Rethink Your Understanding

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 bot...

AI is transforming software engineering, but it addresses only one part of a much larger system. Speeding up code creation doesn’t solve deeper issues like unclear requirements, poor architecture, or slow feedback loops, and in some cases, it can amplify dysfunction when the system itself is flawed. Engineers remain fully responsible for what they ship, regardless of how the code is written. The real opportunity is to increase team...

In this episode, the AI hosts unpack the growing tension between AI hype and real-world adoption in software delivery.

While AI is advancing rapidly, many leaders focus on bold claims, like replacing entire teams, without grounding them in metrics that matter. Leaders find it challenging to determine the right metrics to measure the impact of AI, as well as the success of AI adoption and investments. I challenge this narrative and e...

In this episode, our AI hosts dive into an article advocating for a radical reevaluation of software delivery commitments, shifting away from rigid, project-based models and “precise” estimates toward a probabilistic, continuous-discovery mindset.

Drawing on Vasco Duarte’s insights and the Three Ways outlined in The DevOps Handbook, they explore treating development as a series of adaptive bets, budgeting for change, using experime...

This episode explores the complexities of merging two distinct engineering organizational models after an acquisition.

It highlights the friction that arises when one part of the organization uses small, self-managed, cross-functional teams with distributed leadership, while the other adheres to a traditional Engineering Manager (EM) model where a single manager oversees people, delivery, and agile practices.

The core challenge lie...

This episode addresses a common pitfall in software delivery management: using system-level metrics, such as Flow Metrics, to evaluate individual performance. The AI hosts cover how this practice, while logical to some leaders, can damage trust, produce misleading data, and hinder real improvement. The conversation covers using metrics as a tool to help teams improve, focusing on context, collaboration, and actionable steps. The ma...

In this episode, The AI hosts explore why teams—unlike individuals—rarely get a performance review, and why that needs to change.

Based on my latest article, you'll hear a practical framework designed for Agile and DevOps teams to reflect on how they work, not just what they deliver. We’ll walk through 10 team health dimensions—from collaboration to delivery integrity—and how your team can use them as conversation starters, not scor...

In this episode, the AI hosts explore my latest article on evolving our approach to Value Stream Management (VSM) — which we originally adopted to bring greater visibility to our Agile, Lean, and DevOps efforts. But while preparing for a conversation with an industry leader about the Product Operating Model (POM), I had a realization: we weren’t just managing flow — we were also structuring teams around outcomes.

This episode dives ...

This episode covers a recent shift from the title of Scrum Master to Agile Delivery Manager (ADM). The AI hosts discuss how a change for the role of Scrum Master to Agile Delivery Manager represents a natural progression driven by an expanded need for delivery leadership, the adoption of Flow Metrics and Value Stream Management, and a move towards alternative practices from Scrum to Kanban or other custom Agile practices.

The ADM ro...

This episode, "Beyond Frameworks," reflects on the challenges of leading large-scale Agile and DevOps transformations, even within successful organizations. It argues that despite adopting new structures and frameworks, persistent friction arises from diverging leadership philosophies and a superficial sense of alignment, leading to transformation fatigue.

The AI hosts share my personal experiences and insights from over a decade, h...

In today’s episode, we explore the idea of documenting anticipated outcomes and introduce Purpose-Driven Development (PDD), which focuses on defining expected outcomes for every software project. This approach helps ensure alignment with business goals and moves away from a "feature factory" mindset that prioritizes output over value.

The AI hosts discuss my recent story about resistance to this idea, pointing out that it often come...

In today’s episode, we explore the idea of documenting anticipated outcomes and introduce Purpose-Driven Development (PDD), which focuses on defining expected outcomes for every software project. This approach helps ensure alignment with business goals and moves away from a "feature factory" mindset that prioritizes output over value.

The AI hosts discuss my recent story about resistance to this idea, pointing out that it often come...

This episode dives into various team models within technology organizations, focusing on a comparison between the traditional Engineering Manager (EM) model—where a single manager handles both technical direction and people management—and autonomous cross-functional teams, inspired by frameworks like Team Topologies. I make the case for the latter, highlighting the intentional separation of people management from delivery responsib...

In this episode, we examine the growing trend of cutting Agile leadership roles like Scrum Masters. While often viewed as a cost-saving move, I argue this approach harms team effectiveness and productivity.

The AI host shares my experience evolving the role into an Agile Delivery Manager focused on flow metrics and value stream management, contrasting it with a parent company's decision to distribute these tasks. We discuss the hidd...

In this episode, we shift focus from software delivery to engineering and explore the topic of responsible engineering. The AI hosts discuss a conversation that broadened the definition of responsible engineering, emphasizing the importance of considering the consequences beyond just functionality.

Leadership is key to creating a culture of responsibility by setting clear expectations and encouraging accountability. The discussion h...

This episode addresses confusion surrounding Flow Metrics in Agile environments by emphasizing the importance of understanding Value Stream Management, Flow Items, and the Agile Work Hierarchy. It clarifies how to align Agile work items, such as initiatives or themes, epics, features, user stories, and tasks, with Flow Items, which represent units of value like features, defects, risks, and debts.

The AI hosts discuss the common mi...

The episode is a follow-up to my August 2024 article and Podcast Episode 18 from Season 1: Navigating the Digital Product Workflow Metrics Landscape: From DORA to Comprehensive Value Stream Management Platform Solutions

In this episode, the AI hosts share my perspective on the increasing confusion between Software Engineering Intelligence (SEI) and Value Stream Management (VSM) platforms caused by overlapping features and marketing ...

In this episode, we delve into the article "Beyond Outcome-Focused Metrics: Connecting Work to Outcomes," which emphasizes the importance of aligning software development efforts with tangible business value. We discuss how adopting value stream thinking can transform software teams from perceived cost centers into strategic enablers. By focusing on customer value streams, organizations can enhance strategic alignment, foster conti...

This episode picks up from the last episode, episode 27, Advocating for Refactoring: Prioritization in the Context of Business Goals

We continue the conversion advocating for a proactive approach to managing technical debt, suggesting that teams treat it like a financial loan. I support making continuous "minimum payments" by regularly refactoring code, similar to making minimum payments on a loan, rather than waiting for large-scal...