From Cybersecurity to AI Director: Sarah Self on Leading Transformation at Aviva

0:00I think the bit you have to avoid is just the playing for playing's sake. So even if you are experimenting and learning and innovating on maybe small things that you think might become bigger or might become more impactful, that they still have to have a purpose or an intent. It can't just be because I liked it or I thought it looked cool or, you know, someone else did it. It always has to be tied back to there's a reason why I'm looking at this thing. There's a reason why I'm experimenting with it, because I can see a tie into something good that it could do for us.

0:33Welcome 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-Urmer, 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.

1:18Stay ahead, stay inspired, stay masterful. Welcome to Data and AI Mastery. Hi, Sarah. How are you? I'm good, thank you. How are you? Amazing. Really looking forward to our conversation, Sarah. You have such a fascinating background.

1:40Well, so Sarah, you're the Aviva's AI Director. That's a really exciting role and also former CISO. And I think that's such an amazing blend. So I do have to ask you, how did that come about? How did your career go into cybersecurity and then into AI? Wow. So I think you could describe my career in sort of three areas. So one of those is leadership. I love leading teams. The second is transformation. So actually anything transformational, I am really drawn to. And the third is technology. And so actually, when you think about those three things, it was the leadership, it was the technology, it was the transformation that drew me into cybersecurity.

2:25And that was where I specialized for, gosh, close to a decade, I think. But actually, it's the same things that pulled me towards artificial intelligence, you know, that opportunity to think broadly, to really transform an organization for the good. So those things kind of carried me through. They're very similar, actually, across the two areas. And what sort of similarities? So if you think about cybersecurity, you have to think quite broadly. You have to think people, process, technology. That is completely aligned with how you think about artificial intelligence. Again, it's people, process, technology.

2:59It's quite a broad spectrum of interest. It's the ever-changing nature of it. So in cybersecurity, you're constantly dealing with cyber criminals who are changing their tactics, who are upping their game. I often have described them as, you know, if you take the kind of the criminality and the immorality and put it to one side, they're actually fantastic entrepreneurs. They are constantly evolving. They're constantly looking at different ways to do things and different ways to attack you. Now, fortunately, with AI, we're not thinking about something attacking us, but we are thinking about something that's constantly evolving at a pace, which is phenomenal.

3:35So you do have to think about that, what's happening next, what's happening next. And I love that energy. I just love that constant change. And I love that constant learning, actually, and curiosity. And I think to be successful in either one of cybersecurity, artificial intelligence, and obviously there's a bit of an overlap between the two at times, you have to have that desire for what's coming down the road. Amazing.

4:01When I think about cybersecurity, I think about, you know, how can we secure the organization, how we can help, you know, the workforce think about risks. and when I think about AI, you know, I'm thinking about how do we drive innovation, how do we deliver new outcomes and impact for customers, you know, externally or also internally. What sort of parallels do you see in terms of transformation? Because it feels like, you know, when you transform the organization to be more secure, to innovate, there's a bunch of risks involved and I just wonder if your background in cybersecurity and a sort of risk mindset, you know, is really helpful when it comes to AI as well.

4:41It is because actually if you, they're both about enabling. So I think one of the kind of the things that people often get wrong actually about cybersecurity is they think about it as, oh, it's stopping stuff. And of course you're trying to stop the attacks. But actually the reason why you're trying to stop that happening is because you're trying to enable something for a business or enable something for a customer. You're not doing it to stop kind of business because actually the only, then you are stopping the business. It ceases to exist. So in that way, it's exactly the same, that you're trying to enable kind of the right things to happen.

5:14You're trying to enable the business to utilise the technology in the right manners, to drive the right outcomes and the right benefits, but you want to make it happen. So what you're looking for is you're looking for what are the controls that need to be in place that make it as frictionless as possible? How do you educate people to use things well and to know what to do and what not to do? Because actually you want the outcome. So in that way, again, it's all enablement. You are enabling kind of the business to benefit from whatever it is you're doing. And you want that to happen? I'm curious, how do you balance the risk-reward trade-off?

5:51Because if we think about AI, clearly it can drive a lot of customer value, but you're working with customer data, models are not perfect, they can still hallucinate, make things up. So I guess in the context of such a large scale, how do you balance risk versus customer outcomes? It is a balance, is the first thing to say, but you set really clear guardrails. So actually the outcome that you're trying to get to, So when you say we want to get a customer benefit, we want to do a really improved service or value proposition for a customer. But it's always with a and we need to make sure that we do that safely.

6:25Because if you go for sort of a quick buck on something, which isn't what we would do, it's not what we're about as an organisation. It's about that long term value. Then actually you won't get that if you do it without kind of the right controls and the right guardrails being in place. You might potentially lose customer trust. and that's something that we would never be comfortable doing. So you architect right from the start and you don't look for an either or trade-off. You look to say, how do I reach that customer outcome with the right guardrails and controls and protections in place? And actually, if I can't do it with the right guardrails, controls, protections, then it's possibly not the right thing to be doing.

7:05And I think when you're very clear in terms of what's the outcome you're trying to get to and what does good look like, then actually it's not a trade-off. Because they're one and the same thing. They're all requirements to get to a quality outcome. If I turn around to a customer and said, we've done this thing really, really quickly and really, really cheaply, but actually we didn't protect your data along the way or we behaved unethically along the way, then that's not a good customer outcome. So actually, it's a total failure. The good customer outcome is we've delivered something to you really quickly and we've delivered it to you at a really good price point.

7:39Your data is being completely protected throughout that journey. We've managed it in a sustainable and an ethical manner. That's the customer outcome that they actually wanted. And if you define it in that manner right from the start and you architect in that way right from the start, then actually you're generally in a good position. There's not a trade-off. That's how you do it well. So what does it take in practice to implement a sort of guardrails, the sort of red lines? how does that cascade through in in the organization when you know individuals are implementing projects would love to to kind of understand what does it take yeah so so you have to think um you have to think broadly so technology people and process you need to think about think about all three certainly for us it has been core that you architect in the right guardrails and the right controls from day one and so when you build out whatever the technology solutions are that you're using?

8:35How are you going to use the LLMs in your organisation? Actually having those guardrails in right from the start is really important. The way that we approached that was actually that we built our own platform. We didn't build LLMs, we utilised kind of the industry-leading LLMs, but we built a platform that meant that all of our scale productionised solutions are built out from that platform and that inherently comes with the guardrails associated. So we made sure that, You know, we worked with the right third parties. We made sure that actually we put in, you know, transparency controls, hallucination controls right from day one.

9:12So you do have to architect in that manner and you do have to invest and make sure that you set up in that way. You then need to think broadly. I've mentioned kind of process and people. You know, they are all part of the solution. So how do you educate the people who are going to be using the different technologies or the different solutions? How do you make sure that you are helping them to understand a little bit about AI? How do they use these things? What should they see? If they see or think certain things, what do they do? Making sure that you've got processes aligned to that. So you do have to think broadly around it, but I don't think it's any different necessarily to delivering any other key transformation.

9:56You still have to think about people and process and technology. This is just a different technology that you're working with that comes with some different challenges and some different risks that you need to understand. And you need to make sure that, you know, you factor those in right from the start. 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. Every new follow helps us reach more people and shed incredible work being done by today's Data and AI leader.

10:28All right, let's go back to the episode. you said Sarah that you know it's really important to bring people on the journey and I know you're a big advocate for you know internal upskilling democratizing AI capability across the organization so I'd love to ask you why is that important and you know what are some examples of what you're doing at Aviva yeah oh god it's I it's a personal kind of angle I completely believe in the democratisation of AI. And it's certainly something that we've led with in Aviva as well. So why do I believe that? That's probably a bit of a multifaceted answer is the truth.

11:07Firstly, you can learn a lot from what's happened in the past. So if I use the digital revolution, actually what we saw there was people who had access to the right capabilities, there's a socio-economic advantage to that. So actually, culturally, broadly, I think people having access to things which are fundamentally a power for good in their lives, which I believe AI is a force for good, that is helpful and valuable for human beings and for people in general. So there's kind of almost on that level, you know, I believe it. I also think that it's incredibly important for culture in the organisation.

11:49There's so many headlines around about AI out there and some are true, some aren't, some are quite alarmist. I think the way to help people through that noise, and it's a lot, is to allow them to use things, to allow them to experience things and to know and understand what that means for them in their day-to-day life. If you keep it as a thing over there that only a select group of people are using, I think that adds to the fear, actually. I think that adds to the discomfort. Once you can see something yourself, once you can have a go at using it yourself, all of a sudden you go, oh, OK, I get that now.

12:26I can see why that's sensible. So that's sort of almost the second bit. And then the third bit is probably actually a bit more tied to the kind of commercial benefits, maybe. I have never seen any kind of technology that you need to use. You know, this is so humanistic in kind of how you chat to it. What I have found with just about everybody is they all have a hard moment when they get involved and they use it. And then they start to become more effective and you start to see people welcoming actually the capability into their day to day roles. And I think if you don't do that, that's the commercial angle of it, actually, people will resist.

13:09So if you try to do something really brilliant across the Viva, it's a great customer outcome. It's a brilliant project. It's going to save time. It's going to save money. It's delivering a better outcome. If people feel scared about it, then you will have, I tend to say, organ rejection. Whether it's good or not, if they're scared of it, they'll just go, I don't want it. I don't want it. So you have to drive that kind of culture and that adoption and that sort of natural curiosity to actually get some of the commercial business benefits, as well as thinking about the sort of broader societal picture.

13:43That's wonderful. I love the passion. It's really coming across. And it's so good to hear so many initiatives being run to support people on the journey from learning to training to internal communities and forums to break down the barriers. I mean, that's really fantastic. I smiled when you said, you know, it's an organ rejection. That's such a good analogy. You know, you want to make sure people are supportive. If you want to deliver your transformation, there's no other way. Which leads me to, I guess, the next question, right? Let's say you've got your technology sorted. You've got rails. You've got, you know, programs to support people on a journey.

14:19How do you go about identifying those use cases that, you know, the business will benefit from? Could you give us some examples at Aviva? what gets you excited with AI? We started very much with a view of we wanted to drive customer value and benefit so we wanted to look for solutions that we believed would improve the customer experience improve the customer journey and so that again it drills you into an area quite quite easily if you like if you know that that's the outcome you're trying to get to then you look for areas where you say where is that potentially less efficient for a customer than it could be?

14:53Where is that slower? Or what is the problem that we want to solve? And that led us to some of our early successes. So one of the first things that we put into production and we scaled and then we shared out across multiple products and markets, very proud of that, was a claim summarization solution. And just very simply, our claims handlers that talk directly to customers do an amazing job. But the first thing they have to do when a customer comes on the line is they have to get themselves familiar with that case. So who is this customer? Why are they calling in? What's the case history? What's happened already on this claim?

15:31And invariably, the way that they do that is they say, hello, Mr. Self, nice to see you. Just bear with me one moment whilst I get myself familiar with what's going on. And they put the customer on hold and they read. And the customer sits there on hold. So actually one of the first things we did was we used summarization capability to provide those claims handlers a really succinct but accurate view of the case history all in one place, really easily digestible that they could take accurately and very quickly pick up to the customer. So it reduced those hold times by over 50%. So customer gets a far better experience.

16:13The claim handler loves it because actually they don't want to put the customer on hold. It was easier for them. It was better for the customer. Sounds really simple, but it's a great use of AI. Other ones that I love are things like our medical underwriting, which we're industry leading, very pleased to say around that. And again, this was supporting our medical underwriters. So we have a really inclusive medical underwriting policy at Aviva. And that means that sometimes you are underwriting really complicated personal medical histories. And the way in which you do that and understand that is you look at a huge amount of data.

16:50So again, we delivered capability that meant that we were accelerating how the underwriters could go through that process with 99 % accuracy, you know, brilliant kind of service. And actually, we can therefore get back to customers more quickly, we can go through the process more quickly, we can be confident with the outcomes and their problems that you were relatively, they're well fitted for AI capability. So you've matched them together really nicely, but they had a really clear purpose and intent that was aligned to customer value and benefit. And you could measure that. You can measure how long a process takes you.

17:27I love those two examples because you can drive efficiency and productivity while at the same time driving customer satisfaction. It's a double whammy and And those are always the best implementations. Look, here's a question, I guess, more guidance for other organizations. You mentioned this sort of dilemma between experimentation and also production. How can you balance the two of them? Like, you know, clearly, if you've got a couple of big bets that are going to drive business value, you're going to fall in on them and you get them to production. But at the same time, you don't want to miss on experimenting and seeing what could work in the future.

18:05So how would you balance that, I guess, as a portfolio? guidance? I think you used the right word. Actually, you talked about portfolio. And I think you immediately have to recognize that as in a portfolio, you need some of those different things. I think the bit you have to avoid is just the playing for playing sake. So even if you are experimenting and learning and innovating on maybe small things that you think might become bigger or might become more impactful, that they still have to have a purpose or an intent. It can't just be because I liked it or I thought it looked cool or, you know, someone else did it.

18:38It always has to be tied back to there's a reason why I'm looking at this thing. There's a reason why I'm experimenting with it because I can see a tie into something good that it could do for us. If you start everything with that intent, then actually it's helpful. And then you do carve it up into portfolio. You say, well, actually, what are the big ticket items which we really want to focus on that we believe are big transformational opportunities and make sure that you carve out some time, some money, some capability and capacity to do the different things along the spectrum. I mean, we're quite, I think that's where we're quite fortunate in sort of large businesses like ours is we do work with brilliant people across, you know, we have innovation teams, you know, so you've got that capacity and you've got that, you know, you've got those different skill sets recognized to do kind of those things at different ends of the scale.

19:32I think we're very fortunate in that, you know, we've got amazing kind of transformation teams, business teams. We've got people whose job is focusing into innovation. I think that gives you a real advantage. I think if you're not in sort of the scale of organization that we're in, though, I go back to the word that you used, portfolio. Think of it as a portfolio and make sure that you carve out a little bit that goes along along that spectrum but do it all with a purpose do it all with a view that says you know there's a reason why i'm bothering to do this in the first place and if you can't articulate that in really clear strategically aligned customer outcomes then you're probably just playing with something that you wanted to have a play with yeah it sounds like uh you know we want to move out from just uh having fun but really aligning to to the business outcomes and the organization so so important to have this sort of framework i guess you know everyone's really excited about ai we've talked about the opportunities the outcomes but it'd be great to get your perspective on you know what are the remaining challenges and try and balance out a little bit the hype?

20:43Gosh, I mean, I saw a really interesting thing the other day and it was a graphic and it showed, and it basically showed kind of how far, like how many people kind of across the globe are utilizing AI and how many of them are utilizing it to sort of a deep extent. And if you imagine, you know, it's a big box and it had sort of a blob in it for X many millions of people were represented in each blob and it was only down at the tiny end down here that were actually people who were on that journey and I share that because actually we are so early so I think it's really hard to say what are the what are the challenges that we're yet to because I think we're so early that there is a huge amount that you know we just don't know and we just don't understand yet so you have to go into all of this with I sort of often say to people two things like curiosity and critical thinking so really embracing kind of curiosity and critical thinking and recognizing that we are just at the beginning of this there's a load of hype around it and and I think some of that hype will die down a little bit as well I think some of that hype will start to kind of ebb away a little bit because people go oh hang on a minute we're over hyping this thing I then think we'll actually find that we're probably under hyping some other things and we just don't know them yet.

22:04They're just so early along that journey. I think we, another thing that often plays in my mind is I think kind of through digital revolution again, and I think about social media. I'm a mum, I've got two daughters. And so I think of a lot about social media and kind of how do they utilise technology and be on platforms safely. And, you know, I'm an advocate for them using, I want them to use things safely and well. the I often think back though and I think if we knew now what we know about social media if we knew that however many years ago what would we have done differently then and I often ask myself the same question with AI it's an unanswerable question I don't have the answer by the way but I think asking that question all the time saying what do we think that might be in the future where do we think that might go again that goes to that critical thinking and curiosity is really important.

23:00What do we think we should do now that might be sensible for the future? And I think occasionally just putting the brakes on and having those thoughts and broad conversation is really important because this isn't a technology thing. This is a society, people, the way we work, the way we live thing. And we all need to engage in that conversation and think about it and try and make as sensible a choices as we can and recognize when maybe we didn't make sensible choice that we need to just change that and do something differently all of that is way easier said than done but that is the nature of what we're dealing with here is we need to recognize that it is big it is monumental so sarah can i take you to a quickfire round of question.

23:51First question is, you mentioned aha moment earlier. So I do wonder, what was your moment when it comes to AI? Do you know what, for me, it was really early on, actually, it was a chat GPT thing. I'll be honest, I was playing around and I suddenly realized that I was enjoying the experience I was getting back a quality of response that was different to what I'd had previously and I suddenly and I'd been involved for a while before that but the aha moment was I think just the different emotion that I was feeling utilizing it and there was something in that emotion that made me go oh this is really different this isn't you know just about clever a technology this is making me as a human being feel differently about the interaction and it's giving me it's making me feel a different level of value somehow in what I'm getting back and that for me was the aha moment when it kind of it switched I think a bit from the the sort of the this is very very clever mind-blowing technology to hang on a minute this is fundamentally going to shift the way that I interact with technology as a human being.

25:12And if it's fundamentally going to shift the way that I interact, then actually that's true for everybody. Hey, Sarah, it's been a real pleasure having you on the podcast today. It's been a great conversation. Thank you. No problem. Thank you for having me.

25:32really enjoyable conversation with sarah today aviva's ai director i love her background experience in cyber security and ai and how we could do parallel in those two worlds and the other day it's all about technology people and process and you really to align those three together to deliver a transformation. And clearly, Sarah is so passionate about people and how to bring them on the journey. So we discussed about the importance of training and also internal communities to ensure that everybody has a chance to be part of the AI transformation. Now, we also talked about a range of skills that are even more important in a world that is moving really fast.

26:16And with AI, there's a lot of uncertainties. And those two skills were curiosity and critical thinking. Those are skills that are really worth investing in. Finally, what I really liked, we talked about a couple of use cases where AI can drive productivity and also drive customer value. So we talked about case summarization and underwriting. You know, usually you have one or the other one. In those two scenarios, you could drive efficiency, productivity, but also deliver beautiful outcome back to the customer. So thank you everybody for listening and see you on the next episode.

26:53Thank 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. Be 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.