Easy Agile Podcast Ep.5 Andrew Malak, Chief Product Officer at Spaceship

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.

"Having gone through a cloud migration at Splunk, Greg share's some insightful key learnings, challenges and opportunities" - Chloe Hall

Greg Warner has been involved with the Atlassian ecosystem since 2006 and is a frequent speaker at Atlassian events. Greg has worked as a senior consultant for a solution partner, supported Jira and Confluence at Amazon, and in his current role at Splunk, executed a cloud migration to Atlassian Enterprise Cloud for over 10,000 of his colleagues.

In this episode, Greg and Chloe discuss the cloud migration journey:

📌 The mental shift to cloud migration and how to think beyond the technical side

📌 How to navigate the journey without a roadmap to follow

📌 The four pillars to success for your cloud migration journey

📌 Finding the right time to migrate & thinking about future opportunities    beyond your migration

📌 The unexpected value that can come from a cloud migration

+ more!

📲 Subscribe/Listen on your favourite podcasting app.

Thanks, Greg and Chloe!

Transcript

Chloe Hall:

Hey everyone and welcome back to the Easy Agile Podcast. So I'm Chloe, Marketing Coordinator at Easy Agile, and I'll be your host for today's episode. So before we begin, we'd like to acknowledge the traditional custodians of the land from which I am recording today, the Wodiwodi people of the Dharawal-speaking nation and pay our respects to elders past, present, and emerging. We extend that same respect to all Aboriginal and to Australia Islander peoples who are tuning in today.

Chloe Hall:

So we have a very exciting guest on the podcast today. This guest has been involved with the Atlassian ecosystem since 2006 and is a frequent speaker at Atlassian events. He has worked as a senior consultant for a solution partner, supported Jira and Confluence at Amazon and at his current role at Splunk, executed a cloud migration to Atlassian Enterprise Cloud for over 10,000 colleagues. So welcome to the Easy Agile podcast, Greg Warner.

Chloe Hall:

How are you?

Greg Warner:

Good, and thank you for having me.

Chloe Hall:

No worries. It's great to have you here today.

Greg Warner:

This is one of my favorite topics. We talk about cloud migration and yeah, I hope I can explain why.

Chloe Hall:

Yes, that's exactly what we want for you because I remember when we met at Team 22, you were just so passionate about cloud migration and had so many insights to share and I was very intrigued as well.

Greg Warner:

To give it a bit background about myself.

Chloe Hall:

Yeah.

Greg Warner:

I haven't always been a cloud person. So you mentioned before about being involved since 2006. I was involved early days with when Jira had the several different flavors of standard and professional, when you'd order an enterprise license for Atlassian and they'd send you a shirt. That was one of the difference between one of the licenses. So based a lot in the server versions, over many years. I looked at the cloud as being the poorer cousin, if you like.

Greg Warner:

I'd been to several Atlassian summits and later Team events where there was always things of what was happening in cloud but not necessarily server. I participated in writing exam questions for Atlassian certification program for both server and DC. For me, in the last 18 months, two years now, to make this fundamental shift from being certainly a proponent of what we do doing on server in DC to now absolutely cloud first and that is the definite direction that we as a company have chosen and certainly why I'm so passionate about speaking to other enterprise customers about their cloud migration journey.

Chloe Hall:

Wow. So what do you think it was that you were like, okay, let's migrate to the cloud, as you were so involved in the server DC part of it? What was it that grabbed your attention?

Greg Warner:

I joined Splunk in 2019 and it wasn't all roses in regards to how we maintained Jira and Confluence. It wasn't uncommon to have outages that would last hours. For two systems that were just so critical to our business operations to have that, I was kind of dumbfounded but I thought, hey, I've been here before. I have seen this. And so it was a slow methodical approach to root cause our problems, get us to a version that was in long-term support, and then take a breather.

Greg Warner:

Once we got to that point where we didn't have outages, we kind of think of what the future would be. And for me, that future was exactly what I'd done before, what I'd done at Amazon, which is where we would move all of our on-prem infrastructure, Jira, Confluence, and Crowd to public cloud, whether it would be a AWS or GCP, something of that flavor. I'd done that before. I knew how we were going to do that to the extent that I'd even held meetings in my team about how we were going to stand up the infrastructure, what the design was going to be.

Greg Warner:

But there was probably one pivotal conversation that was with our CIO and it was in one of those, just passing by, and he's like, "Greg, I've seen the plans and the funding requests." He's like, "But have you considered Atlassian Cloud?" Now, the immediate personal reaction to me was like, we are not going to do that because I'd seen the iterations. I'd seen it over time. I'd worked for a solution partner. I'd worked with customers in cloud, never really thought we could be enterprise-ready. So my immediate reaction was not going to do that. I said, "I'm not going to answer that question right now." I said, "I don't know enough to give you an answer."

Greg Warner:

And I'm absolutely glad I did that because I would've put a foot in mu mouth had I given the immediate response that was... So yeah, I took that question, went and did some analysis, spoke to our technical account manager at the time, and really looked at what had been going on and where was cloud today? Where was it in its maturity? And the actual monumental thing for me was that I think it's actually ready. People make excuses for why they can't do it, but there are a bunch of reasons why you should. And if we look at us as a company, with our own products that we are moving our own customers to cloud, and we are using cloud services, like Google Workspace and Zoom and a variety of SaaS applications. What was so different about what we did in engineering that couldn't go to cloud? And that was like, okay, I think the CIO was actually asking me a much bigger question here.

Greg Warner:

So the result of that was yes, we decided that it was the right time for Splunk to move. And that is a monumental shift. And I know there's a lot of Jira admins out there that are like, if you do this, you're putting your own jobs at risk. The answer is no, you're not. And even within my team, when we had we'd discussed this, there was emotional connection to maintaining on-premise infrastructure and were we giving our own jobs away if we do this? There's all those... No.

Greg Warner:

And there have actually been two people in my team that got actually promoted through the work of our cloud migration that otherwise wouldn't have because they could demonstrate the skills. But that's kind of like the backstory about how we decided to go to cloud. And I think as we are thinking about it, there is a mental shift first. Before you even go down the technical path about how you would do it, change your own mind so that it's open so that you're ready for it as well.

Chloe Hall:

Yeah, I love that. It's so good. And I think just the fact that you didn't respond to your CIO, did you say that?

Greg Warner:

Yep.

Chloe Hall:

That you didn't respond to your CIO straight away and you weren't like, "No, I don't want to do that." You actually stepped away, took that time to do your research, and think maybe cloud is the better option for Splunk, which is just so great and really created that mental shift in yourself. So when you say that your employees, like everyone kind of has that beef that, oh, we're going to lose our job if we move from on-prem to cloud and those employees ended up getting promoted. How did their roles change?

Greg Warner:

When we moved from on-prem to cloud, you no longer have to maintain the plumbing, right?

Chloe Hall:

Yeah.

Greg Warner:

You no longer have to maintain all the plumbing that's supporting Jira, Confluence, BitBucket, whatever is going to move. Now we thought that was the piece that's actually providing value to the organization. And it wasn't until we went to cloud, we actually realized it wasn't. Like what we can do now is different. And that's what my team has done. They've up-leveled.

Greg Warner:

So in the times since we moved from Jira, Confluence on-prem to cloud, we now get involved a lot more with the business analysis and understanding what our project teams want. So when someone from engineering is requesting something that has an integration or a workflow, we've got more time to spend on that than are we going to upgrade? Are we on the current feature release? Is there a bug we have to close? Log for J as a prime example where the extent of where we covered was logging a call with the Atlassian enterprise support and then telling us, "Yep, it's done."

Greg Warner:

Whereas other colleagues within the ecosystem that I spoke to spent a week dealing with that, right? Dealing with patching and upgrades. So the value for our team in the work we do has shifted up. We've also done Jira advanced roadmaps in that time. So we've been able to provide things we would've never got to because we're too busy to the plumbing, to the extent now that we have a very small footprint of on-prem that remains and that's primarily FedRAMP and IO5. It's not quite certified yet. It's going to get there. So we have a very small footprint and I'm the one who has to do the upgrades and now you look at it like, oh my god, that's going to be this couple-week tasks we going to do where I could do all this other better work that's waiting for us in cloud. You don't realize it until you have it removed how much you used to do.

Greg Warner:

And so we used to do two upgrades of Jira year and two upgrades of Confluence a year. We put that down to about a month's work of each. By the time you do all of your testing and you're staging and then do that. So you're really looking at four months of the year you were spending doing upgrades. We don't have that anymore. It's completely gone. And so now we make sure that we do things cloud first. We don't bring across behaviors that we were doing on-prem into cloud. So that's probably one thing we learned was that don't implement server DC in cloud.

Chloe Hall:

Yeah, that's so great. It seems like it's opened up a lot more opportunity for you as well. So I think something that I kind of want to look into and understand a bit more is that people focus a lot on the technical aspect of the cloud migration. What other aspects do you think need to be considered?

Greg Warner:

Certainly people. I mentioned at the very front here the mental mindset and that really started with my team, to get their mind around how we're going to do this cloud migration. There isn't necessarily yet a roadmap that says these are all the steps need to take to get ready for your cloud migration. So we had to invent some of those and one of those two was, what did we want to get out of the cloud migration?

Greg Warner:

I speak to other Atlassian customers. You talk about they're running a project, the project is the cloud migration, the start and the end is the cloud migration day. No, completely wrong. The cloud migration actually has a beginning, a middle, and an end. What you're talking about here, about this first changes is in the beginning, and that should be we're moving to cloud because it should be fundamentally better than what we have today.

Greg Warner:

If it's not better, there's no value in doing the activity. So we started with a vision and that vision was that all of the core things had to work from day one and they had to work better. So create issue, edit issue, up to issue, that just needs to work. There should be no argument whether it does or does not. That needs to work and work better. Create a page, edit a page, share a page. That stuff needs to work in Confluence without any problems. We also need to make sure that there are people in the organization who this could be a fundamental change of how they work, depending on how much they work with Jira and Confluence. So appreciating that there is some change management and some communications that needs to be ready as you do your cloud migration to ensure that your vision is going to work, but also acknowledging you will break some things. You're not going to be able to do a cloud migration and shift you from A to B without nothing.

Greg Warner:

It will go wrong. So we were aware of that and for that, what I would always tell people was that we're really fixed on the vision of making it sure it's better than it was today, but flexible on the details, how we get there. We will probably find different ways as we go along because things will change. Cloud changes itself. You'll discover things you didn't know before. There was a Jira admin that made a decision 10 years ago, you now found that. So yeah, very, very fixed on that vision that day one that we had to have this unboxing experience that when people got to use Jira and Conference Cloud for the first time, they could see why we'd spent so much effort to make sure it was polished and things just worked. And as you went a bit further out, there might be things to do with apps that might not be quite the same.

Greg Warner:

That's okay. And then further out, things you just ultimately can't control. And for that, we had 76 integrations of teams that had written automations from all over the company. We're never going to get to find out what they do, but we knew that some of those would probably break. And so just dealing with some change control and allowing those people to know this is coming, what the rest endpoints will be, how to set up their API keys. We did a lot of that, but we did have one integration that broke and that integration broke because the entire team was on PTO or leave that week. We can't avoid that one. But it was good to see other teams actually jumped in because they'd been involved in updating theirs to go help fix that. So that was okay. We had one integration that we really gave the white glove support to and that was for... We have a Salesforce to Jira integration that's a revenue-generating integration.

Greg Warner:

We gave that a lot of attention to make sure that just worked. But the 76 others, we provided a runbook. The runbook was essentially teams, you do things like this. So they knew how to change and update to the new system. But yeah, certainly the beginning, middle and end. The beginning is all those shifts that you're going to have to change and probably some history about design decisions. The middle is in fact your cloud migration and the end, middle to the end is everything you do with it afterwards. So that's where the real value comes from in your cloud migration. It's once you're in, what can we do with it?

Greg Warner:

And we are towards the end of that now. There have been things that I couldn't have planned for that people have done. So we did your advanced roadmaps, saving the forest there, but also we're encouraging our staff to extend the platform. That used to be really difficult and we've worked with Atlassian to understand what should that look like? And we've settled on using it Atlassian Forge. And so now we have our first app this week, in UAT, in Atlassian Cloud to solve business problems that we have. That's a custom Atlassian Forge app. And we're encouraging our engineers to build those and so they can extend and get that real value through the cloud migration.

Chloe Hall:

Yeah, wow. You've come so far and it's nice to hear that you're towards the end of it and all the opportunities are coming with it and you're seeing all the value. It's all paying off as well. I think I just want to go back to that moment where you talk about there isn't essentially a roadmap outlay. There isn't someone or something to follow where it says this is where you need to start. These are the steps to cloud migration. And I think a lot of people, that's what they fear. They're like, we're not sure exactly where to start. We're not sure what roadmap we'll follow. How do you navigate that in a way?

Greg Warner:

So I get back to that when I talked about the vision. We said we're fixing the vision flexible details. Early on when we signed for cloud migration, it was in the first week after we'd signed for it, that same CIO asked me, "Greg, what's our date? When are we moving? Because you've sold me that this is so much better. Where's the action? When are we get this?" And we took a good six weeks after we signed to actually understand the tooling that's available. So for Jira, there's really two options. There's the Jira site import and the Jira cloud migration assistant. And on Confluence side, there's one that's called the Confluence cloud migration assistant. Better kind of understand how those technologies work. And for a couple weeks there, my team actually considered if we did the migration ourself, we could probably save the company a bunch of money and we would own it.

Greg Warner:

We would know how this thing worked. We got about four weeks in and decided that was a terrible idea. Do not do that. Any enterprise customers I talk about that say we're going to do it ourselves, do not do that. Do not do that. And part of the reason is that there's really four pillars to success for your cloud migration. Jira migration, Confluence migration, apps, and users. And we did not know how to do apps and users and we probably could have gotten away with Confluence and Jira. But we said, look, this is something that we actually need to have a partner involved. And so we did ask for partners to provide their way of doing it, knowing what they knew about us. And we did provide as much detail as we can. We had two partners actually provided completely different methodologies how to get there.

Greg Warner:

So this is that flexible on the details, but we really had to make a decision on what worked for us. So when it really came down to Jira, would we do a big bang approach and just switch it over in the course of a weekend or did we want to do cohort by cohort over time? And we decided for us, because we are a 24/7 organization that's supporting our customers, doing the big bang switchover, that was the best way to do it. So that's one of the reasons we chose the partner we did. But that partner didn't necessarily have a roadmap of where they want to go. But we did then explain what we want to get out of this. That was the first thing, was about it needs to happen on a weekend. So that then filters down what your choices are. The ecosystem apps part is really important to make sure that one, there may have been apps installed in your system that have been there for 10 years and you're not sure why they're there anymore because it was four Jira admins ago.

Greg Warner:

Nobody knows what's there. But if they don't have a cloud migration pathway, you really should consider they're probably going to hit their end because there is no equivalent. So you can rule them out. Identify the ones that do have a business process with them. And for that, Salesforce for us, we had to find a cloud-first connect that would work. So that meant that we knew that was going forward. But really, I think the key thing that we invented that we didn't know about was that we created this thing called an App Burn Down. And that's where we looked at all the apps we had. We had about 40 apps. We said, okay, which ones are not going to go to cloud? Which ones don't have a migration pathway? Which ones are going to replace something else? And so we started to remove apps over the course of about three months.

Greg Warner:

So people would see that we're starting to get away from on-prem design decisions and old ways of doing things. But we also said, but once we get to cloud, this is the pathway out of it. So that we said, look, we're going to turn this app off but you're going to get this one instead, which is the cloud-first app. So people could see how we're going to make the jump over the river to get there. But it meant that we would, over time, identify apps that weren't used. If we turned them off and nothing happened, it's fine. But also we did come across some where they were critical to a business use. And so if we didn't have an answer for those yet, it gave us time to find one. And with your user base, typically it's your colleagues, that's going to be your most critical customers. They're going to ask, okay, you're turning it off. When do I get the functionality back?

Greg Warner:

And by doing that App Burn Down over time, that does buy you time to then have that answer. So it's a much easier conversation than I'm simply turning off functionality, I don't have an answer for you yet. There are things like that. It wasn't necessarily a roadmap, but working with a solution partner is absolutely the right way to go. Don't try and do it yourself. They also work with Atlassian and they have far better reach into getting some of these answers than you can possibly ever have. And I have on at least three different occasions where our solution partner did go and speak directly with an ecosystem partner to find out what's the path forward. How can we make this work? So it is good. The migration is really a three-way collaboration between yourself, your solution partner, and Atlassian. And you all have the same goals. You want to get to cloud and it does work really well.

Chloe Hall:

Wow. Yeah. So sounds like hope everyone got that advice. Definitely don't take this on your own. Reach out to solution partner. And I really like how you said you went to two different solution partners and you found out what their ideas were, which ways they wanted to take you, so you could kind of explore your options, work out what was the best route for Splunk. And it's worked very well for you as well. Having that support I think as well. Yeah. Sorry, you go.

Greg Warner:

The choice of the partner is really important and it's probably one of the earliest decisions that we made to get that one right. And I remember several times thinking about, have we got the right people on board? Did we speak to... And it was an interview process to the extent that when we had our final day after we'd been working with Atlassian and with our partner for six months, one month after our migration was completed and we're all done, we had one final Zoom call with all of us and took a photo and did that. But it kind of felt like a breakup, to be honest, because we'd been in each other's faces for six months and working. We're now all saying goodbye. We might not see each other. It was like the weirdest feeling. But it did work. And so yeah, it is a real fundamental choice.

Greg Warner:

Just take the time, make sure they understand what we want to do, make sure you understand how they're going to do it. But yeah, if we have done it ourselves, we would've got ourselves all caught up in knots, wouldn't have been a successful migration or so. I'm a technical guy. I want to solve it. I want to be like... But I think the actual right answer was no, you don't need to know how this works 100% because you're going to do this hopefully just once. And so focus on the real business value things about dealing with stakeholders and the change and making design decisions that are really important for you because you're going to own those probably the next decade rather than worrying about how do I get my data from A to Z?

Chloe Hall:

Yeah. It definitely would've felt like a breakup for you because you would've been working side by side for so long, dealing with so much. Are you still in contact with them or...

Greg Warner:

Yeah, we had this fundamental thing we always said is we're always, if there's a problem, we're always cautiously optimistic, we're going to solve it. We did engineering challenges that we went through, but I did say right early on is, the ecosystem is only big and we're all going to bump into each other at some point. So yeah, let's make sure that we're still friends at the end of this. And I didn't realize how important that was until later when I was in New York for Christmas and I arranged to meet the project manager that worked for us. She lives in New York, so how about I meet you so... So we met each other at the hotel and she's like, "I have never met a customer outside of work to do this." Yeah, I gave the story about it felt like a breakup, but she did say that at the beginning you said we'll be friends after.

Greg Warner:

Yeah it is because it can be really hard. I've been on the consultant side where you kind of have to have some hard conversations and sometimes... You want to make sure that everyone understands the problem. You're trying to make it better so that at the end of it, you can still be friends like that. That is the thing. There probably will be engagements later on that you might need them again. So you want to make sure that you have your choice of best in breed partner to choose from. You have those relationships. They understand what you want to choose. So yeah, it is really important to choose the right partner. Don't necessarily based on price but choose the partner that's going to work for you, understands what you're trying to get out of your cloud migration and they'll be there in the future when you need them for another cloud migration or a much more gnarly project. Try and be friends at the end of it.

Chloe Hall:

And definitely it's good that you have that friendship now because they have that understanding about your business and what you want and the value of it. So if you do need help again, it's a lot easier to bring them on board straight away. So now that you've performed a cloud migration and you're coming towards the end of it, do you look at the process any differently to when you were at the very beginning?

Greg Warner:

Yeah, I thought we were just executing a data migration just yeah, on-prem to cloud.

Chloe Hall:

Yeah.

Greg Warner:

Pretty straightforward, nothing big. I was pleasantly surprised as we're making some of these decisions as we went along, that it was more than that. There were business processes that we could improve. There was the beginning, the middle, and end. I didn't realize that until actually after the end. So when we did our cloud migration, it was actually the week before Thanksgiving in the US. It was November 19. And even that decision was made in just going for a walk at lunchtime. When should we really do this? And I kind of came down again, spoke to my project manager and said, "How about we do this in the cloud migration the week before Thanksgiving?" Because 50% of our workforce is located in the US and a large proportion of that will be on leave or PTO before.

Greg Warner:

So by doing it over a weekend before then we're ensuring that... Like when you open a new restaurant. You don't want to have all of your tables full on the first night. We knew that we were going to have everybody using Jira and Confluence day one after a migration because we're going to break some stuff. They actually turned out to be really exceptionally good idea. And I encouraged people to find... Look at your data and work out when is low time to do this? I've been involved in Jira and Confluence for a long time and just thought it's task tracker and it's a wiki. There's nothing there that I don't really know about. But one of the decisions we made was actually that when we completed the data migration and it was ready to go, I always said if we waited, do we get a better result? And the answer was no.

Greg Warner:

We should make this available to people now. And so we opened it up on a Sunday morning in the US, which was starting to be business hours in Australia. We started making teams aware that they can now go ahead and use Jira and Confluence. And it was the feedback that we immediately got from those teams that were starting to use Jira service management in cloud for the first time, about, "Wow, this is so much better than it was on-prem." And people said, "I can actually see the attention to detail you've made on fields and descriptions and the changes you've made." And it started to impact people's workday that this was better than it was. I didn't expect that to come back. And so I have a montage that we share with the team of all these Slack messages from people saying, "This is really good. This is much better than we had before."

Greg Warner:

What I didn't also realize is that when we moved from on-prem to cloud is the data that we had became more usable and accessible. Hadn't planned that. It seems obvious now, but when we put it in cloud and it has all the security controls around it and now no longer has the requirements of things like VPN to get access to it, people could build new things to use it to be able to interact with your issues, to interact with pages. And so we started with 76 integrations and over space of three months now we had this big jump in the first three months up to about a hundred something and now we're going to Forge And what it means is people who have had this need to be able to get to the data can now get to it. I didn't see that coming. I just thought we were just server cloud. But yeah, having a more accessible has led to improvements in the way that our teams are working but also how they use it in other applications that just simply wasn't available before.

Chloe Hall:

Yeah. Wow. That's great. And it's good that you were able to receive that feedback straight away from the teams that you had in Australia. I think that's really good and it sounds like it's created such a good opportunity for you at Splunk as well now that you're on cloud.

Greg Warner:

Yeah, it's certainly a business leader that can propel you forward and I eagerly come in now and look at what are other teams going to do with it. And so when we had the first team that said they want to build a Forge app, I'm like, Sure. We should not discourage that at all. Extend the platform. That's why we spent the money and time to do it. What can you do with it now? And we did certainly make Atlassian aware on the product side, like how we're using it and where we'd like to see improvements. If you look at the server DC comparison, I used to be that person that would look at the new features in cloud and ask that question about, when is that new feature coming to on-prem? To going to being that customer who's now, I have that feature today, right? And I'm using it because we don't wait for it.

Greg Warner:

So you mentioned about things you didn't plan from the roadmap. There are design decisions that I talk to enterprise customers that I need to make aware of about. One of them is to do with release tracks. In enterprise cloud, you can choose to bunch up the change to cloud and then they get released periodically every two weeks, every month. When I looked at that, came back to one of our principles about don't implement server in cloud, why would we do that? Atlassian has far more data points on whether this works for customers at scale than we do. So why would we hold back functionality? So as a result we don't do release tracks. We let all of the new functionality get delivered to us as Atlassian sees fit. And the result of that is our own engineering staff, our own support staff who use Jira, get the notifications about new products and features and this is fantastic.

Greg Warner:

Again, why would we implement server, which is where you would bunch up all your changes and then go forward? The other thing too about our cloud migration journey is don't be blinked that you're just doing a cloud migration today and then the project ends. There are things you need to be thinking about as you go along, but what's the impact in the future? So for us, we have multiple sites. Enterprise customer have multiple sites. So there are design decisions that we've made so that we can, in the future, do cloud to cloud migration. You will move sites. Your organization could be bought or could be buying companies. So you do mergers and acquisitions. And so as part of that, we have some runbooks now that talk about using the cloud-to-cloud tooling so we can move a Jira project from a site here to a site there, how we'd move users here and users there.

Greg Warner:

And that actually came about through the assistance with our TAM, not focusing just always on the cloud migration date but also what's that look like six months later? What's it look 12 months later? So that you don't perform your cloud migration and then lock yourself in a corner that later on now I have to unwind something. I had the opportunity to fix it. So yeah, I do encourage migration customers to also think six months, 12 months beyond their cloud migration. But what could also happen and then speak to your solution partner about design decisions today that could affect you in the future.

Chloe Hall:

Yeah. So you definitely need to be thinking future-focus when you're doing this cloud migration. I know you've addressed a lot of the opportunities that came out of the cloud migration. Was there anything else that was an unexpected value that came from it that you wanted to share?

Greg Warner:

The other value is make it more accessible. We have seen people use it in different places that we hadn't thought about. So some of the things that we were doing before, we had to have a company-owned asset to get on the VPN and just things like that. That actually restricted people in where they could do work. Whereas now we've, as long as you've got a computer or mobile device connected to the Internet, absolutely you can use a mobile device support, you can get access to it. Approvals that used to be done on a computer are now done on a mobile device. Those things. But I think the integrations has been probably been the one thing I'm most... We're not the catalyst. We kind of pushed it along but seeing people get real use out of it and using the data for other purposes. We have seen people build some microservices that use the data from Jira that we couldn't do before. Again, you're just unlocking that potential by making it more usable and accessible.

Chloe Hall:

After going through the whole migration journey and, like you said, you're coming towards the end of it, what were the things that stood out to you that you're like, okay, they didn't go so well? Maybe if I was to do this again, how would I do this better next time?

Greg Warner:

So I get back to that day one unboxing experience. You know you want to give it that best experience. And we delivered that for people in Australia and APAC as we opened it and they got to use Jira for the first time and it worked fine. And that is mainly the result of a lot of emphasis on the Jira piece because we said, we know this is going to be hard. It's got workflows, issue schemes, notifications schemes. This is going to be hard.

Greg Warner:

So we started that one really early and then probably about 60% down through our migration journey, we started on Confluence. We thought how hard can Confluence be. It's a bunch of spaces and pages. It can't be that hard. We actually hit some migration challenges with the engineering tooling with Confluence, which meant that the Confluence UAT was delayed. The Jira UAT was fantastic. Ran for a month. We found some problems, got fixed, got answers. We were really confident that was going to be fine.

Greg Warner:

And then we hit this Confluence piece. We're like, wow, this is going to be a challenge. And there was at least one time I could think of. It was a Saturday morning at breakfast where our solution partner sent me a Slack message about, I think we've got a problem here with some tooling. What are we going to do? Towards the middle of the day, I was kind of scratching my head. This could be a real blocker. We actually worked with Atlassian, came up with the engineering solution, cleared that out. That was good to see, like in the space of 12 to 24 hours, there was a solution. But what it meant was that it delayed the Confluence UAT and it made a week. And there was something we found to do with the new Confluence editor and third-party apps right at the end of that week. And we had to really negotiate with our stakeholders to make this go ahead.

Greg Warner:

Because again, if we'd waited, we'd get a better result. No, we really should go. We know that there's this problem. It's not system-wide but it affects a small group people. So we did it. But for about a hundred people they have this really bad Confluence experience because of this thing. And so for me, I couldn't deliver on that thing I promised, which was a day one experience that was going to be better than what it had before.

Greg Warner:

Now we did work with Atlassian and app vendors to get some mitigation so it wasn't as bad on day five. It wasn't day one but it wasn't perfect. But I would certainly encourage people to make sure that you do treat Jira and Confluence with as much importance as each other. They do go together. When I did our cloud migration, we did it on a weekend and I remember coming back after dropping my kids at school on Tuesday and sitting in the car park. I was like, wow, we actually pulled that off.

Greg Warner:

If we'd propose to the company to move your company email system and your finance system on a weekend, the answer would be no because it's too big a hat. But what we'd said is we're going to move all of our Atlassian stack in a weekend, which really is two big systems, Jira and Confluence. So if I had the time again, we would've started Confluence much, much earlier and then we wouldn't have the need to rush it at the end. And that really did result in a bad day one experience for those people. We have worked with Atlassian since then. We're getting that resolved. We know other Atlassian guys have the same problem. I would start early and don't underestimate the complexity that could happen. There will be some things outside of your control.

Greg Warner:

I talk about this Confluence problem and the migration tooling, which is actually do at scale. Not every customer will see it. We saw it, I conducted customer interviews when we were doing our solution partner decision and the customer actually told me this. Like I should have started Confluence because we had this problem, we wasted some time, and we did it. I even have my notes. But it wasn't until later, same problem, you even had the answer and they told you and you still waited. So I'm spending a few minutes on this podcast talking about it because it happened to me. It's probably going to happen to the next person. So if I could do one thing and that is just encourage you to start it earlier. You're going to end up with a much, much better migration and hopefully can deliver on that day one experience that I couldn't do.

Chloe Hall:

Yeah, no I'm so glad that you've shared that with the Easy Agile audience as well because now they know and hopefully the same mistake won't keep getting repeated. Well, Greg, my final question for you today, and I don't know if you want that to be your answer, but I think it's really good just for the audience, if there's one key takeaway that they can go away with them today from the podcast, what would be that one piece of advice for everyone listening to start their migration journey?

Greg Warner:

The first thing to do is to prioritize it. So if you're an Atlassian customer that's using on-prem Jira or Confluence and you don't have a timeline and you don't have a priority to your cloud migration, start there. Open up the task, which is start to investigate Atlassian Cloud and choose a date. Because yeah, there will come a situation down the track where you might be asked by your CIO and so it's better to have an answer prepared already. I would encourage people to start to look at it because it is the future. If you look across the industry, people are moving to SaaS. It's really a question. Do you want to maintain and be that customer wondering when that feature's coming to cloud or do you want to be that customer in cloud who has it today? We have seen a monumental shift to when we moved to cloud in functionality, availability, all the good things that cloud delivers. And it's one of the biggest promoter... The person that used to write exam questions for servers now saying go to cloud.

Greg Warner:

Absolutely. So when I've spoken to other enterprise customers, particularly at Team, I said like, when do you plan your cloud migration? I was like, wow, we're going to start it in three years. I'm like, three years? You need to go back to the office next week and start like 12 months because yeah you will... There is absolutely a competitive advantage to doing it. And it's not just me being now as biggest cloud opponents. We see it, we see it every day and for me, this is one of the most influential projects I've been involved in with Atlassian since 2006. This one here is going to have a long-lasting effect at Splunk for a long time and I'm happy to speak to yourself at Easy Agile and others about it and here at their cloud journey because I want to go to Team next year. I want to make sure we have these conversations in the whole way about, I got that one thing. It's either I started my Confluence migration earlier or I actually put in a timeline of when we should start our cloud migrations.

Chloe Hall:

Yeah, beautiful. That is some great advice to take away, Greg. And so honestly, thank you so much for coming on the podcast today. You have provided some brilliant insights, takeaways, and also because there is no roadmap, I feel like your guidance is so good for those who are looking to start their cloud migration. Yeah. We really appreciate you sharing your knowledge.

Greg Warner:

All right. Thanks for having me on. Thank you for listening.

Chloe Hall:

No worries.