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- [00:00]
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- [02:22]
Mia Sorrenti
Every year, technology stretches the definition of progress to new horizons. But with the advance of AI and other technologies, the very idea of Progress doesn't seem to accommodate the levels of growth we continue to see. Every new discovery is the beginning of a new beginning. AI will soon be surpassed as the latest innovation by either quantum computing, hyper automation or a new endeavor which has yet to be discovered. Hello and welcome to the age to come, brought to you by Intelligence Squared in partnership with IBM. I'm producer Mia Sorrenti. Throughout this year long program, we will explore together how new technologies will reshape human experiences over the arc of a lifetime. And you're about to hear the first of those live conversations, recorded live in London. Journalist Kamal Ahmed explores how innovation is the driving force behind meaningful growth. Kamal was joined by guests Prashant Jajodia, Laura Gilbert and Lee Ellis, who together examined what sustainable, resilient growth looks like for the UK and how empowering both businesses and individuals can push the boundaries of what is possible while securing future prosperity. Let's join Kamal, Laura, Prashant and Lee now with more.
- [03:30]
Kamal Ahmed
Thank you Intelligence Squared and thank you IBM, and welcome. There's a very, very big word behind me and that word is growth. Although intelligence is slightly longer, but the word growth and I think this is going to be an excellent conversation with some experts in the field around how we know there is something. We know it is transformational, but how it leads to growth and equitable growth, not only here in the uk, but also all around the world is something that is at the top of every executive, every politician, every parent, every young person looking for their first job is at the top of their priority inbox. And to discuss this and to give us some examples of where things can really work, I am joined by a marvellous set of people. Now to my left. I don't know whether that means politically, but who knows? Laura, we may find out, is Dr. Laura Gilbert. She's Visiting professor in practice at the School of Public Policy. She's also senior director of AI and head of the AI government program, the Tony Blair Institute. And from September 2020 to January 2025, Laura worked at 10 Downing street as the founding director of 10DS, the data science and analytics team and the incubator for artificial intelligence. Welcome, Laura. It'll be fascinating to hear your experience of trying to get policymakers and politicians to understand the first thing about artificial intelligence. To her left, Leigh Ellis. Welcome, Lee, Chief Digital Officer for Royal London, responsible for improving customer experience and driving business growth through digital channels. Over 20 years experience in financial services industry. Before Royal London, you were Managing Director at Barclays uk and on the far left Prashant Jejodia, you are Managing Partner for the Financial services sector at IBM Consulting for UK and Ireland. Also a member of IBM's Consulting's Global Leadership Team and IBM's Global Executive Committee. Over 25 years experience as a global business leader in financial services and prior to IBM, worked at Citi for a number of years as the head of technology for emea treasury and with Polaris Software Labs as the SVP of their UK business. Now, Prashant, before we came on stage, we were sharing some of our. We may be similar ages, maybe sharing some of our war stories of what we thought about technology in the past. I just have one admission before we start on the panel. I got my first job in the early 1990s and there was this new fangled piece of technology called the email. And at the Guardian newspaper where I'd got my first job, which was really a dream for me as a. As a young journalist, fairly fresh out of university, you had to walk over to a special computer terminal where the emails arrived with this very complicated address that you had to then type in. And I turned to a colleague at the Guardian and I said, this will never work. Faxes are so convenient. There is no way that this is going to catch on. And Prashant, I think you've had a similar, maybe not quite that experience, but I think one thing that's worth just remembering before we start this conversation, anyone who thinks they can predict the future probably won't be building the future. But, Prashant, you had something similar with a brilliant old boss in your city days.
- [07:07]
Prashant Jajodia
Just to be clear, I believed in email right from the start. But this story is about my boss. I'll not name him. At Citi, he was the Head of Treasury, fantastic person, brilliant, warm. He used to live in Brussels. Once a week he would visit London. And we would know that John is visiting today because before he arrived into office, the fax machine would just rattle with a whole bunch of John's emails. His secretary is sending him the emails on fax. John would come into the office, diligently write the replies on the email, another secretary would just email it back to Brussels and the email responses would go in. So he didn't believe in emails.
- [07:45]
Kamal Ahmed
I would have liked John, I would have gone on well with him. But let's. There's a little bit of work for you to do as well, because we want this to be a conversation, not just a sort of panel discussion which you listen dutifully to. So before we start, there is going to be A question for you all, which is going to pop up on the screen at any moment, just to set a bit of context before we actually go to the question itself. And I found some of this stuff put together by the brilliant people at Intelligence Squared and IBM quite surprising in a positive way. So the UK is the third largest AI market globally, after the US and China. Now, last year, of course, we had the big announcements about the US tech firms pledging tens of billions of pounds of worth of investment in the uk. Jensen Huang predicting that this country would become an AI superpower. Now, Arvind Christian, the CEO of IBM, has often said that hyperscaling investment when you're not sure what the destination looks like makes little economic sense at this stage. So this fear that we are building something when we do not know the destination, like a sort of motorway going to a greenfield site, when the greenfield site could remain empty, I think is one of the concerns that is often said. So let's have our first question about Britain's AI economy. In order to become the primary destination for AI investment, what should Britain prioritize? Domestic infrastructure and AI deployment? B investment in the efficiency and productivity of public services. C, fostering and retaining talent. Now, of those three options, give us an answer about what you think. Let's say we've sort of got a cab, roughly, is where we are. So, Prachat, let me come to you first. The infrastructure question and the retaining talent question, those are the two top ones. But on the infrastructure and investment and AI deployment, how is IBM and how are you thinking about how much you are helping support your customers, your partners, your clients with the work they need to do and actually building the capabilities to make the decisions they need to make.
- [10:01]
Prashant Jajodia
Yeah. So we are working extensively with our clients in using AI to transform their business. That's primarily our mission, like Arvind said, obviously infrastructure is only a small part of the story. It's about really using AI to reimagine your business and transform your customer service. So what we are doing with our clients is we are helping them build a case for transformation, build the guardrails required to implement AI. There's lots of things required to implement AI at scale. We're helping them there, we're helping them with the talent because, you know, you cannot, like you see in this response, you need to retain and attract talent to get AI. So I think those are the three areas we are working on.
- [10:49]
Kamal Ahmed
Yeah, Laura when in number 10, but also outside and advising policymakers now with deep expertise that Tony Blair Institute has brought to this debate over many years and actually is now one of the leading, you know, thought leaders in this space. When you're thinking about the infrastructure versus talent, does it mean you have to do, is it an. And is it both? Because often you find with politicians, they're slightly obsessed with building things and not obsessed so much with maybe the talent of the people that are necessary to make AI the success. Or is that not how you felt it when you were in number 10? What were the key priorities for you?
- [11:31]
Laura Gilbert
Yeah, I mean, in terms of these specific questions that the skills and talent rest on. The other two, really. So, you know, I would prioritize that, but you don't get skills and talent unless you have something to build on. And the other one was public services, which of course, big fan of. If it's a rubbish place to live, no one wants to come here. So they're all quite interdependent, really. And I think you're also missing something from there which I think is probably the most important for me, which is an environment in which it is possible to grow and build businesses. And particularly in the tech space, if you have a place that is attractive for your tech entrepreneurs, for people that might want to come over and build new types of business, grow new industries, that I think is the most important thing. And they then bring the talent and they provide the skills to improve the public services and they bring with them the infrastructure. And I think what politicians of all flavors have been very keen on in the recent past, for very obvious reasons, is growth. And that's very much a focus of sort of policy thinking. The problem is it's really, really hard to do. And I think, particularly if you're a politician, they don't really have a strong background in delivery. So what it really means to deliver a policy can be very opaque to them and they probably not necessarily, the majority of them, well versed in, say, economics. So it is a very difficult area to get government to do well, I would say.
- [13:05]
Kamal Ahmed
How did you find Laura dealing with public services? Interesting, Prashant. I saw on your LinkedIn with some of your execs, the stuff that you were reposting around, for example, IBM now working with the nhs, NHS England. I was at an event, BI Intelligence Squared with Demis, Hassabis and DeepMind famously were heavily criticized when they started first trying to get involved in. In health work, or not, or getting involved with the NHS to help their health work, which is all about disease discovery and solving for disease, which has been Demis's one of Demis's major projects out of DeepMind and isomorphic labs. And you were just reminded in that conversation that you often get public rejection of somehow technology taking their data and they're quite negative and worried, maybe sometimes for good reason. But I just wondered, do you feel that tone has changed and where IBM is now with this, it's not just private companies that are an important part of this new ecology, but it is also public services, which is around this growth issue that really matters.
- [14:22]
Prashant Jajodia
We are massively passionate about public services. We do a lot of work building mission critical systems for the UK economy. I'll give you an example and you're absolutely right, of course. When we are building mission critical systems, we have to be very sensitive around how we use the data, the security and the governance around it. But the impact is enormous. I'll just give you a very small example of what we did with University Hospital in Coventry. We used AI for a very important use case. Like we all talk about long waiting times for appointment at NHS and yet so much time is lost because people don't turn up for appointments. We used AI data science to mine it and set up a notification engine on the back of that and we were able to save 7,000 hours every week from lost appointments. So it's a massive capacity which is made available.
- [15:32]
Kamal Ahmed
And how do you relay that to the patients, to the staff that have maybe have this more negative maybe of view? Is it, are you able then to explain how AI can really help in terms of service provision?
- [15:47]
Prashant Jajodia
I mean in this example the patients actually much appreciated getting, you know, reminders and notification at the right time saying that there is an appointment and if you, if you can't make it, then click this button and reschedule it. It's a convenience which we in busy lives, otherwise keep thinking, okay, we'll call up someone and reschedule the appointment. So it is a big plus. So talking to them about how it can help them in their day to day life I think is a big sell for the public.
- [16:19]
Kamal Ahmed
Laura, what was it like connecting the AI conversation with public service delivery? We've all heard, haven't we, the SCAR stories of NHS and computers and for many in the public it's obviously not the same, but for some members of the public it might be. Well, they always get it wrong.
- [16:39]
Laura Gilbert
Yeah, so there's a lot of interesting points here. One of them, the appointment scheduler, sounds fantastically convenient for people. We hate having to phone a clinic and talk to them about canceling our appointments. So people don't do it there's an interesting systems point because we looked into building exactly the same system. We went to the GPS and said, we're going to build this. And they said, don't you dare. Do you realize that is the only time we get to do our paperwork? But they're not joking because actually the system has adapted and adjusted, and it's almost on purpose that they literally need those gaps to do their work. You can be in a place where you're fixing something with technology, where you're not fixing necessarily the core problem or all of it. And this comes over and over again. Your sort of point about trust and adoption as well is very interesting. We were talking earlier about stories. One of the first things we built was an algorithm, and it was to solve a particular problem. And the problem is that in the UK we kill about 22,000 people a year. With bad prescription profiles. People can have up to about 19 prescriptions simultaneously. They interfere, they do harm, they cause people to die, they cause people to fall and become immobile and not be able to live independently. And it costs the National Health Service about a billion pounds a year. A quarter of that is in wasted medication, where you're getting prescribed things that aren't really doing anything. But the rest of it is in direct harm to people. And so we built an AI pharmacist. Essentially, it sits next. It never make decisions. It sits next to the pharmacist. It can go through their pharmacy records and it can pull out bad prescriptions and help you to sort of fix them. If we went to the public and said, guess what? We build an AI pharmacist, obviously people don't want that, you know, the response is immediately, no, that sounds awful. But when you start from the problem and say, do you realize that we're killing 22,000 people a year? Suddenly this is a good story. It starts to really get people to understand why you're doing the thing. And I think that what I was trying to do in government was bring in a lot of the behavioral science about the way people respond to things, bring in a lot more understanding of the public. There are very good reasons time and time again in government. How do we get people to trust AI? Stop doing that. That's clearly a bad idea. People can and should trust a brand, a company, a person with their data based on two criteria. Do I think they're competent? And government, in many ways, in individual deployments of things, has failed on that. So are they competently going to protect my data, use it, and do they intend Me? Well, there are a lot of companies that are deploying AI tools that I fully believe are incredibly competent and are doing them very well. But I don't want a company to build an algorithm that understands my emotion from my face and then uses it to market to me. I'd rather they didn't. So there's, you know, when you're sort of looking at the interplay between government and the public and we do it very badly, trust in government is lower than trust in Facebook advertising. It's sort of bad news. But the way that you turn that around is that you explain openly and clearly to public why are we delivering everything from this piece of technology to this new policy? That maybe doesn't sound great, but there is a reason. And then you actually have to be trustworthy both in terms of the competence and the thing you're trying to do.
- [20:17]
Kamal Ahmed
Lee, can I bring you in? There was an interesting point there which was heavily supported by you in the audience around talents and skills. And I've been discussing obviously AI as probably the top business issue over a number of years, but certainly since ChatGPT sort of blew up the sort of consumer side of AI, of course there's been much AI well before that, but the consumers suddenly engaged with this thing just at Royal London. I've been getting from a lot of enterprises that I've been speaking to that training workforce and helping your workforce understand and your colleagues understand the opportunities. But what this is is a very important part of the sort of adoption journey. And I just wondered how Royal London had thought about its sort of inside first sometimes before you even start to think about outside.
- [21:11]
Lee Ellis
Yeah. And maybe just for the audience who don't know Royal London or don't know it well, maybe just explain for context. So Royal London are a pensions, life insurance investments company in the UK. It's got about 10 million policyholders across our product range. We're mutual, so we're customer owned. Very proud of the fact that we're able to return our profits to our members. Just this year launched back 199 million back to members. So that give back is really important. And for Scal, we've got about 200 billion assets under management. So quite punchy numbers for an organization. It's about 5,000 workforce strong. You're absolutely right in terms of trying to pivot the organization to adopt the emerging technology and make sure that they're able to go on that journey with us is quite the challenge. And we tend to attract people for the sentiment and the mission of the company which is to try and prove the financial resilience of the UK population at large, trying to engage them in certainly in their pensions and their financial products. They need to see them through their later life stages. So we attract really good talent towards that mission. I think en masse though, we've seen really great adoption of the early manifestation of the language models in the workplace. So CoPilot, we've obviously OpenAI's ChatGPT models underpinning that we rolled it out last year across the organization we had phenomenal adoption. I think we outpaced the tech companies on the adoption that we had and not just initial adoption but ongoing active usage. So really quite impressive and surprising engagement from colleagues to want to embrace the technology and use it day to day. Of course the fight for the top tier talent, the ones that really in the engineering space know how to bring it forward. I mean that this where we rely on our partners like IBM to help augment our capability in that area and bootstrap us really for the, for the future that's coming.
- [23:34]
Kamal Ahmed
I think it surprised a lot of people in the technology space the enthusiasm with which large retail consumer populations adopted the sort of consumer facing LLMs that they were suddenly allowed to grab onto despite the fact that they were often wrong. It became part of the sort of joy of trying to use them a bit and seeing what was wrong and what was right. And I remember I was having dinner with Jimmy Wales, the founder of Wikipedia and ChatGPT. It's about three days in market and of course the first thing that pompous egotists like me do is look myself up. And it had sort of eight or nine things that were wrong. And Jimmy Wales said just give it a couple of weeks and moan at it and it'll get it right. And it was astonishing. Sort of journey of just sort of action. But that's one side of it. But what about the actual training for how Royal London thinks about its actual business? I was struck, I was at an event around agentic AI. Obviously there are different levels of AI at agentic AI and they said that one of these big enterprise, global enterprise companies said that real adoption came and business transformation came when they actually trained their teams on how to make their own LLM models and become coders themselves and solve problems that their customers had or their colleagues had and that exponentially changed the way the organization thought about. They moved it out of the sort of chatgpt. Oh, this is quite good fun. I wonder who the new actors are, you know what in XYZ and and into an actual training model which I thought was quite an interesting way of the business enabling their teams. Is that a journey you've been on or how does that work?
- [25:31]
Lee Ellis
Yeah, I think we're in the middle of it right now. I do think that point on adoption probably stems from the fact that it's not introducing it to colleagues in the workplace as a new thing. We're introducing it as something they already have, you know, in their personal life and are dependent on. And so I think the adoption comes from well thanks for giving me what I already have personally. And now I can be more productive and less wasteful while I'm spending 15 hours at work a day. So I think that's why we've seen such high adoption. We've implemented again in partnership with IBM what we call a center of enablement. So trying to take it beyond, at your point, personal productivity and get into starting to create agents underpinned by the LLMs for more business processes shifting away. We kind of tend to think about it in three ways. One is the personal productivity. So yeah, things like Copilot in Microsoft, GitHub, Copilot for our engineers we're starting to look at called code and run a few experiments there in our engineering workforce. So there's that adoption of how do we leverage for personal productivity, make your day easier, smarter, more efficient, effective. We then have created this center of enablement with IBM to then try and put more take it away from just general productivity and put proper guardrails around it so you can actually improve the efficiency and effectiveness of your internal processes. And immediately I think we've got like 30 different use cases running through that at the moment. Couple coming out the other side of the pipe now, but things like using IT for assessment of medical data, for claims processing, it's like incredibly effective and efficient for things like that. Using it in our HR department for sort of highly repeatable processes internally, quite a safe ground to experiment with. But I think embedding that discipline guardrails in an enterprise context is incredibly important. You have to apply that beyond your personal productivity lens. The third one, and maybe we can save a bit of the conversation for later. And I think you was kind of hinting at this at the beginning, but it starts to go beyond your day to day and it disrupts the value chain of your business. And how do we deal with that? And that's where there's one I'm most excited about because I think there's most opportunity lies in that margin. But also it's the one that's least understood at the moment. And for rural London as a business, if we think about quite a lot of our inflows, we're not really a direct to consumer business. Most of the products that book with us come through financial advisors, for example. And so what becomes really quite interesting is where does that financial advice come from in the future? I think we've got 91%, maybe you can fact check me on this. 91% of the UK population can't afford or don't have access to an IFA. So where does the rest of the, you know, where does that population go to for advice? I personally, I think the answer is going to be gone here and go like what? What should I do? In fact, I think Lloyd's study, Lloyd's Bank Group run a study last year already showed that I think it's 28 million UK adults that are willing to take advice from their assistants. And as that pivot probably in the next six months starts to, you know, we start to see agendas ki become a bit more ubiquitous, bit more accessible to folks and we allow what's our current generative version of it, give you advice. What about when it can actually take action on your behalf? I think that will really disrupt the business model and the value chain for Royal London. I think we're really thinking hard about, you know, how do we, what role do we play in that?
- [29:43]
Kamal Ahmed
We were just talking before we came on. I found this journey with many enterprises and organizations I talked to that the first response is how do I do something within my division or literally within my own workflow, which is just a bit more efficient and does the boring stuff and the sort of joke is how do I read a PDF 4% faster? That's the first sort of opportunity you see is how do I make my bit. But then how do you turn that vertical approach into a horizontal approach which actually becomes disruptive and prashant. When you're in a leadership role in a business, it is difficult to know what will really move the needle in terms of roi. This return on investment, but also that rather lovely phrase return on intelligence. How do I leverage the opportunities that large language models which in the end. Kate Crawford said this to me at the Mobile World Congress in Barcelona. I was interviewing her. Kate Crawford is the AI professor from the University of Southern California. She said, we always have to remember that Most of the LLMs at this stage that we are using are broadly statistical probability at scale. That's what these things are doing. So let's not over romanticize what's happening here. And of course, artificial General intelligence, quantum computing, et cetera, is a world maybe not that many years away, but still away from us. But Prashant, how do you help Royal London, others understand where to move the needle? Because that's the big issue is I don't know what of this is going to really change things and then really help my roi.
- [31:17]
Prashant Jajodia
So just before I answer that, I think absolutely generative AI and LLMs is what you said, and it is powerful in itself, but Agentic AI makes it two steps up. So just in simple terms, Agentic AI combines actions along with the power of LLMs. So you can then now start to reimagine your workflow. And I think using that can have significant implications on your business. So IBM has a IBM Institute of Value where we publish client research. We published a paper recently, enterprise in 2030. As part of that, we interviewed 3,000 CxOs on exactly this question that where is the value? So this surprised me as well, that 79% of the CXOs believe that AI is going to generate significant revenue. Like Lee was saying, fundamentally transform the business and generate significant revenue. On the other hand, 29% of the people only knew where the revenue will come from. So people don't have an idea of where it will come from. But absolutely a lot of people believe that AI. The benefit of this will be agentic AI will be fundamentally transforming the business, generating new revenue pools. What people are doing, and it's absolutely the right thing to do, is initially implementing AI to drive productivity, which is what we as IBM Arvind set out this vision for IBM in 2024, saying we will implement AI into our own business, whether it is our finance, our hr, our technology, our customer service. We'll implement AI to improve our business processes. And he put an audacious goal of saying we will save 3 billion pounds. $3 billion. So $3 billion is what we will save. Of course, we have now recently released that we have exceeded that outcome. We have saved 4.5 billion for the company, but that is just the beginning. So we are now recycling that savings into the business, making investments into the business to ultimately generate more growth.
- [33:40]
Kamal Ahmed
Lee, what most. What most surprised. I mean, interesting, isn't it? It's not what you're planning for. It's the surprises that sometimes are the breakthroughs. I just wondered what most surprised you with the partnership with working and the way you've been working around AI.
- [33:53]
Lee Ellis
Yeah, and I was just going to be able to. I think it Starts on the other side of the P and L rather than the revenue. It does start with the reduction in cost to serve. Just huge efficiency but potential certainly across our business. And also it gives us greater flexibility with how we service and are available for customers. Up until fairly recently we've been the telephony first, telephony first business. And look, we pride ourselves on, we've got five star customer service for our contact center colleagues. A lot of the policies that we offer, especially in a protection space and for pensions where you're making sort of complex big decisions, you need help, you need guidance, you need a human touch through that. But a big element is day to day transactional processing. I think there's huge savings. That's where our initial focus is, has been on the surprise side, I think. I mean we talked about it earlier, the speed of adoption, the pace of adoption is a surprise. I think the bit that we all need to watch for is the doubling up of the capability. I think it's in our nature to think about things as they are right now and we think about the models in the context of what can they do for us right now. It's quite hard to imagine the doubling of capability and the implications that come with that. And that concept of what is exponential power increases actually translate to and what the societal impacts and implications of it. I do, I do this with my kids to try and help get their heads around this. I don't know if you've ever played the game, but you know, you imagine the chessboard, you start with a penny on square one and you go to 2p 4p and I kind of like, hey, what would you rather take a million million quid now or do you want the chess board? They all take, they take the million. But the action that happens on that last row of the board, the doubling up, I forget what the number is now, it's 800 squillion quid or something. But the doubling up that occurs through that last row is hard to get your head around. And I think we're just approaching now with respect to AI and where we're at in the positioning. I think we're on the last row of the chessboard.
- [36:40]
Kamal Ahmed
Laura, when you're coming to the advice you give, yours is more on the policy side, but obviously with a massive connection with business. Case use case. What has surprised you about transformation? Because I think it is often. I think you're absolutely right, Lee. We obviously do sort of what we did yesterday, tomorrow, as long as yesterday wasn't a complete screw up. Broadly, we're creatures of habit and actually we are cautious often and slightly risk averse. I just wondered, Laura, how you think about those types of issues and when you're giving advice about where the UK needs to be thinking about where growth comes from.
- [37:20]
Laura Gilbert
Firstly, I very rarely give policy advice. I'm an engineer and mostly I build stuff. But we do build tools that can give policy advice based on data and evidence and real world examples and evaluations and that sort of thing. I think that, you know, when it comes to talking about transformation, because I'm an engineer and I was working previously and now in institute institutions that are majority policymakers and people who are thinking in that space, they tend to be very impractical a lot of the time they don't really think through what does it mean to actually get it done, what's the delivery mechanism? And as an engineer, of course, it's incredibly important to get that right. So when I'm thinking about I'm going to build a new tool for improved public services. Great. We've got some lovely bits of evidence showing this is really bad and we definitely know we've got the technology to make it really good. The first thing I'm going to think about is, can I get this to the user? And in a government space, it's not infrequent that the answer is no, or that the opportunity cost, the amount of political capital, the number of people I will have to persuade, the blockers I will have to power through, are so great that that I should go and do something else instead and pick something where I do have a pathway to delivery. So when you're looking at organizational change, including government change, there's a few things that are really important. It's a practical exercise. Every time a strategy document passed my desk, I just rolled my eyes. Because we have so little evidence of government strategies working. Some do. But that is a starting point I always find quite bizarre. If you come across an analysis of how would we change this, as in, what people do we need? What you do you need to make new laws? Do you need, you know, how much money do you need? Who are the people that you need to go through? That's when you succeed. So a lot of the time I am going around telling governments, stop using side packs, use demonstrations of something that's working, stop writing strategy documents, you've got enough of them. I build a delivery plan. And when you're looking at the internal transformation as well, stop giving people videos about AI, because that really doesn't work. You have to Put it in their hands, give them an actual problem to solve. This idea of let's go off and teach people to code as a hypothetical. It doesn't work at all. You can give them Claude and say, well look, I tell you what, you can go now and you can enter in Claude saying make me the game Frogger and it will go and do that for you and you can see how. And it can explain it to you. If you get people to do that sort of thing physically with their hands, ideally something that relates to the outcome, then that's how you actually change things. So I think that my view on transformation is that we need to think more like engineers and less about policy.
- [40:23]
Kamal Ahmed
And you've actually built exactly as you've described, products that can help.
- [40:29]
Lee Ellis
Yeah.
- [40:29]
Kamal Ahmed
On the policy side actually think more clearly in a data led way about what are the right ways to think about policy implementation. That actually works well.
- [40:42]
Laura Gilbert
Yes. And when you. So I mean government's very interesting. I had no interest in politics at all and I'd been running a medtech startup for 10 years and was very tired. And so we sold it 2nd of March 2020 and that was not a good time to go for what I thought I was going to do next, which is a nice bit of relaxing consulting on account of the whole pandemic. So some months later I thought to myself I really should get a job. And I saw this sort of Downing street job advertised and I'd had. Well, it was about 2 in the morning, I'd had some wine so I thought chuck a CV at that. And then she was very surprised to get it, really genuinely did it, about whether to take it. I wasn't sure if I wanted to go and work with politicians. And then I thought it's going to make a great story. So sort of went off into that space. But you then do get firsthand information about how policy is made and how government operates. And as you can imagine, some of it's inspiring, some of it's horrifying and I think there were a few assumptions that I went in with. One of them was that the civil stamps are the good guys and the politicians are the bad guys. That's really not true at all. It's very mixed across the board. I think I expect it to be a lot more organized than it was. I mean my two first observations were those like what is the organization in this place that makes things happen? And it's very subtle, it's very fluid, it's very based on personal influence and dynamics and it can Change any second. So when you're looking at a government policy, it may be the case that a team of researchers in the right department, who've hopefully got some statisticians and data scientists in there, have done a really serious look around the world. They've figured out the evidence, they've figured out the levers, they've figured out the system, the knock on effects. If I do this, what else will happen that might be the case. There's also a decent chance that it's four white blokes who all went to the same school in a room with a pizza and a beer. So a lot of what I ended up doing in that job was actually I went in thinking, everyone's going to bloody love my numbers and statistics. Really, they did not. It was much more about figuring out how people work and how to influence them. Politicians, in fact, I very, very rarely not never met a politician who doesn't believe they're doing the right thing. They really think, and they may have sat on the backbenches for 14 years watching these other people doing it. I know how to do that. They're completely wrong. We'll do it like this. They then come in and you have to talk to them about it and you have to explain to them that perhaps their assumptions they've made about the way the Asylum system works, etc. Are in fact very provably wrong. Like numerically, all the evidence. And if you go into a room with anyone, and particularly a very senior person or a minister, and you say to them, here's a lovely bit of data that shows you're wrong, the impact of that is you don't get invited to the next meeting, that's it, you're done.
- [43:48]
Kamal Ahmed
But these are all good lessons, these are all good lessons for business as well. That around who gets invited to the
- [43:54]
Laura Gilbert
meetings, who gets invited to the meetings,
- [43:55]
Kamal Ahmed
how you think about diversity, how do
- [43:57]
Laura Gilbert
you get people to the diversity? I mean, 87% higher chance of a good business decision being made by any measurably more rather than less diverse group of people. Very, very important to get those sorts of things right. But yes, when you are looking at this kind of, you know, how do you get people to make policy better? It's not actually the tech is a tool that you can use to do it, but when we build that technology, we don't just go, we're just going to build a tool that will make decisions. No, I build it to change the way that the policymaker thinks about policy and it will ask them the right Questions where they sit back and go, oh, I hadn't thought about that. What's the knock on effect on the stakeholders? It will suggest to you, if you do this, what are the groups of people that will be affected? And you know, it's very interesting when you do that. If you, for example, you know, I remember one of the politicians suggesting different methods for asylum. Could you stop people getting permanent settled status? It's very easy for that to become a policy incredibly quickly if it's politically salient and for the people delivering it to believe that that's the best thing to do. But the odds of them sort of having a framework to think through what happens then, you know, those people can never get a mortgage, for example. Did you know that when you sort of suggested this? So the technology for me is the tool by which, you know, first of all, you build it with an understanding of human behavior, what they care about, how to get a person in a space where they can change their mind and then you try to make sure that that tool improves their thinking process rather than makes decisions for them. Yeah, sorry, I'm quite passionate about that.
- [45:48]
Kamal Ahmed
That's excellent. And I think is a model not just for public service, but is a public sector, but also for the private sector. Now Lee, you said, you said the word scary the first time we've stepped into some of the more negative zone of where we are at the moment in AI development. Of course we are at a situation now where such huge gargantuan amounts of money are being spent on a future that we haven't actually yet built. We're seeing some use cases come through and as you say, Laura, we need to have the right systems to be able to do that. If you just think that just this year alone the big technology players are going to spend, or say they're going to spend something like $650 billion on AI build out, which is a number, of course, that far exceeds the GDP of most countries in the world and that if the tech Bros don't quite fulfill what they say they're going to fulfill, and we have quite a lot of evidence that that is not always the case, that there's going to be some kind of massive effect or backlash or a collapse in various markets, et cetera, et cetera. And I think this time now to come to the second poll question, which is, which is around sort of hubris really. So please do grab your phones. Let's answer the next question which is around this idea of are we racing towards a cliff edge or are we Racing towards sunny uplands. So the question is, if the AI boom falters, what will matter most for Britain's economic resilience and productivity? And I think we're then going to come into digital sovereignty, the position of the UK in the world, et cetera, et cetera. So three answers are to those possible answers. A Firstly, we should ensure the solution promotes strategic autonomy and reduces reliance on any single geographic region or specific vendor ecosystem. Second approach, a diverse economy which is not over reliant on AI. Are we actually in the hubristic phase and we will get to a future where AI is far less of what we imagined it could have been at this stage? And also that thinking more carefully is a C option. Long term capital investments should happen to support innovation cycles of maybe a broader type than we're presently having. Prashant, could I bring you in on the question of racing towards the cliff edge? The possibility of the building of the hyperscalers being a major problem and that in itself adding to the sense of fear that Lee sort of just mentioned in passing. But this idea of is it just very, very scary? I just wondered what your broad thoughts are on the answer to if the AI boom falters, what will matter most for Britain's economic resilience and productivity?
- [48:54]
Prashant Jajodia
AI is here to stay is my personal view. The AI boom falter to me equates to will these tech companies investing this billions of dollars, will they get the return on their investment? That is still to be answered. But AI is here to stay. So therefore for Britain, which is not investing billions of dollars, we are investing 25, $30 billion in AI infrastructure. But what I would suggest is to drive growth, differentiate. So how do we differentiate? Britain is great in certain industries like financial services, fintech, healthcare, tourism. So instead of going horizontal, I would go vertical, use apply AI in these industries to further transform themselves. These industries, we are differentiated because of data, because of regulation, because of access. These are unique properties to Ukraine. So if I apply AI in these industries, make them even more differentiated, I can drive growth. I mean that would be my recommendation.
- [50:00]
Kamal Ahmed
Lee, just thinking about the business, your business and your sector, how do you protect yourself from sort of hubris? And boy, we've got to sort of bet on black now. And if we don't bet on black now, boy are we going to be behind and your board is sitting there saying, Lee, what have we done today on AI? And every question is about what are we doing? How are we transforming things? How are you changing things?
- [50:25]
Lee Ellis
You sound like the board.
- [50:26]
Kamal Ahmed
And so not maybe on the bigger macro questions, but of course do answer. But on that question of hubris and then faltering, how do you mitigate risk in a situation that could be, as you said, scary?
- [50:42]
Lee Ellis
Yeah. I think the only thing you can really do is try and build an engineer flexibility into our business model. I think you guys were alluding to it earlier. If anyone tells you they can predict the future probably beyond 12 to 18 months max, I would say I wouldn't write a strategy paper beyond that at the moment. Probably not going to be right. So I think the smartest move right now is to try and hedge and flexibility into your architecture, flexibility into business model, flexibility into your services. At Royal London, I'm primarily there as a technologist rather than, you know, than the business expert. The thing that I'm focused on at the moment is working towards how we make sure that our products and services can be consumed and distributed in a, in a way different to what they are right now know the current model is an IFA phones up, they've got a client, they want a pension or protection policy. I'd like to help the business be a bit more diverse, diversified and flexible in how it can respond to what I think is the inevitable change around us. Funny enough, like someone on the board. You mentioned the board. We've got the chair of the board, Isabelle Hudson, she's an absolutely fantastic.
- [52:03]
Kamal Ahmed
This is recorded, right?
- [52:07]
Lee Ellis
Genuinely, she's absolutely fantastic. And the most important thing and attribute that I think she has is curiosity. So as soon as the language model was launched, she's on there, putting Royal London in there and then she's coming back and asking, interrogating us on, you know, why are we showing in this position, that position, from that simple question it spawned, you know, it pivoted out a section of our brand and marketing department to pivot from SEO, the search engine optimization, to start building out a generative engine optimization capability so that we start to refactor our content and external content so that it shows up in a language model with search results and it shows up with the intention and sentiment that we want to convey as a business. So we're on that journey. The next part, I think is going to be incredibly important is creating our, our equivalent of a USB C cable, the universal plug into Royal London for us, that's trying to introduce an MCP or a model context protocol layer across our architecture so that we allow the agentic AI as it starts to develop to not only say, okay, you searched, he was asking about a protection policy. Royal London's great. That's the number one. Oh, and by the way, they're a mutual. They work in your interest. You know, that's a good one to go for. But then if you want to, if you want to now open that policy, I can open it for you. And I don't want to have a dead end when that occurs. I want to have the flexibility to continue to serve the amazing IFAs that we have partnerships with, but to also open up new routes as they develop. I think we got a hedge. You got a hedge for that future.
- [53:47]
Kamal Ahmed
And that is. Lee, I think there you've nailed the notion of transforming rather than just simply efficient, making more efficient your present processes in the. The idea of connecting horizontally the services that you do and then leveraging how you kind of use that. And as you say, SEO to Geo for my industry has been a huge change and we are not aggressive enough. I don't believe in thinking about how that changes the way people search for the news and I think that's a very important part of it. Now, Laura, the answers have come up to that question. Before we get to the C, I'd actually like to talk about the A which fewest people have gone for this idea around. One of the big arguments, isn't it, is that somehow UK we're going to be left as a sort of servant of China or the us, and that's going to mean that we are going to be. We're going to lack autonomy in the direction of travel that the UK takes. Now, I know, Laura, that Tony Bone Institute and you yourself have done work around digital sovereignty. This idea that. Shouldn't Britain have his own chat GPT? Does that matter? Well, I wanted to ask you about this and why you answer with such clarity. Why the answer to that is no. And how should we think about digital sovereignty and to Prashan's point, how we lean into what makes the UK great without trying to pretend we're ever going to play on the stage of the US or China in particular.
- [55:25]
Laura Gilbert
Yeah, I mean, the point on diversification and playing to your strengths is super relevant. There's a huge opportunity cost when you chase something big like building our own LLM, there's a certain amount of public money, not enough. Do you spend it on that or do you spend it on the thing that we've got a chance at being first at, which would be something in the how do you use this technology to be the best in the world? And it's, I think, quite achievable if we're smart about it and it comes with it. The sovereignty angle is interesting. Right. I'm not at all nationalistic. I've never been very nationalistic, but I'm sort of almost starting to come around to the idea a little bit. And the reason that I did actually was related to large language models. I was in Estonia and they were putting out a digital assistant and they'd built it to understand the Estonian language, which was difficult at the time with large language models because it's quite niche, it's about a million speakers and there's not a huge amount of literature. So they were struggling with the actual language itself. But they'd also built this virtual assistant who was in. In Albanian dress with the face of the average Albanian woman and understood the cultural context. And I thought that's what happens when you use large language models. You're not just using the language, the technology, spending your money on it, but you're also importing the culture of the people that built it. And if everybody does that, then the next generation down maybe loses an appreciation of the culture of the place that you.
- [57:00]
Prashant Jajodia
You're from.
- [57:01]
Laura Gilbert
Maybe that's not a bad thing. I think it is. I think we lose diversity as a species. I think that's harmful. It's higher risk. And you're also losing something intangible. Even in the uk, a US or a Chinese model probably doesn't have a decent cultural inherent understanding of what the different types of tea are like. Is it supper for your children? Is it, you know. So the sovereignty is more than just supply chain resilience, although it is very strongly in that place as well. The other thing is we don't have the purchasing power to sort of beat out these sort of superpowers. But if you start working together, then that starts to change a little bit. So I think, if I remember rightly, and I'd need to check this, I think the EU is about 20% of GDP and the Commonwealth is about 15%, if I remember right. Ish. If those sorts of groups of countries come together and start agreeing jointly on what they care about and what they demand from a model built by, say, China or the USA in order to use it, suddenly your purchasing power starts to look quite a bit more impressive. You've got the chance to actually impact these sorts of things. And if we go further and invest jointly in open source everything from models to solutions, then every individual country doesn't have to fund that. You can fund a bit of it and you can share it. And an awful lot of countries, the Majority in the world, I would say by a long margin are countries that do feel that they have something to protect. They do want to roll out solutions to their public in a way that's affordable and it's important actually for the whole world to bring up the, the bottom end as well. You know, when we're talking about global productivity and you know, the happiness index, other things that matter, that rich, poor divide, that's the key indicator. It is, you know, countries who have more poverty, have the higher crime rates and spend more on health and you know, all of those sorts of knock on effects. So you know, working together, I think in the open source space with other countries to build shared services is I think the way you actually approach your own sovereignty.
- [59:20]
Kamal Ahmed
Prashant, just the, the lower answer, the C. The C answer around the long term capital investment over innovation cycles must be, I'm assuming as a senior executive at IBM, sort of music to your ears because the, the risk of rushing to solutions in the world we are presently in is high. And the notion of patient capital in a world where we see those, I mean quite remarkable numbers in terms of what is being invested in now, in a world where the chips we may be using in six weeks time are going to be different from today's, let alone what may be in a year's time. But when you're advising your clients, how do you get the right balance between being of course at speed, being passionate, wanting to get solutions, get things to happen, but also thinking about that slower innovation cycle which our audience has said is the best way to de risk. The AI doesn't quite do what we imagined or in the way we imagined it would do it.
- [60:29]
Prashant Jajodia
Well, I'm always asking my clients to commit to us multi year long term the challenge with AI and I hear this lot from our CEOs, many CEOs think of AI as a software. I'll buy the software, I'll invest money, buy the software, roll it out and I'm done. But we've discussed here so clearly that it is actually a business transformation which will happen over years. It requires cultural change, it requires reimagining processes. So this requires a consistent program of work which you have to plan and invest over years. And the same applies to government or to countries. And this is where no wonder that that has come up as a big area that for uk there's a lot of investment in uk. I think UK is the third largest in the world in terms of attracting investments in AI and other places. But our challenge is long term investment and there has been lots talked about. I'm not a policy expert like Laura, but there has been discussion about can our pension funds invest consistently? Not to point at Lee and Roy London, but can our pension funds consistently invest into innovation and infrastructure of the country? So yeah, I think as in business, as same applies to the country, this will require long term consistent investment and effort to drive the transformation.
- [62:14]
Mia Sorrenti
Thank you for listening to the Age to Come. This episode was recorded live in London as part of intelligence squared and IBM's the Age to Come series. There's a link in the episode description for you to find out more about the upcoming live events and give this feed a follow as we'll be releasing podcast episodes throughout the year.
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