Easy Agile Podcast Ep.20 The importance of the Team Retrospective

In this insightful episode, we dive deep into one of the most common frustrations in engineering and dev teams: retrospectives that fail to drive meaningful change. Join Jaclyn Smith, Senior Product Manager at Easy Agile, and Shane Raubenheimer, Agile Technical Consultant at Adaptavist, as they unpack why retrospectives often become checkbox exercises and share practical strategies for transforming them into powerful engines of continuous improvement.

Want to put these insights into practice? We hosted a live, hands-on retro action workshop to show you exactly how to transform your retrospectives with practical tools and techniques you can implement immediately.

Key topics covered:

• Common retrospective anti-patterns and why teams become disengaged
• The critical importance of treating action items as "first-class citizens"
• How to surface recurring themes and environmental issues beyond team control
• Practical strategies for breaking down overwhelming improvement initiatives
• The need for leadership buy-in and organizational support for retrospective outcomes
• Moving from "doing agile" to "being agile" through effective reflection and action

This conversation is packed with insights for making your retrospectives more impactful and driving real organizational change.

About our guests

Jaclyn Smith is a Senior Product Manager at Easy Agile, where she leads the Easy Agile TeamRhythm product that helps teams realize the full benefits of their practices. With over five years of experience as both an in-house and consulting agile coach, Jaclyn has worked across diverse industries helping teams improve their ways of working. At Easy Agile, she focuses on empowering teams to break down work effectively, estimate accurately, and most importantly, take meaningful action to continuously improve their delivery and collaboration.

Shane Raubenheimer is an Agile Technical Consultant at Adaptavist, a global family of companies that combines teamwork, technology, and processes to help businesses excel. Adaptavist specializes in agile consulting, helping organizations deliver customer value through agile health checks, coaching, assessments, and implementing agile at scale. Shane brings extensive experience working across multiple industries—from petrochemical to IT, digital television, and food industries—applying agile philosophy to solve complex organizational challenges. His expertise spans both the technical and cultural aspects of agile transformation.

Transcript

This transcript has been lightly edited for clarity and readability while maintaining the authentic conversation flow.

Opening and introductions

Jaclyn Smith: Hi everyone, and welcome back to the Easy Agile Podcast. Today I'm talking to Shane Raubenheimer, who's with us from Adaptavist. Today we're talking about why your retrospectives keep failing and how to finally fix them. Shane, you and I have spent a fair amount of time together exploring the topic of retros, haven't we? Do you want to tell us a little bit about yourself first?

Shane Raubenheimer: Yeah, hello everyone. I'm Shane Raubenheimer from Adaptavist. I am an agile coach and technical consultant, and along with Jaclyn, we've had loads of conversations around why retros don't work and how they just become tick-box exercises. Hopefully we're going to demystify some of that today.

Jaclyn Smith: Excellent. What's your background, Shane? What kind of companies have you worked with?

Shane Raubenheimer: I've been privileged enough to work across multiple industries—everything from petrochemical to IT, to digital television, food industry. All different types of applied work, but with the agile philosophy.

Jaclyn Smith: Excellent, a big broad range. I should introduce myself as well. My name is Jaclyn. I am a Senior Product Manager here at Easy Agile, and I look after our Team Rhythm product, which helps teams realize the benefits of being agile. I stumbled there because our whole purpose at Easy Agile is to enable our customers to realize the benefits of being agile.

My product focuses on team and teamwork, and teamwork happens at every level as we know. So helping our customers break down work and estimate work, reflect—which is what we're talking about today—and most importantly, take action to improve their ways of working. I am an agile coach by trade as well as a product manager, and spent about five years in a heap of different industries, both as a consultant like you Shane, and as an in-house coach as well.

The core problem: When retrospectives become checkbox exercises

Jaclyn Smith: All right, let's jump in. My first question for you Shane—I hear a lot that teams get a bit bored with retros, or they face recurring issues in their retrospectives. Is that your experience? Tell me about what you've seen.

Shane Raubenheimer: Absolutely. I think often what should be a positive rollup and action of a sequence of work turns out to normally become a checkbox exercise. There's a lot of latency in the things that get uncovered and discussed, and they just tend to perpetually roll over. It almost becomes a checkbox exercise from what I've seen, rather than the mechanism to actively change what is happening within the team—but more importantly, from influences outside the team.

I think that's where retros fail, because often the team does not have the capability to do any kind of upward or downstream problem solving. They tend to just mull about different ways to ease the issues within the team by pivoting the issues rather than solving them.

I think that's where retros fail, because often the team does not have the capability to do any kind of upward or downstream problem solving. They tend to just mull about different ways to ease the issues within the team by pivoting the issues rather than solving them.

Jaclyn Smith: Yeah, I would agree. Something that I see regularly too is because they become that checkbox, teams get really bored of them. They do them because they're part of their sprint, part of their work, but they're not engaged in them anymore. It's just this thing that they have to do.

It also can promote a tendency to just look at what's recently happened and within their sphere of influence to solve. Whereas I think a lot of the issues that sometimes pop up are things that leadership need to help teams resolve, or they need help to solve. It can end up with them really focusing on "Oh well, there's this one bit in how we do our code reviews, we've got control over that, we'll try to fix that." Or as you say, the same recurring issues come up and they don't seem to get fixed—they're just the same complaints every time.

Shane Raubenheimer: Absolutely. You find ways that you put a band-aid on them just so you can get through to the next phase. I think the problem with that is the impact that broader issues have on teams is never completely solvable within that space, and it's no one else's mandate necessarily to do it. When an issue is relatable to a team, exposing why it's not a team-specific issue and it's more environmental or potentially process-driven—that's the bit that I feel keeps getting missed.

When an issue is relatable to a team, exposing why it's not a team-specific issue and it's more environmental or potentially process-driven—that's the bit that I feel keeps getting missed.

The pressure problem and overwhelming solutions

Jaclyn Smith: Yeah, I think so too. The other thing you just sparked for me—the recurring issue—I think that also happens when the team are under pressure and they don't feel like they have the time to solve the problems. They just need to get into the next sprint, they need to get the next bit of work done. Or maybe that thing that they need to solve is actually a larger thing—it's not something small that they can just change.

They need to rethink things like testing strategies. If that's not working for you, and it's not just about fixing a few flaky tests, but you need to re-look at how you're approaching testing—it seems overwhelming and a bit too big.

Shane Raubenheimer: Absolutely. Often environmental issues are ignored in favor of what you've been mandated to do. You almost retrofit the thing as best you can because it's an environmental issue. But finding ways to expose that as a broader-based issue—I think that should be the only output, especially if it's environmental and not team-based.

The problem of forgotten action items

Jaclyn Smith: Something I've also seen recently is that teams will come up with great ideas of things that they could do. As I said before, sometimes they're under pressure and they don't feel they have the capacity to make those changes. Sometimes those actions get talked about, everyone thinks it's a wonderful idea, and then they just get forgotten about. Teams end up with this big long backlog of wonderful experiments and things that they could have tried that have just been out of sight, out of mind. Have you seen much of that yourself?

Shane Raubenheimer: Plenty. Yes, and often teams err on the side of what's expected of them rather than innovate or optimize. I think that's really where explaining the retrospective concept to people outside fully-stacked or insular teams is the point here. You need, very much like in change management, somebody outside the constructs of teams to almost champion that directive—the same way as you would do lobbying for money or transformation. It needs to be taken more seriously and incorporated into not just teams being mini-factories supporting a whole.

You transform at a company level, you change-manage at a company level. So you should action retrospective influences in the same way. Naturally you get team-level ones, and that's normally where retrospectives do go well because it's the art of the possible and what you're mandated to do. I think bridging the gap between what we can fix ourselves and who can help us expose it is a big thing.

I see so much great work going to waste because it simply isn't part of the day job, or should be but isn't.

You transform at a company level, you change-manage at a company level. So you should action retrospective influences in the same way.

Making action items first-class citizens

Jaclyn Smith: Yeah, absolutely. I know particularly in the pre-Covid times when we were doing a lot of retros in person, or mostly in person with stickies on walls, I also found even if we took a snapshot of the action column, it would still end up on a Confluence board or something somewhere and get forgotten about. Then the next retro comes around and you sort of feel like you're starting fresh and just looking at the last sprint again. You're like, "Oh yeah, someone raised that last retro, but we still didn't do anything about that."

Shane Raubenheimer: I think Product Owners, Scrum Masters, or any versions of those kinds of roles need to treat environmental change or anti-pattern change as seriously as they treat grooming work—the actual work itself. Because it doesn't matter how good you are if the impediments that are outside of your control are not managed or treated with the same kind of importance as the actual work you're doing. That'll never change, it'll just perpetuate. Sooner or later you hit critical mass. There's no scenario where your predictability or velocity gets better if these things are inherent to an environment you can't control.

Product Owners, Scrum Masters, or any versions of those kinds of roles need to treat environmental change or anti-pattern change as seriously as they treat grooming work—the actual work itself.

Jaclyn Smith: Yeah, that's true. We've talked about action items being first-class citizens and how we help teams do that for that exact reason. Because a retro is helpful to build relationships and empathy amongst the team for what's happening for each of them and feel a sense of community within their team. But the real change comes from these incremental changes that are made—the conversations that spark the important things to do to make those changes to improve how the team works.

That action component is really the critical part, or maybe one of two critical parts of a retro. I feel like sometimes it's the forgotten child of the retro. Everyone focuses a lot on engaging people in getting their ideas out, and there's not as much time spent on the action items and what's going to be done or changed as a result.

Beyond team-level retrospectives

Shane Raubenheimer: Absolutely, consistently. I think it's symptomatic potentially of how retros are perceived. They're perceived as an inward-facing, insular reevaluation of what a team is doing. But I've always thought, in the same way you have the concept of team of teams, or if you're in a scaled environment like PI planning, I feel retrospectives need the same treatment or need to be invited to the VIP section to become part of that.

Because retrospectives—yes, they're insular or introspective—but they need to be exposed at the same kind of level as things like managing your releases or training or QA, and they're not.

Jaclyn Smith: Yeah, I think like a lot of things, they've fallen foul of the sometimes contentious "agile" word. People tend to think, "Oh retros, it's just one of those agile ceremonies or agile things that you do." The purpose of them can get really lost in that, and how useful they can be in creating change. At the end of the day, it's about improving the business outcomes. That's why all of these things are in place—you want to improve how well you work together so that you can get to the outcome quicker.

At the end of the day, it's about improving the business outcomes. That's why all of these things are in place—you want to improve how well you work together so that you can get to the outcome quicker.

Shane Raubenheimer: Absolutely. Outcome being the operative word, not successfully deploying code or...

Jaclyn Smith: Or ticking the retro box, successfully having a retro.

Shane Raubenheimer: Yeah, exactly. Being doing agile instead of being agile, right?

Expanding the scope of retrospectives

Jaclyn Smith: One hundred percent. It also strikes me that there is still a tendency for retros to be only at a team level and only a reflection of the most recent period of time. So particularly if a team are doing Scrum or some version of Scrum with sprints, to look back over just the most recent period. I think sometimes the two things—the intent of a retro but also the prime directive of the retro—gets lost.

In terms of intent, you can run a retro about anything. Think about a post-mortem when you have an incident and everyone gets together to discuss what happened and how we prevent that in the future. I think people forget that you can have a retro and look at your system of work, and even hone in on something like "How are we estimating? Are we doing that well? Do we need to improve how we're doing that?" Take one portion of what you're working on and interrogate it.

You can run a retro about anything. I think people forget that you can have a retro and look at your system of work, and even hone in on something like "How are we estimating? Are we doing that well? Do we need to improve how we're doing that?" Take one portion of what you're working on and interrogate it.

Understanding anti-patterns

Shane Raubenheimer: Absolutely. You just default to "what looks good, what can we change, what did we do, what should we stop or start doing?" That's great and all, but without some kind of trended analysis over a period of time, you might just be resurfacing issues that have been there all along. I think that's where the concept or the lack of understanding of anti-patterns comes in, because you're measuring something that's happened again rather than measuring or quantifying why is it happening at all.

I think that's the big mistake of retros—it's almost like an iterative band-aid.

I think that's the big mistake of retros—it's almost like an iterative band-aid.

Jaclyn Smith: Yeah. Tell me a little bit more about some of the anti-patterns that you have seen or how they come into play.

Shane Raubenheimer: One of them we've just touched on—I think the buzzword for it is the cargo cult culture for agile. That's just cookie-cutting agile, doing agile because you have to instead of being agile. Literally making things like your stand-up or your review or even planning just becomes "okay, well we've got to do this, so we've ticked the box and we're following through."

Not understanding the boundaries of what your method is—whether you like playing "wagile" or whether you're waterfall sometimes, agile at other times, and you mistake that variability as your agility. But instead, you don't actually have an identity. You're course-correcting blindly based on what's proportionate to what kind of fire you've got in your way.

Another big anti-pattern is not understanding the concept of what a team culture means and why it's important to have a team goal or a working agreement for your team. Almost your internal contracting. We do it as employees, right?

I think a lot of other anti-patterns come in where something's exposed within a team process, and because it's not interrogated or cross-referenced across your broader base of teams, it's not even recognized as a symptom. It is just a static issue. For me, that's a real anti-pattern in a lot of ways—lack of directive around what to do with retrospectives externally as well as internally. That's simply not a thing.

A lot of other anti-patterns come in where something's exposed within a team process, and because it's not interrogated or cross-referenced across your broader base of teams, it's not even recognized as a symptom. It is just a static issue. For me, that's a real anti-pattern in a lot of ways—lack of directive around what to do with retrospectives externally as well as internally.

Jaclyn Smith: Yeah, I think that's a good call-out for anyone watching or listening. If you're not familiar with anti-patterns, they're common but ineffective responses to recurring problems. They may seem helpful initially to solve an immediate problem, but they ultimately lead to negative outcomes.

Shane, what you just spoke about there with retrospectives—an example of that is that the team feel disengaged with retrospectives and they're not getting anything useful out of it, or change isn't resulting from the retrospectives. So the solution is to not hold them as frequently, or to stop doing them, or not do them at different levels or at different times. That's a really good example of an anti-pattern. It does appear to fix the problem, but longer term it causes more problems than it solves.

Another one that I see is with breaking down work. The idea that spending time together to understand and gain a shared understanding of the work and the outcome that you need takes a lot of time, and breaking down that work and getting aligned on how that work is going to break down on paper can look like quite an investment. But it's also saving time at the other end, reducing risk, reducing duplication and rework to get a better outcome quicker. You shift the time spent—development contracts because you've spent a little bit more time discovering and understanding what you're doing.

A common anti-pattern that I see there is "we spent way too long looking at this, so we're going to not do discovery in the same way anymore," or "one person's going to look at that and break it down."

The budget analogy

Shane Raubenheimer: I always liken it to your budget. The retrospective is always the nice shiny holiday—it's always the first to go.

I always liken it to your budget. The retrospective is always the nice shiny holiday—it's always the first to go.

Jaclyn Smith: It's the contractor.

Shane Raubenheimer: Yeah. It's almost like exposing stuff that everybody allegedly knows to each other is almost seen as counterintuitive because "we're just talking about stuff we all know." It often gets conflated into "okay, we'll just do that in planning." But the reality is the concept of planning and how you amend what you've done in the retrospective—that's a huge anti-pattern because flattening those structures from a ceremonies perspective is what teams tend to do because of your point of "well, we're running out of daylight for doing actual development."

But it's hitting your head against the wall repeatedly and hoping for a different outcome without actually implying a different outcome. Use a different wall even. I think it's because people are so disillusioned with retrospectives. I firmly believe it's not an internal issue. I believe if the voices are being heard at a budgeting level or at a management level, it will change the whole concept of the retrospective.

Solution 1: Getting leadership buy-in

Jaclyn Smith: I like it, and that's a good thread to move on to. So what do we do about it? How do we help change this? What are some of the practical tips that people can deploy?

Shane Raubenheimer: A big practical tip—and this is going to sound like an obvious one—is actual and sincere buy-in. What I mean by that is, as a shareholder, if I am basing your performance and your effectiveness on the quality and output of the work that you're promising me, then I should be taking the issues that you're having that are repeating more seriously.

Because if you're course-correcting for five, six, or seven sprints and you're still not getting this increasing, predictable velocity, and if it's not your team size or your attitude, it's got to be something else. I often relate that to it being environmental.

Buying into the outputs for change the same way as you would into keeping everyone honest, managing budgets, and chasing deadlines—it should all be part of the same thing. They should all be sitting at the VIP table, and I think that's a big one.

Buying into the outputs for change the same way as you would into keeping everyone honest, managing budgets, and chasing deadlines—it should all be part of the same thing. They should all be sitting at the VIP table.

Solution 2: Making patterns visible

Jaclyn Smith: I think so too. Something that occurs to me, and it goes back to what we were talking about right at the beginning, is sometimes identifying that there's a pattern there and that the same thing keeps coming up isn't actually visible, and that's part of the problem, right?

I know some things we've been doing in Easy Agile TeamRhythm around that recently, attempting to help teams with this. We've recently started surfacing all incomplete action items in retrospectives so people can see that big long list. Because they can convert their action items to Jira items or work items, they can also see where they've just been sitting and languishing in the backlog forever and a day and never been planned for anything to be done about them.

We've recently started surfacing all incomplete action items in retrospectives so people can see that big long list. Because they can convert their action items to Jira items or work items, they can also see where they've just been sitting and languishing in the backlog forever and a day and never been planned for anything to be done about them.

We've added a few features to sort and that kind of thing. Coming in the future—and we've been asked about this a lot—is "what about themes? What about things that are bubbling up?" So that's definitely on our radar that will be helpful.

I think that understanding that something has been raised—a problem getting support from another team, or with a broken tool or an outdated tool that needs to be replaced in the dev tooling or something like that—if that's been popping up time and time again and you don't know about it, then even as the leader of that team, you don't have the ammunition to then say "Look, this is how much it's slowed us down."

I think we live in such a data world now. If those actions are also where the evidence is that this is what needs to change and this is where the barriers are...

Solution 3: The power of trend analysis

Shane Raubenheimer: Certainly. I agree. Touching on the trend analytics approach—we do trend analysis on everything except what isn't happening or what is actually going wrong, because we just track the fallout of said lack of application. We don't actually trend or theme, to your point.

We do trend analysis on everything except what isn't happening or what is actually going wrong, because we just track the fallout of said lack of application.

We theme everything when we plan, yet somehow we don't categorize performance issues as an example. If everybody's having a performance issue, that's the theme. We almost need to categorize or expose themes that are outward-facing, not just inward-facing. Because it's well and good saying "well, our automated testing system doesn't work"—what does that mean? Why doesn't it work?

I think it should inspire external investigation. When you do a master data cleanup, you don't just say "well, most of it looks good, let's just put it all in the new space." You literally interrogate it at its most definitive and lowest level. So why not do the same with theming and trending environmental issues that you could actually investigate, and that could become a new initiative that would be driven by a new team that didn't even know it was a thing?

Jaclyn Smith: Yeah, and you're also gathering data at that point to evidence the problem rather than "oh, it's a pain point that keeps coming up." It is, but it gives you the opportunity to quantify that pain point a little bit as well. I think that is sometimes really hard to do when you're talking about developer experience or team member experience. Even outside of product engineering teams, there are things in the employee experience that affect the ability for that delivery—whatever you're delivering—to run smoothly. You want to make that as slick as possible, and that's how you get the faster outcomes.

Solution 4: The human factor

Shane Raubenheimer: Absolutely. You can never underestimate the human factor as well. If everything I'm doing and every member of my team is doing is to the best of not just their capability, but to the best of the ability in what they have available to them, you become jaded, you become frustrated. Because if you're hitting your head against the same issue regardless of how often you're pivoting, that can be very disillusioning, especially if it's not been taken as seriously as your work output.

If everything I'm doing and every member of my team is doing is to the best of not just their capability, but to the best of the ability in what they have available to them, you become jaded, you become frustrated.

We run a week late for a customer delivery or a customer project, and we start complaining about things like money, budget overspend, over-utilization. But identifying systematic or environmental issues that you can actually quantify should be treated in exactly the same way. I feel very strongly about this.

Solution 5: Breaking down overwhelming action items

Jaclyn Smith: We tend to nerd out about this stuff, Shane, and you're in good company. You've also reminded me—we've put together a bit of a workshop to help teams and people understand how to get the most out of their retrospectives, not just in terms of making them engaging, but fundamentally how to leverage actions to make them meaningful and impactful.

We've spoken a lot about the incremental change that is the critical factor when it is something that's within the team's control or closely to the team's control. That's how you get that expansion of impact—the slow incremental change. We've talked about sometimes those action items seem overwhelming and too big. What's your advice if that's the scenario for a team? What do you see happen and what can they do?

Shane Raubenheimer: I would suggest following the mantra of "if a story is too big, you don't understand enough about it yet, or it's not broken down far enough." Incremental change should be treated in exactly the same way. The "eat the elephant one bite at a time" analogy. If it's insurmountable, identify a portion of it that will make it a degree less insurmountable next time, and so on and so forth.

If we're iterating work delivery, problem-solving should be done in rapid iteration as well. That's my view.

Jaclyn Smith: I like it.

The "eat the elephant one bite at a time" analogy. If it's insurmountable, identify a portion of it that will make it a degree less insurmountable next time, and so on and so forth. If we're iterating work delivery, problem-solving should be done in rapid iteration as well.

Wrapping up: What's next?

Jaclyn Smith: I think we're almost wrapping up in terms of time. What can people expect from us if they join our webinar on July 10th, I believe it is, where we dive and nerd out even more about this topic, Shane?

Shane Raubenheimer: I think the benefit of the webinar is going to be a practical showing of what we're waxing lyrical about. It's easy to speak and evangelize, but I think from the webinar we'll show turning our concepts into actual actions that you can eyeball and see the results of.

With our approach that we took to our workshop, I think people will very quickly get the feeling of "this is dealing with cause and effect in a cause and effect way." So practical—to put that in one sentence, an active showing or demonstration of how to quantify and actually do what we've been waxing lyrical about.

the benefit of the webinar is going to be a practical showing of what we're waxing lyrical about. It's easy to speak and evangelize, but I think from the webinar we'll show turning our concepts into actual actions that you can eyeball and see the results of.

‍Jaclyn Smith: Excellent. That was a lovely summation, Shane. If anyone is interested in joining, we urge you to do so. You can hear us talking more about that but get some practical help as well. There is a link to the registration page in the description below.

I think that's about all we have time for today. But Shane, as always, it's been amazing and lovely to chat to you and hear your thoughts on a pocket of the agile world and helping teams.

Shane Raubenheimer: Yeah, it's always great engaging with you. I always enjoy our times together, and it's been my pleasure. I live for this kind of thing.

Jaclyn Smith: It's wonderful! Excellent. Well, I will see you on the 10th, and hopefully we'll see everyone else as well.

Shane Raubenheimer: Perfect. Yeah, looking forward to it.

Jaclyn Smith: Thanks.

Ready to end the frustration of ineffective retrospectives?

Jaclyn Smith and Shane Raubenheimer also hosted a live, hands-on webinar designed to turn retrospectives into powerful engines for continuous improvement.

In this highly interactive session, they talked about how teams can:

• Uncover why retrospectives get stuck in repetitive cycles
• Clearly capture and assign actionable insights
• Identify and avoid common retrospective pitfalls and anti-patterns
• Get hands-on experience with Easy Agile TeamRhythm to streamline retrospective actions
• Practical tools, techniques, and clear next steps to immediately enhance retrospectives and drive meaningful team improvements.

👉 Watch the recording here.

TL;DR

Henrik Kniberg, the agile coach behind Spotify's model, discusses how AI is fundamentally transforming software development. Key takeaways: AI tools like Cursor and Claude are enabling 10x productivity gains; teams should give developers access to paid AI tools and encourage experimentation; coding will largely disappear as a manual task within 3–4 years; teams will shrink to 2 people plus AI; sprints will become obsolete in favour of continuous delivery; product owners can now write code via AI, creating pull requests instead of user stories; the key is treating AI like a brilliant intern – when it fails, the problem is usually your prompt or code structure, not the AI. Bottom line: Learn to use AI now, or risk being left behind in a rapidly changing landscape.

Introduction

Artificial intelligence is fundamentally reshaping how software teams work, collaborate, and deliver value. But with this transformation comes questions: How do we maintain team morale when people fear being replaced? What happens to code quality when AI writes most of the code? Do traditional agile practices like sprints still make sense?

In this episode, I sit down with Henrik Kniberg to tackle these questions head-on. Henrik is uniquely positioned to guide us through this transition – he's the agile coach and entrepreneur who pioneered the famous Spotify model and helped transform how Lego approached agile development. Now, as co-founder of Abundly AI, he's at the forefront of helping teams integrate AI into their product development workflows.

This conversation goes deep into the practical realities of AI-powered development: from maintaining code review processes when productivity increases 10x, to ethical considerations around AI usage, to what cross-functional teams will look like in just a few years. Henrik doesn't just theorise – he shares real examples from his own team, where their CEO (a non-coder) regularly submits pull requests, and where features that once took a sprint can now be built during a 7-minute subway ride.

Whether you're a developer wondering if AI will replace you, a product owner looking to leverage these tools, or a leader trying to navigate this transformation, this episode offers concrete, actionable insights for thriving in the AI era.

About Our Guest

Henrik Kniberg is an agile coach, author, and entrepreneur whose work has shaped how thousands of organisations approach software development. He's best known for creating the Spotify model – the squad-based organisational structure that revolutionised how large tech companies scale agile practices. His work at Spotify and later at Lego helped demonstrate how agile methodologies could work at enterprise scale whilst maintaining team autonomy and innovation.

Henrik's educational videos have become legendary in the agile community. His "Agile Product Ownership in a Nutshell" video, created over a decade ago, remains one of the most-watched and shared resources for understanding product ownership, with millions of views. His ability to distil complex concepts into simple, visual explanations has made him one of the most accessible voices in agile education.

More recently, Henrik has turned his attention to the intersection of AI and product development. As co-founder of Abundly AI, he's moved from teaching about agile transformation to leading AI transformation – helping companies and teams understand how to effectively integrate generative AI tools into their development workflows. His approach combines his deep understanding of team dynamics and agile principles with hands-on experience using cutting-edge AI tools like Claude, Cursor, and GitHub Copilot.

Henrik codes daily using AI and has been doing so for over two and a half years, giving him practical, lived experience with these tools that goes beyond theoretical understanding. He creates educational content about AI, trains teams on effective AI usage, and consults with organisations navigating their own AI transformations. His perspective is particularly valuable because he views AI through the lens of organisational change management – recognising that successful AI adoption isn't just about the technology, it's about people, culture, and process.

Based in Stockholm, Sweden, Henrik continues to push the boundaries of what's possible when human creativity and AI capabilities combine, whilst maintaining a pragmatic, human-centred approach to technological change.

Transcript

Note: This transcript has been lightly edited for clarity and readability.

Maintaining Team Morale and Motivation in the AI Era

Tenille Hoppo: Hi there, team, and welcome to this new episode of the Easy Agile Podcast. My name is Tenille Hoppo, and I'm feeling really quite lucky to have an opportunity to chat today with our guest, Henrik Kniberg.

Henrik is an agile coach, author, and entrepreneur known for pioneering agile practices at companies like Spotify and Lego, and more recently for his thought leadership in applying AI to product development. Henrik co-founded Abundly AI, and when he isn't making excellent videos to help us all understand AI, he is focused on the practical application of generative AI in product development and training teams to use these technologies effectively.

Drawing on his extensive experience in agile methodologies and team coaching, Henrik seems the perfect person to learn from when thinking about the intersection of AI, product development, and effective team dynamics. So a very warm welcome to you, Henrik.

Henrik Kniberg: Thank you very much. It's good to be here.

Tenille: I think most people would agree that motivated people do better work. So I'd like to start today by touching on the very human element of this discussion and helping people maintain momentum and motivation when they may be feeling some concern or uncertainty about the upheaval that AI might represent for them in their role.

What would you suggest that leaders do to encourage the use of AI in ways that increase team morale and creativity rather than risking people feeling quite concerned or even potentially replaced?

Henrik: There are kind of two sides to the coin. There's one side that says, "Oh, AI is gonna take my job, and I'm gonna get fired." And the other side says, "Oh, AI is going to give me superpowers and give us all superpowers, and thereby give us better job security than we had before."

I think it's important to press on the second point from a leader's perspective. Pitch it as this is a tool, and we are entering a world where this tool is a crucial tool to understand how to use – in a similar way that everyone uses the Internet. We consider it obvious that you need to know how to use the Internet. If you don't know how to use the Internet, it's going to be hard.

"I encourage people to experiment, give them access to the tools to do so, and encourage sharing. And don't start firing people because they get productive."

I also find that people tend to get a little bit less scared once they learn to use it. It becomes less scary. It's like if you're worried there's a monster under your bed, maybe look under your bed and turn on the lights. Maybe there wasn't a monster there, or maybe it was there but it was kind of cute and just wanted a hug.

Creating a Culture of Safe Experimentation

Tenille: I've read that you encourage experimentation with AI through learning – I agree it's the best way to learn. What would you encourage leaders and team leaders to do to create a strong culture where teams feel safe to experiment?

Henrik: There are some things. One is pretty basic: just give people access to good AI tools. And that's quite hard in some large organisations because there are all kinds of resistance – compliance issues, data security issues. Are we allowed to use ChatGPT or Claude? Where is our data going? There are all these scary things that make companies either hesitate or outright try to stop people.

Start at that hygiene level. Address those impediments and solve them. When the Internet came, it was really scary to connect your computer to the Internet. But now we all do it, and you kind of have to, or you don't get any work done. We're at this similar moment now.

"Ironically, when companies are too strict about restricting people, then what people tend to do is just use shadow AI – they use it on their own in private or in secret, and then you have no control at all."

Start there. Once people have access to really good AI tools, then it's just a matter of encouraging and creating forums. Encourage people to experiment, create knowledge-sharing forums, share your own experiments. Try to role-model this yourself. Say, "I tried using AI for these different things, and here's what I learned." Also provide paths for support, like training courses.

The Right Mindset for Working with AI

Tenille: What would you encourage in team members as far as their mindset or skills go? Certainly a nature of curiosity and a willingness to learn and experiment. Is there anything beyond that that you think would be really key?

Henrik: It is a bit of a weird technology that's never really existed before. We're used to humans and code. Humans are intelligent and kind of unpredictable. We hallucinate sometimes, but we can do amazing things. Code is dumb – it executes exactly what you told it to do, and it does so every time exactly the same way. But it can't reason, it can't think.

Now we have AI and AI agents which are somewhere in the middle. They're not quite as predictable as code, but they're a lot more predictable than humans typically. They're a lot smarter than code, but maybe not quite as smart as humans – except for some tasks when they're a million times smarter than humans. So it's weird.

You need a kind of humble attitude where you come at it with a mindset of curiosity. Part of it is also to realise that a lot of the limitation is in you as a user. If you try to use AI for coding and it wrote something that didn't work, it's probably not the model itself. It's probably your skills or lack of skills because you have to learn how to use these tools. You need to have this attitude of "Oh, it failed. What can I do differently next time?" until you really learn how to use it.

"There can be some aspect of pride with developers. Like, 'I've been coding for 30 years. Of course this machine can't code better than me.' But if you think of it like 'I want this thing to be good, I want to bring out the best in this tool' – not because it's going to replace me, but because it's going to save me a tonne of time by doing all the boring parts of the coding so I can do the more interesting parts – that kind of mindset really helps."

Maintaining Code Quality and Shared Understanding

Tenille: Our team at Easy Agile is taking our steps and trying to figure out how AI is gonna work best for us. I put the question out to some of our teams, and there were various questions around people taking their first steps in using AI as a co-pilot and producing code. There are question marks around consistency of code, maintaining code quality and clean architecture, and even things like maintaining that shared understanding of the code base. What advice do you have for people in that situation?

Henrik: My first piece of advice when it comes to coding – and this is something I do every day with AI, I've been doing for about two and a half years now – is that the models now, especially Claude, have gotten to the level where it's basically never the AI's fault anymore. If it does anything wrong, it's on you.

You need to think about: okay, am I using the wrong tool maybe? Or am I not using the tool correctly?

For example, the current market leader in terms of productivity tools with AI is Cursor. There are other tools that are getting close like GitHub Copilot, but Cursor is way ahead of anything else I've seen. With Cursor, it basically digs through your code base and looks for what it needs.

But if it fails to find what it needs, you need to think about why. It probably failed for the same reason a human might have failed. Maybe your code structure was very unstructured. Maybe you need to explain to the AI what the high-level structure of your code is.

"Think of it kind of like a really smart intern who just joined your team. They're brilliant at coding, but now they got confused about something, and it's probably your code – something in it that made it confused. And now you need to clarify that."

There are ways to do that. In Cursor, for example, you can create something called cursor rules, which are like standing documents that describe certain aspects of your system. In my team, we're always tweaking those rules. Whenever we find that the AI model did something wrong, we're always analysing why. Usually it's our prompt – I just phrased it badly – or I just need to add a cursor rule, or I need to break the problem down a little bit.

It's exactly the same thing as if you go to a team and give them this massive user story that includes all these assumptions – they'll probably get some things wrong. But if you take that big problem and sit down together and analyse it and split it into smaller steps where each step is verifiable and testable, now your team can do really good work. It's exactly the same thing with AI.

Addressing the Code Review Bottleneck

Tenille: One of our senior developers found that he was outputting code at a much greater volume and faster speed, but the handbrake he found was actually their code review processes. They were keeping the same processes they had previously, and that was a bit of a handbrake for them. What kind of advice would you have there?

Henrik: This reminds me of the general issue with any kind of productivity improvement. If you have a value stream, a process where you do different parts – you do some development, some testing, you have some design – whenever you take one part of the process and make it super optimised, the bottleneck moves to somewhere else.

If testing is no longer the bottleneck, maybe coding is. And when coding is instant, then maybe customer feedback – or lack of customer feedback – is the bottleneck. The bottleneck just keeps moving. In that particular case, the bottleneck became code review. So I would just start optimising that. That's not an AI problem. It's a process problem.

Look at it: what exactly are we trying to do when we review? Maybe we could think about changing the way we review things. For example, does all code need to be reviewed? Would it be enough that the human who wrote it and the AI, together with the human, agree that this is fine? Or maybe depending on the criticality of that change, in some cases you might just let it pass or use AI to help in the reviewing process also.

"I think there's value in code review in terms of knowledge sharing in a large organisation. But maybe the review doesn't necessarily need to be a blocking process either. It could be something you go back and look at – don't let it stop you from shipping, but maybe go back once per week and say, 'Let's look at some highlights of some changes we've made.'"

We produce 10 times more code than in the past, so reviewing every line is not feasible. But maybe we can at least identify which code is most interesting to look at.

Ethical Considerations: Balancing Innovation with Responsibility

Tenille: Agile emphasises people over process and delivering value to customers. Now with AI in the mix, there's potential for raising some ethical considerations. I'm interested in your thoughts on how teams should approach these ethical considerations that come along with AI – things like balancing rapid experimentation against concerns around bias, potential data privacy concerns.

Henrik: I would treat each ethical question on its own merits. Let me give you an example. When you use AI – let's say facial recognition technology that can process and recognise faces a lot better than any human – I kind of put that in the bucket of: any tool that is really useful can also be used for bad things. A hammer, fire, electricity.

That doesn't have so much to do with the tool itself. It has much more to do with the rules and regulations and processes around the tool. I can't really separate AI in that sense. Treat it like any other system. Whenever you install a camera somewhere, with or without AI, that camera is going to see stuff. What are you allowed to do with that information? That's an important question. But I don't think it's different for AI really, in that sense, other than that AI is extremely powerful. So you need to really take that seriously, especially when it comes to things like autonomous weapons and the risk of fraud and fake news.

"An important part of it is just to make it part of the agenda. Let's say you're a recruitment company and you're now going to add some AI help in screening. At least raise the question: we could do this. Do we want to do this? What is the responsible way to do it?"

It's not that hard to come up with reasonable guidelines. Obviously, we shouldn't let the AI decide who we're going to hire or not. That's a bad idea. But maybe it can look at the pile of candidates that we plan to reject and identify some that we should take a second look at. There's nothing to lose from that because that AI did some extra research and found that this person who had a pretty weak CV actually has done amazing things before.

We're actually working with a company now where we're helping them build some AI agents. Our AI agents help them classify CVs – not by "should we hire them or not," but more like which region in Sweden is this, which type of job are we talking about here. Just classifying to make it more likely that this job application reaches the right person. That's work that humans did before with pretty bad accuracy.

The conclusion was that AI, despite having biases like we humans do, seemed to have less biases than the human. Mainly things like it's never going to be in a bad mood because it hasn't had its coffee today. It'll process everybody on the same merits.

I think of it like a peer-to-peer thing. Imagine going to a doctor – ideally, I want to have both a human doctor and an AI doctor side by side, just because they both have biases, but now they can complement each other. It's like having a second opinion. If the AI says we should do this and the doctor says, "No, wait a second," or vice versa, having those two different opinions is super useful.

Parallels Between Agile and AI Transformations

Tenille: You're recognised as one of the leading voices in agile software development. I can see, and I'm interested if you do see, some parallels between the agile transformations that you led at Spotify and Lego with the AI transformations that many businesses are looking at now.

Henrik: I agree. I find that when we help companies transition towards becoming AI native, a lot of the thinking is similar to agile. But I think we can generalise that agile transformations are not really very special either – it's organisational change.

There are some patterns involved regardless of whether you're transitioning towards an agile way of working or towards AI. Some general patterns such as: you've got to get buy-in, it's useful to do the change in an incremental way, balance bottom-up with top-down. There are all these techniques that are useful regardless. But as an agilist, if you have some skills and competence in leading and supporting a change process, then that's going to be really useful also when helping companies understand how to use AI.

Tenille: Are you seeing more top-down or bottom-up when it comes to AI transformations?

Henrik: So far it's quite new still. The jury's not in yet. But so far it looks very familiar to me. I'm seeing both. I'm seeing situations where it's pure top-down where managers are like "we got to go full-out AI," and they push it out with mixed results. And sometimes just completely bottom-up, also with mixed results.

Sometimes something can start completely organically and then totally take hold, or it starts organically and then gets squashed because there was no buy-in higher up. I saw all of that with agile as well. My guess is in most cases the most successful will be when you have a bit of both – support and guidance from the top, but maybe driven from the bottom.

"I think the bottom-up is maybe more important than ever because this technology is so weird and so fast-moving. As a leader, you don't really have a chance if you try to control it – you're going to slow things down to an unacceptable level. People will be learning things that you can't keep up with yourself. So it's better to just enable people to experiment a lot, but then of course provide guidance."

AI for Product Owners: From Ideation to Pull Requests

Tenille: You're very well known for your guidance and for your ability to explain quite complex concepts very simply and clearly. I was looking at your video on YouTube today, the Agile Product Ownership in a Nutshell video, which was uploaded about 12 years ago now. Thinking about product owners, there's a big opportunity now with AI for generating ideas, analysing data, and even suggesting new features. What's your advice for product owners and product managers in using AI most effectively?

Henrik: Use it for everything. Overuse it so you can find the limits. The second thing is: make sure you have access to a good AI model. Don't use the free ones. The difference is really large – like 10x, 100x difference – just in paying like $20 per month or something. At the moment, I can particularly strongly recommend Claude. It's in its own category of awesomeness right now. But that of course changes as they leapfrog each other. But mainly: pay up, use a paid model, and then experiment.

For product owners, typical things are what you already mentioned – ideation, creating good backlog items, splitting a story – but also writing code. I would say as a PO, there is this traditional view, for example in Scrum, that POs should not be coding. There's a reason for that: because coding takes time, and then as PO you get stuck in details and you lose the big picture.

Well, that's not true anymore. There are very many things that used to be time-consuming coding that is basically a five-minute job with a good prompt.

"Instead of wasting the team's time by trying to phrase that as a story, just phrase it as a pull request instead and go to the team and demonstrate your running feature."

That happened actually today. Just now, our CEO, who's not a coder, came to me with a pull request. In fact, quite often he just pushes directly to a branch because it's small changes. He wants to add some new visualisation for a graph or something in our platform – typically admin stuff that users won't see, so it's quite harmless if he gets it wrong.

He's vibe coding, just making little changes to the admin, which means he never goes to my team and says, "Hey, can you guys generate this report or this graph for how users use our product?" No, he just puts it in himself if it's simple.

Today we wanted to make a change with how we handle payments for enterprise customers. Getting that wrong is a little more serious, and the change wasn't that hard, but he just didn't feel completely comfortable pushing it himself. So he just made a PR instead, and then we spent 15 minutes reviewing it. I said it was fine, so we pushed it.

It's so refreshing that now anybody can code. You just need to learn the basic prompting and these tools. And then that saves time for the developers to do the more heavyweight coding.

Tenille: It's an interesting world where we can have things set up where anyone could just jump in and with the right guardrails create something. It makes Friday demos quite probably a lot more interesting than maybe they used to be in the past.

Henrik: I would like to challenge any development team to let their stakeholders push code, and then find out whatever's stopping you from doing that and fix that. Then you get to a very interesting space.

Closing the Gap Between Makers and Users

Tenille: A key insight from your work with agile teams in the past has been to really focus on minimising that gap between maker and user. Do you think that AI helps to close that gap, or do you think it potentially risks widening it if teams are focusing too much on AI predictions and stop talking to their customers effectively?

Henrik: I think that of course depends a lot on the team. But from what I've seen so far, it massively reduces the gap. Because if I don't have to spend a week getting a feature to work, I can spend an hour instead. Then I have so much more time to talk to my users and my customers.

If the time to make a clickable prototype or something is a few seconds, then I can do it live in real time with my customers, and we can co-create. There are all these opportunities.

I find that – myself, my teams, and the people I work with – we work a lot more closely with our users and customers because of this fast turnaround time.

"Just yesterday I was teaching a course, and I was going home sitting on the subway. It was a 15-minute subway ride. I finally got a seat, so I had only 7 minutes left. There's this feature that I wanted to build that involved both front-end and back-end and a database schema change. Well, 5 minutes later it was done and I got off the subway and just pushed it. That's crazy."

Of course, our system is set up optimised to enable it to be that fast. And of course not everything will work that well. But every time it does, I've been coding for 30 years, and I feel like I wake up in some weird fantasy every day, wondering, "Can I really be this productive?" I never would have thought that was possible.

Looking Ahead: The Future of Agile Teams

Tenille: I'd like you to put your futurist hat on for a moment. How do you see the future of agile teamwork in, say, 10 to 15 years time? If we would have this conversation again in 2035, given the exponential growth of AI and improvements over the last two to three years, what do you think would be the biggest change for software development teams in how they operate?

Henrik: I can't even imagine 10 years. Even 5 years is just beyond imagination. That's like asking someone in the 1920s to imagine smartphones and the Internet. I think that's the level of change we're looking at.

I would shorten the time a little bit and say maybe 3 or 4 years. My guess there – and I'm already seeing this transfer happen – is that coding will just go away. It just won't be stuff that we humans do because we're too slow and we hallucinate way too much.

But I think engineering and the developer role will still be there, just that we don't type lines of code – in the same way that we no longer make punch cards or we no longer write machine code and poke values into registers using assembly language. That used to be a big part of it, but no longer.

"In the future, as developers, a lot of the work will still be the same. You're still designing stuff, you're thinking about architecture, you're interacting with customers, and you're doing all the other stuff. But typing lines of code is something that we're gonna be telling our kids about, and they're not gonna believe that we used to do that."

The other thing is smaller teams, which I'm already seeing now. I think the idea of a cross-functional team of 5 to 7 people – traditionally that was considered quite necessary in order to have all the different skills needed to deliver a feature in a product. But that's not the case anymore. If you skip ahead 2 or 3 years when this knowledge has spread, I think most teams will be 2 people and an AI, because then you have all the domain knowledge you need, probably.

As a consequence of that, we'll just have more teams. More and smaller teams. Of course, then you need to collaborate between the teams, so cross-team synchronisation is still going to be an issue.

Also, I'm already seeing this now, but this concept of sprints – the whole point is to give a team some peace of mind to build something complex, because typically you would need a week or two to build something complex. But now, when it takes a day and some good prompting to do the same thing that would have taken a whole sprint, then the sprint is a day instead. If the sprint is a day, is there any difference between a sprint planning meeting and a daily standup? Not really.

I think sprints will just kind of shrink into oblivion. What's going to be left instead is something a little bit similar – some kind of synchronisation point or follow-up point. Instead of a sprint where every 2 weeks we sit down and try to make a plan, I think it'll be very much continuous delivery on a day-to-day basis. But then maybe every week or two we take a step back and just reflect a little bit and say, "Okay, what have we been delivering the past couple of weeks? What have we been learning? What's our high-level focus for the next couple of weeks?" A very, very lightweight equivalent of a sprint.

I feel pretty confident about that guess because personally, we are already there with my team, and I think it'll become a bit of a norm.

Final Thoughts: Preparing for the Future

Henrik: No one knows what's gonna happen in the future, and those who say they do are kidding themselves. But there's one fairly safe bet though: no matter what happens in the future with AI, if you understand how to use it, you'll be in a better position to deal with whatever that is. That's why I encourage people to get comfortable with it, get used to using it.

Tenille: I have a teenage daughter who I'm actually trying to encourage to learn how to use AI, because I feel like when I was her age, the Internet was the thing that was sort of coming mainstream. It completely changed the way we live. Everything is online now. And I feel like AI is that piece for her.

Henrik: Isn't it weird that the generation of small children growing up now are going to consider this to be normal and obvious? They'll be the AI natives. They'll be like, "Of course I have my AI agent buddy. There's nothing weird about that at all."

Tenille: I'll still keep being nice to my coffee machine.

Henrik: Yeah, that's good. Just in case, you know.

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Thank you to Henrik Kniberg for joining us on this episode of the Easy Agile Podcast. To learn more about Henrik's work, visit Abundly AI or check out his educational videos on AI and agile practices.

Subscribe to the Easy Agile Podcast on your favourite platform, and join us for more conversations about agile, product development, and the future of work.

In this episode, I was joined by Sherif Mansour - Distinguished Product Manager at Atlassian.

We spoke about styles of product management and the traits that make a great product manager. Before exploring the idea of a shared brain and the role of a product engineer.

Sherif has been in software development for over 15 years. During his time at Atlassian, he was responsible for Confluence, a popular content collaboration tool for teams.

Most recently, Sherif spends most of his days trying to solve problems across all of Atlassian’s cloud products. Sherif also played a key role in developing new products at Atlassian such as Stride, Team Calendars and Confluence Questions. Sherif thinks building simple products is hard and so is writing a simple, short bio.

Hope you enjoy the episode as much as I did. Thanks for a great conversation Sherif.