From FOMO to Strategy: How Haleon's CDAO Richard Moule Is Educating Leaders on AI

0:03Welcome to Data and AI Mastery, the podcast where we bring you cutting-edge insights, practical advice, and inspiring stories from the leaders shaping the future of data and AI across the globe. I'm your host, Raoul-Gabriel Urma, founder of Cambridge Spark, the leader in transformational data and AI upskilling, career development, and progression. In each episode, I will be diving into real-world case studies of companies harnessing the power of AI to drive innovation, reduce costs, and create new business opportunities. So, whether you are an aspiring data scientist, AI engineer, or seasoned executive, this show is designed to give you the tools and knowledge to stay ahead in a world where data is transforming every aspect of business.

0:48Stay ahead, stay inspired, stay masterful. Welcome to Data and AI Mastery.

0:56Hi Richard, how are you? I'm very good, how are you? Doing great. I'm really excited for our conversation today, Richard, for several reasons. One, you're the Chief Data and AI Officer at Helion, a really exciting role, really exciting company, especially because I've been using the Centrum Vitamins and the toothpaste, Sensodyne as well, and Voltron. So, you know, on a personal basis, it's very exciting. Kilian is one of the largest, you know, global healthcare consumer company. And second reason is you have such an interesting career. You've worked at EY, at Reket, and really senior leadership role.

1:32So, Richard, I can't wait to deep dive in your background and how you think about AI today. Looking forward to it.

1:45So let's get to it. So maybe before we go into the AI space, you've had such a cool career. I'd love to get a bit of an insight from your perspective. You know, you've worked in finance, in retail, in sports, now consumer healthcare. What's the thread throughout your journey? I suppose the thread is that there's always new passwords in this space, right? Going all the way back, there was advanced analytics, there was big data, there was machine learning, there's generative AI. there's always something new coming down the track, but actually at their very core, they are all the same thing. Fundamentally, it's about how do you take large data sets that are very difficult for humans to personally process, and how do you make better decisions, right?

2:31So how do I synthesize large amounts of information, you know, get to the point where I'm making a business decision, which is better than the one I would have made otherwise. And I think going back even before I got into analytics, when I worked in things like market research, I had to have university and academia, it was the same thing. I was looking at large panel data sets. And then I was looking at large retail data sets. And it's always been the same thing. And I think at the very core of it all, there's no difference between a really advanced model and a Power BI dashboard if you can make a better decision off the back of it.

3:04It's just a tool that allows you to harness And I've always found that intriguing.

3:12My next question is, what do you find exciting about the data and AI space, Troy, career, and especially today? It's just how fast moving it is. I mean, for me, I've always been a very intellectually curious individual. The idea of sort of being in a job or a function that was in stasis and that was it, that may appeal to some people. That just doesn't appeal to me. I like the idea that it's constantly breaking barriers, that there's new frontiers that we can look at. I love the challenge of the fact that actually it's an area that's often poorly understood by people outside. And if you can play that role of translator, it's almost like a modern priesthood in a way, in terms of trying to explain things to people and bring them on the journey.

3:56So it's the curiosity and the fact that the translation job that you can play in the business has always appealed to me. Amazing. and that really resonates the fast pace definitely if you're intellectually curious what a what cool times to be living right now so i guess that leads me to the the next question around in the world of we went through ai agentive ai agentic ai now i'd love for you to describe a little bit how do you think about it and uh you know as part of your role right as a cheap data and also how do you think about it and how would you think about the differences this is a conversation that's been live for me over the last um 18 months or so that i've been at hayeon which is again going back to my my first point there is a common theme through all these things they're not distinct things they are evolutions development slightly different ways of of doing similar tasks and what i've tried to explain to senior leadership and people within the business is that what you're seeing are parts of a whole it's a progression of technology they're not separate things going back to the buzzword comments as we move through those buzzwords it's not fundamental it's just a development of what has gone before and i think if people can understand that that machine learning and generative ai and llms and agentic are all the same thing they're all components of the same things and that kind of it's bringing people on the educational journey that really helps me in my role if they can understand and get their head around these concepts so i've been spent a lot of time trying to make sure that people do understand these concepts spending time on the education spending time explaining things to people that they may not initially understand how it relates to their role, but hopefully they go away afterwards thinking, actually, I do better understand this space.

5:40I can speak to people in technology, vendors, et cetera, in a more informed way. That's really interesting. So as part of your conversation, is there any misconception you had to debunk as part of that sort of educational journey? Yes. I mean, the key thing that I had to debunk was that these are absolutely separate things, right? So I think the key one, you know, the one that's topical now is really not very long ago, generative AI was everything. Everybody was running around trying to do generative AI. And now everybody wants to do agentic. And I think, you know, explaining to people that actually these things are linked, that they could be part of systems and all those kind of those kind of concepts is is important because I think if people in their minds segment them, and the most common question we get is, why aren't we doing agentic or are we doing agentic?

6:31And then when you scratch under the surface, that's not really what people mean. They're using that because that's the latest iteration of the language, not because they understand the difference between generative AI and agentic. So it's kind of almost a buzzword bingo where they're pulling out the latest term without understanding how they necessarily relate to each other. That's super interesting. and when you hear that question is there any expectations that that people have around what's the opportunity available or you know is there some FOMO that is driving are we doing agentic AI or what what do people expect to get from it there's definitely a FOMO right I mean there's clearly an element where there's pressure internally and very much externally whether it be from from board investors from general expectations of a company of our size that we should be doing it.

7:20I always have a more structured strategic approach to how you approach these types of things. So I think it's really more about, again, taking people through and explaining, actually, what is the role that something like a Gentic can play? What is it not relevant to? Because it's not the answer to everything. There are much more basic capabilities in many circumstances that meet your need. So people come to me and they say, I want this. And I say, that's okay that's a bit of machine learning we already have that capability we can deploy that yeah but i want a gentic um yeah but you don't need a gentic to do that right so we're trying to work out what are the what's the right tool for the right job um and this goes back around to the kind of educational discussion which is you know i need people with some knowledge i need people with a reasonable level of knowledge where they know not to ask me for a specific technology they understand technologies and how they are um slightly different to each other but they're asking me for solutions and they understand that then we will look at the technology solutions in the background and come back with the best fit that's the way that we want to operate not people saying i want to gentic when it's not the it's not the answer to their requirement that's great and i guess when it comes to education what are some of the initiatives that you would recommend or you have put in place to i guess address that gap and ensure that everyone can really work together with the same sort of fundamental base?

8:43So we started at the top of the organization, right? So we started with Cambridge Spark and we decided to go executive team and executive team minus one and go for really in-depth educational program, you know, where they're dedicating significant amounts of time to understanding this topic. Brian, our CEO, was very clear that he wanted us to be the best educated leadership team in the FTSE 100. when it came to AI. And we made sure that the leadership team goes first, that we're led by people with a very good understanding in this space. But we're also deploying best-in-class platforms across the organization where people can go on learning journeys to whatever extent they want to.

9:25So if they want a basic understanding of terminology, et cetera, the foundational things that they need to understand, they can do that. If they want to go all the way through into how do I do, you know, how do I operate in low-code environments, certified coding, all the way through to actual coding, they can do that as well. And we're now developing it to the extent where we're looking at roles and job families and saying for you in your role as an analyst in finance, you should probably do these 10 modules. If you're somebody working in technology and data science, you know, an early part of your career, you should go all the way through this journey and do all 20 modules and get to a very high level of skill.

10:02So we've gone from a very low knowledge environment when I joined 18 months ago. And I think actually we're now probably par for the course. You know, we're probably average for the industry. By the time we fully implemented this, I want us to be very much industry leading in terms of our knowledge. Wow, that's super cool to see the commitment to investing into your people, bringing them on the journey and ensure everybody has the knowledge and skills need to succeed. So that's really cool to see. I 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.

10:41Every new follow helps us reach more people and shed incredible work being done by today's Data and AI leader. All right, let's go back to the episode. I was thinking, now that you've invested so much in getting people on the journey and have the knowledge in AI, you must get a lot of demand and interest for, hey, we can use AI in my business function, agentic AI, can you help me with certain projects? So how do you think about ownership in this environment where everybody wants to get on the journey? So I think this is where you start to get to a strategy discussion. People throw around strategy a lot.

11:23Very often it's not quite what they mean. in this circumstance when there are very significant pieces of work that need to be done to enable what people want in terms of everything from the basics of master data. And I say basics, it's very complicated, but master data management or all of those kind of elements, engineering, you can't just turn it on. There isn't, you know, I know a lot of people want there to be, but there isn't a magic AI tap that we can turn on. You have to do all the foundational work. And so we are working on that. We're working on what is the tech strategy to make this possible.

11:56And that's a roadmap over X number of years where we start with certain areas of the business and we'll work our way through the organization. I don't want the rest of the company, the business to worry about that. What I want them to worry about is what is their business strategy and where given the knowledge that they now have in that senior leadership cohort, where do they now believe that AI could play a role in that journey, right? So they're better informed, much better informed than they were 12 months ago. We've had some really enriched conversations now where they're coming to me sort of saying, I think there's an opportunity here.

12:29Whereas before it was, as I said, it was just I want to do a Gentic. That's not the conversation we want to have. We want to have conversations saying, I'm trying to achieve this outcome. To do that, I need best in class forecasting that works at every level of the organization. That's OK. We can do that. We have solutions that deliver your business requirement, but they now have the knowledge to know that AI can play a role in that and that it can deliver value for them. So much better conversations, business strategy, tech strategy, marrying up. Wow, I love it. That's a fantastic evolution, right?

13:01So if I play this back, there's some foundational sort of infrastructure, data and so on that is required and you don't want people to worry about this. you know and like it's it's it's available but now it sounds like the conversation has shifted very much like business outcomes problem solving but using common language uh that we now have in the air world and understanding of the art of the possible so if we take that i guess to the next step richard is say now we want to implement and deliver on those business outcomes and actually make the work happen how do you think about ownership of ai spend and ai tools you know do see this as centralized or different business function would have ownership around their own sort of a implementation?

13:44How do you think about it? So there's two, there's two big elements to this, I think. One is oversight and governance. And one is, you know, this isn't free, it costs money. And I think there is, there is a little bit of a conception that the build costs money. But once you've got it running, it's kind of it's a free resource. It's not right. And we need to start having conversations about the fact that how does this process work today? What does this process cost to run? It could be external vendors, it could be contractors, it could be lots of technology that we use. There's lots of different things that we do.

14:15If we replace this with an AI system, particularly anything in the sort of agentic realm, what does that cost to run? We need to start comparing those things. The cost longer term will evolve, or comes up in a second. The key thing we're looking at at the moment as we move into an agentic world is governance, though. We've always had strong governments at Haley, and I was very impressed when I joined how advanced it already was in terms of our responsible AI programs and how we made sure that people weren't taking risks with the company's reputation and so on and so forth. So that's very strong.

14:46But that's in a world where we were building select standalone use cases, right? So we're deploying a generative AI capability, that kind of thing. Agentic is materially different. It fundamentally runs processes in the business end to end. And I think once when we're building those things, it needs to remain centralized. You need people who understand what they're doing. You need to understand people who understand how to build efficiency, how to build cost models. But once it's built and it moves to BAU, that should naturally migrate to owners within the business. Tech will stay involved from an oversight and governance perspective, those kind of things.

15:22But it should be owned by a business function. We need them to own their usage, own their budgets, be responsible for how they use this technology. and then I think they'll be more disciplined in terms of how to use it. We shall see. We'll see. I mean, it's really amazing to hear your perspective and really refreshing. I like what you said around clearly agent TKI. I guess the complexity and the risk has grown big time with all those agents owning end-to-end workflows, like you've said, and that requires expertise. At the same time, I imagine from a business function, you want to go as fast as you can and start to show results so how do you find the right balance between governance innovation and speed and keeping everyone happy or all the stakeholders involved so the the most common phrase that i use with my team is we've got to raise the floor and raise the ceiling right um and what i mean by that is going back to the stuff i was just talking about about you know the foundational work that needs to be done i'm super lucky in an executive team who I've explained this to, they give me the time to walk them through a topic that probably isn't top of their agenda, but to explain why it matters and actually how, if we do the foundational work I'm asking them to let me do, we will leap ahead when it's done.

16:41We will accelerate away from our competition. So I think that's important, but people still want shiny new things. And I think a strategy that says, could you just wait five years while we fix everything over here, it's just not going to fly, right? So what we're looking at is where is the low-hanging fruit? Where can we build capabilities rapidly? Now, the answer to that question is typically in unstructured data, right? So where do we have unstructured data today, documentation, that kind of thing, where we can start to harness this technology to accelerate things so people feel that they're deriving value, that we're moving forward, that we're building the principles and the discipline in teams and functions so they're starting to build this into their day-to-day lives while we build some of the stickier, large, big data problems in the background.

17:27So as we work our way through those, and we are systematically working our way through them rapidly, we're still giving people capabilities and tools so the organization feels like it's progressing. So we raise the floor by fixing the foundations. We raise the ceiling by giving people a sense that the technology is coming, they're deriving value from it. Great. Sounds like you really need a balanced approach, kind of like, like you say, tackle both ends at the same time to drive the value. And speaking of value, how do you think about measurements of value creation? Because having spoken to many clients and leaders, there's definitely a big focus right now on cost savings, automations for obvious reasons.

18:09AI can do really well. But there's still some question marks around growth levels when it comes to customer experience, delight, revenue creation, and so on. how do you think about it and how do you find the balance between those two or is there any specific focus you think right now in the market so i think the stickiest problem in this space today is visible pnl value from from these builds um now it's not because these builds don't deliver value it's because people haven't quite got used to the concept this isn't a halion problem this is an industry-wide problem but i think it fundamentally comes down to having much stricter business cases in terms of how you deploy these things i'm going to spend x with an understanding that it will deliver y and it will deliver y from these 10 outcomes um you know so i think if we as we start to scale it we'll have to move into that world um but it's going to be tricky because people aren't used to it that's a great uh wisdom that you're sharing we're not used to costing a process in its own right and think about all the resources involved you know labor contract and it spend and all of that so just starting documenting that before you know you can actually improve it automate and so on but there's probably a big lag from we've automated this process or parts of it and realizing the benefits of automation uh from a resource point of view so that that takes time also to cascade through so it it does it takes a lot of time to cascade through but but again it's it's understanding all the dimensions of costs that sit around it right and i think going back to some of the work i used to do at ey that was that was part of my role was doing process mapping understanding what it takes to take out a mortgage and a lot of people who sit within a within a bank they make they see one stage of that process as an example and they go well that's how you take out a mortgage but it's it's a process that goes on over 18 months it's a huge undertaking and there's multiple touch points and it's the same within our business to get things done within our business you see the five percent of the process you work on but actually to expand that out into a much broader understanding of how the business works is going to be interesting and i think some of the transformation programs that we have operating today will very much be looking at that kind of that kind of thing so that that maturity and that thinking is is absolutely coming out here great so it sounds like in order to realize you know the the benefits of agentic care across the whole organization and in any organization having people that you know have the knowledge and skills to think about process identification defining it costing it mapping it and then using ai around it it's going to be extremely beneficial skill set to have around no it will and we we've already started doing some of these these things and you know the things that a lot of ai is good at is back-end automation type stuff, right?

21:05It's not the cool stuff that people necessarily want to hear about, but it is good at it. And I'm talking about things like invoices and, you know, all of those kind of things. And that's where we started. We started looking at processes that actually, when you look at them in the round, they're hundreds of hours of people's time. It's being spent on absolutely pointless menial tasks of matching invoices and dates and times and all of those kind of things. That's where the short-term unlock is going to be because they're identifiable processes. We know the process exists. We know all the people that work on it.

21:37We know all the people who work on it don't enjoy it. We know they could be doing something much higher value for the business, like out there doing sales calls, dealing with retailers, that kind of thing. That's perfect. Let's go for that because it's a limited process that we can observe. And we'll work our way through these processes and start automating them one by one. Yeah, it's quite exciting stuff. Yeah, super exciting. And I guess to counter that a little bit, Is there any use cases, you know, we're concerned that you don't think we're ready yet or, you know, it's not quite working? Yeah, I think the biggest barrier I've noted is in spaces where we have a high degree of human interaction today.

22:20So, and what I mean by that is we have some processes in the business where the end-to-end process looks long. And, you know, it is long. It could be, you know, four or five weeks to get something relatively simple done. But when we've broken those processes down and looked at them, 75 % of that is human negotiation and interaction. It's not actually a technical process. It's about people deciding, is it black, is it white, or is it gray? Let's get to the gray and move forward. I think that's very hard for AI to address that kind of thing. It can truncate those kind of processes a little bit. But if it is fundamentally at its heart a negotiation, it can't really help with that.

22:58It can provide better data for better conversations and all that kind of stuff, but it can't change human nature. Yeah, that's fascinating to hear. And I guess it shows the importance of a human loop and human judgment, you know, as kind of fundamentals. What keeps you up at night when it comes to AI transformation and, you know, also fellow leaders like yourself in the industry? It depends on the day. So lots of things. But the main thing that keeps me up at night is a lack of discipline, I think. you know again going back to how do we how do we do this over time we we can go for a model where you know a thousand flowers bloom and all that kind of stuff but i suspect a thousand flowers will wilt um and i suspect that will lead to fatigue and it will lead to people becoming disillusioned with this space um we have to move through this in a disciplined way if we genuinely want to unlock value and so particularly given my role there's lots of people in my team who are doing things which are much more technical and on the ground, I feel like my role is to be the overall shepherd to make sure that we stay on track with the direction of trouble I want this to move in, which is, yes, we're doing the foundations.

24:05Yes, we're doing the hard yards in terms of master data management. Yes, we're reducing the complexity of our estate. Nobody cares about these things, but I know they need to happen for us to win in the future. So I'm trying to make the organization move at a pace and discipline that means that we're moving in an appropriate way and not trying to build things on you know business functions or data sets or areas that simply aren't ready for it because it's it's a waste of our time and effort

24:36hey richard i could uh talk to you for hours i love the passion and uh clearly your your commitment to to the success of this transformation but uh i need to take you to a quick fire round of questions is that okay i'm ready all right so in the age of ai how do you think about hiring is there what do you look for now in people you're hiring is there like a difference compared to maybe five or ten years ago so there's a couple of things really i think if i'm looking at you know what kind of questions i would ask people now rather than compared to a few years ago i want to understand how people are harnessing technology doesn't have to be ai just to be clear how they understand how they harness technology to improve their own personal productivity right so you know i'm not one of these people who thinks that ai is going to replace people on maths i think it will augment people i think will become more efficient they'll become faster at what they do roles will change all of those kind of things but people will become augmented um and i think the funny thing is in their private lives people are ai natives already they just in many cases they don't realize it their mobile phone netflix their bank amazon they're using ai every day most people now the younger people are using chat gpt all the time so that their ai natives in the same way that people were digital natives 10 15 years ago that needs to come into the workplace and people need to you know be less resistant to it in the workplace and understand that all they're doing is augmenting themselves and making themselves more efficient in terms of how they they operate so do they understand that concept of augmentation how are they seeing it are they how did they done that in their work in their work life is an important thing for me fantastic any recommendations on how to stay up to date in this space at the moment like you know podcast or resources like how how do you ensure that yourself you know you're up to date in this really fast moving environment um well i i take the call so i speak to i speak to people who say they've got new ideas um it's you know it's a there's a certain percentage of my time i can dedicate to that frankly but i do dedicate the time the time to it if people say they've got something genuinely new they want to show me i'm prepared to to listen um i do make i'm at london tech week next week so i go along to the conferences i walk the floors i meet people listen to what they've got um and i think it's you know you've got to you've got to have an outside in perspective in my kind of role you have to be able to bring that in i was at sap sapphire in um madrid it was a hardship for me to go to Madrid for the week, as you can imagine.

27:09So I was there the whole week looking at what they got, listening to what people were saying about their capabilities. So it's an important part of what I do. Amazing. Yeah. So conferences, staying open-minded to new ideas and so on. This seems really important. I can ask you a couple of more personal questions. Sure. What's the best piece of career advice you've ever received? I think the best piece of career advice I received was from a partner when I was at EY. And he basically said, always look for the overlapping self-interest. And it sounded quite Machiavellian at the time. But he basically, his point was, that's how you build alliances, right?

27:46So effectively, by understanding not what is difference in terms of what you're trying to achieve and what I'm trying to achieve, identifying the overlap of your self-interest is how you build an alliance with somebody and start to work together. And that can then develop into better and better relationships. if you map everybody in your environment around what your overlapping self-interests are you start to create networks of people who have the same interest in achieving things and i've always applied that in my career since oh that's really really insightful advice actually my next two questions are a bit more on the fun side so what was your favorite subject back at school i would say history but i took politics and onto university but i think probably you can see behind me it's it's it's either ai books or history one way or another so probably history i should have done a history degree in my life would have gone a different way amazing so i guess the history of ai must be or intelligence uh could be a really interesting uh yes there's books over there about about that exactly that beautiful and final question what's your favorite music genre music genre i'm a i'm a kid of the 80s 90s i'm afraid so much to my children's disappointment.

28:54I listen to a lot of grunge and that kind of thing. Excellent. To be fair, my son's kind of into it. My son walks around the house in a Red Hot Chili Peppers t-shirt. So he's on board with my music taste, which is all good. Playing Californication. Yes, exactly. Beautiful. Well, Richard, it's been an absolute delight to have you on the show today. I've really enjoyed the conversation. Really enjoyed it. Thanks a lot.

29:24Such a fantastic conversation with Richard. I could speak with him for hours and there's so many really interesting nuggets to recap. So the first one was, in a world that is moving so fast, we've got AI, Gen AI, LLMs, agentic AI, it's really easy to get excited on technology. But at the end of the day, what really matters are business outcomes, right? So we talked about the importance of upskilling leaders to be AI fluent, but we've focused on business outcomes. What is it going to do for your business function? How do you measure success? What's the ROI? And once you've got that, an AI team with business team can really collaborate really, really effectively.

30:04So that was really interesting. Secondly, I really like what Richard said about, you know, as part of transformation, you got to raise the floor, but also the ceiling and the art of the possible. And it's a balancing act. You got to get people on the journey and be competent with the tools. But you also got to think about, you know, growth and new experiences that you can deliver with AI. And on this topic, processes and how often an organization will not spend enough time understanding the true cost of a process, the resources involved, labor, contractors, IT, and so on, so that when you use AI or when you automate it, you have a clear benchmark so you can, like, evidence the success.

30:48So more organizations really need to think about first principles spend more time understanding their own workflow, their own processes before they jump on the AI journey so that, you know, the business case is really, really clear. So thank you everybody for listening and see you on the next episode. Thank you for tuning into this episode of Data and AI Mastery. If you found value in today's discussion, make sure to subscribe so you never miss an insight from the leaders driving the future of data and AI. And if you're a data and AI leader looking to upskill your workforce with the fundamental data and AI skills to transform your business, Cambridge Spark is here to guide you every step of the way.

31:29Be sure to reach out to us on LinkedIn or on our website, cambridgespark.com. Until then, be sure to keep pushing the boundaries of what's possible with data. And remember, mastery comes with continued learning and action. Until next time, stay ahead, stay inspired and stay masterful.