Rethink Your Understanding

In this episode, the AI hosts explore why the agentic era is shaping up to be more than another AI feature wave.As software begins to act on behalf of users, engineering and product leaders may need to rethink the systems beneath the interface, from data quality and secure APIs to durable state, long-running workflows, and human approval checkpoints.They discuss why trust will likely build gradually, starting with lower-risk tasks before expanding into higher-stakes transactions. The bigger idea is simple: this looks more like a major systems shift, similar to cloud or continuous delivery, than a surface-level product enhancement.Link to the article: Software for Humans, Systems for Agents, originally published April 06, 2026.Connect with me on LinkedIn

In this episode, the AI hosts unpack my recent career reflection article, Staying Was the Hard Move, and the counterintuitive truth that long tenure doesn’t have to mean stagnation.They explore what it actually takes to lead through the “hard middle” of digital transformation: modernizing legacy architecture without breaking customer trust, scaling engineering practices through years of growth, and evolving from tactical management into executive leadership focused on team outcomes.It’s a story about compounding impact, how resilience, culture, and sustained reinvention can become the real advantage.Link to the article: Staying Was the Hard Move, originally published February 28, 2026.Connect with me on LinkedIn

I keep seeing “Agile is dead” headlines, now repackaged for the AI era. My take: AI isn’t killing Agile. AI is illuminating constraints that were already in the value stream.AI can do market research, write documentation, write code fast - it can’t take accountability. As AI compresses execution time, rebundles responsibilities, and enables smaller teams with faster release cycles, the real work shifts to human judgment: decision-making, validation, security, governance, and operating safely in production.This episode reframes Agile and agility as an enduring capability, and explores what must evolve when software delivery accelerates dramatically with AI.Link to the article: Agile Isn’t Dead and AI Isn’t Killing It Either, originally published January 24, 2026.Connect with me on LinkedIn

AI is now table stakes in software engineering hiring, but it is also warping the signals we used to trust.In this episode, the AI hosts cover my article about a growing pattern I call “AI-fluent, fundamentally lost”: candidates who can produce impressive output with prompts, yet struggle to explain the logic, constraints, and architectural trade-offs behind what they ship. The result is a new kind of risk: “glass cannons” that look productive fast, but can drive long-term maintenance cost and technical debt when fundamentals and judgment are missing.They cover the arguments for a more durable hiring approach that evaluates both system-level reasoning and AI-assisted execution, treating AI as a productivity accelerator, not a replacement for critical thinking.Link to the article: AI Fluent, Fundamentally Lost, originally published December 07, 2025.Connect with me on LinkedIn

In this episode, we unpack a new challenge in software hiring: AI is boosting productivity while also creating an illusion of mastery. Candidates can generate impressive AI-assisted code, yet struggle when the conversation moves to fundamentals like composition vs. inheritance, tradeoffs, and architectural decision-making. The result is a distortion of traditional hiring signals, where output can mask gaps in understanding.The AI hosts dig into why fundamentals still matter most in enterprise systems, where reliability, durability, and accountability matter more than raw speed. Great engineers don’t just produce code, they can debug it, validate it, and challenge AI-generated work with sound judgment. We close with what hiring practices must evolve to measure next: architectural reasoning and system-level decision-making, the areas where AI can assist, but not substitute.Link to the article: When AI Isn’t Enough, originally published November 29, 2025.Connect with me on LinkedIn

This episode breaks down the Product Operating Model and what it really takes to succeed in a modern software organization.The AI hosts explore why POM is not a plug-and-play framework, but a system that only works when architecture, funding, and team design actually support long-lived product ownership. We clarify the most common misconceptions, from the belief that POM replaces DevOps to the myth that it calls for larger teams, reframing the model around small, empowered groups owning a complete slice of value.They also discuss why shifting from project funding to product funding is essential, and how Value Stream Management provides the visibility needed to understand how work truly flows across the organization. If you’re trying to implement POM or make sense of the friction around it, this episode gives you a clear, practical view of what the model demands and how to make it work in your unique context.Link to the article: When the System Fits, the Product Operating Model Works, originally published November 27, 2025.Connect with me on LinkedIn

This episode explores why modern engineering organizations should move beyond activity metrics and project thinking and adopt a system built on measurable business outcomes. The AI hosts break down how Value Stream Management and the Product Operating Model work together to give teams long-lived ownership, product margin accountability, and the visibility needed to surface friction, align priorities, and understand where value is actually created.They also examine the leadership shift required to make it all work. Flow metrics show how efficiently teams deliver, Realization metrics show whether that delivery matters, and AI is rapidly amplifying both by revealing bottlenecks and opportunities in real time. For leaders navigating transformation, this conversation shows how to rethink your operating model and unlock the performance your organization is capable of.Link to the article: Why Value Stream Management and the Product Operating Model Matter (and What Comes Next), originally published November 05, 2025.Connect with me on LinkedIn

In this episode, The Price of Alignment, we explore what happens when innovation meets bureaucracy. When a large, centralized organization acquires a smaller, agile one, the push for alignment and consistency can come at a steep cost.Drawing from the story of two companies, “LegacyTech” and “AgileWorks,” we examine how forcing uniform management models onto autonomous, microservice-based teams can unravel the very agility and speed that made them valuable in the first place.The conversation dives into Conway’s Law, bounded contexts, and the critical leadership lesson every executive should learn before integrating two very different worlds of software delivery.Link to the article: The Price of Alignment, originally published October 21, 2025.Connect with me on LinkedIn

Today's conversation is a short follow-up to season 2, episode 48. In this episode, Beyond the Beyond: AI Across the Value Stream, we explore how artificial intelligence is reshaping software delivery, not as a magic fix, but as a mirror reflecting the strength of your existing systems.Drawing insights from the 2025 DORA Report and emerging Software Engineering Intelligence trends, this episode unpacks why AI’s real potential lies in amplifying disciplined engineering practices and end-to-end visibility.We’ll examine how leaders can move beyond creation and release metrics to apply AI across the full value stream, from idea to operation, turning insight into flow, and flow into measurable business impact.Link to the article: Beyond the Beyond Delivery: AI Across the Value Stream, originally published October 11, 2025.Connect with me on LinkedIn

When companies remove Agile Leaders, roles like Scrum Masters or Agile Delivery Managers, they often assume the system will self-regulate. But what really happens when no one is accountable for team health, continuous improvement, or flow?In this episode, the AI hosts unpack Phil's article about the quiet erosion of agility inside modern organizations, where well-intentioned efficiency moves end up dismantling the very disciplines that make agile work. Drawing from real-world transformations, he argues that while frameworks fade, the mindset of agile leadership must endure.Link to the article: What Happens When We Eliminate the Agile Leader?, originally published October 09, 2025.Connect with me on LinkedIn

In this episode, the AI hosts explore how artificial intelligence might reshape the very design of software teams. The “two-pizza rule” once defined how agile, cross-functional teams operated, but AI is changing what small and effective really means.As AI and automation expand what individuals and small teams can achieve, leaders must rethink the scale, structure, and collaboration required. We unpack what this shift means for engineering management, decision-making speed, and value delivery, and how organizations can use AI not just to optimize code, but to redefine how high-performing teams operate in the era of intelligent systems.Link to the article: From Two Pizzas to One: How AI Reshapes Dev Teams, originally published October 02, 2025.Connect with me on LinkedIn

AI’s full potential in software delivery isn’t in writing code faster, it’s in transforming the entire value stream. In this episode, the AI hosts explore why most delays happen in ideation and release, not coding, and how AI applied narrowly to delivery can actually amplify dysfunction. Drawing on insights from Mik Kersten, Laura Tacho, John Cutler, Atlassian’s 2025 AI Collaboration Report, and the DORA 2025 findings, we discuss how leaders can use Value Stream Management and flow metrics to measure AI’s true impact, reduce systemic waste, and accelerate idea-to-value across the enterprise.Link to the article: Beyond Delivery: Realizing AI’s Potential Across the Value Stream, originally published September 30, 2025.Connect with me on LinkedIn

In this episode, our AI hosts dive into Smarter Pull Requests: Balancing AI, Automation, and Human Review, a handbook redefining how teams approach code reviews in the age of AI. The framework shows how to integrate AI and automation responsibly, ensuring speed without sacrificing human judgment on quality, security, and design.We cover rules files, evidence-based PR templates, and AI gate checks with tools like GitHub Copilot and CodeRabbit, all reinforced by CI pipelines. These practices raise the baseline of consistency while letting reviewers focus on architecture and business alignment.We’ll hear how AI reinforces, rather than replaces, human accountability in code review.Link to the article: Smarter Pull Requests: Balancing AI, Automation, and Human Review, originally published September 28, 2025.Connect with me on LinkedIn

This episode takes you inside the evolving role of a VP of Engineering — far beyond a standard job description.The AI hosts discuss why the role is highly contextual, shaped by company size, leadership culture, and organizational maturity. I share the core accountabilities I’ve been held to: ensuring software quality and resilience, fostering people engagement, retaining and developing talent, and building the skills teams need to stay competitive.The conversation also explores the VP’s strategic importance in business alignment, global talent management, and transformation initiatives, while contrasting the role with that of a CTO. Finally, we touch on the personal evolution required to succeed — developing V-shaped skills, mentoring leaders, and balancing the rewards of culture-building with the challenges of tough decisions.Link to the article: So, What Does a VP of Engineering Do?, originally published August 21, 2025.Connect with me on LinkedIn

This episode advocates for a system-wide perspective when adopting AI in software development, extending beyond mere code generation to encompass the entire value stream.Recent studies show initial productivity dips and extra effort with AI tools, but this is just a transitional phase, not a failure of the technology. Robust delivery metrics, like those from SEI tools or Value Stream Management platforms, are key to pinpointing bottlenecks where AI can drive the greatest impact, avoiding indiscriminate application.Ultimately, the conversation advocates for intentional AI adoption grounded in measurable outcomes across all roles within the delivery system, including product, QA, architecture, and even business functions, to achieve sustained competitive advantage.Link to the article: AI in Software Delivery: Targeting the System, Not Just the Code, originally published August 09, 2025.Connect with me on LinkedIn