A (2:54)

Absolutely. So one of the first things that we think about organizations, we are talking about organizations that somehow depend on technology or have a technology function. And what I talk a lot in the book is about how to structure the technology function because it's really challenging for most organizations to think about how to hire, how to think about the connection between the technology function and the rest of the company and how to leverage the resources that they have in the company to not only speed up processes, but really to rethink how the processes will be done better in a tech way, in a tech first way. And what I talk about also in the book is innovation and how to create a culture that also supports innovation.

B (3:46)

Yeah, yeah, well, and I think this is getting to be more and more important even for those companies that still don't think of themselves as a software company or a technology company. I know the quote from years and years ago, software is eating the world comes from a couple decades ago. I think a lot more companies are, are needing to think like this. So let's talk, let's get to definitions here. And, and I'd love to hear how you define this platform mindset and what makes it different from some of the more traditional ways of thinking about an organization.

A (4:22)

The main thing to, to start with is that platform for me is a software platform. So it's a piece of code that, or a system that encapsulates co complexity. So for instance, if you think about, you have your company and you have to authenticate users to talk to your service. So then you have to use an authentication service and let's say you have like four or five services. What I've seen in many organizations is that they create four or five ways of authenticating users and they very quickly get in trouble because then they cannot even identify what is a unique user. If the same user is using multiple services, there is no way to correlate that that's the same person and consolidate the data. And then when we start thinking about platforms, how can we think about these pieces of functionality that are common, that can be encapsulated and reused multiple times by different organizations and then gain agility and promote like more uniformity across the company so that a central technology team can, can be more leaner and, and empower the whole organization.

B (5:40)

Yeah, yeah. And so in your book, a platform Mindset, you talk about this and as well as the, the multiplier effect that that approach provides. Can you explain, you know, what, what do you mean by the, the multiplier effect? And, and maybe share an example where adopting a platform mindset led to a breakthrough or in innovation and, or efficiency.

A (6:03)

So. So, Greg, one of the stories that I talk in my book is when Satya Nadella assumed CEO in Microsoft, like many years ago, we had, like many computing platforms in the company. So we had the Windows team building their own, their own infrastructure, the Office team building their own, their own infrastructure. We had Bing and we had Azure as an emerging platform. But internally, not everybody was adopting Azure and there was not really clean communication between all the owners, business owners, between these different parts of the organization. And one of the things that I experienced firsthand, like, seeing Satya operate is that he started creating a culture that fostered collaboration across the teams. And then very quickly, what was taboo, like saying, like, oh, Bing is going to adopt Azure or Office is going to adopt Azure. It started becoming more and more natural. And then we evolved to organize a company around this concept of one Microsoft. We have one platform that will power the company and regain a lot of agility by doing so. And, and what it takes to create this multiplier effect, like this idea that I'm encapsulating a whole bunch of code, a whole bunch of processes, reducing the multiple teams into a single team, and then using this combined platform, this combined team to power lots of experiences in the company. And that's what I call the multiplier effect. You build it once, reuse it many times, and you get more agility because you can innovate and build more differentiated solutions for users while reusing platforms that will enable you to have that agility and leverage the engineering resources a lot better.

B (7:59)

Yeah, yeah. And that's definitely when that comes together, that's a powerful approach. And certainly Microsoft is an example of how that, how that mindset can come together. And that said, though, you know, any organization, I mean, Microsoft certainly is not a small company. Lots of moving pieces, lots of teams. I'm sure there was a lot of, a lot of things that needed to change along the way. And I think a lot of organizations struggle with a transition to a platform, even the mindset, let alone on the execution. And in your experience or what you've seen, what are some of the biggest roadblocks that an organization or team might encounter? And how can leaders help to overcome some of those roadblocks to foster this cultural shift?

A (8:46)

Yeah, this is a great question, Greg, because I feel, although there are some technical roadblocks to adopting platforms, and then I can go over them in a little bit, but by far, I think I've seen that more often than not, the roadblocks are more cultural than, than technical. And this idea of, like, teams are used to operate in a way that they have a lot of autonomy because they control the full stack. They control the whole. If I am an Office365 business owner and I control my whole stack, I don't need to depend on anybody else. I have full control of my destiny. But if you come to me and say, now, Marcus, you cannot do that because you have to depend on this new platform there, which is called Azure, it will be more efficient. But then now we have to coordinate with them and say, oh, I don't want to coordinate with them. And it's not only coordination, it's trust and it's collaboration. And it's like joint timelines to deliver products and joint commitments to all the key stakeholders. And building this culture of trust and culture of collaboration, I think is really the key. That's why even the subtitle of my book is Building a Culture of Collaboration. Because I think in addition to all the technical roadblocks, creating these mechanisms that allow people to collaborate are really key. And just to give a concrete example, one of the things that Satya did really early on in his CEO tenure at Microsoft was he changed the evaluation at the end of the year, the performance evaluation for every employee, so that everyone now had to write, how did you empower others? Right. How did they contribute to the success of others? And this seems like a small thing, right? Like it's a line, that paragraph that you write in your review form, but is like, directly tied to your compensation and directly tied to the direction you wanted to get to guide the company. And that was, in my opinion, I think, very fundamental for making people more open to collaborate. Because they say this is so fundamental for the success of Microsoft that my compensation and every vice president compensation will be tied to we collaborating and we're building something together.

B (11:14)

Yeah, that's really powerful. And to your point, it's really not just talking about being more collaborative. It's putting something behind that and emphasizing it in a big way. Because back to your other point as well, I think when it comes to technology, I think a lot of companies think that they're going to implement some platform or tool or thing and it's going to solve all of their people and process challenges or hurdles or things. But quite often it's the people and processes that help it make either be successful or run into more roadblocks or things like that. And so I think that cultural Shift is so powerful. I know another aspect sort of maybe compounding things right now is just the pressure for a lot of teams and from a lot of stakeholders to adopt AI and kind of navigating the challenges with that and really the unprecedented demands that it places on infrastructure. So how is a platform mindset essential for building systems that can handle everything from the massive computational data and governance needs of things like generative AI, but also some of the mindset shifts as well?

A (12:36)

Yeah, and this is a really important point, especially because we are early stages of AI and I feel two important points here that I feel that are important, that we highlight is first of all, there is no AI without data. And then coordinating the data in a kind of organized and uniform way across the company, I think is really crucial. And when we talk about platforms, my experience has been like that one crucial platform that people normally get wrong is the data platform. Like they don't have a data lake. Many times they have multiple data lakes and the data is not consistent. And then they don't have even basic definitions of what a customer is, what transaction is, and so on. And then when they try to cross reference this data across these multiple systems that they have, normally there are impedance mismatches and semantic mismatches because these things are not really well defined. So we're spending time to sort through the data and then at least to get the data into a common place where we can use AI to derive insights, I think is key. And this is one of the most important things that I think we can do. But the other thing that regarding AI and platforms that I would like to say is that since we are so early on, I think there is also some room for experimentation and a little bit of let the flowers bloom and see what is the strategy for implementing some functionality using AI or using modern development tools that leverage a lot of AI, how it will work well for your company and your processes. So I feel allowing for a little bit of experimentation and exploration of all these technologies that are blossoming now, I think is relevant before we decide on which approach we're going to take and then like and how to standardize this approach into a platform that we're going to enforce in the company. So I think it's kind of a combination of like, let's create some basic platforms to consolidate data, let's explore a few of the new frameworks and ideas that are coming and. And they'll keep coming. Right. I think we are like in this very fast environment now with lots of new ideas regarding AI. That keep happening every week. So it's important for us to keep track of that and then consolidating to more standardized processes and platform once we have good indications that these things are really working well for us.

B (15:23)

Yeah, yeah, definitely. I totally agree that there's lots of room for innovation still and just yeah. Exploration to your, you know, to your point is there's, there is no de facto way to do things at this point. So in many areas, so lots of room there. There's a lot of talk about the ethical considerations of AI as well. And yet, you know, every organization's a little bit different of course, but often some of those ethical considerations almost feel like an afterthought and something that is kind of retroactively applied or retroactively at least thought about how do you do all of, all of what you're saying in a way where ethical considerations are kind of baked in to the process so that it doesn't have to be kind of thought about afterwards and stuff like that?

A (16:22)

Yeah, Greg, I think this is a relevant question not only for ethical considerations of AI, but almost like it's like pervasive if you're talking about software development. Because every time that we boating things after the fact we risk incurring in what we call technical debt. Right. Like that we thought about building this solution this way and then we had another idea. Then we adapted and extended our solution to this other scenario and we cut corners and now we have kind of a Frankenstein solution that doesn't work really well. And then in case of ethical considerations for AI, I think it's even worse because it's really hard to boating after the fact. And then I think there is lots of examples of decisions that were taken into account using some AIs that like we're trained with partial data, with data that didn't represent all the cases that we, we needed to, to represent. I was talking to a professor that he studies melanoma and it's, it's a completely new disease like melanoma in, in different skin colors. So, so if you train in one skin color and we are going to look at melanoma in another skin color, like the, even the patterns look very different. So then the solutions simply don't work. Right. And to that they say that like, you know, not only for ethical AI but for everything in technology, the more you can anticipate and then try to bring more intentionality about the scenarios that you want to solve and think ahead of the fact that those scenarios should be incorporated in your solution, you are better off. And it's easier said than done because many times you cannot foresee all the possible scenarios. But in those cases, I think I feel that it's relevant to take note that, okay, either we should rebuild this platform or this piece of functionality, reconsider this scenario and without trying to create these Frankenstein solutions, this technical debt. Because this is what gets a lot of startups and a lot of companies into trouble. Because in early days, if you think about the startup, they build a solution for a problem, but that's not really the right problem that they want to solve. They don't have the right unit economics and then they tweak it a little bit to another problem and then they finally get the problem right. But then the software is all wrong at that point because it was built with a series of adaptations and modifications and that's what create all this technical debt. And just like any other form of debt, right, it accrues over time and we have to pay at some point.

B (19:16)

Yeah, yeah. So the last topic I want to talk about with you is something that you talk about in the book as well, and it's leading through, you know, we're certainly various aspects of the economy and everything are, you know, let's say there's, there's upturns and downturns and a bit of uncertainty. Maybe that's an understatement. But how can leading with a platform mindset help organizations to, you know, not only steer through some of this uncertainty, but also maintain innovation and retain top talent during, during times like this.

A (19:54)

I think it's crucial for us to, to execute in a very agile and mean way if you want to remain competitive. And I feel that especially top talent, they are attracted by tools and processes that work and then that give them freedom to think and innovate. So the more we can think about structuring systems and platforms that encapsulate a lot of your complexity and build this substrate or this platform that we can use to free you up so that you can think about real differentiating products and problems and innovation on top of that substrate of that platform that you created, the better off you are. And I feel what a lot of companies get it wrong is that they don't really invest in creating these platforms. They keep trying to rush and build solutions that are a good market fit, but then they don't have a common platform and then they, they keep getting more and more bloated because anything else that they want to do, they have to build it from scratch and build it from scratch. So I think the sooner that companies can realize that if they invest in structuring the technology systems and the technology area as a whole in terms of platforms, the better off they will be using a linear structure and less people, more agility, more ways to experiment and to build the products that the customers need.

B (21:36)

Yeah, yeah. And so, you know, obviously in a large organization there's lots of different teams and people that are, that are responsible for those different pieces. What kind of qualities should a leader cultivate in their teams to help those teams guide them through whether it's periods of rapid transformation or disruption or all of the above and everything we're going through now. What kind of qualities do leaders really need to build in their teams?

A (22:10)

I think one of the things that I spend a lot of time in the book is talking about growth mindset and then this almost seemed like cliche, but I feel that it's actually the most important quality. Above all, of course people have to be technically strong and good and they have to be accountable and then they have good morals and values. But above all else, I think they should be willing to learn and willing to adapt. And then they should treat failure as expected because especially now in times of AI, we see technology is moving so fast that maybe what we are building this week is not going to be relevant next week. But if you have this curiosity and this willingness to learn and adapt, I think we will be always better off. Right. We can always find a way to leverage our expertise to build something interesting and differentiating.

B (23:07)

Yeah, and you touch on a great point there as well, is leaders need to be willing. Not every experiment is going to achieve the desired results. That's why it's an experiment. If you want to call that failure, you can. I prefer if you learn something out of it, it's not really failure. You're learning at least from it. So I think leaders that really understand that and empower their teams to get to the best answers more quickly, I mean, definitely that's a powerful thing. As you're looking ahead and certainly thinking about this in a long term perspective as well, what are some of the trends that you're seeing, whether that's in platform development or on the organizational side of this that you know, you're excited about and see shaping the future of how businesses are run.

A (23:57)

Yeah, for my daily job, like Greg, I think I'm exploring a lot the use of AI in several fronts. I think like one thing that helps me a lot is like to do research, like in my day job, like I need to think about the future and understand the innovations that are coming. And it's really hard to keep track of all with all the news and all the publications that are coming. So using AI to help me keep track of like what's going on and keep ahead of the curve there I think is super relevant. And then on the dev side I think also like these tools that help us speed up development. We call it vibe coding. Right. That is like using AI to speed up core development. I think it's they're making a lot of progress and it's really interesting to see how they are shaping up. So I've been busy just keeping track of the technology changes these days.

B (24:52)

Yeah. Love it. Love it. Well Markus, thanks so much for sharing your ideas and insights. One last question for you. I like to ask everybody, what do you do to stay agile in your role and how do you find a way to do it consistently?

A (25:05)

Yeah, for me it's just like staying curious. One of the things I enjoy the most is talking to students, talking to university professors. Keep an eye for all the innovation that is happening and it's super exciting times for us in computer science and the industry of computing now. So I am quite excited every day that I wake up and what are the things that I'm going to do today.

B (25:35)

Love it. Well again I'd like to thank Marcus Fontura, Technical Fellow at Microsoft and CTO for Azure Core at Microsoft, for joining the show. You can learn more about Marcus and Microsoft and his book by following the links in the show notes. Thanks again for listening to the Agile Brand brought to you by Tech Systems. If you enjoyed the show, please take a minute to subscribe and leave us a rating so that others can find the show as well. You can access more episodes of the show@theagilebrand.com that's theagile brand.com and contact me. If you're interested in consulting or advisory services or are looking for a speaker for your next event, go to www.gregkilstrom.com that's G R E G K I H L S t r o m.com the Agile brand is produced by Missing Link, a Latina owned, strategy driven, creatively fueled brand production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay curious and stay agile.

A (26:45)

The Agile.

B (26:50)

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