Beyond Technology: Building a Data & AI Culture That Lasts — Insights from Capgemini’s Leaders

0:00With a surface level of understanding, there's only a certain value that organizations will be able to derive from AI. I think the longer term change really comes from proper understanding around the capability of the tools, taking hold at all levels within an organization, and for there to be a consistent effort driven through organizations and commitment to that longer term cultural change.

0:27Hello and welcome back to the Data and AI Mastery podcast. I'm really excited about today's episode as we are doing something a little different. That's right. We're bringing you a special conversation with three leaders from Capgemini as part of a really exciting collaboration. Each leader is playing a vital role in transforming how people and technology work together. I am joined by Claire Williams, Vice President for Analytics and AI in the retail and CPG sector, who leads Capgemini's data and AI transformation work with global consumer health organizations. Arlene Carsley, Head of Workforce Transformation, who focuses on building the culture, skills and mindset needed for lasting change.

1:15and Mansuk Man, Managing Consultant in Workforce Transformation, who sits at the intersection between data, AI and talent strategy, ensuring that technology and people move in sync. Together, we explore what data and AI transformation really mean in practice, how organizations are putting them into action, where the mandate for change begins, and why success depends as much on people as it does on technology. Hello, welcome to the show. Good to see you. Hi, Claire. How's it going? Oh, good, Rahul. How are you? Amazing. Great to have you on board. Hi, Arlene. How are you? Yeah, I'm really well, thank you.

1:58How are you? Fantastic. Great to have you on the show. Awesome. And Mansouk, how are you? I'm doing great. Thanks, Rahul. Good to see you. Great. Well, I'm so looking forward to our conversation and great to have you here. The age of AI and transformation is a big topic today, so I can't wait to get to it. To kick off, Claire, you're leading Capgemini's data and AI transformation work with some of the most global consumer health organizations. So, Claire, what's prompting organizations to think about AI transformation now? What's the urgency? Yeah, urgency. I mean, it's in three levels, really.

2:40obviously as the tech revolution people finally now get AI I mean the revolution that's coming through LLMs and chat GPT and the commoditization of that and just the coming into the general population's knowledge is there and if you don't move on that you're really going to really going to struggle to be able to move so I think technical technological advancements is absolutely one to become fully authentic as an organization and to augment your processes with AI, allow people to focus on the things that they really, really enjoy doing rather than the drudgery. Super, super important. And all those things are things that your competitors are going to be doing.

3:19So you need to be able to do that. Some of these things as well are helping organizations leapfrog their competition, get ahead of the game when it comes to all the economical issues that are around the world at the minute things like tariffs conflicts cost of living all of those kind of things put additional pressure on the system and be able to get ahead in terms of the right forecast the right pricing model the right products about pack sizes and all of those things super super important driven by data and without getting your leadership to a space that they're confident in doing it like that getting the platforms technology in place to a point that it can fulfill on that, you're not going to be able to be competitive.

4:03So that's what I know. Fantastic. And Mansu, how does that resonate with you? It resonates a lot, actually. I think it's also interesting at this point where organizations have been investing in AI and in technology, but probably in quite a fragmented way. And the conversation has suddenly shifted now to value. And what return are we getting on those investments? and they're realizing they're not getting the return. It's just based on technology and it's just based on tools. So I think there's a bigger conversation now about data and AI-driven culture and how do we make sure our people are ready, our leadership's ready, they're able to make the right decisions around it.

4:43I think I've definitely seen a shift in that. And the client that we are with currently, the challenge for them was also about building that culture. around data and AI. They've not historically used data a lot or trusted data a lot in the organization. And then they've got ambitious goals around AI, but haven't seen a lot of return on that investment as well. So I think that's a really exciting space to be in to help organizations get the foundations right in terms of their people, talent, and everything else that goes with it. So I think that's also the urgency where they're looking at it more as a wider transformation rather than just a technological piece of work.

5:22So Claire, to continue with you, I'd love to get your perspective on who is driving this urgency for the transformation. Are you seeing the board, you know, a bit of FOMO, we're hearing a lot about AI, we've got to do something about it and that's putting pressure on the execs or are you seeing it coming from the CEO themselves or even bottom up? Like, I'd love to get your perspective. Yeah, no, it's a really, really, really good question. and fortunately in this situation it is on the board as being absolutely driven by the board they are very humble in terms of their acknowledgement of their own skill set and they have got a massive thirst to learn themselves and curious about it all what don't want to be outranked in terms of their skill set exception stuff like that that's phenomenal but if I think about the journey I've personally been on with this is fine over the last two years um these things two years ago were very much bottom up you know siloed it's a pain it's an absolute nightmare and nothing's trusted it's the world where success and stuff like that um so that bottom up has um resulted in the lockdown in terms of the the board and sponsoring this wanting to sort themselves out wanting to elevate this to the point that even the ceo has the success of the data and AI transformation on his own personal forecast, which I think is absolutely phenomenal.

6:49And when I speak to others around their own data and AI transformations, my advice to them is unless you've got full C-suite engagement and it isn't just driven by the CIO, CDO, then you're really going to talk. And I think that's phenomenal because as we've talked about, it's not about just the technology and the tooling. It's about the people, the transformation, the culture, the thinking value for being data driven. Fantastic. And Matsuk, what about you? Like, you know, when you speak to clients, what do you see the balance between, you know, board mandate, executive mandate or, you know, the people within the organization kind of like, you know, getting on the journey and say, we've got to do something about it?

7:30I think it's a combination of all of them, really. So, again, like, so Claire and I, our most recent experiences with the same client. So there is, I think I'm just, I've learned a lot working on that particular piece of transformation, because it definitely is the board is interested and the board is holding the organization to account to say, okay, what are we doing? And how are we moving forward? And, you know, we need to have regular updates. And it's not the only transformation they're also going through, but there is a huge emphasis on the fact that the board is interested and we want to know and we want to know about the value.

8:03And I think they've shifted the conversation more to value than just what are we doing? And, you know, what's the plan longer term? But equally, I think generally with clients and I think generally with organization, I can say that as an employee myself, Capgemini themselves have gone through our own transformation around this, how the skills that we need in this AI world for consultants, that's changed a lot. And seeing the investment that we've made over the last two years or so and upscaling 350 ,000 people in AI skills and just having that forward thinking mindset gives the employees a lot more reassurance that they are in the right space, that the organization's investing in them.

8:48I read this recently, the World Economic Forum had this really cool term that they've come up with, which is FOBO. So it's the fear of becoming obsolete. And there are a lot of people who actually do think like where organizations aren't really investing in AI skills and AI in general. There is a genuine fear and anxiety amongst employees. But what would this mean for us? I think where there is the right kind of investment and that also fuels that reassurance for people as well. So I think it's coming from all ends and it just needs to be balanced in the right way that we don't end up making quick decisions only to try and make everyone happy.

9:28It needs to be done in the right way. And these transformations are longer term transformations, not just something that we can fix overnight as well. And what about you, Arlene? How do you think about balancing quick wins versus longer term change in the people context? I think there's at the moment there may be a view that people, the organisations will sort of get on the AI bandwagon and that they're looking to drive some of these quick wins. But personally, you know, I think with a surface level of understanding, there's only a certain value that organisations will be able to derive from AI.

10:09I think the longer term change really comes from proper understanding around the capability of the tools that taking hold at all levels within an organisation and for there to be a consistent effort driven through organisations and commitment to that longer term cultural change. like so much of it i believe is related to a shift in mindset behaviors ways of working a great deal more collaboration is required to really understand the power of the tools and to to use that as a way to reshape workflows so yeah i think there's some good good quick wins around efficiency plays i'm sure that like i say many many people are using it in their day-to-day But I think the true value of AI will only be unlocked when we really drive to cultural change within organizations at scale.

11:05Great. Well, I like that you're talking about timelines. That's kind of a wonderful question I had maybe for you, Claire. You know, when we talk about transformation and ROI, how should we think about the timeline? Like, what should we expect? It's a really, really good question. I was smiling as Mansit was talking about the timeline. And I kind of come back, I guess, one of my earlier points was that delivering a point solution to deliver value versus an enterprise transformation where you're taking people on a massive change journey. Things take time, right? You don't design a target operating model and then switch it on the next day.

11:46People have got to get used to it. You've got that whole upskilling process, et cetera, not to mention actually getting the foundational data in a space that is trusted. And you've got this kind of like triangle I always talk about where you're getting your exec board team members all skilled up, gend up on AI that, you know, going through Cambridge Sparks program, et cetera, and stuff like that, getting really, really excited about that. you've got your employee base um upskilling themselves as well around data and ai you've committed a massive pipeline of work in terms of delivering data products use cases ai um across the board but if you don't have those data foundations in place at the same time you can't unleash all that excitement and you just create this pressure cooker of frustration and that's why it takes time to actually transform but i think what's really important in that timeline you know three three five year horizon is to focus on okay what are the incremental wins you're making along the way and make sure that everything that you're doing has an outcome to it like and when i talk about an outcome talk about a business value associated with it and you have that right measurement framework in place because otherwise you're at risk of coming up with a load of activities done um that haven't nudged you along and then three five years later you haven't transformed and i think that's really really important as well so patience um but with but but celebrate along the way be committed to the process i i like that what are some examples of um maybe quick wins that one might think about as they start the transformation journey good question um i think um multitude of them and and mounsuk please feel free to chip in as well so guessing everybody on board with the fact yes you need to have the ownership of data from within the business from those that have created created that data you own the data we're not in a situation anymore where some um technology team owns the data they don't own the data they're there to make sure that data is trusted of quality etc so decision points like that and alignment on that are really really key plus getting the first time you um create a data products, AI products that has reused in more than one area of the business.

14:06I think that's also a big win. If you look at the North Star of where you're getting to for an organization that trusts data, that has a curious employee base, that is fluent in AI, anything that nubbers the dial along any of those, we should be celebrating. I'd love to get your perspective on how you think about the business being engaged as spot of transformation and a spot of showing quick wins like what what are you seeing in practice yeah it's super critical I mean you can never do a transformation to people I think that's really critical we talked about um the importance of board engagement um and board sponsorship um and ultimately the board is absolutely representative of the business and those that absolutely are able to execute on activating the value off the back of a transformation are those in the business um so those that are out visiting pharmacists um to um help them grow their their pharmacy footprint their business and have the right products on shelves you know the tools that they're armed with um likely to be data and ai ones that help them in that situation so them being comfortable with that and then being able to execute on that is the only way you're able to activate that value similarly somebody out um in a shop floor um in in a factory um being able to understand um trends of data and where continual quality concerns on a tube of toothpaste for example are likely to impact them being data literate enough confident enough to understand the importance of that um and the impact that that has on the supply chain um super key so So unless those people are involved and not just those that sit in the business from a global perspective, but those that actually out in the markets are able to actually activate on that, is the only way you're going to get that value from a transformation and get it fully adopted, truly end to end.

16:07I don't know whether you'd add to that. Yeah, I think that business engagement has been quite big. I think when we started this work, I think we had probably three or four kind of clear priorities that were given to our team. And one of them was about how do we make data and AI a business-wide topic rather than it being a very tech topic, essentially. Like it's not something that just people in the digital and tech organization need to worry about. So how do you build that business sponsorship? How do you strengthen their awareness? They have to own this. They have to own the strategy as well.

16:39So where we've written the data and AI strategy, it's been written with the business rather than, you know, on an ivory tower written by the CDO on their own. I think they've been very forward thinking in that perspective is that we need to bring that business along that journey. So building the right networks to engage with them, keeping them updated. and that goes back I think Raul to your point around quick wins is if you the transformation is obviously going to be long it's going to take quite a few years and as it should but if you only focus on that I think you lose the business and people end up going and creating these siloed solutions in their part of the business because they feel like they have that fear of missing out so you've got to build that engagement very early on and you've got to have a really clear strategy a really clear vision that they then buy into.

17:25They know it might take three years to get to where we need to get to. But if you don't engage them, you don't show them early value, like Claire said, those early success stories that are coming out from the value that it's unlocking already, you just end up creating a bigger problem, a worse tech stack in the future because everyone's gone and just done their own thing because they don't trust that something as an organization centrally, we're going to do it. So I think that's been a really important part of this. And equally amplifying, I think not many organizations are starting with nothing.

17:55There's a lot of work that's been happening that's amplifying what already exists as well in the organization. So where have we seen value before, surfacing some of the good work that's been done, and then building that trust in the organization and the business is really important. So I think engagement with the business and the key point that came out quite early on in our conversation was it's not just a tech transformation. it's not something that just your cto is going to own it very much is a business transformation across the board and they are if not more um at least an equal partner in this and just to add to that actually like it's well it's well documented that like 70 percent of transformations fail they fail to deliver value and that genuinely sits across three common themes one idea was a bad one in the first place consult the right individuals check that it actually works check with those that are like those football teams at the pharmacist check that um the problem that we're trying to solve is the right problem to solve um and shoot those ideas down very quickly and when they're not the right ones focused on um the second one being and we talked about measurements that you measure the right things so you focus so much on being on time on quality on budget you forget to go okay well whose life is going to change what value is it going to deliver and that value conversation again you can only get um affirmation of that value conversation from the business teams because as i said they're the ones that are able to activate it and then that third one that mansit was really um clearly articulating there is that you the other reason they fail is you focus so much on the technology change so much on that you forget about the people along the way that's great so clearly business engagement is uh so important And as you said, getting everyone working together.

19:46What does good look like? Well, good looks like enhanced workflows where there's clarity in terms of roles and responsibilities. I think we recognize that AI adoption disrupts workflows. And without that sort of good change management, business engagement, then resistance is inevitable. So the successful adoption of AI relies upon transparent communications really create explanations around the impact on roles and early involvement of affected teams as well I think that there's uh there's a huge emphasis on talent roles and skills as well making sure that we've got the right line of skills and that matters more than having many specialists and having a level of fluency across entire organizations is key, I think, to really unlocking the potential OVI.

20:41I think it's interesting to consider what's the right start point for a data and AI transformation. And for me, e-skilling is absolutely key to that. When there's a level of understanding across an entire organization at various different levels, then it's possible to start to think about things like the operating model, how the organisation's structured after workflows and processes. But until then, until there's like a common language and understanding across the entire organisation, it's very difficult to place AI in the context of a given organisation and then for it to realise its potential there.

21:21I think we recognise that AI adoption disrupts workflows and without that sort of good change management, business engagement, then resistance is inevitable. So the successful adoption of BI relies upon transparent communications, really clear explanations around the impact on roles and early involvement of affected teams as well. I think that there's a huge emphasis on talent roles and skills as well, making sure that we've got the right blend of skills. And that matters more than having many specialists and having a level of fluency across entire organisations is key, I think, to really unlocking the potential of AI overall.

22:07I think it's interesting to consider what's the right start point for a data and AI transformation and for me, skilling is absolutely key to that. When there's a level of understanding across an entire organisation at various different levels, then it's possible to start to think about things like the operating model, how the organisation's structured, different workflows and processes but until then until there's like a common language and understanding across the entire organization it's very difficult to place AI in the context of given organization and then for it to realize its potential there.

22:48I hope you're enjoying today's conversation if you're finding the insights useful please do take a moment to subscribe to the Data and AI Mastery podcast and leave us a review on Apple Podcasts Spotify or YouTube every new follow helps us reach more people and shed incredible work being done by today's daytime AI leader. All right, let's go back to the episode. Of course, transformation can fail. And if that's the case, what are some early signs that you'd recommend, you know, executives to think about to avoid, you know, being on the failure journey? Like, what recommendations are there? You know, things you should be watching out for ahead of time.

23:30i think first one i'd say there and like please fans that come in as well um would be um that you haven't got the right level of sponsorship to me that would scream absolute alarm bells people aren't bothered about it people aren't turning up possessions um and having that you know having a thirst for knowledge and knowledging um skilling themselves up on what that conversation is you've got a real problem you haven't got that buy in there if you're constantly as well having to justify the investment needed for that transformation. I think that's another one. Obviously, all investment needs to be justified.

24:03And I come back to my previous point around making sure you're measuring the value and the impact. But if you don't have the buy-in, don't have the funding for a transformation, they are big alarm bells. And thirdly, if the organization still keeps investing in that siloed behavior for the need of one part of the business, rather than looking at holistically for the value for the entirety of the business. that's also a bit of a trigger i think just the on the strategy piece like you know do you have a unified strategy or not do you have a strategy in the first place or not and that was probably like a starting point for us is like what what is the strategy over the next to be defined it will evolve and that's absolutely fine but we need to have a strategy as an organization and that strategy can't be focused on just tools um it's moving away from that it has like different pillars it has pillars across like how do you if it's an ai transformation you can't do that with our data so what's your day what's your strategy around getting the right foundations what do we want as an organization to do what's the message we want to build reusable products we don't want to um have siloed uh production and like a terrible tech stack at the end of this um what what are we going after i guess like you know again it's not about tools like what are the use cases you're trying to unlock that will have you know deliver the most value in in the organization we can't solve everything and we can't bring in ai for every single thing but equally what are our high value use cases and you do that with the business taking time to actually get that right and then making sure that doesn't sit on a shelf somewhere um a metaphorical shelf or on a folder more realistically now uh in somebody's on somebody's laptop is um is the key thing so then you share that strategy with the business you get that buy-in but you build it with them um as well i think that's that's been really really key and i think where organizations are starting without that um you're you're shooting in the dark essentially and then you realize a few years down that you have to do a lot of rework go back and do that um after which is you don't see the return but also it just requires a lot more rework and a lot more investment in the future to undo some of the work that you've done over the years as well that was a really good point you made that around ai for ai's sake ai for everything i mean that's the other risk in this triangle situation right that everybody comes back from their training around being competent around being an ai fluent leader etc and goes right we need ai everywhere although actually you need value everywhere what is the right tools technology techniques to be able to get to that value and i know you said put in a folder somewhere i always like align it to and you probably get this mindset um a pretty picture your kids brought home from school stuck on the fridge it's great it's really exciting but it's not adding any value apart from obviously bringing the smile to your face but i think um that's that's the tricky balance to have getting the foundation is right governance structures people bought into stuff what are maybe misconceptions that you're saying organization have when it comes to you know preparing their workforce to get on the adoption journey i think the biggest misconception is really thinking that it's about training training and communications that they need to be trained on the new tools and actually ai adoption is more about mindset behaviors and trust that word that keeps coming up time and time again so I think the clients that we work with often assume that only technical teams need upskilling in my view the biggest change is for non-technical roles and to make sure that that upskilling is something that is embedded organization wide as well I think it's important that leaders see AI as a true workforce transformation the misconception is that it's a tech project and it's simply not AI only works when people understand it, when they trust it, when they feel empowered to use it.

27:50So it goes much further beyond understanding the technology and training people on the tools. There's a more deep-seated cultural transformation that needs to take place. Manso, can you tell us a little bit, how does it work when you have to ensure technology is adopted and we won't get our right out of it but also we need to get people on the journey how do you kind of like make sure tech and people move in sync rather than like you know Claire you pointed out earlier we don't want silos because that's a recipe for disaster I think they have to right the conversation shifted so much as well like it's it's not like any other technology transformation we've done before um there is more awareness of that in the businesses now um anyway it's impacting how we work how the business delivers what it delivers but also now who we work with so as you go into more of that agentic world so it's bringing it's making sure that you know you've got your um i guess your chros your people leaders as much part of the conversation as much part of that um transformation as you have the cdo and the ctos as well.

29:02So I think one of the things that we've got currently, which I think is quite impressive and forward thinking of the client is around having a steerco for the transformation, which doesn't only have your expected people like your CDO and your CTO or your CIOs, but also has the head of business strategy who sits on the transformation steerco. And that makes sure that whatever we're doing in all different streams links to business strategies. I think that becomes a meeting point. That's where both come together. But equally, you have the head of talent who owns and is accountable for part of the transformation as well.

29:40So just from the exact sponsorship perspective, it goes beyond sponsorship. I think it goes through that accountability piece and having a role in the transformation. I think that's really key. So we're not going, we're not doing things completely differently. We're looking at it from a technology data perspective. We're looking at it from an operating model perspective. We are looking at it from a people perspective. And as we're having that conversation, you've got the right people around the table to have that and they have the accountability to deliver on that as well. And then I think the other one is that we've spoken quite a lot about literacy, but I think what it really means is it's that acknowledgement that organizations that will be successful are not just ones who invest in assets and building lots of assets over a period of time.

30:25Organizations that do invest in their people, making sure they've got the right skills, making sure they've got the right mindset, making sure they feel safe enough, I think, to experiment with AI as well. Like there's a lot of skepticism. There's a lot of fear sometimes. There's still not, a lot of people will use Copilot to do, you know, to help with some of the work. But how many people will freely say that they've done that? I think there is, again, that conversation that is still, we're still on the back foot with some of it. So I think there is building that culture and making sure people have the right skills is really important.

31:01Because if you don't do that and you keep investing in and you keep reimagining how work should look like, you end up just with anxiety across the organization around all of this and people doing it in the background, not following the guardrails around it. I think it's building that open conversation as well and giving people the skills and the safety and the safe space to experiment with it. As you were talking, it made me think as well, one of the things we've done brilliantly, I think, with this organisation is not just focus on the cultural change and the upskilling of the wider business teams.

31:34actually looked inwardly from a data and AI perspective and a digital and tech perspective and gone, okay, there was upskilling needed in those teams, but it's more around the art of storytelling. And that's been super credible for me, like being able to articulate the impact with data and AI has rather than, oh, I've built this dashboard today. Well, what's the impact of that? Or I've got this problem with enterprise data management and data governance. Okay, well, what does that actually mean? How do you tell that story in an elegant way that gets business buy-in that also um gets board buy-in and how do you um articulate impact the challenges the risks and ultimately that value i think that's been really good that's very true actually so we've taken um the group a group of leaders within the data function on workshops on storytelling specifically and and there's been a lot of buy-in around it is because I think when we started back in the day, there was, you would ask questions like, you know, we're just doing the work and we need to get the work done.

32:38And yes, it makes sense to us. But where you're trying to build that business engagement, you're trying to build that, you know, business buy-in for it as well. And you're trying to tell the value story. It's a separate skill to develop as well alongside that. So it's been really good that people have really bought into it and built that skill up and, you know, that learning around that as well. So, yeah, that's a good point, Len. So Arlene, in that context then, how do you see roles evolving over time, you know, now that AI is for everybody? I think we're going to see, and I think this relates to my earlier point around that quotes the right start point to really embed AI adoption within an organisation.

33:22I think we're going to see a real shift from doing work to orchestrating work. So we know that AI is increasingly going to handle all of the routine tasks and data gathering, basic analysis. but what that means is that the human aspects of skills is becoming ever more critical so the ability to apply a strong decision making problem solving relationship building creativity and judgment those skills are going to become more important and I think every role within an organization is going to need to have a level of data fluency as well so all employees don't need to become data scientists but they do need to be ai and data literate and they need to be comfortable interpreting machine generated insights so again that level of upskilling and where historically it may have been the domain of more technical roles setting within technology or data functions it's something that we're seeing is absolutely necessary across the broader organization as well Other changes, we are expecting more hybrid roles to emerge as well.

34:37And those human skills, which I mentioned, are increasing and in value immensely. Now, if I take you to, you know, this idea of balancing quick win versus long term transformation and long term goals, Any recommendation on how to balance it? Because you can imagine, you know, you have publicly listed companies, you know, with quarterly earnings, you have private companies that do not necessarily need to go through that process. So what are you seeing in terms of showing quick wins versus yet we need to think, you know, three to five year timeline? I think from at least what we've seen, there is that point on like, you know, you've got to balance both.

35:23So there is, you're obviously, you've got investors, you've got the board that you're answering to. They want to see things quickly. They want to see you're on the right path. But you've also got people who want to see that, you know, there's school stuff happening. You've got new employees coming in and they want to see that, you know, you're an organization that's up in front when it comes to technology and you're making those investments and all that. So I think the quick wins piece comes from across like multiple stakeholder groups are interested in that. If you only do the long term goals, I think you'll just lose interest.

35:57People will just lose interest in what you're trying to do. But I think some of the things that you're, I'm not sure if it's a quick win, but I think that going back to that piece around having the right strategy is really important because that also then helps you get, be able to very clearly articulate what you'll achieve in the longer term, what you need to get there, and then equally what are the things you already are doing that are providing that that are you know adding value so going back to Claire's point around sharing the success stories early on from the things that you the products that you've already created shifting the conversation from just you know let's get another tool and building that right narrative so we've actually spent about a year I would say in just getting the right narrative across the organization on what data and AI is, what is it going to unlock for us as a business.