AI at scale: Accenture’s journey to empowering 10,000 developers with Copilot
Episode Details
Transcript
After a successful initial rollout of GitHub Copilot that increased developer productivity, Accenture now has given the tool to more than 10,000 developers worldwide — with a burgeoning waitlist of teams eager to deploy other Copilots.
In this episode, we hear from Karen Odegaard, Accenture's Global IT Managing Director, about how the firm is using generative AI and GitHub Copilot to transform the developer experience and reinvent the way developers do their jobs.
According to Karen, driving adoption isn’t just throwing new tools at people. It’s making sure the value is clear, and the tools are effective enough to change behaviors and mindsets. She shares her fascinating perspective on business transformation in the age of AI that any organization can learn from.
Link to full episode transcript.
Produced by Larj Media.
Hayete
Welcome to Pivotal. I'm Hayete Gallot, corporate vice president for commercial solution areas at Microsoft. I work with customers around the globe to transform their business through technology. At the center of every transformation are people who give technology its purpose. And that doesn't change with the advent of AI. It's actually being accelerated. People spark visionary ideas for leveraging technology. The release of AI technology like ChatGPT this year is exciting, but it has led to big question as we grapple with the best way to harness those tools to enhance and support the people behind the work. We like to talk about technology. I love to talk about it. But we often forget that technology is most effective when it supports people with purpose. This season will demystify AI by talking to the innovators using new AI technology to uplift their industries and augment their people, from education to journalism to surfing. And it just illustrates what AI is about. Everybody thinks it's about tech. No. Everybody's using AI, and that's what we're gonna show you on this season. I'm excited to share my conversation today with Karen Odegaard. She's the global IT managing director at Accenture. So Karen gets to play with all the GNAI technology at Accenture and making sure she changed the way Accenture works as a whole. Very exciting. And so in this episode, we're gonna really focus on the developer experience and Harsch leveraging GitHub Copilot, to reinvent the way developers go about their jobs. So, Karen, thank you for taking the time to meet with us today. You've been at Accenture over sixteen years. Can you tell us a bit about yourself and your current role at Accenture? Yes. Karen
So I have been here for sixteen years. I flew by. My role today has evolved. I sit within our global IT organization, and I am enterprise real estate, so things like all of our Microsoft products and a number of other platforms, and I spent more than ten years in consulting. And so I'm a software engineer by design, and so I kinda grew up on that track. Hayete
I'm sure that's very helpful in your role today. Karen
I think so because, you know, you understand technology from the ground up. And, you know, I've had the opportunity to go take training at Stanford, understand foundation models, how they really work, you know, understand the dependency around data and how data essentially shapes, right, these solutions. So I think that's been helpful in terms of understanding the use cases, you know, where you could potentially get low hanging fruit where data is less of a dependency, actually. I can look at this as a technology transformation end to end. I'm just not releasing products to people. I understand all the inputs and the outputs and the end you know, how to drive the end to end journey, which has been my role here, just be and being able to shape that. And then there's a combination of products and then custom things that you you build. Right? And so I a lot like you know, today, we often get the question because we do have our own enterprise chatbot, like, what is the difference between that and Copilot and how to use it, and you can explain the differences right from the ground up so that people can understand the difference between the the technology and the dependencies. Hayete
So when did you first become aware of GenAI, and how did you react when you saw the technology in action? Karen
I think when the world did. I think I remember it was like November of twenty twenty two, maybe, when GPC came out. It piqued our interest, and a couple of months later, we decided, okay, okay, I think we need to move on this thing. And so that is when we started our journey. So it has been a little bit over a year, and fast forward a year. We're using generative AI across all of our corporate functions, so it's been quite a journey. Hayete
So GenAI has changed your role at Accenture. Tell us more about this. Karen
Yeah. So I was responsible for what we call kind of our enterprise cross platform capabilities, so things like Microsoft three sixty five and all the new emerging products like Veeva and others that were really out for Microsoft ServiceNow, you know, our internal company portals, everything that was cross functional. And ironically, our GenAI work started in that space where we could take advantage of unstructured data. And so although I own this giant real estate, the only thing anyone has asked me about in the last six months is GenAI, and that moved to be kind of a full time focus for me, focused on that transformation across all of our corporate functions since, you know, the CIO organization is the one organization that works across every single silo, and so we're bringing it together from a technology perspective. Wow. Hayete
That's a big role. And we only ask you about Gen AI. It must be frustrating at time, Karen
but it's still very interesting. Hayete
So in terms of Gen AI, I'm particularly interested how you're approaching developers and your experience with our assistant for developers, GitHub Copilot. Can you tell us about how you're leveraging GitHub Copilot? Karen
Yes. So like all of our journeys, we started pretty small. We started with a pilot. I think with all of our JNI use cases, we really wanted to to focus on understanding the value that it could deliver for our organization. So we partnered with GitHub to do a pilot early on, and how we identified who to start with. You know, within Accenture, within IT, especially, we really focus on being first and best credential for our ecosystem partners. And so IT within IT, we started that journey. We do a lot of development. You know, we're a thirteen thousand plus person organization. We leverage resources out of our delivery centers, which is in Manila and India, where most of our development work happened. And within that part of the world, they're very receptive to new tools. And so, you know, when this was coming out, we looked at our delivery centers, where are they gonna be the most receptive to using new tools and provide us feedback. And there was a lot of marketing around the product, so people knew this was out there. And so we tapped a couple of teams in our Philippines delivery center to be part of the four fifty developers that we piloted this on early on, and that's how the journey got started. So today, we are using it across Accenture. We've now scaled from that four fifty to about ten thousand developers, that are using it in the day in their day to day work. So a lot of it is focused on kind of internal development, but also development for client delivery work that we do as well. Hayete
I'm very interested on how we're gonna scale. But before we go there Yes. I'm curious. So you get with the first team, four hundred fifty developers. What was their reaction initially? Was there skepticism? How did they approach the tool? Karen
I think with all of Gen AI, there was a little bit of skepticism, but a lot of more interest, I would say, than skepticism. And I think once people started using the tool and saw, oh, wow, this is actually real. It can actually generate code that I can actually use. The adoption started from there. So So a little skepticism, I would say, but more optimism in terms of art of the possible and where this could potentially go. Hayete
So when you do this kind of pilots, you always wanna get feedback. So how did you go about getting the feedback to know how where you had to adjust maybe or what was next in the pilot? How did you set that up? Yeah. Karen
So we created a couple of feedback loops. I think the best feedback we got was live real time for users where we set up these office hours where they would come in and kind of share their perspectives and give us feedback. We also did surveys, of course, and I think a majority of them actually filled out the surveys, which we know end users don't love surveys, but this group was committed to giving us feedback. So we did get, that response. And then we also had a Veeva engaged community where people can also just share with each other and share with, our teams that were driving out the pilot, their feedback. Hayete
Did anything surprise you as you were going through the pilot where you're like, oh, I did not expect this? Karen
I don't think we initially expected a lot of the code that was generated to actually be used by end users, but that was one of the things that we saw was that, yeah, it was actually retained and and used, you know, in their real live work. And so I think that was surprising, and that really helped us to understand that, you know, this would add value in our environment and users would adopt this. Hayete
When you think about pilot, people are always curious in particular with Gen AI about the rate of adoption. What did you see and observe with the developers as you rolled out the GitHub Copilot? I think we saw Karen
So adoption was really high, you know, ninety plus percent. Mhmm. I think you know, I know we'll talk about scaling, but, you know, we want that to persist as we think about scaling the organization. But in pilot mode and especially early days, the novelty was there. Everyone wanted to use it, so we saw a really high adoption. Hayete
So you said earlier that you went from four fifty. You're already at ten thousand users. Right? And it must be that you were very happy with what you saw in the pilot. How did the experience of your developers change? What was their level of satisfaction? How did you measure that to assess? Is there value in this product? Karen
So most of that was via surveys. You know, every one of of them said they would continue to use this in their day to day. They would recommend it to their colleagues. And then, of course, the metrics that GitHub helped us collect show that users were actually using this. So I think those measures helped Hayete
the for Accenture. Yeah. So when you think about GitHub, Copilot, and you think about responsible AI, how did you approach this as your developers got to use the tools and adopt them? Karen
Yeah. So I think like most organizations, the reality is when we think about responsible AI, we we didn't, at that a year ago, knew what that translated to us as an organization and how we needed, you know, the controls we needed to build for all of our solutions. So very early days, we really heavily partnered with GitHub and Microsoft, understood their responsible AI commitments, what we could depend on our partners for versus what we needed to manage and maintain in house, and what kind of qualitative and qualitative quantitative testing we needed to do against the outputs to make sure that they adhere against our own internal responsible AI controls. So for GitHub, luckily, you guys were very mature. So as it went through our internal assessment, it was deemed low risk, and we didn't really have to do much above and beyond understanding the commitments and the controls that you and Microsoft already had built within the product for us. Hayete
So with every customer I talk to with GenAI, there's an aspect around user satisfaction and then there is the aspect of having to justify to a CFO investing in thousands and thousands of users. Karen
Yeah. Hayete
So when you think about your pilots and and the data points, how did you build that case? What was, like, the key things that you use to justify to your your CFO? Here is here you go. I wanna give that to every developer in our company. How do you go? So I Karen
think the data that came out of the pilot, the adoption metrics, the productivity metrics, looking at, you know, with all our GenAI, use cases, we look at the foundation. What are people doing today? What is the throughput that we're seeing today? How can that be accelerated, and how can that translate to reduced operational costs or workforce optimization? But the reality is, across everything we're doing with Gen AI, we are just not there yet. So what we how we think about this is this is a strategic investment. We saw the value, right, for a small group of four hundred plus people. We know that can translate if we drive the adoption across the organization. So we think about the the decision we made is we're making a strategic investment now to get the value later, at least the the return on investment later. Because we are just not at the scale and maturity yet that we're going to see that immediately. And that's a conscious decision, but at Accenture, we believe, right, in the power of generative AI and that it's gonna deliver on the promise. And so we're we're making that strategic investment upfront. Hayete
What what kind of metrics did you see from a productivity perspective? How did you think about productivity in the context of developers as you were observing the pilot? Karen
Yeah. So we saw more builds, shorter sprint cycles, just general throughput increased across our developers. And, obviously, if developers are able to generate more code and shorten some of the cycles, you know, long term is some of those resources can be pivoted to do different types of work. So those are the main things that we've we looked at. Hayete
And then if you think about your developers, I know they filled surveys. Mhmm. And so what how did the satisfaction look like? Like, if you were to go today and say, I'm taking GitHub Copilot from you, what would be their reaction? Karen
I think we've seen this consistently with all GenAI tools. Once you give this to people and they realize the power and that it is an assistive technology in their day to day work, I I literally get emails from people, don't take these tools away from me. You know, I really depend on it now within my day to day, so GitHub is no different. So that's what we're seeing and hearing from our developers. The new way of working, you you can't go back. Right? It's It's like taking taking this away, taking teams away, for example. Right? It's just ingrained in how they work now. That's that's awesome Hayete
to hear. So you went from four fifty Karen
Mhmm. Hayete
To ten thousand. What did you learn in the pilot that maybe influenced the way you rolled out at scale? Because ten thousand were getting to scale. What was different maybe in the way you approached the pilot and the scale rollout? Karen
Yeah. So the pilot was really a proof of concept, is essentially how we looked at it. Scale is very different because we're at ten thousand today. We're planning to get to fifty thousand by the end of our fiscal year this year. And I think there's more work to do around change and adoption for us, because it's not just I think we were able to just put a tool out there, and people they had the novelty of using a new tool. They opted into it. Here, where, at scale, you're telling people this is how you need to work. Right? They're not and some of them are opting in, but some of them are being told, work differently, deliver more value for the organization. So there's more to do, I think, around change and adoption. Obviously, there's more automation that needs to happen from a provisioning perspective. We also want to be able to track value at scale, and I honestly say we haven't a hundred percent figured that out yet, but that is something that we're looking to do as well. So I think those are the big differentiators when it comes to scaling beyond the four fifty that we started with. Hayete
And how did you pick the next ten thousand? How did you choose the cohorts? Because I know every customer I Karen
talk to is always, how Hayete
do you select cohorts? How do you define which ones you're getting at first, second, third? So I'm just curious. Karen
Yeah. I would say it's easier in this use case, actually, because you're talking about a specific skill set and role. So we're looking at within our organization who is doing this type of work. You know, it starts with that, but also there is technical constraints, right, who's leveraging the supported IDEs across our organization. We want to continue to deliver value for our clients. So where can we deliver more value for our clients? So it's a hybrid of that. And also, with a lot of our Gen AI products, there has been a wait list. Right? There are people who actually want to use this. They wanna contribute to the future of Accenture, the future of the products, and so some of it is is also people just opting in. So those are the two ways that we're kind of that we've created our backlog for for users that we're provisioning access to. Hayete
So Karen, you talked about a waiting list. I think it's very novel. I haven't heard a lot of customers doing that. So how did it come to be? How do Karen
you approach this? So with GitHub Copilot and some of you the other products we're rolling out, we saw really quickly that people wanted access to these products before we are ready to deploy them at scale. But I used it as a way to gauge demand because demand will tell us us whether people will use this or not. So there is the business case, but there's also do people our people proactively under because we are very focused on making sure our entire employee base understands the foundation for GenAI. They know what our ecosystem partners are doing. And so are they well versed on the market? Do they understand, even before we can train them, how these tools can change how they work? And, actually, we're using our GenAI bot for people to go put themselves on the wait list. So we part of this is also me just trying to just figure out how to reinvent how we currently work. And so we want everything to be Gen AI led. So the wait list is go to our enterprise chatbot, ask to be added on the wait list for this particular product that you want. And it's so easy for people that I think that's the other thing. So we literally and we didn't do any massive campaigning either. It's like if people are asking if they go to our Gen AI destination, this is how you can go get on the wait list. And we're at tens of thousands of people on the wait list for for these products. And again, that that that makes it clear to us that there is demand across our employee base and that they will use these products. Hayete
That's actually awesome because not only are you gauging the demand, but you're actually using GenAI to get people to submit their name on a wait list. It's like you actually get started on GenAI already and see how you can interact with a chatbot. That is just awesome. I think it's very novel. I think we should all think about it. It's like, you know, when you launch product, sometimes you do a wait list to create excitement, or you can raise prices. But in this case, you guys get people to be very excited about the product. It's awesome. Exactly. Yeah. It's very interesting this notion of wait list. I haven't seen that in a lot of places. This is actually very, very interesting approach. I learned something here. I I should think about this. Now in terms of you said now it's gonna be a new way of working. It's kind of this is the way you work. It has to be ingrained in their day to day. When you think about hiring new developers, how are you now onboarding developers? What has it changed for you? Karen
I think just knowledge transfer has always been a big thing in our business. Understanding code that's already written usually takes existing resources to do that. I think that's one change I'm seeing in terms of how our developers are working. Outside of that, I think we need more time. Right? And we need to grow the adoption because while we've given it to ten thousand users, we don't necessarily have ten thousand users using it at the the rate that we're looking for now because we still have more work to do around change and adoption. But I'd say just the notion of how people do knowledge transfers is probably the biggest immediate change outside of the obvious code generation productivity benefits we're seeing. Hayete
Yeah. We've all had that problem. Right? You inherit code and you're like, what is this spaghetti? And What does it do? Yeah. How do I make sense of it? I I that's one of the thing that I really when GitHub came out, I was excited. I was like, you can actually look at the code and say, what is this? Yeah. And then you can accelerate the way you build a new code, right, and integrate with it. Exactly. Karen
So now Hayete
you look at GenAI across the board. Yes. You seem really well underway with GitHub Cofilo. What's next? What's next? What are you looking at tackling now with JNI? Karen
My gosh. Everything. So I'm looking across all corporate functions, across all partners with Microsoft. We're also rolling out Microsoft three sixty five Copilot at scale across our organization. We're trying to get Power BI. All the co pilots, essentially, we are looking at them where we use the baseline products because I think that's going to accelerate our journey, right, if we can leverage off the shelf products. When those products when this journey started, there was a lot of custom experimentation. But now we have products that are enterprise grade, enterprise ready that we can consume at scale. So it's really just pushing this out to to your question earlier, the resources that can get the most value out of these types of products because we're not at the point yet where we're gonna give it to everyone, but who's playing these roles? Who can benefit immediately? Who wants to be part of the reinvention? And one of the big areas of focus for us, we can't do this so much with developers, but with some of your the other products like three sixty five Copilot, it's focusing on leaders. We feel like people follow leaders. Leaders are gonna be changing change agents and really drive the change across our organization. So we're giving some of these tools to them first because what we're seeing happen is when we give it to them, their cohorts also need it, you know, to support them and to also be as productive, and so I think that strategy has worked really well for us. So, yeah, it's continued to, you know, partner with our ecosystem partners, understand where they're going with GenAI, be that first and best credential for for us and for them as well across all the GenAI offerings. Hayete
How are you leaders adopting the new tech? Are they as enthusiastic as those people who get themselves on the wait list? What are you experiencing there? Karen
In our environment, I'm surprised that there are a lot of leaders, yes, that are just as enthusiastic as our junior people. So I think that's part of the group. The other part, it's it's a business imperative. Right? We are selling this to our clients. We are supporting their their transformation, so they have to be well versed on these two. And in some cases, clients are already using the products, you know. And so, you know, they, you know, they need to be able to collaborate in that way also. So, yes, I would say, generally, as I look at adoption, adoption, you know, adoption holistically is still higher among non leadership groups, but by very little, and they're also using it just in different ways. So I would say, yes, there are leaders also putting themselves on the waitlist just as much as, you know, the average employee, and we definitely have more enthusiasm, I would say, than I've seen for, you know, previous technology shifts here in terms of our leaders asking to be able to use these tools. Hayete
I like your point about business imperative. Yeah. The that's why maybe you're seeing that shift in technology adoption Karen
in help just contribute to that evolution. Right? Because a lot of this is, you know, even for for Microsoft, right? You're learning from your customers how they're using these products and that helps, you know, you figure out your roadmap and how it should evolve over time. Truly. Hayete
And and I think when leaders really lean in, it also paves the way, right, for the the teams to to realize this is not a if. It is we're gonna do this. And I know I had to do the same in my organization. No. I don't like enthusiasm because I have a lot of tech people. But still, you gotta lead the way, and, you gotta be able to innovate with them. Karen
So it's exciting. You find yourself also your as a leader when you understand you're instructing your teams to work this way. So I'll say to my developers, use GitHub Copilot to figure out if you can shorten, you know, this this delivery cycle here so we can release faster. Right? Go use my Microsoft three six five Copilot to create this job description. So I find myself working that way now. Hayete
It it's so true. I realized, you know, when we launched m three sixty five Copilot, at first, you have the first scenarios that, of course, we give and illustrate. But the creativity and how each job has a very different need. And it's actually interesting you said clients shape and give us feedback. Even within Microsoft, what we're seeing is different groups will use m three sixty five Copilot for very different thing. Our marketing teams versus our sales team. Karen
Right. Hayete
They will rely more on specific features, and they will push us more. Or their their meta prompt that they create to extend the skills of their co pilot will be very different because they have a different reality. Right. But that's where the the value comes, right, and the creativity. Exactly. And how you make that co pilot your co pilot. Karen
Exactly. Hayete
I I just love that. Exactly. Karen
Yeah. Hayete
So, Karen, it's great to see the adoption that that you're seeing and now you're rolling out to ten thousand users. If you had to think back about the biggest challenges that you face that now you're really paying attention to, what would you call out? Karen
I think I'd call out two things. I think, you know, we're a year into our journey with almost a year into our journey with GitHub Copilot and just GenAI as a whole. And as I mentioned, there was a lot of, you know, novelty in the beginning of using new tools, and so we saw adoption. We're we're not seeing that same rate as we scale. We're seeing adoption, but not quite at the same rate, which makes sense, right, as you go you're going from a very small group to a very large group. So that is where we realized we have to put in more effort. And I know we talked about the difference between scaling, and that's why I say change and changing the behaviors and mindsets, not just throwing out a new tool for people. I think that's going to be a challenge for all of us because that has a huge dependency also on the value realization piece of this. Right? I talked about making a strategic investment now, but at the end of the day, the CFO is gonna come back and say, great. I made that strategic investment. What where when. Right? Not what what if, but when are we going to see that return on investment? And so I think the continuous work around value measurement is another big effort and focus and and challenge in all this because you have to make sure people are using it, you change their behaviors and mindsets as a as a major dependency, to that. Hayete
I think it's two fantastic points. Like, you're not just giving them a tool. It's about changing the way people work. I think you used the word ingrained in the way they work. Yes. That's really when you start getting the value. And in measuring with Gen AI, it's interesting because I remember when we started in Gen AI, people were talking about productivity. And I was like, well, at the end of the day, it's not like people are or people are like, we're gonna save time. But it's not like people are gonna go from thirty nine hours a week to twenty hours a week. It's just they're gonna go from maybe less menial work to more high value work. How do you measure that? It's a very different way to think about productivity. Karen
Yeah. And I Hayete
think that's all we are all, like, working through right now with Gen AI to say, how do you then turn into financial numbers? Like, I can see you with developers' shorter life cycle for to get from ideas to code. When you're checking your code, less more success than not. Like, there is things that are actually quite concrete with developers. But as you move through function in a company, we really need to think deeply about the value realization. And what are those ways of measuring? Because measuring a marketing person and the productivity we're getting Yeah. Through the Gen AI is very different from a developer. And I think we're all learning. There is no formula. We're getting into new territory. We all have the instinct that this is super powerful. Yes. And people say it. They're satisfied. And now we need to put that in numbers. So I I really like what you pointed out here. I think this needs to be top of mind for all of us as we roll out those tools. It is. It needs to be part of the fabric of the way we work, and then we need to be able to justify and measure. And by function, it's gonna differ. And and I think that's a challenge for all of us. Karen
Yeah. And I think it's harder because in the environment we're in right now, it's an option. It's an option as a way to work. It's very easy for me to be able to articulate value. Like in the waitlist concept, I could have told people, go fill out a request on ServiceNow. You know, that's how you get on the wait list. But instead, it's just ping a bot that takes you ten seconds. Those are the types of use cases where it's the only way you can get things done that I'm very easily able to articulate value. So I hope in this space, eventually, it becomes a de facto standard. Right? Where it's it's the way of not of working, not a way of working. Hayete
And I think for us, it's been interesting because that's why I was asking about onboarding and how it may be changing the way you work because your point about de facto, if you're a new developer, you come at Accenture and we said, this is the tooling you're using. This is how you do things. You're not trying to change management, do change management on the behavior. It's like, this is your tooling. This is how you work. Yeah. You're an information worker. You all know you're getting teams. But when we rolled out teams, as you know, at first people were like, what is this tool? Right? I'm used to doing emails. When do I do emails? When I do this I'm thing and I have all those teams. It just there is but now people are coming into Microsoft. Nobody is thinking about what did I use before, and now I have Teams. They just know this is my tooling. So I think there is this as well onboarding, and I'm hopeful it's gonna help us with as people come onboard that we can create that movement, and it becomes that de facto you're talking about. Karen
Exactly. Yeah. Makes sense. Hayete
So if you had an advice for our listeners, what would you say to them? Because you you are handling this across all corporate function. I know every customer is asking me, where do I start? Where do I prioritize? Yes. Karen
I think it's a journey. And like everything else, a journey doesn't happen overnight. And I would say find that low hanging fruit because that is where you can quickly prove out value. The reality is introducing new technologies or shifting evolving business process or changing the way people work takes time, takes effort, takes investment. But there are some areas where, you know, GenAI can solve for now that it couldn't solve for before. Like, if I take the example of, like, performance feedback, that's something everybody in our companies did manually. And so GenAI can now help generate that content. That's a really obvious quick win, doesn't involve, again, this big business process transformation effort, and people can get value really quickly. Quickly. So identify those quick wins within your organization, and that'll help you understand the power of generative AI and the value that it can deliver so that you can scale that across the things that do require more complexity to roll out. Awesome. Hayete
Yeah. I have another question for you. You're a consulting firm. Yes. Right? So you're learning a ton internally. How are you then translating that to your external efforts? Right? Because you're learning a ton. Your clients are gonna face the same issues you're facing. So I'm just curious as to how you're connecting the dots here. Karen
Yeah. The dots are heavily connected. As I said, because we try to be leading and bleeding edge and be that first and best credential for our clients. We're using ourselves as a test bed for everything first. So if I think about the work we've done with GitHub or with m three sixty five Copilot is we had a lot of learnings outside of that because we are an organization that operates at scale, so we end up running into things well before a lot of your customers do. So, for example, I'm having conversations with Rajesh Jha on some product enhancements that maybe your customers are not asking for now, but, you know, through a few weeks later, a few months later, the market needs it. But Accenture has already experienced it because of the scale the massive scale that we're operating in. So that is what we do. We we can take just a a department that's the size of a, like, a a whole company, right, for for some of your other customers and prove out things really quickly, and then we shape that into offerings. So for three sixty five Copilot, if I take that as an example, some of the data governance work that we've done translates to offerings to help our clients mature data governance for m three sixty five that we then offer. So that is how we work with literally everything that we do is because we're a test bed first, we shape and deliver offerings and requirements for you, right, as a partner to help evolve products and help our our customers to mature in their journey. Hayete
We're always thankful for Accenture's push in helping us Yeah. Shape our product offerings. It's true that you guys have such a scale that you really get to experiment at levels ahead of many of our clients, so it we really appreciate it. Thank you so much, Karen. It was very insightful. I'm excited to see how you get to the next fifty thousand, and and I can't wait to see how you push us across all of our GNAI products so that it's better for our clients. Thank you. Karen
Great. It's been a great partnership, and, yeah, we're looking forward Hayete
to it as well. Thanks, Karen. Thank you for listening to Pivotal. I'd love to hear your story in your pivotal moment, So don't hesitate to follow me and share on LinkedIn. All this information is also available in the show notes. Our show is produced by LARJ Media. That's LARJ Media. Special thanks to Li Niang and our partners at We Communications. 
