#107: Is the UK ready for AI?

– Intro music of The Green Room begins, followed by an introduction from our host. –

Jenny Haskel (Host)

When you look back at history, every generation has a technology that changes everything.

Steam powered the Industrial Revolution.

Electricity transformed homes and businesses.

And the internet connected the world.

Now, artificial intelligence promises not just to reshape our daily lives but almost every industry, organisation and job.

Over the last few months on The Green Room, we’ve spoken to leading names across business to explore what this really means – for us, for organisations and for the UK.

Today we’re bringing those conversations, and more, together to answer our final big question of the series: Is the UK ready for AI?

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Oliver Seal (Guest)

I think every industrial revolution to date has, in the end, created more jobs than before the industrial revolution

Mike Manby (Guest)

We created this technology. Humans created it. And there will always be a need for us to be at the front and centre of that creativity, the adoption of it and the evolution of it.

Anne-Marie Malley (Guest)

I don't think we are going to lose our jobs to AI. I think we're going to lose our jobs to the people who have best enabled themselves with AI.

Tom Cope (Guest)

It’s so fundamental to unlocking the investment, unlocking the projects, to unlocking the energy infrastructure that’s needed to power it all.

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Jenny Haskel (Host)

Only a couple of years ago, AI was still largely discussed as an emerging technology. The speed of change has surprised everyone – but now businesses and governments are investing heavily, and individuals are adopting new tools at an unprecedented rate.

AI isn’t just something we’re seeing and hearing more of, it’s influencing everything.

I’m Jenny Haskel, one of the new hosts on The Green Room, and with the help of the expert guests we’ve had join us on those sofas behind me, we’ll bring our AI series to a close - as we explore one final big question before our summer break - Is the UK ready for AI?

When the podcast started in 2019, our first ever episode asked: What will be the last job on earth? To celebrate our 100^th episode, we thought we’d ask the same question again.

We even invited back one of our original guests – Anne-Marie Malley, Deloitte UK Vice Chair.

Anne-Marie Malley (Guest)

The things I think we've seen in the last seven years from an AI perspective, are just the speed of development of the technology. And the fact that the technology now does things that we never even imagined that it could. So there is reasoning now happening within that technology. We didn't, we couldn't have imagined that back then. That pace of adoption, I mean, when GPT-3 came out, I think within two months that was 2020, within two months there were 100 million users. We've never seen that speed of adoption. And I think when we talk about, and Ben and I will talk about work, and the future of work, one of the big challenges usually is user adoption. Here we've kind of got the opposite. The people have adopted and the organisations are then catching up to then work out, how do we actually leverage this?

And then I think the application of it. So, it's everything, everywhere. And how do we manage it, how do we govern it, how do we help it? So lots has happened I think, which is all just very exciting and I think we now just need to work out, how do we make this work?

Steph Dobbs (Host)

Just a bit of change then!

Ben Legg (Guest)

Yeah. I think the other thing is that you guys had the best information at the time, and you got maybe half right, half wrong, and I think even now you hear a lot of people saying, you know, ‘this is where the world's heading’ with absolute certainty. No one knows. That's the reality. Yeah, we can try to be better informed and we'll have opinions and some of them will be right. But we also just need to keep our finger on the pulse because it will keep changing.

Jenny Haskel (Host)

Ben Legg, former COO of Google Europe, highlighting one of the biggest challenges we all face when it comes to AI – if the technology is changing so quickly and we can’t be sure what’s coming next, how do we keep up and how do we prepare for the future?

For Bruce Daisley - who’s run YouTube UK and Twitter EMEA – it’s a challenge for businesses, but one that requires a mindset shift. He joined us, on The Green Room sofas, along with Deloitte’s Human Capital Consulting leader, Kate Sweeney.

Bruce Daisley (Guest)

I think the interesting challenge is that probably businesses used to think that there'd be these big surprise, disruptive shocks once every five or ten years, and they feel like they're happening every year right now. And so, to some extent, knowing that you're going to get something unexpected shouldn't be a disruption to the business plan. It should be just an anticipation of the business plan. I think that's the challenge that, before that you might turn up somewhere and explain in your budgeting, “look, this unexpected shock happened.” And I think to some extent now we’ve just got to bake it in, we've got to expect that there's going to be something that knocks the train off the tracks.

Obviously, it makes doing business immensely complicated because you lay down your best laid plans, you've got all your good intentions, and then something completely unexpected happens. But I think that's the current reality that just the cycle of news, the cycle of global politics, is just making things a lot harder to predict.

Kate Sweeney (Guest)

I completely agree with Bruce around the uncertainty, but I think as well, it's that sense that AI is here. It's kind of, fundamentally everybody knows they've got some really big competitive decisions they've got to make, and the uncertainty around “how do you make them?” I think at the moment. You've got, on one side, people are really struggling, loads of experimentation but very difficult to scale, so people don't have the confidence to say, “we know this is the right way to go”. And the flip side as well, huge investments, huge investments in technology, and everybody's looking at kind of token consumption and token pricing, and we're not sure how to model it. So again, I think that forward view feels like a really difficult environment to be making decisions within and having any sort of certainty.

Bruce Daisley (Guest)

I'll add something to that actually, because I hear you exactly, and the really interesting thing, I turned up at about three or four events in the last few months, and on stage there were all these really compelling stories being told about what people are doing right now, what businesses are doing. And I was sort of chairing a couple of round tables, and the conversations that I ended up having were people who came up to me afterwards and said, “we're not actually doing much right now”. And you've got this simultaneous challenge where everyone recognises there's a fast pace of change, it's kind of overwhelming in the disruption it represents. But simultaneously, a lot of people are looking at their working week and thinking, “yeah, we've not actually changed much ourselves yet”.

And so you've got, I think, the added complexity that there's so much noise, there's so much fearfulness that you're going to miss the moment, and then additionally, people are thinking, “I just don't know what our answer to this is”. So it adds to the, probably adds to the anxiety of the moment, where everyone just thinks, “I don't know if people are doing more than us”. You know that feeling when you're in an exam and someone puts their hand up and ask for more paper and you're like, “Hang on, hang on. What? I'm nowhere near!” And it's just like, at the moment, we're surrounded with loads of people putting their hands up and asking for more paper, and we're not sure how we should interpret that I think.

Jenny Haskel (Host)

The challenge for firms is huge – as we’ve just heard from Bruce and Kate – leaders are trying to work out how to implement a technology that is fundamentally changing how we work and operate, while also trying to deal with external forces and pressures in an uncertain world.

But the promise of AI is too big to ignore – so those decisions aren’t just important, they’re needed now. Because they set us on a path to a world where AI acts as a catalyst - empowering every business for smarter decisions, greater efficiency and new growth.

But also a future where it simultaneously upskills our workforce and creates opportunities nationwide.

An opportunity that’s too big for an organisation to drive alone, this needs a whole country.

For governments, the questions become more important, the challenges become larger and the outcome becomes more defining for a generation. Because countries aren’t just working out how to implement AI, but they’re competing globally on innovation, adoption, infrastructure, talent and skills.

All with an end goal of economic growth - billions generated in new wealth – the creation of new jobs, a boost to our skills base and a fairer, more prosperous future in the UK.

Here’s Pearson’s UK CEO, Sharon Hague.

Sharon Hague (Guest)

Here in the UK, you know, businesses, the economy as a whole. We need and want to see growth. There's a lot of government energy focussed on how can we achieve growth? And AI if adopted well, at scale, has the potential to help us all unlock that growth, which will obviously be a huge benefit to the country as a whole.

Sharon Hague (Guest)

You've got the technology rapidly developing. You hear so many stories of people that get themselves familiar with a tool, and then they go back two weeks later and it can do something it couldn't do when they first started. So the pace is incredible. And what we're really seeing is this growing gap between the pace at which the technology is moving and then the pace at which people can keep up.

Which not only requires businesses and organisations to really rethink how they are structuring the work, it's also rethinking completely how they keep their workforce skilled, how they retrain people, how they upskill them. Because I think when this technology started to emerge, there was very much a focus on, we're going to recruit people with these new skills, you know, we're going to recruit 100 data scientists, and we're going to do… but of course, that can't possibly keep pace with the rate at which the technology is developing.

And the key to success is how you get the people and the technology working together. So you've not got one replacing the other. You're actually using the technology to really augment what people can do.

And I think that's why it's a pivotal point, because this realisation is happening that actually this isn't just a sort of swap some stuff out and everything else settles back in place. It's a really, fundamentally rethinking how you shape roles, how you train and skill people, and also the skills that people need are fundamentally changing.

Sharon Hague (Guest)

And I think here in the UK, we have many of the strong foundations to make this a success for the economy as a whole. But we're really at a key sort of inflection point. So now is really the moment to focus on how we can move from seeing the potential to really delivering that at scale. And of course, you know, the big tension point is how do we get people and the technology working together in a way that actually takes things forward?

Jenny Haskel (Host)

The path forward requires three fundamental things to be in place for the UK’s AI strategy to succeed – building foundational compute, capturing investment value and transforming work. But beneath those 3 pillars are a number of other questions and challenges that need answers and solutions.

As Sharon Hague mentioned, transforming work requires the upskilling of an entire nation. It needs people and technology to work together. And businesses need to reinvent how they think about our working processes and structures.

When organisations started talking about AI, many assumed the biggest challenge would be buying the right tools, but the conversations today are much different.

Mike Manby (Guest)

Technology is not the challenge here. Technology can do pretty much anything you need it to do. It really comes down to people, business processes, how they've been configured in the past. And because there's so much change hitting our industries, there's so much transformation and there's a lot of overlays that we’re bringing into organisations. And our experience has shown many users, people are experiencing up to ten changes at once, where ten years ago it was only two. And so I think because of this ambition and desire to really drive the transformation, there's a lot that our people are having to work through. And therefore I think it really comes down to people being part of that, you know, the solution, but also the challenge because of the speed of which this disruption is happening.

Jenny Haskel (Host)

Mike Manby, technology and transformation consumer industry lead at Deloitte UK highlighting the need for businesses to focus on people, not technology to solve this problem.

He joined us in the studio, along with ‘The Tech Humanist’, Kate O’Neill.

Mike Manby (Guest)

In our experience, we have found about 70% of our technology-driven programmes are not successful when change, and that human element are not built into the programme from the start. And I think for us, you know, we've spoken for many years about tone from the top and culture really sort of driving that message. But actually, change only happens when you've got the whole spine of the organisation lined up, excited about the change, but also being clear about what are they going to get out of it and what's their role in contributing to it. So I think for me, that people element is as, if not more, important than the technology.

Kate O’Neill (Guest)

And I think this word, ‘enablement’, you know, it comes up a lot, but it is one that I think requires a sort of multifaceted consideration, right? You have to be thinking about what does it look like to enable our organisation to operate at different sort of AI assisted levels all the time, and it requires a lot of different lenses. I think it's why it matters so much to build the trust and the transparency for all of the people around the entire organisation to sort of pipe up and say, you know, what I recognise is that we're not doing this in a way that's consistent with this vision we're trying to cast. For example, maybe we're having these like stand-up meetings every day and then they need to be in person. Do we really need to have them in person? Do they really need to be every day? Could we, you know, maybe have a certain portion of the people who are not contributing that day only send their transcription bots to the meeting, you know, and what are we going to do about putting a policy in place that's respectful but also recognises not everybody needs to be in every meeting all the time, yet they might need to get the transcript of that meeting. And can we start adapting some of those processes and adopting some of those processes into a more future ready approach?

Jenny Haskel (Host)

AI is a technology tool, but it’s also much more than a new IT roll-out. It requires people across the business to come together, drive the strategy, lead from the top and re-think old assumptions.

Something Kate Sweeney and Bruce Daisley expanded on when they joined us on the podcast.

Here’s Bruce, building upon the need to move away from our week of scheduled meetings.

Bruce Daisley (Guest)

And it's such a leap from what we do right now. So I've said to people, “can you imagine an environment where your world, your working week isn't constructed around your calendar to the same extent?” And people have said, “yeah, I think I'd be uncomfortable with that”.

And that's a really interesting start point, because if you're uncomfortable with breaking away from this rigid way that your work looks now, then I suspect you're not going to be prepared for these changes and these disruptions.

Kate Sweeney (Guest)

The businesses that I'm seeing that are making strides - are where they're actually asking a completely different question, not “how do we AI what we do now?” but “how do we actually completely rethink? How do we get down to the tasks and talk about what do we want our agents to be doing? What decisions do we need humans to be taking? How is that whole world going to look different?”

And at that point, you can start seeing how you can completely imagine a bit of an organisation, you can scale it up, and then you're going to have people in agents working in a totally different way, taking decisions in a different way, using information in a different way. And I think until we get there, we're not going to see the big step forward.

Kate Sweeney (Guest)

We're all used to the next drop of software that comes and you just intuitively work it out. And actually AI isn't intuitive. It's a completely different way of working. It's a completely different way of thinking about how you do your daily tasks.

And until organisations start tackling that challenge, I don't think we're going to see AI scaling beyond a little bit of personal productivity.

Jenny Haskel (Host)

So it’s clear that while AI is a powerful technology, it’s the humans who implement and use it, that help to determine the overall usefulness and impact it can have.

So, if the UK is to achieve its AI ambitions, it will be measured by whether people actually feel the benefit. Whether AI improves the experiences, decisions and interactions we have every day.

In a special live episode of The Green Room, Julia Lo Bue-Saed, CEO of Advantage Travel Partnership, who work with independent travel businesses across the UK, and Deloitte’s Tom Astill, shared their insight.

Julia Lo Bue-Said (Guest)

I think we've seen a real cross-section of different approaches. I think for a lot of businesses that are investing in AI and are using it to really empower their overall process, they're starting to see the dividends. And I think - back to kind of what Tom said - I think the added element for the travel industry, it's about not - I said before - not just telling the customer what they're prompting, what they think they want. How do you really ensure that you can deliver what you know they need, or what you can encourage them to think about? It's using AI and using the power of AI to help support that process. But I think we're living through a really significant time now where you've got almost democratisation of AI, empowering, giving us all opportunities, to empower businesses and consumers around decision making.

Tom Astill (Guest)

In most cases, we're not sort of seeing an experience that is primarily human and in person being suddenly replaced by something that is now AI. It's actually something that is already digital, having a different type of technology power it and make it available. Long established are the idea of chat bots and service centre bots that do things. The idea is these can now actually just be better with more data, more information, and more human like.

Jenny Haskel (Host)

But one word is crucial in all of this – and it continued to come up, again and again across every conversation we had.

Trust.

Because being able to move with confidence is the element that changes everything. It determines whether people adopt the technology or avoid it. Whether something moves from the experimentation phase to full-scale adoption or stays stuck.

In our episode dedicated to trustworthy AI, Simon McDougall – Chief Strategist for Privacy and AI at ZoomInfo and Avtar Benning from Deloitte’s Trustworthy AI offering, helped us understand why trust and AI isn’t as simple as we may want it to be.

Avtar Benning (Guest)

I think the challenges, you know, with these AI models - some of these closed black box models - there's a misconception there that you need to… well, firstly, you can't understand each and every component of those systems. But you don't need to understand each and every individual component of these models to gain trust. You need to be able to understand certain components of these models but enough confidence around reliability, accuracy. So there are a lot of things you could do around the peripherals of these models to gain trust.

It's the classic aeroplane analogy. You don't understand how a jet engine works, but you're confident enough to get on an aeroplane. And that's because there's a lot of testing that's been performed. There's a lot of controls and risks around it that are well understood. So I think it comes back down to not necessarily needing to know all the individual details, but enough confidence that it will perform reliably that gives you trust.

Simon McDougall (Guest)

And I think one of the key things there is that this is not a new conversation. We have had trust conversations for ever and ever.

One of my favourite stories around this is going back to 1891 - I think the US President’s name was Benjamin Harrison, I'm doing it from memory so fingers crossed on that one - they electrified the White House. They put lights into the White House, very exciting at the time. Revolutionary. The President and the First Lady refused to touch the light switches because they were worried they were going to get electrocuted. To the point where there was somebody employed at the White House, one of their jobs was to turn the switches on and off on behalf of the president.

And now, obviously, we have all, probably a number of times today used electric switches and turned them on and off. And we don't worry about that. Now, why is that? That is not because we've all become electricians and we haven't tested these particular circuits very well. But there are layers of assurance and testing and common use and life experience - all these things come together to build trust. So we use it unthinkingly. And if somebody does get electrocuted by a light switch, that's something you tell your friends about, and it becomes a newspaper story, and everyone wonders what happened, and there's an investigation into it.

Again, going back to my first point, one of the challenges we have here is that compared to other technologies, AI has become distributed and widely used very quickly, there’s this post-ChatGPT AI in particular. So we haven't had time to build all of these components up. And we're going to have to build some of them as we go along. But we need all of those components before we really get to a stage of saying we are going to trust AI.

Jenny Haskel (Host)

Earlier, Kate Sweeney spoke about the pressure that businesses are facing to implement this technology. So, when that’s matched with the challenges outlined by Avtar and Simon, a trust gap starts to appear, slowing adoption – Kate Sweeney is seeing that with her clients.

Kate Sweeney (Guest)

A lot of what I'm seeing is that actually people aren't really, they aren't really trusting the AI. So they'll use it a little bit to sort of take meeting minutes and sort of accelerate reading of documents, but actually starting to use AI in their output is something that they're not yet confident and not yet trusting. And I think we've got a huge trust gap to overcome if we're going to start really accelerating and scaling AI.

Jenny Haskel (Host)

And what Kate says is shown in the data. A new Deloitte report, which will be published later this year, surveys 25 thousand workers across the UK about their use of AI. Almost two thirds had tried an AI tool, but only one tenth use it multiple times a day, and only half of respondents thought it saved them any time.

So how do we bridge that gap? Back to Simon.

Simon McDougall (Guest)

The trust conversation itself is not new. AI is the new thing and it's raising trust questions. And one of the traditional models talks about gaining trust through three different values you have to display. And that's: ability, benevolence and integrity.

And so when they say ability, it’s, do you believe that this, that the thing you’re going to trust, the organisation or the person, you know, actually has the ability to do what they say they're going to do, you know, are they competent in this? Benevolence is, are their interests, the same as mine? So, it's an emotional thing. Are our values aligned? Or are they always going to be a bit sneaky about things? Integrity is, are they actually going to follow through? Do they have principles? Will they deliver on something that they say they're going to do? Benevolence is being well meaning, integrity is saying, “Yeah, we will always deliver.”

And to have trust in something, you have to build up a confidence in those three values, which are all slightly different.

Simon McDougall (Guest)

And if I go back again to my days as a regulator, we did work with Manchester University in 2018, 2019 on explainability and trust, and we were running these large workshops called Citizens Juries with lots of people who are representative of the UK, so not specialists, people who are just representative of the UK. And at the same level of understanding.

And what we found was that people were willing to trust AI in certain contexts much more than others. So, for instance, if it was a diagnostic tool which was approved and used by the NHS, where there was already a huge level of trust and is being used for their benefit, people would say, “fine, we're going to go with that because we can see the benefit and we trust the NHS.” If it was a hiring decision by a faceless corporation, they were less willing.

Jenny Haskel (Host)

Oliver Seal, who leads Deloitte UK’s digital education practice, joined us for our episode with Pearson’s Sharon Hague and raised the issue of national confidence. Because readiness isn’t just about removing barriers – it’s also about recognising our own advantages.

Because when we consider the 3 pillars of AI success that we mentioned earlier – building foundational compute, capturing value in the UK and transforming work – it’s clear that to tackle all the challenges, we need both confidence and existing strengths to rely upon.

Oliver Seal (Guest)

I think one of the biggest hurdles is that, as a country, I feel like we've lost our mojo a bit. And so I think one of them is about confidence. And as Sharon puts it, really making sure we then back that confidence with investment around digital skills. I think there's a bit of a feeling that we are expecting government to do it for us, and I think we need to - both at an individual level and a business level, and in a business ecosystem - think about how we can work first, then move to hit those barriers.

Sharon Hague (Guest)

I think the UK has some tremendous strengths. You know, you've talked about higher education, our reputation internationally, international research, education – the UK is very, very highly valued. There's a real opportunity for us to lead in this field, which is something that is going to continue to be an area of focus, attention and growth into the future. So I think there's huge benefits from a national scale.

Jenny Haskel (Host)

So Oli is saying we have the responsibility to drive our AI goals forward – as individuals and as businesses – to really start on that journey today. It’s not solely about governments.

But there are some fundamental considerations that are needed at a national level. Because while digital skills and the adoption and trust we’ve spoken about are central to transforming work, there’s other important national barriers that need addressing too.

Because AI doesn’t just live in a digital world – it lives in the physical one too, a world with infrastructure and a need for investment. Here’s Deloitte’s Anne-Marie Malley once more.

Anne-Marie Malley (Guest)

There are some strategic considerations that could have a significant impact on this, I think. So one is the cost and resilience of energy. So, energy consumption, going to double by 2030. Given the current geopolitical situation, where is that coming from?  And what is that going to do to drive costs? And I think countries like the UK - we've got a brilliant, natural and built assets - should be moving much more quickly to make sure that we have got clean, abundant, cheap energy to drive the AI requirement that will be. Because if we don’t, it’s going to become a cost play. And then it will start to limit.

Ben Legg (Guest)

And that will create hundreds of thousands of jobs, probably, building that infrastructure.

Anne-Marie Malley (Guest)

Absolutely. So I think we, in the UK, need to move more quickly, I think every country needs to think about how they're going to do that.

The second thing is the cost of compute. I mean, that has gone up between 24 and 25, that went up 36%. So that is a direct cost on to your organisation. How are you going to manage that? And that is moving again in one direction. And then what are governments going to do in terms of the regulation around AI? That’s quite the unknown and that could have a very positive or a very limiting impact on it too. So I do think there are some strategic questions that need to be answered and some very strategic action that needs to be taken at the national and at the global level to make sure that we have the foundations, the energy, the cost of compute, in a position where we can continue to use and exploit AI.

Jenny Haskel (Host)

Back in November 2025, Carol Yan from Amazon Web Services, joined us in The Green Room, alongside Deloitte’s Tom Harris, where they discussed some of these strategic questions just posed by Anne-Marie – including how our national AI ambitions and energy industry overlap and what is needed to see success.

Carol Yan (Guest)

Because when you think about AI and you imagine it for a second, you're probably imagining maybe some coders or maybe actually you’re imagining the prompt. Right? You put something in and you get, now something, get something, automated back. But we probably don't actually think about is that behind every single prompt, behind every single line of code, it has to be powered by electrons.

So, everything that you're putting in, something is running in the back to make sure it's being run. And then I saw a really interesting quote from the International Energy Agency’s report that AI is emerging as a general purpose technology, much like electricity. So, you see the emergence of AI, but now we're starting to see the role that the energy sector has to play in in powering that.

Tom Harris (Guest)

I think we're, we're pretty uniquely placed to, to, to both win the AI race, but also to reach our net zero goals. To do so, I think we need to be faster and bolder and more deliberate and think about the, the, the problems together in a system thinking kind of way.

And in order to do so, we've got to unleash more energy abundance, cheap renewable energy. And that's going to take policy advancements, it’s going to take regulatory change. It's going to require teamwork and cross-industry participation, and it's going to create the incentives for capital to flow into those challenges as well. I think if we do that, the UK can get on course to deliver both those goals.

Carol Yan (Guest)

I think, at a macro level, from, from our perspective, there's always going to be a focus on smarter infrastructure. There's always going to be a focus on making sure that's powered by carbon free energy. But I think most importantly, one of the things we talked about is deeper collaboration and a focus on the business outcome you're trying to drive. And, you know, if we do all of those things which, you know, might be easier said than done, AI isn't going to be a problem.

Jenny Haskel (Host)

It’s a belief echoed by Deloitte experts Caroline Brown and Tom Cope who joined us on our most recent episode.

Only by reframing our understanding of AI and being able to see the potential road bumps ahead are we able to ensure that our future can be AI-ready.

Caroline Brown (Guest)

Here's the interesting thing, the most advanced kind of AI that runs on the really leading edge, newly developed, compute power. You can put those anywhere, right? So model training, it happens in a data centre somewhere. You're not waiting on an immediate answer because the model is training the model. So it's going through cycles and cycles and cycles of training.

So those are the data centres that the really big tech companies, the hyperscalers like Microsoft and Google and AWS, they can put them anywhere, right? So they choose Iceland where there's loads of geothermal energy and where the climate is very much cooler, so you don't need to apply as much cooling or in the Nordics for the same reason.

But for what we call the inference model, so the stuff where you ask ChatGPT, a question of, “I've got this rash, what might it be?” One of the most common ChatGPT use cases is enquiries about your own health, did you know? Frightening! You want a really quick answer. You don't want to be waiting while it sends the query to Iceland, and then comes all the way back.

So those data centres, they can be very much smaller because we don't need to do the big model training, but you need them to be closer. So generally they get put near cities.

Caroline Brown (Guest)

Now let me ask you, would you want somebody building a data centre at the end of your garden? I think all of us... if they look like distribution centres, big, massive warehouse buildings, nobody wants that in their line of view. And so, everybody wants all of the benefits of AI. Nobody wants to look at it at the end of their garden.

Tom Cope (Guest)

I think in the UK we do have some real national assets. We’re an island and we have enjoyed a rich history of some impressive, complex, pieces of energy infrastructure which come with grid connections that have been decommissioned.

Tom Cope (Guest)

So I think you have to start there, because it’s almost speed to megawatts, gigawatts - depending on your ambition - where it’s existing infrastructure that unlocks a lot.

Caroline Brown (Guest)

For me as a sustainability person, the thing that's most important when we look at our use of AI over the next 15, 20, 30 years, is to ensure that given its high energy needs, that AI doesn't compound the problems that we're experiencing as it relates to availability of power in the UK energy system. That it doesn't create tensions over how land gets used, that it doesn't create tension over how water gets used, and remembering that we need water for cooling. But the opportunities that AI presents, actually in solving some of those challenges to my view, completely outweighs some of the constraints and the concerns.

There was a study, it's about a year old now, I think, that estimated that from a sustainability perspective, using AI to optimise our energy systems, to help with accelerating the development of new low-carbon materials, particularly some of those really challenging materials like steel and cement that we know contribute significantly to greenhouse gas emissions. When we think about how we help electrification across big industrial processes that have traditionally used fossil fuels, we know because it's already doing it, that AI is going to deliver significant gains and benefits, and allow us to accelerate.

So the study that I referenced reckoned that use of AI could contribute over 5 billion tonnes of carbon emission reductions over the next 15 years, in contrast to the 1 billion tonnes of carbon emissions it generates through use of AI. So for me, in essence, emitting one to get five back, feels like a pretty good trade.

Hannah Gowen (Host)

But there’s the M-word, which is money, which comes into most things, right? So, Tom, I guess there's a big question here of, who funds all of this? You touched on governments there, whose responsibility does it fall on to fund this large-scale change?

Tom Cope (Guest)

I think it depends which part you're talking about. You know, very, very, you know, the large hyperscalers, which I'm sure everybody's heard the term, you know, are strategically incentivised and aligned to invest in these assets.

I think where it becomes a lot more complex is, you know, the value chain that you need to invest in to unlock, unlock the plug to go in and the electrons, is a lot more complex and actually isn't aligned with their normal, you know, with what they've been doing for the last decade with regards to tech, you know, operations, logistics, etc.

So if you take nuclear, for example, you know, there is not, there is not a... Sorry, there isn't a project being developed globally at the moment, largely in the West. If I just generalise a little bit, that hasn't got the hand of the state around it in some form, and that's important, you know, it's not a negative thing. It shows the importance of that technology.

But there is, there's a whole leap of kind of de-risking that needs to be done by government, through business models, funding models. A lot of the work that we advise our clients on, skills, just to... I know we are talking about financing, but, you know, all of these things need to come together to unlock that.

And it's going to require such a large degree of coordination to make those risk ratings drop down to an acceptable level to really underscore what are sensible levels of return, that ultimately, if you don't get that right, the cost of your power goes up. The cost of the... you know, your your compute then goes up and actually all of a sudden, you know, AI becomes an evil thing because it becomes a premium product that isn't accessible by everybody.

Tom Cope (Guest)

I think one of the biggest challenges is AI and data centres are so prominent at the moment and it’s almost insatiable demand and everything needed to happen yesterday, when you’re talking about the scale of power infrastructure that’s needed, there’s a really long tail.

Jenny Haskel (Host)

If the technology ends up ringfenced with those who can afford it, then we won’t fully grasp the opportunity ahead.

And that equal access is about a nationwide reach too – AI can’t be something felt just in our biggest cities.

Back to Pearson’s UK CEO, Sharon Hague

Sharon Hague (Guest)

Because I think there is the potential, isn't there, that you perhaps have - it could be regional or it could be a divide between very large companies and small and medium sized enterprises. So it's incredibly important that we work collectively to make sure that those opportunities aren't concentrated in only certain types of organisations, benefitting - or in certain parts of the country. And I think you've touched on some of the policy around, how directing of investment and so on to different regions and support of industry in different regions.

But I think, you know, UK companies as a whole, there is a big opportunity for encouraging - public private partnership can be a really good way to help spread the opportunity. I think UK companies actually invest far less in training and learning in their organisations than, for example, European counterparts. So creating incentives for companies of all sizes to invest more. You know, the government has been doing a lot to create more vocational and technical pathways through education and much of that - to the ability for that in different parts of the country to reflect the jobs that actually exist in that area, rather than it being, you know, training lots of young people for a particular pathway where actually there aren't any jobs in that region.

So I think that's going to be really positive. And then, you know, I think there are also things that, from a policy perspective, can be done to encourage large companies through their supply chains to actually help with that kind of upskilling and making sure that many organisations of different sizes, through the supply chain, benefit from some of these developments.

Jenny Haskel (Host)

So, “Is the UK ready for AI?” One thing is clear from the guests we’ve had on the podcast - readiness isn’t a destination, it’s a choice.

A choice to build the infrastructure to power AI.

A choice to invest in the foundations needed to capture its value.

And a choice to transform work - by experimenting, challenging old ways of thinking, developing new skills and building trust at every stage.

-- Outro music of The Green Room begins --

Being ready for AI isn’t having the most powerful technology, it’s creating the conditions for people to use it, trust it and benefit from it.

Ben Legg (Guest)

There's so much more to learn, and I haven't yet met any workforce, I've met the odd individual who is probably mastering all of those. I think if anyone said I need to pick something for the next quarter or three for my personal learning plan, I say become a power user of AI for the tasks and activities you like to do.

Kate Sweeney (Guest)

It is very, very clear that AI adoption is happening where it's being role modelled by leaders. And I think leaders have to be really brave and start experimenting themselves

Simon McDougall (Guest)

If you are honest and transparent around what you're doing, if you're clear about what the risks are and you are clear you're striving to manage them, they'll often accept a few bumps in the road within reason, if they know that you are striving to manage it.

Kate O’Neill (Guest)

When people can sense that sort of both sides are accounted for, they will lean in and they'll be part of that process.

Anne-Marie Malley (Guest)

It's everything, everywhere. And how do we manage it, how do we govern it, how do we help it? we now just need to work out, how do we make this work?

– Outro music of The Green Room begins followed by our host talking. –

Jenny Haskel (Host)

That’s all for this series of The Green Room, we’ll be back in September with more big questions.

If you missed any of our series exploring the AI opportunity, you can find them all wherever you’re listening or watching to this podcast. And don’t forget to hit follow or subscribe on the channel so you don’t miss an episode when we return.

This podcast is produced by our very own pod squad. Original music by Ali Barrett.

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