Easy Agile Podcast Ep.26 Challenging the status quo: Women in engineering

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Gerald shared that companies often face the same challenges over & over again when it comes to implementing agile, but the real challenge and most crucial is overcoming a fixed mindset.

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"Gerald helps massive companies work better together while keeping teams focused on people and on the customer. I'll be revisiting this episode."

Gerald also highlights the difference between consultants & coaches, and the value of having good mentors + more

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Transcript

Sean Blake:

Hello, and welcome to this episode of the Easy Agile Podcast. Sean Blake here with you today. And we've got a great guest for you it's Gerald Cadden a Strategic Advisor and SAFe Program Consultant Trainer at Scaled Agile, Inc. Gerald is an experienced business, an IT professional, Strategic Advisor and Scaled Agile Program Consultant Trainer SPCT at Scaled Agile. Thanks, Gerald. Welcome to the Easy Agile Podcast. It's really great to have you on as a guest today, and thank you for spending a bit of time with us and sharing your expertise with our audience on the Easy Agile Podcast.

Sean Blake:

So I'm really interested and I'm interested in this story that... For all the guests that we have at the podcast, but can you tell me a little bit about your career today? I find that people find their way to these Agile roles or the Agile industry through so many diverse types of jobs in the past. Some people used to be plumbers or tradies, or they worked in finance or in banking. How did you find your way into working at somewhere like Scaled Agile?

Gerald Cadden:

Good morning, Sean. Thanks for having me here guys. I'm very happy to be here with you guys today. Career things are always an interesting question. I'm 53 and so when I look back I wonder how do I get to where I am? And you can often look at just a series of fortunate events. And I worked in retail shoe stores and then I decided to do something in my life. Did an IT diploma then did a degree and I started working in the IT side. I pretty much started as a developer because that was where the money was and so that's where you wanted to go. I didn't stay as a developer long. Okay. All right. I was a terrible developer so I wasn't good at it. It was frustrating.

Gerald Cadden:

I moved into some pre-sales work and that led me to doing business analysis and I really liked the BA work because I got to work with people and see changes. I could work with the developers, still got to work really directly with the customer which was much more interesting for me. So I spent a lot of time in BA doing the development work, doing business process reengineering my transitioned over to rational unified process. When it was around spent countless hours writing use cases doing your mail diagrams, convincing people on how to make the changes on those. And then Agile came along and I had to make a complete brain switch. So all of this stuff that I'd learned and depended on as a BA suddenly disappeared because Agile didn't require that as an upfront way of working. It required that to be in the background if you wanted it and it was more a collaboration.

Gerald Cadden:

So about 2004, 2005 started working with Agile a lot more by this time I was living in the U.S. So that's where I got my agile experience, stayed there for a long time. Got great experience and then I moved over to working with SAFe around 2011. The catalyst for that as I was working for the large financial firm in New York with a team there. And we were redesigning a large methodology for them to implement Agile at scale. Went to a seminar in 2011 at an Agile conference saw Dean Leffingwell presentation on SAFe and just looked up and went, "Well we can stop working on our methodology. It's done."

Gerald Cadden:

So hardly after that meeting I ran outside and tackled Dean Leffingwell because I wanted him to look at my diagrams and everything and give me some affirmation that I was doing the right thing. Dean is got a very frank face and he pulled his frank face and he looked at me and just said, "You know what? Just use SAFe?" And I'm like, "Yeah, we will." And so I started my SAFe journey around that time and we implemented that financial company and I've been on that journey ever since.

Sean Blake:

So take us back 10 years ago to 2011. And you're working at this financial company, you've heard of this concept of SAFe really for the first time you started to implement it. How did the people at that company respond to you bringing in this new way of thinking this new framework? It sounded you already had the diagrams on the frameworks and the concepts forming in your mind did you find that an easy process? I think I already know the answer, but how complex was that to try and introduce SAFe for the first time into an organization of that magnitude?

Gerald Cadden:

Yeah, this is a very large financial firm, a very old financial firm so very traditional ways of working. So what's interesting is the same challenges SAFe comes up against today they're present before SAFe even began. And so the same challenges of the past management approaches trying to move to faster ways of working was still there. So as we were furiously drawing diagrams in Visio, trying to create models for people to understand it was hard to create a continuum of knowledge and education that would get people to move from the mindset they had to the mindset we wanted them to have. And it was an evolving journey for myself and the team that I was working with. I work with a really great guy and his name is Algona, a very, very smart man.

Gerald Cadden:

And so the two of us we're always scratching our heads as to how to get the management to change their minds. And we focused on education, but it was still a big challenge. I finished on the project as they started with SAFe. I moved to different management role in the company that we continued the work there. Michael Stump he used to work for Scaled Agile I think he works now at a different company, but he continued a lot of that work and did a really good job and they did implement SAFe. They made changes, but they faced all the same challenges. The management mindset overcoming moving away from the silos to a more network structured organization. Just the tooling, just the simple things was still a challenge and there's still a challenge today. So the nature of the organization is still evolving even in the modern day Agile world.

Sean Blake:

You mentioned there that part of the challenge is around mindset and education. Have you found any shortcuts into how you change a team's mindset? The way they approach their work, the way that they approach working with other teams in that organization? I assume the factor of success has a lot to do with, has the team changed their mindset on the way they were working before and now committed to this new way of working? And can you talk to us a little bit about how do you go about changing a team's mindset?

Gerald Cadden:

Maybe I'll change the direction of your question here, because what I've found is usually you don't have to work too hard to change the mindset of a team. Most of the teams are really eager to try new things and be innovative. You only come across some people in teams who may be their career path has got them to a certain point where they're happy with the way the world is and they don't want to change. The mindset you really need to change is around that leadership space and that's still true today. So the teams will readily adapt if management can create the environment that allows them to do it and if they can be empowered. But it's really... If you want to enable the team it's getting the leadership around them to change their mindset, to change the structures that are constraining the teams from doing the best job they can.

Gerald Cadden:

And so that for me was the big discovery as you went along and it's still true today. As Agile has been evolving I've noticed that people don't always put leadership at the top of the list of challenges but for me it's always been at that top of the list. A lot of people want to look at leadership and say things about them unflattering things, but you have to remember these are human beings. And the best way to come to leadership is to really begin with a conversation, help them understand. They know the challenges, but we need to help them understand what's causing the issues that are creating those challenges.

Gerald Cadden:

As you work with them and educate them you can to open their minds up a little more. Does that mean they'll actually change? Not necessarily. Political motivations, ideologies other things constrained leadership from moving. But conversations and education I think are the way to really approach leadership. And getting to know them as a person, take an interest in their challenges, take an interest in them as an individual. So create that social bond is an important thing. As a consultant that was always hard to do because as a consultant you're always seen as an external force and it's hard to build that somewhat social relationship with that leadership and build that trust.

Sean Blake:

Yeah, that's so true. Isn't it. I remember on an Agile transformation that I was on previously, how Agile coach really would spend just as much time with the leadership team as they would with us the Agile team. And it seems strange that the coach was spending so much time trying to really coach the leadership team on how they should think about this new way of working, but you put it in the right context there it's so important that they create that environment for their people and for their teams to feel safe in trying something new. Yeah, that's really important.

Gerald Cadden:

I think if you looked at how Agile evolves, when you look at the creation of the Agile manifesto and its principles and then the following frameworks like ScrumXP, et cetera it evolved from a team perspective. So everybody made the assumption that we needed to create these things for the teams to follow, but as people worked with teams they found that it wasn't the teams at all the teams adapt, but the management and the structures of the organizations are not adapting. And so that's really where it went.

Gerald Cadden:

I can't recall the number of countless Scrum implementations you worked on and you just hit that ceiling of organizational challenges. And it was always very frustrating for the teams. I think there's a an opposite side to that too is that too many in the Agile world just look at the teams as the center of the world and you can't approach it from that way either the teams are very important to delivering value to the customers, but it's the organization as a whole that delivers value. And I think you really have to sit back and just say, "The teams are part of that how do we change the organization inclusive of the teams?"

Sean Blake:

Okay. That's really interesting. Gerald, you've spoken a bit about teams and mindset, when you go into an organization, a big auto manufacturer or a big airline or a financial services company and they're asking for your help, or they're asking for your training, how do you assess where that organization is up to? What's their level of maturity from an Agile point of view? Do you have organizations that are coming to you who have in their mind that they're ready to go SAFe and then you turn up on day one and it turns out no one has any real idea about what that type of commitment looks like?

Gerald Cadden:

Yeah, it's a good question. Because I think as I look back at the history of this, in 2011, 2012 when SAFe really got going, as you went forward I mean, there was no concept of where to begin. Consultants were just figuring it out for themselves and like most consulting or most methodologies they got engaged in an IT space and at the team level. And people would try to grow from the team level upwards. And at some point we need to know I've struggled a lot with this because I was just trying to figure out where it is that. So my consulting hat was always on to sit down, talk to people about their challenges, find a way to help figure out how to solve the challenges whether it was going to be Scrum or SAFe or whatever is going to be right.

Gerald Cadden:

Those are just tools in the toolbox. But when Scaled Agile as I was working with... Excuse me, as I was working with SAFe, Scaled Agile brought out the implementation roadmap. It produced so much more clarity that came later in my time with SAFe and I wish it had come earlier because it really began to help me clarify that initial thing that we call getting over the tipping point. How to work with the organization you're talking to, work with the right people, understand their challenges, help them understand what causes those problems, which is the more traditional ways of working the traditional management mindset, help them connect SAFe as a way to overcome those challenges and begin to show them. If you looked at the roadmap it's this contiguous step-by-step thing, but what you find in reality is there are gaps between those steps and in those gaps is the time you as a transitional team are having lots of conversation with the management.

Gerald Cadden:

If you put them through a training class they're not going to come out of the class going, "Oh, wow that's it. We know what to do." It takes follow-up conversation. You have to have one-on-ones one on many conversations, cover topics of gains so you can remove the assumptions or sorry the misassumptions. So it's a lot of that kind of work that the roadmap its there for those who are implementing SAFe today use it. It is one of the most helpful tools you'll have.

Sean Blake:

Awesome. Yeah. I think just acknowledging the difference between the tools in the toolbox and then the other fact that you're dealing with humans and you're dealing with attitudes and motivations and behaviors and habits there's two very different things there really. It sounds you need to take them all together on that journey.

Gerald Cadden:

Yeah. A side to that we train so many SPCs like SAFe program consultants. We train them, training them out of classes all the time with us and our partners. The thing that you can, you can teach them about the framework, but you can't necessarily teach them how to be a good consultant or a good... I want to say I use the term consultant and coach, right?

Sean Blake:

Yes.

Gerald Cadden:

Sometimes I like to say a good consultant can be a good coach, but a good coach can't necessarily be a good consultant because there's another world of knowledge you need to have like how do you sit down and talk to executives? How do you learn the patients and the kinds of questions you need to ask, how do you learn to build those relationships and understand how to work the politics? So there are things outside the knowledge of an SPC that they need to gain. So young people coming in and running to do this SPC course I want to prepare you for everything, but it gives you the foundations.

Sean Blake:

So when you're in a organization or you're coaching people to go back to their organization how do you teach them those coaching skills so that when they come in and they've got to learn the politics, they've got to identify the red flags, they've got to manage the dependencies, they've got to bring new teams onto the train. How do you go about equipping that more human and communications of the toolbox really?

Gerald Cadden:

I think you can obviously teach the fundamentals of the framework by running through the training courses. But mentoring for me is the way to go. Every time I teach a training class I make it very clear to people when they go back and they're starting a transformation don't go this alone. Find experienced people that have done this and the experience shouldn't just be with SAFe their experience should be having worked with large organizations having experience with the portfolio level if necessary. Simply because there are skills that people develop over years of their career if they don't have at the beginning.

Gerald Cadden:

I mean, if I look back at some of the horrific things I had said in meetings and in front of executives my boss would put his hands up in front of his face because I was young and impulsive and immature and I see that today. So when I first came to the U.S I worked with some younger BAs and they would say things in a meetings and you quickly have to dance around some things to, "We didn't really want to say that right now." So I think mentoring is the skill. We can teach you the tactical skills, but teaching you the political skills, the human skills is something that takes mentoring and time.

Sean Blake:

Mentoring so important in that context. Isn't it?

Gerald Cadden:

Yeah.

Sean Blake:

Okay. So let's rewind 12 months ago to March 2020, a month that's probably burned into a lot of people's mind is the month that COVID changed our lives for the foreseeable future. I know that Easy Agile had a lot of content out there, articles about how to do remote PI Planning, how to help your virtual teams work better together and we didn't know that COVID was coming we just saw this trend happening in the workforce and we had this content available.

Sean Blake:

And then I was checking out our website analytics and we had this huge spike in what I assume were people in these companies trying to work out for the first time, how to do PI Planning virtually, how to keep very literally their release trains on the tracks in a time where people were either leaving the state, working from home for the first time, it's really like someone dropped the bomb in the middle of these release trains and people scrambling on how we are we going to do this virtually now? Did you have a lot of questions at the time on how are we going to do this? And how have you seen companies respond to those challenges?

Gerald Cadden:

Yeah. I remember being in Boulder, Colorado in January of 2020 and I just come back from vacation in Australia and that's when COVID was coming around and you were hearing about things in January, 2020. I was talking with my colleagues and we were wondering how bad this is going to be within two months the world was falling apart. And for us I think a good way to tell that story is to look at what Scaled Agile did. We knew our business that it was very reliant on our partner success and it still is today. And so as we began to see the physical world of PI Planning and training, as we began to see that completely falling apart the company had to quickly adapt.

Gerald Cadden:

We already had a set of priorities set for the PI and we implement Scaled Agile internally in the company. At the time we're running the company as a train itself because it's 170 all people. So they had to reprioritize the different epics, we pushed a new features and it was all about what do we need to change now to keep our partners afloat by getting them online and a really good team at Scaled Agile in a really cross-company effort to get short-term online materials created to keep the partners upright so they could keep teaching. They could find ways to do this, to do PI Planning, to do they're inspecting adapts all online. And so we pushed out a lot of material just simply in the form of PowerPoint slides that they could then incorporate into tools like Mural, Al tool. SAFe collaborate we went about developing this and we've been maturing that over time.

Gerald Cadden:

And so now we're in a world where we have a lot more stability. We saw a big dip like everybody else, but the question is, are you going to come out of that dip? And so what we did notice within probably even the second quarter of that year where the tail end of it we saw it starting to come up again, which our partners starting to teach more online. So the numbers told us that the materials we're producing were working. So for us it was just a great affirmation that organizing yourself the way we did organize yourself, the quick way we could adapt saved us. So Scaled Agile could have gone the way of a lot of companies and not being able to survive because our partners wouldn't have survived. We had the ability to adapt. So it's a great success story from my perspective.

Sean Blake:

Well, that's great. We're all glad you're still around to tell the story.

Gerald Cadden:

Yes we are.

Sean Blake:

And Gerald, whether you're reflecting on companies you've worked with in the past, or maybe even that internal Scaled Agile example you just touched on. Are there specific meetings or ceremonies or checking points that are really important as part of the Agile release train process? What are the things that really for you are mandatory or the most important elements that company should really hold onto during that really set up stage of trying to move towards the Scaled Agile approach?

Gerald Cadden:

So I interpret your question correctly. I think for me when you're implementing the really important things to focus on as a team first of all is the PI Planning. That is the number one thing. It's the first one people want to change because it's two days long and everybody has to come and it can cost companies a quite a significant sum of money to run that every 10 to 12 weeks. And so you will run very quickly as I had in the past in the car company you run very quickly into the financial controller who wants to understand why you're spending $40,000 a quarter on a big two-day meeting. And so they lie, they start questioning every item on the bill, but that's the most significant one.

Gerald Cadden:

PI Planning is significant. The inspect and adapt is the other one simply because at the end if you remove that feedback cycle, what we call closing the loop if you remove that then we have no opportunities to improve. So those two events themselves create the bookends what we get started with and how we close the loop, but there are smaller events that happen in between the team events are obviously all important. But more significant for me is the constant, the event for the product management team or program management team how are you going to filter them, excuse me.

Gerald Cadden:

Who are going to need to get together on a regular basis to ensure that then we call this the Sync. So this is the ART Sync or the POPM Sync. You need to make sure those are happening because those are these more dynamic feedback loops and ensure the progress of good architectural requirements or good features coming through so that when you get to PI Planning the teams have significant things to work on. So if you had to give me my top three events, PI Planning, inspect and adapt, and the ART Sync and product POPM Sync.

Sean Blake:

Awesome. I know there's always that temptation for teams to find the shortcuts and define the workarounds where they don't have to do certain meetings or certain check-ins, but in terms of communication it must be terribly important for these teams to make sure they're still communicating and they don't use the framework as an excuse to stop meeting together and to stop collaborating.

Gerald Cadden:

Yeah. I mean, I went through when I started implementing at the large car company in the U.S I decided to rip the bandaid off. They had several teams working on projects and they weren't doing well, when I looked at the challenges and decided we're going to implement SAFe some of the management they were, "Are you crazy? Why would you do this?" But they trusted me. And so we did rip the bandaid off and we formed them all into a not. We launched set up. And I remember at the end of the PIs some of the management have had a lot of doubts that were coming up after they sat through the PI and they said they just couldn't believe how great that was.

Gerald Cadden:

Even though the first PI was a little chaotic they understood the work and the collaboration, the alignment, just the discussions that took place were far more powerful for them. And teams were happier, they were walking out to a different environment. So it changed the mood a great deal. So I think the teams their ability to be heard in one of the most significant places is during PI Planning, they get that chance to be heard. They get that chance to participate rather than just be at the end where they're told what to do.

Sean Blake:

Mm-hmm (affirmative). So it really empowers the team.

Gerald Cadden:

Yeah. Absolutely.

Sean Blake:

That's great. So as a company moves out of the implementation phase and becomes a little bit more used to the way of doing things what's the best way for them to go about communicating that progress to the wider organization and then really evangelizing this way of working to try and get more teams on board and more Agile release trains set up so that it's really a whole company approach.

Gerald Cadden:

Yeah. A good question. So I think first of all the system demo that we do. So the regular system demos that take place, this is an event where you can invite people to. So when you get to the end of the program increment, the 10, 12, or the eight, 10 or 12 weeks and you're doing your PI system demo that's a chance for you to invite people that may be in the organization who are next on the list and they're going to be doing this, or they're curious, or if you have external suppliers who you're trying to get on board as part of the training have them come. Have them come to these events so they can just participate. They can see what goes on and it takes away some of the fear of what that stuff is. It gives them work much.

Gerald Cadden:

So the system demo whether you do it during the PI, but definitely the PI system demo and you want that one. So more ad hoc things and one of the things that I've seen organizations really fail to do is when they're having success the leadership around the train need to go out and I hate the term evangelize, but go out and show the successes. Get out and talk about this at the next company meeting present where they were and where they are now. But as part of that don't share just the metrics that show greater delivery of value show the human metrics, show how the team went from maybe a certain level of disgruntlement to maybe feeling happier and getting better feedback, show with how the business and technology have come closer together because they're able to collaborate and actually produce value together rather than being at odds because the system makes them at odds.

Sean Blake:

Awesome. Gerald is there anything else you'd like to share with our audience before we wrap up the episode? Any tips or words of encouragement, or perhaps some advice for those who are considering scaling up their Agile teams.

Gerald Cadden:

I think that the one piece of advice again, I'll reiterate back to the earlier point I made is as you are going through the implementation process and you're starting to launch your train and train your teams figure out how you're going to support them when you launch. Putting people through an SPC class or through all the other classes they won't come out safe geniuses. They'll have knowledge and they'll have the enthusiasm and have some trepidation as well, but you need good coaching. So figure out as you're beginning the implementation pattern where you're designing the teams et cetera, figure out what your coaching pattern is going to be. Hire the people with the knowledge and the experience work with a partner for the knowledge and experience. They shouldn't stay there forever if you work with consultants.

Gerald Cadden:

Their job should be to come in and empower you not to stay there permanently, but without that coaching and coaching over a couple of PIs your teams tend to run into problems and go backwards. So to keep that momentum moving forward for me it's figure out the coaching pattern. The only other one I would say too is make sure that you get good collaboration between product and the people who are going to be the product management role on architecture, get rid of the grievances, have them work together because those can stifle you. Get in and talk about the environments before you launch. You don't want funny problems when you, "Oh, the architecture is terrible." Okay. Let's talk about that before we launch." So just a couple of things that I think are really important things to focus on before you launch the train.

Sean Blake:

Awesome. I really appreciate that Gerald. I've actually learned a lot in our chat around. It's the same challenges that you had 10 years ago it's the same challenges that we have today. The really the COVID is the challenge of how do you focus on the mindset change. We've talked about the teams are eager to change. There might be a few grumbly voices along the way, but really it's about leadership providing a welcoming and safe environment to foster that change and the difference between being a coach and a consultant, the importance of mentoring. Wow we actually covered a lot of ground didn't we?

Gerald Cadden:

I may get some hate mail for that comment, but...

Sean Blake:

Oh, we'll see. Time will tell. Thanks so much Gerald for joining us on the Easy Agile Podcast. And we appreciate you sharing your expertise with us and the audience for the podcast. Thanks for having you.

Gerald Cadden:

Happy to do it anytime. Thanks for having me here today.

Sean Blake:

Thanks Gerald.

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.

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.

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