers game – Recruitment and people management generate real value when viewed as careful matchmaking between what work a company offers and what employees are looking for, rather than just transactional headcount filling.
3. Make HR less transactional – Slow down important conversations around hiring, onboarding, and employee experience. These decisions deserve the same rigor companies apply to external product development, not just checkbox processes.

Product managers and product owners are drowning in documentation, vision statements, roadmaps, and backlogs. But what if AI could handle the heavy lifting, freeing you up to actually talk to customers?

In this episode, Dave and Peter explore how large language models are changing product management. They go beyond the obvious use cases (like generating user stories) to discuss upstream opportunities: building product strategy, validating market positioning, and testing ideas against competitors.

You'll learn:

• Why documenting your product strategy matters (and why most PMs skip it)
• How to prompt AI to be critical, not just complimentary
• The danger of accepting AI outputs without evaluation
• Temperature settings, context windows, and other practical techniques
• What to do with the time you get back (hint: talk to real customers)

Dave and Peter also share a key practice: write down what you expect before you prompt. This simple step helps you critically evaluate AI responses instead of accepting them at face value.

If you're a product manager, product owner, or anyone building digital products, this conversation will help you use AI as a tool for better thinking, not just faster output.

In this episode, Peter Maddison and Dave Sharrock welcome Justin Trombold, President and Founder of Antison Advisors, to discuss the parallels between agile transformation and generative AI adoption in organizations.

Justin shares insights from his work helping companies navigate generative AI readiness, revealing that the biggest challenges aren't technical; they're organizational. From end-user proficiency to cross-functional collaboration, the conversation explores why companies struggle to move beyond "toy apps" to create real business value with AI.

Key topics covered:
• Why organizations need an AI strategy before investing in tools
• The critical importance of end-user proficiency with LLMs
• How cross-functional collaboration enables AI success
• Why annual planning cycles may be holding your AI initiatives back
• The parallels between agile adoption and AI transformation
• Moving from efficiency gains to true value creation

Whether you're leading AI initiatives, managing agile transformations, or wondering why your organization's AI investments aren't paying off, this conversation offers practical frameworks for thinking about organizational readiness in the age of generative AI.

THREE KEY TAKEAWAYS:

1. End-user proficiency is everything. 
2. Define the sandbox before choosing the toys.
3. Innovation in planning matters as much as innovation in products. 

Contact us: [email protected]

#GenerativeAI #AgileTransformation #OrganizationalChange #AIReadiness #DigitalTransformation #LLM #CrossFunctionalTeams #Innovation

In this episode, Peter Maddison and Dave Sharrock welcome Rick Delisi, Lead Research Analyst at Glia and co-author of "The Effortless Experience" and "Digital Customer Service," to discuss how AI is transforming customer service in banking and credit unions.

Rick reveals why the future of contact centers isn't about eliminating human interaction; it's about automating the routine so humans can focus on building real relationships. Learn how banks are breaking the age-old trade-off between efficiency and customer experience, and why starting with internal-facing AI tools is the safest path to transformation.

Discover the surprising truth about which customer satisfaction metric actually predicts loyalty (hint: it's not what most companies are measuring), and why customer expectations for AI are shaped more by bad experiences with other companies than by anything your organization does.


THREE KEY TAKEAWAYS:

1. AI for Everyone, Not Just Customers: AI can transform your entire organization, from helping frontline agents with real-time guidance, to giving managers instant analysis capabilities, to enabling executives to make data-driven strategic decisions. The most successful implementations use AI across all levels: customers, agents, managers, and executives.

2. Start Internal, Then Scale Outward: Begin with internal tools that help agents, managers, and executives first. This builds confidence, allows teams to experience the technology firsthand, and creates incremental improvements that build organizational trust. By the time you roll out customer-facing AI, your entire team understands and trusts the system.

3. The best predictor of customer loyalty isn't satisfaction scores or Net Promoter Score, it's the Customer Effort Score. Ask customers, "How much effort was required for you to get what you needed?" after each interaction. Low-effort experiences drive loyalty, and this metric gives you actionable insights into where to improve your service processes.

CONTACT US:
Email: [email protected]

Definitely Maybe Agile explores the complexities of adopting new ways of working at scale, covering digital transformation, agile practices, and DevOps in enterprise environments.

#AI #CustomerService #Banking #DigitalTransformation #ContactCenter #CreditUnions #CustomerExperience #Glia #FinancialServices #AgileTransformation