CR085: Driving change in public services with data and AI with Craig Suckling, UK Government

There is huge potential for the leverage of data (and AI) to provide the most modern Citizen digital experiences, but that has to be balanced with managing deep legacy challenges and, of course, security risk.
 
In a special episode this week, Dave and Rob talk to Craig Suckling, UK Government Chief Data Officer about modern public services, the challenges and opportunities in unleashing data in a safe and secure way, how AI fits into all of this and how Governments can lead the way in secure and ethical AI implementation.
 
TLDR:
01:56 The Chief Software Officer?!
05:40 Cloud conversation with Craig Suckling
57:55 Christmas with the kids and Lego!
 
Guest:
Craig Suckling: https://www.linkedin.com/in/craigsuckling/

Hosts:
Dave Chapman: https://www.linkedin.com/in/chapmandr/
Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/
Guest host Sandeep Kumar: https://www.linkedin.com/in/sandeepkumar99/

Production:
Marcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/
Dave Chapman: https://www.linkedin.com/in/chapmandr/

Sound:
Ben Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/
Louis Corbett:  https://www.linkedin.com/in/louis-corbett-087250264/

'Cloud Realities' is an original podcast from Capgemini

Creators and Guests

Host

Dave Chapman

Chief Cloud Evangelist with nearly 30 years of global experience in strategic development, transformation, program delivery, and operations, I bring a wealth of expertise to the world of cloud innovation. In addition to my professional expertise, I’m the creator and main host of the Cloud Realities podcast, where we explore the transformative power of cloud technology.

Host

Esmee van de Giessen

Principal Consultant Enterprise Transformation and Cloud Realities podcast host, bridges gaps to drive impactful change. With expertise in agile, value delivery, culture, and user adoption, she empowers teams and leaders to ensure technology enhances agility, resilience, and sustainable growth across ecosystems.

Host

Rob Kernahan

VP Chief Architect for Cloud and Cloud Realities podcast host, drives digital transformation by combining deep technical expertise with exceptional client engagement. Passionate about high-performance cultures, he leverages cloud and modern operating models to create low-friction, high-velocity environments that fuel business growth and empower people to thrive.

Producer

Marcel van der Burg

VP Global Marketing and producer of the Cloud Realities podcast, is a strategic marketing leader with 33+ years of experience. He drives global cloud marketing strategies, leveraging creativity, multi-channel expertise, and problem-solving to deliver impactful business growth in complex environments.

What is Cloud Realities?

Exploring the practical and exciting alternate realities that can be unleashed through cloud driven transformation and cloud native living and working.

Each episode, our hosts Dave, Esmee & Rob talk to Cloud leaders and practitioners to understand how previously untapped business value can be released, how to deal with the challenges and risks that come with bold ventures and how does human experience factor into all of this?

They cover Intelligent Industry, Customer Experience, Sustainability, AI, Data and Insight, Cyber, Cost, Leadership, Talent and, of course, Tech.

Together, Dave, Esmee & Rob have over 80 years of cloud and transformation experience and act as our guides though a new reality each week.

Web - https://www.capgemini.com/insights/research-library/cloud-realities-podcast/
Email - [e-mail]

CR085: Driving change in public services with data and AI with Craig Suckling, UK Government
[00:00:00] Are we, are we recording, Marcel? Sandeep, can you check that button's on? Is it red? Is there a red light? I saw him press A button.
Welcome to Cloud Realities, an original podcast from Capgemini. And this week we have a special conversation. We're going to dive into the world of data in public services, how you deal with that mass of data and the deep history of data, and how do you get that to show up to the citizen as a new series of people.
exciting services that are low friction and easy to use. I'm Dave Chapman and I'm Rob Kernaghan. And joining us on the show, uh, we have, first of all, a guest presenter, Sandeep Kumar. Hi Dave, this is Sandeep. I'm with Capgemini. I'm the global head of Citizen Digital Services, which is very close to my heart.
We are delighted to have you here. It's one of the few times we've had a guest [00:01:00] presenter on as well. It's very exciting. My pleasure. Look forward to a fun chat. What could possibly go wrong? Everything, I suspect. With you guys around. It's a bit uncomfortable. It's early, isn't it? A shot's fired early. You haven't heard the show.
We can go in two minutes. My word. Also joining us this week, I am delighted to say we have Craig Suckling, who's the UK Government Chief Data Officer. Craig, welcome. Thank you very much. It's great to be here with you guys. Delighted to talk to you. Thanks for fitting us into a very busy schedule. No, always happy to come in and, um, have a good conversation about data and AI and how we can drive change across government.
So it's great to be here. Do you notice how much more polite he was than Sandeep in that intro? No. Didn't put a little digging, did he? No, he didn't. That's a, that's a plus. You, you're the guys who will be asking me the questions, so I need to be nice to you. Fair. Sandeep doesn't know how he's left itself exposed.
Yes quiet. It's a long way to do this. Uh, but before we get into all of that. What's confusing you this week, Rob? Uh, well, Dave, I've [00:02:00] got a simpler one this week, because you've, you've, you've Oh, thank God for that. He berated me for having a confusing confused, so Sometimes Rob's confusion means he can't express the confusion in a clear way, so the confusion itself is confusing, never mind the output of the confusion.
However, you'll be glad An inception of confusion. Yeah, yeah, yes. Sometimes we end at a really weird point. But the, um, the one this week, Dave, is new role. Okay. So, this idea of software will eat the 10 years ago, it's now become a reality. Have you made this up? Have you seen this somewhere? No, I've, I read about it.
Okay, yeah. Yeah, yeah, yeah. Um, and uh, will it, my confusion is, I absolutely see the importance of the role, because everything software defined, engineering, whatever, blah, our entire lives are configured through software. Is it going to replace the role? of the CIO. Because actually, with cloud, with AI, with the algorithm rising, with all the things that we talk about, [00:03:00] is the CIO now going to have to become the chief software officer?
And the tradition of I own that IT is going to go, because we're democratizing everything, yeah? Decentralizing things. It's all about software and integration, as opposed to now, I've got a big budget and I'm going to run a data center. And my confusion is Are we seeing the change of the CXO portfolio and the CIOs now under even more pressure?
So, I definitely see that change. Whether that results in a Chief Software Officer role, I'm not 100 percent sure, but I think it's pretty inarguable at this point that the, how the original IT top team was framed as of, let's see, even five years ago, I mean, certainly ten years ago, but even five years ago, that's feeling pretty different to me now, these days.
Yeah, and I don't see it as being fundamentally different from the shift from the CIO to the CTO. Because, uh, the technology now has got a software [00:04:00] paradigm, but it's still technology in my mind. Yeah, I think the one thing with the challenge, a name like the Chief Software Officer implies an increasing focus on just the technology.
Just the tech, yes, exactly. to actually focus more on, like, the business and the culture and the org change as part of that as well. So provided that's incorporated into the name, I can see that. So the chief software culture integration. Exactly. A great big moniker for the, I mean, that's quite a role, isn't it?
Yeah. I think it's not just you confused about it, I actually think it's also the name of the roles are almost microcosmic of the change that the IT business interface needs to go through a little bit. To your point, Craig, it's like, it's not good enough that IT these days are in the basement and trying to do things cheap and cheerfully.
And it's almost, to a certain extent, not IT's problem that they're in that position. You know, that's just the way that technology has, has gone over the last, you know, 20 years. But what we're seeing now, [00:05:00] probably post cloud, post the unleashing of data and then as we go into AI age is you've gotta really rethink how you integrate technology into business, don't you?
And increasingly, technology is the business as well. And so like these things are becoming, the borders are blurring on this as well. And I, I think you're right. I think you. A trend I see is that we'll see less of a traditional, you are just a technology focused person who sits back in the basement and more of a, how do you actually integrate into the business lines more effectively as well.
I mean, I think, I think we've actually helped with this one. I you have actually, you've clarified that might be a first, I mean, first usually we exit this with even more complexity, . Alright, let's get onto our major subjective of the day. Craig, good to see you. How are you doing today? You alright? Very good, thanks.
Good to, good to be with you guys. Why don't we just set with, just set out some basics for us a little bit. So, maybe set out your role and mission within UKGov. And actually for those, like, from different countries and may not appreciate [00:06:00] the sort of structure of UK government, do you just want to give us a little pen picture of, of how it functions at the highest level?
Sure thing. So my role is UK government chief data officer, and I sit within a central team called our central digital and data office. And the remit and scope of my role is to help with driving the strategic agenda for data and the transformational agenda for data and AI across the UK government. And that extends across all of our ministerial departments, everything from health through to education through to benefits and pensions, you know, through to defense and everything in between. Right. I'm also really working with and focused on local authorities and how we really get, you know, better use of data, better use of automation and AI across local authorities and into public sector.
So I have a broad scope of role. I wonder what you do on a Sunday. If I answer honestly? Anything that is not technology. [00:07:00] And it's multifaceted in so much that I focus on some of the technology aspects and yes we want to create new modern capability and technology, but a lot of the focus as well is how do we shift culture?
across government as well. How do we build the future skills that need to be in place? And of course, there's a big, there's a big challenge there because government was one of the first to use technologies to do processing. So you've got a long tail, a legacy to tackle within all that as well. So it's like this extra massive complexity associated with it.
There's a long tail of legacy. There's also a huge inheritance of data. That's got huge value and opportunity if we can do more with it as well. Yeah, absolutely. Um, I mean, I always talk about this. I go to the National Archives every now and then and they sit on 800 years worth of data. Wow. All the way back to the Magna Carta.
And so like, you know, there's a huge amount of really valuable information and assets that we can, if we start to deliver on sharing and exposing that in the right, trusted, and secure way, you know, we can create [00:08:00] so much better opportunities on how we create, um, public services for citizen businesses. We can collaborate more effectively across teams as well.
So, in your pen picture of government, you've got the big central departments, and then you've got the local authorities that sit outside of that. Give us a sense of how the sort of chain of command works. So, how decentralized is it versus centralized? How much kind of, how much mandation power do you have versus kind of nice to have stuff?
Yes, so, so I'm a big decentralist and so like, you know, one of the things I'm very intent on is not creating bottlenecks in what I do. In my role and in my central department and function, I talk a lot about this balancing act of autonomy plus uniformity, right? And for me, my focus is I want to have the teams, the departments, the functions across government being enabled to work really autonomously in how they use data and ai, how they drive on value and change so that they can do so.
We need a broad aperture for this to happen, not to be constrained by some central function. But we want everybody to be [00:09:00] working at uniform best practice at a really high bar. So a lot of what my team does is create those conditions. How do we create good standards, good policy, how do we create great frameworks, how do we give people access to the right tools, so that they have the best capabilities available to them to do really good high value work.
But can do so independently. Right, right. So there's a mission across government at the moment, for those who aren't aware outside of the UK, the UK has had a new government for the last three to nine months. Yep. They've just done a budget. So the UK Chancellor, Rachel Reeves, just announced, in her budget speech talked about public services and described it quote unquote, uh, we will join up services across government to increase efficiency and bring costs down, which is good mission, a good mission.
I think it talks a lot to citizen experience as well as, um, you know, a huge amount of opportunity to join up data within that agenda. So let's just dig into that a little bit. What does that look like from your seat at the moment in terms of like immediate action off the back of [00:10:00] that? Yeah, so I mean, you know, I, I should say as well that there are a number of other missions across government.
This one is an underpinning one for sure, but we also have themes like breaking down barriers of opportunities for all creating safer streets across the uk. And all of these are imperative to actually how we transform public services to create good citizen and business experiences, right? Central to all of that is how we use more data, how we use more analytics in ai.
to be able to also transform the experience and one of the things that I'm focused on, you know, when I think about that transformation agenda is how do we create experiences for our citizens that are singular? If I am a new parent and I'm registering the birth with the NHS, have to also go speak to DWP about childcare benefits, have to go speak to DFE about educational material.
I should be able to opt in to have a singular experience that allows for data to share. Also not, as a citizen, also not have to self discover. So it should be proactive, is the other piece. If I, um, opted into a service where I want to do my own tax self assessment, I [00:11:00] should receive a reminder at that time.
Wouldn't it be great if it was filled out already with data on me and data from third parties outside of government, and all I have to do is sign it off. Right. filling in your self assessment tax form. I mean, it's a joy for me, so I maybe work priority. Is it your favorite moment in the new year? Yeah, there's a, there's a view in that.
If you think about it, if you put the human or the citizen, I should say at the center, um, it's like all those life events and the triggers, but the government departments can be quite siloed in the way they have traditionally operated. And the process has been verticalized against the department. You know, it looks like the budget that drives the department has there been much conversation about this life events triggering and proactively pushing because at the moment it's I have to go and pull from the department.
Sometimes the department's push when they, one thing's about that concept of a citizen centric approach. model that triggers as events occur around the outside based on the data. Is, is that the sort of themes that are being discussed at the moment? Yeah, it's a focused part of [00:12:00] my strategy to help to drive more of that.
And I'm working with many others across government because it's a big initiative. Central to it is that we have to have strong citizen trust on that as well. And so, you know, a lot of this rests on the ability for citizens to opt in, to allow for their data to be shared, to allow for events to fire across different departments as well.
Right. And so very much focused on. Yes, we want to have that experience. It should be proactive. It should be singular. It should be uniform, wherever you engage with government. But it also needs to be founded on strong public trust, and it needs to be one in which there is transparency and consent and the ability to opt in and out where you don't feel comfortable as well.
And where is that, where's that process up to? So would you say that you've got that nailed? So how that, how that functions, or is that, is that just in conversation at the moment? So, you know, as part of the new missions that have been announced, we're really working on how do we start to drive this in a more, you know, Progressive way in a fast way.
There are already components that we see that are starting to hit at the edges of this really well. So for example, we have [00:13:00] the one login service across, um, government now, which is a service that allows for citizens to opt in to allow for themselves to be uniquely identified across a number of core departments so that you don't have to continuously log into different ways and different silos, but you have a singular way of being authenticated through services.
So that's the first building block in this space. Um, I'm focused a lot right now, probably most of my time, on data sharing between departments and how we start to unlock that. And like I was saying earlier, that's going to be fundamental because if we can share data and again, it is always within the provision of where it is ethical, legal, trusted.
to be able to share and relevant. Um, then that allows for us to create the foundations to start to drive those transformations and services. And as a citizen though, when you're thinking about that trust, there must be a large part about explaining to society the value they'll get and building that trust model up.
Because, I mean, to be fair, a lot of the citizens who are [00:14:00] involved in this system don't understand how it operates. They're only recipients of experiences associated with that. So is there a drive around the sort of, how have you thought about the sort of raising awareness of why this is going on, the benefits, and sort of like the evangelizing of the this is going to be much better for all when we get there?
A hundred percent. So like I speak about value exchanges in this instance. And I don't mean value in terms of pound value, I mean in terms of societal and broader base value. And so I'll give you an example, right? Like this is one that's in the public domain. We've been working with the Department for Education.
They've been announcing, uh, creating an open educational data store, right? Which will allow for, um, curriculum and exam scripts and other bits of information from across the department to be exposed as an open store into a cohort of ed tech startups. And they'll be using that data to create more personalized AI educational products.
We'll deploy those products back, pardon me, into schools, so that students all have better ability, better access [00:15:00] to personalized AI products to break down barriers of opportunity. Now that's a value exchange because breaking down barriers of opportunity is one of our core missions. And if we can start to draw those circles more around how data that we can deliver outwardly into the economy or across government can deliver on benefits back to society.
And to your point, make that really clear. Then I feel that citizens will be more willing to lean into this as well. I, One very quick point, while you were saying that, it's just, uh, in seeking point on this, uh, element of consent, um, as we see that there's a lot of fluidity in the usage of data, especially when you think about sharing, sharing between the different departments and, uh, we'll think of new use cases in which the data can be beneficial to citizens.
How do we manage the incremental, um, Consents because you might have taken the data for a particular [00:16:00] purpose from the citizen But we think there is value in enriching that with some element from the tax department perhaps and then offering a better service um I don't know my opinion here is it could be that gdpr just locks you into a very point in time, have you got consent for this?
Is there some thought process around how policy can help a more fluid view of consent? Because if it isn't the right intention. I think it lives and dies on the user design around as a citizen, can I have real clarity in a single place around where my data is being used and for what events. And so I can essentially just switch on and off different things that I'm comfortable or not comfortable with having done with my data.
And so, you know, this is very nascent for us still, but in, in terms of our think big idea and roadmap where we want to go on this, we really want to make sure that there is, uh, I I talk about like features of trust, right? So like [00:17:00] real transparency and clarity around where data is used, real control from citizens in terms of what they can or not do with their data on an event level basis.
Yeah. Um, and then. You know, explain ability around where your data is being used. And also if I'm interfacing with algorithms across services as well, which is going to be another key feature. We can talk a bit more about like how we're trying to create more transparency on where we are interfacing with algorithms in these services.
And is there a conversation going on about the need for legislation and change legal frameworks around this as well? Because it feels like you know, because we're changing the dynamic of how data flows, et cetera, there's probably a policy change that needs to occur in parliament. Is that starting to raise its head and say, yeah, we should be changing certain things about how technology law works.
Yeah, so we're taking a, um, long look at legislation today, and you've seen recently, you know, there's a bill passing through right now around DUA. Yeah. Um, and data access. And that, you know, looks to [00:18:00] strengthen, you know, how businesses across the economy can share on data. You know, again, where there is the right consent to do so.
Um, we have the Digital Economy Act from 2017. which seeks to help to drive better sharing of data across departments as well. We know that there are areas where there are still gaps. Where data sharing does not have a legal gateway. And so, you know, the approach we're taking here is we want to be bolder on this.
Um, we also want to be targeted to identify where there is real valuable areas where we want to share more data and see if we can strengthen legislation applicable there. Legislation is not going to be everything. It's one part of a broader whole of system change we need to get right though.[00:19:00]
Let's maybe move the conversation slightly on specifically then to data and some of the challenges that you've got across government of enacting some of this stuff. So, you know, when you look, data is often problematic even in large scale organizations, never mind across the governmental system that you described before.
It's often locked into structured systems. It's often, Unshareable for technical reasons, not just for things like legislative reasons or, or legal reasons. Um, and it's often the barrier to entry for many organizations moving their environments forward and of course, in the next three years, in terms of adopting AI in the way that is being evangelized.
all over industry at the moment. Give us an insight into the challenge that you have with the data swamp in government. How are you looking to try and join it up? Are you taking a macro view [00:20:00] that says, architecturally I want to sort of understand every little bit of it, I want to kind of get how You know, kind of how it comes together.
I want to understand like a master data management structure. Or are you taking a little bit more of a, well I'm going to try and just do this one little bit, and if I can do this one little bit, then I can build on to the next bit. What's going on in your head with all of that? Yeah, so this is a multifaceted, complex topic.
And so, I'll probably start with some of like the core principles. That I'm really convicted on the first is, this has to be a decentralized federated ecosystem when we think about doing more with data. Um, we have seen from the last decade or so that centralizing, on data into a single monolithic lake is a slow death march to nowhere.
It's not much fun to this. It's like the poor people who are involved in those programs to get the edict centralized and then they fight and fight and fight. You must say it's a very sort of soul destroying approach to data, isn't it? Yeah, and I always have two core arguments on [00:21:00] this. The first is that You know, you can't scale centralization.
There's too much complexity. There's too much variability and variety and data, especially if you look at government. To be able to have a single team, a single capability that owns everything, yeah, knows everything, and can deliver it outwardly really quickly, just you can't scale it. The second thing is it's really bad for resiliency.
If you have a single point of attack on data and if someone as a bad actor had access to that big honey pot, isn't it? Yeah, you get the you get the entire treasure chest in one fast swoop. Yeah, so very focused on decentralization What that means however, is that we need to then have departments being able to build modern capability that allows for them to own and operate data In a more, you know, mature way, essentially.
Left within department though. Left within departments. And so, the challenge on that is, we need to work more with departments who are typically tight on budget, short on skills, short on [00:22:00] capacity, struggling with legacy. And we need to enable them to be able to modernize their capability more across the system.
Now we're focused on doing that with both a top down and a bottom up directive, and I can talk a bit more about that. Top down is, we spoke a little bit about missions earlier. It's really imperative that when we think about data and data sharing, that it is baked into the outcomes we're trying to drive through missions.
Not that we're just doing data because we'd all love better data governance, that's headless. In terms of, like, what we're thinking about. Many have died on that hill. Many have died on that hill. And the second hill is the Data Lake hill. Yeah, there's two hills. They're looking at each other going, but the idea sounded so good when we started.
So If only people would understand! So Understanding the value and the imperative of data in non data terms as it aligns to the priorities of government is a really important lever for us to pull to drive change. We spoke a little bit about legislation and policy, and yes, we also want to do more to mandate that.
We want to [00:23:00] focus more on how we unlock funding as well, but then bottom up, we want to work with departments to give them better access to skills, better access to tools, better I believe, and this is typical across both public and private sector, that probably 80 percent of data activity is manual effort on managing quality and mastering data.
Yeah, no doubt. In a world of AI now, we are seeing a lot more of this being able to be augmented or automated more directly and take that capacity out of the manual thing so that they have more time to focus on the value added. So those are some of the things you need to get right. Yeah. The scale is vast, and so, you know, you mentioned earlier, like, do we tackle it all?
The approach we're taking is, where are there first meaningful high opportunity areas that we can start to focus on? Use case based. Use case based. And it's a balancing act of, we want these things to be valuable, to deliver on outcomes. Uh, we want them to be feasible enough to move quickly. Um, but we want them to be imperative enough to start to drive strategic scale as well [00:24:00] and start to evangelize these quick wins early as we unlock things.
And I think, I remember back, there was an experience I had with UKGov where my passport photo was shared with the DVLA for my driving license. Right. And I went in and they said basically because you had signed up within a certain period, the trust is there, so we'll reuse your photo. And it massively cut out a a load of faff for me, but it was a very simple use case where it was a simple picture of me that was shared and now it just, and it was like, click, click, click, bang, gone.
DVLA generally didn't actually really Yeah, they've done, there's a lot of that sort of stuff going on, but it's, it's a very simple use case, but for the citizen It's removed so much toil associated with the process interacting with the departments. And I think little things like that help build the confidence in the system around how people see the value.
And we might think that's trivial, but actually when you experience it, you go, Oh, hang on, that's great. And we need to chip away on this incrementally. The other thing you mentioned there is that, There's actually quite a lot of good stuff that happens today. [00:25:00] Yeah. And we shouldn't beat ourselves up too much around government.
There are some very good use cases and examples of this working. We need to elevate those. And I'm really focused in my central role on where we see good data sharing, good use of AI at scale in different areas. How can we help to productize that up right, and share it with others? How do we start to scale that benefit?
Um, and then, yeah. Shout about it a lot. You may not have this articulated, so if not, that's fine, but in your head, how far can citizen services go? You talked about things like sort of moving to more of a proactive stance, like removing friction, you know, single points of entry, etc. Could it get to a point where it's as easy as, you know, you've got an app on your phone and you're kind of very straightforwardly interacting on, uh, on your sort of relationship with government, almost tick box style?
Yep. Do you think it could get there? Yes. And we see progress with this already, like, if you look at areas like the NHS, the NHS app, that's starting to [00:26:00] move in that direction more. Right. But we have this opportunity to have this single, uh, In your palm, you know, way of interfacing with government. Now having said that, we also need to be aware of all of the different population groups across the UK.
Because not everybody is digitally savvy. And there is a lot of people that, you know, really require more human interaction. Whether they suffer from, from disability or whether they are from a, um, you know, a demographic where they haven't had as much access to digital skills. So yes, we're focused on that digital experience.
But we also need to cater for others in the market. For sure. Yeah. And do you think, so that might be a good place to bring the inevitable AI topic into the conversation, right. It strikes me that for those that are uncomfortable doing anything other than natural language, language interface, that AI would've a natural place there.
So you can create like a digital structure even behind creating like accessible to all services. Yeah, a hundred percent. I think we are seeing this opportunity now and again, like progress is beginning. I think we are [00:27:00] now maybe five weeks in to having launched our first gen AI chat bot on, on gov uk.
Okay. And that's, you know, in, uh, what we call public beta essentially as a phase of release. Um, that's the first instance of our starting to use Gen ai. In a meaningful way in the public domain to allow people to navigate some of the common queries we see on Gov uk. What does it do? What does the first one do?
Well, it allows for you to look at things like accessing more useful advice on tax or where to identify, you know, where you can get access to critical services. Mm. Through Gov uk lets you navigate things in a more natural language conversation and direct you to the right places. Right. In a, in a, in a far more easier way.
I see that building. going forward, right, to allow for us to have more natural interfaces in a lot of our services. I spoke earlier about the fact for that to be backed by strong transparency. And so one of the things we have in my department that we have, you know, spearheaded over the last 18 months is a policy called the Algorithmic [00:28:00] Transparency Recording Standard.
It rolls off the tongue. First, nah, I like it. There's an acronym in there somewhere, isn't there? We love a good acronym, yeah. With the Algorithmic Transparency Recording Standard, we are working with all departments to publicly disclose, wherever they are using algorithms, that citizens and businesses are interfacing with.
And you can go on to the ATRS hub, which is available online, and identify where there are algorithms in use today. We see this as a first important step. Because as we start to scale AI, we have to scale transparency of use of AI as well. I think there's a really important leadership role for governments in this, isn't there?
Because, you know, everybody's wrestling with, how do you do responsible AI? And how do you do AI responsibly? And I don't think we've cracked it yet. And just because of the pace of the technical innovation, regulation inevitably is trailing that a little bit. And I don't think, I, I can't remember in my career, a a pace of innovation that's been as fast as what we've seen [00:29:00] with Gen AI over the last couple of years.
Yeah. So what, how are you balancing that out? Like awaiting regulation from certain areas, but needing to act now and be responsibly. I like, I like this notion of, of transparency, but what other elements are in the conversation for you? Yeah, and you're right. So it's, it's, it's nascent, but it's moving quick and it, it means that we have to shift how we think about adoption.
as well. Um, and, and how we, we drive this. And you can divide this conversation into two parts. One is like, you know, how do we think about AI and the AI economy across the UK? And then how do we think about AI adoption internally into government? And maybe to talk to the internal factors. So examples like the HRS policy I just gave are a test and learn for us to be able to identify in a first way, like where is that, like,
How do we learn from that to inform wider policy and thinking around regulation we might have in the future? But, you know, the other dynamic to this is like, Because we're so early on in the process, so [00:30:00] we want to make sure that we are creating a broad aperture of kind of like experimentation and test and learn where it is in a non-G regrettable risk adverse way.
And so like, I almost have two threads to like ai. One is, you know, when we think about tools like a co-pilot to allow for you to do better software engineering mm-hmm . Mm-hmm or to allow for you to collaborate more across teams, uh, to be able to search across SharePoints more easily. Right. There is a low risk.
Broad horizontal like innovation things that you can just relatively small blast radius exactly and allow for that to be accessible widely and you know, importantly, help us to understand the business case around like, what does that mean in terms of driving more productivity gain? There are also then specific use case areas where we want to unlock real.
outcome led value with AI and data, because data is fundamental to this. And on those, we're going with fewer, deeper, to deliver on getting out of just a proof of concept into production level scale. And I think there's a big [00:31:00] point there in, we've struggled with productivity over the past. There's been a conversation that said it's not gone up.
Yeah, basically for 10 years productivity is flatlined. You mean even since ERP was deployed? Yes, it's actually maybe it was ERP's fault. I can't believe it. I've seen in business cases maybe 10 - 20 percent productivity goes wrong. Well, David, it didn't happen. God forbid, the ERP wars, as you call it, in the 90s didn't quite deliver what they said they were going to do.
No, we didn't get that from them. Before we get on to your soapbox and rants about ERP systems, there is a point here is it does feel like, and you use the word, Productivity is this could be a point of a lift where we get out of that, that, that flat lining and we actually get, you know, more power behind the system that allows us to be more productive.
Yeah. It feels like that's happening, but it's a, in my head, it's that careful balance with making sure we do it, In the right way, because if you ran to productivity too fast, you're going to get what I call the AI bloopers, which is I'm sure there's going to be some unfortunate mistakes. And we've seen [00:32:00] quite a few happen already.
It's that it's that control balance that I think is so key. And that's why, you know, Where there are low risk, non regrettable, broad things, that's where we have less guardrails to just make sure we're doing broader experimentation to understand. But where there are other things, especially if it is in use around citizen services, we're being a lot more meaningful, a lot more thoughtful about how we go and deploy these sorts of solutions out there as well.
So there is a, it's a case by case basis, essentially, around these things. I mean, productivity is an interesting area. kind of like factor to this because, and you know, you talk about the ERPs, like the other thing here is like, we need to do a better job, I think, across all industries of actually following up and tracking the value that we said we'd unlock in the first place.
How often do you go back? Don't look back. You don't want to look back, do you? You go, oh my God, we'll find out. Well, I think when you're in your 25th year of delivering an ERP service, a lot of people have retired during the program, innit? Right, right, right. Right. Right. Just one quick point on the [00:33:00] ERP, I'm just saying it is very topical at the moment because you've got all of the government shared service transformations happening where there's a restacking there.
Is there an opportunity to learn from the past on those ones? Um, so understand the question more. So in terms of how we get better at ERP deployments? Yes, because there's a lot of it happening in government at the moment.
Look, I, I think a, a few things here. Um, some of the things that we're trying to drive as best practice across government is a cloud first policy. Yeah. We believe that whether it's an ERP or something else, that we want to have scalable, secure, resilient, like infrastructure underneath it. I think the other thing which is gonna be a dynamic that I'm keen to see how it progresses over the next year is we see a lot of these ERP vendors now investing in AI and agents to help to actually drive the next generation of what this looks like. So I believe, and I have hope, that we will see a lot more [00:34:00] agility in how we're actually able to consolidate, modernize, drive out new capability around ERPs. I echo that actually, we joke about it, but I think the agentic layer is absolutely key to scaling AI across organizations, I think. Because to your point earlier, things like co pilots are radically effective, but they're radically effective at a very individual level. So you, you know, it's a field of stars in terms of then how that joins up and we'll see how that gets adopted and there's an interesting adoption paradigm there somewhere.
But in terms of the, you know, how an organization actually functions. You know, moving from being locked into configured process, to being, to being able to automate that process in a that's at a far higher level than previously, to then dynamically reconfigure that process based on the conditions in which it's acting, feels to me to be a real strong step forward.
It's a huge amount of opportunity [00:35:00] there and you, you come back to like what we were talking about earlier on, things like data sharing. There, there is a world in the future where we will have agents that help to broker and execute on that sharing, establish the right agreements, reference the right legal gateways, ensure that there is strong trust in how the sharing is happening, and do that on our behalf.
Data, data sharing, just to establish an agreement typically takes six months, right? So, like, I think, and like the example on that is, again, I think some of the other biases, and I'm guilty of this, that we see when we think about AI and agents is we tend to always think about chatbots. A lot of the value for me sits actually in where there is cost, effort, intensive work that happens behind the scenes.
And that is everything from showing data. That is everything from how do we migrate legacy code? Yeah. With more agentic. Yeah. Like bots helping to do that. Um, how we manage data quality. And if we can start to drive more automation with agents across those functions, we create the capacity in those teams to focus on delivering some of the more of the front end things, the more valuable like citizen facing services with AI as well.
Where's the [00:36:00] conversation up to, uh, while we're on AI, where's the conversation in government up to about the relationship between humans and ai, whether that's. Intra government, so like internal if you like, or citizen to AI interfaces. Have you guys got into that conversation? Um, so yeah, so I think, um, a lot of this is based on, I was talking earlier about features of trust and like, you know, what does it look like in a future where we do have more AI to human interaction?
I guess we can divide this into two pieces. There is the citizen experience and then there is the civil servant experience. So with the citizen one, you know, we spoke a little bit earlier about, you know, transparency explainability where it is happening, control, the ability to opt in, verifiability, you know that this is a trusted, verified service.
And you know, those are all going to be really important features of the interface that we provide. When we think about the civil service and like, you know, people that work across departments, we're thinking differently about like, what do the skills of [00:37:00] those people look like in the future? And I don't mean about like us deploying, you know, thousands of AI engineers, because that's the people that build, but I'm talking about like the people that interface, right?
And in that instance, we need to think about how do people change how they work, moving from a doer to a director, really starting to orchestrate and you can imagine all of us having a team of agents. That we now start to prompt and have execute on our behalf. And so there is a shift from doers to directors.
There is also an increasing need to have critical thinking as part of our culture. What I mean by that is we cannot. Afford to switch off our brains when we see the outputs from ai. Yeah. We need to be savvy, we need to be critical enough to understand the nuance, the accuracy. We're not yet through the woods in hallucinations really.
Right. Really interesting point that, and we had a, I dunno whether you've ever read any Dave Snowden's work. He wrote about the FIN framework and various [00:38:00] really good stuff. We had Dave on the show, uh, and he was observing that a number of the. So, you know, the top end schools in the U. S., for example, are taking information age machines like computers out of the classroom.
Yeah. Because it's like, you can now take for granted that you're going to have information. Right. And now you're going to have information that's actually been correlated for you, you know, kind of expressed for you in a funny tone or in a serious tone or whatever you want it to be. So therefore, being an information worker as a human is actually of less value.
And the bigger question would become, with Because this is essentially better information. With better information, what better decisions can you make? I'm really encouraged by this because I think this is an opportunity that's addressable for everyone. I'll give you an example. We ran a hackathon a while back now, which was all about taking non technical people and having them get more hands on and engaged with how to prompt engineering into AI.
And the person who won that was a 30 year tenured [00:39:00] ea who knew nothing about technology, but knew everything about the corporate information that she had worked through. Oh, amazing. Over the course of her, you know, tenure of 30 years and she was the most savvy, the most. intuitive in just adopting how to prompt on information than how to perceive the output and accuracy.
Right. And so, like, if someone who has no technical background but a strong background in just working in an organization can adopt and, you know, win the hackathon, you know, the opportunity is there for everybody to embrace this. I mean, that's built on, Understanding how everything works within the system and it's presented as humans have this weird propensity that when they read something they tend to want to believe it, and it's that protection to say that you've served accuracy, so you're able to work against it as opposed to things that might not be as accurate as you hoped.
100%. And if you look at this from like a data perspective that goes into what is training our AI, a lot of the data is the formerly captured data. There's no [00:40:00] data that exists around the nuances of how you execute a process to create a briefing for a minister or how you think about negotiating, you know, around like different perspectives on writing a new policy.
That's an informal process that doesn't have a data trail. It's not there to train AI. Those are the areas that will continue to be valuable for us in the future around like how do we have that nuanced thinking that goes over and above just some of the basics that AI will deliver on as well. I think the.
The point you raised around the 30 year tenure year is an interesting one. Yeah. Because the experiences gained in a not technical world. Do you see any danger in AI making it harder for us to create those very unique individuals that sort of enrich the system? I think, I think the bar, the bar is gonna continue to raise 'cause we will see AI increasingly doing more and more.
You know, work on our behalf. But I think what that allows us to do is, you know, we talk a lot about, you know, this fact that people have more capacity to be more creative, to think more, [00:41:00] you know, in more broad, innovative ways. I also think it will, you know, be very important for humans to stay in the loop, to think about things like ethics and like to think about like, Is this really the right thing?
Because the data might tell us, and essentially AI is based on prediction and on what the data informs, and whether that is generative or classical AI. But we still have a valuable role to play in the loop to think about when we're making decisions based on best information, is this the most ethical thing for us to do?
And I don't see AI automating that just yet, right? And in fact, it can be very cold in its decisioning. Right. And the human And the emotional reaction or the emotional decision is sometimes the right thing to do because it's more caring. You're just saying that now because it makes me sound good. Yeah.
Previously you said that AI's a bit soft.
but it is that, is that there's always those logic problems. The trolley issue that, you know, there's, there's, there's challenges to the ethics that underset the, the algorithm will make a cold decision, but it may not be the [00:42:00] right decision to make. Yeah. From a human perspective, and how do we make sure that that humanity stays within the system so we don't lose out on the, that connection?
I like that phrasing. Humanity stays in the system around it, right? And, uh, I mean, I won't pretend to have all the answers on this one because it's like Nobody does. It is just, yeah. And the event horizon, no one can hazard a prediction two years beyond, right? Like, it's just Too fast. So we will continue to think about like, what did the future of skills look like?
Cloud transformation to take one of the big last paradigms was one of the fastest moving sort of changes in technology. It impacted organizational shape. It impacted culture, obviously impacted the tech stack. It allowed. some legacy to be retired, but it also took some legacy with it. And [00:43:00] it maybe took, you know, you've probably got your own number, but five to seven years to sort of get some repeatable patterns and get some understanding on what it took to lead through a cloud transformation or, let's give it a broader name, a digital transformation.
AI transformation, which is, A phrase that's starting to be used now? I think it's probably being used a little early. Yeah. I don't think people, you know, to the last section of the conversation, I don't think people really understand yet how all the elements come together to create enterprise wide transformations yet.
But I, we're probably not far off, at least, you know, kind of emergent frameworks for what that looks like. Right. You've got gen AI involved, you've got, you've got copilot and personal productivity, AI involved now, then you've got. Aspects of culture change and things like that that we've dotted on. In the middle of such a complex organization that you are, what does that feel like as a leader?
You know, you're dealing with so much ambiguity. Yep. And you're dealing with such fast moving [00:44:00] components around you. Where do you start with that? Yeah, it's um. It's a challenge, right? And I think it's, uh, it's definitely multifaceted. If we think about what does that new platform look like for AI and like, how is that going to change, not just technology, but like how we organize, how we are culturally, you know, engaged with technology, you know, how we, we manage our work across government.
Um, there's a few strands to hit on. The first is, you know, we've been very much focused on There are foundational components that come before AI from a technical standpoint that need to be in place. You mentioned cloud. Cloud is going to be very important to have broad storage facilities for data that is scalable, resilient, and flexible.
Like I said earlier, data does not need to be in a single place, but we do need to have common standards and the best approaches to how we store data and cloud is a good basis for that. Um, [00:45:00] we then want to make sure that data can be fluidly exchanged. where it needs to be used by those teams that are creating the right AI applications across government.
And so we spoke about data sharing. That's another strategy, a part of that. That data needs to be held in close proximity to the AI algorithms, the AI applications that are then using them. We are seeing the new ground for gains as an inference. And less and less should we be focused on the pre trained type area.
That's probably best left for the big foundation model companies. When we think about practitioners across government, we want people to be able to utilize data, close proximity to an LLM, build applications that drive really good experiences at the inference level. And so we think about that stack that we need to unlock end to end.
And in my role, you know, we're focused on how do we create common platforms that allow for some of these things to happen. Data exchange and sharing. Um, how do we create access to common tooling for things like AI workbenches, for being able to leverage the right AI models. [00:46:00] But how do we allow for departments to have the autonomy in how they adopt that?
Because what we don't want to create is any sort of vendor lock in either. And so we're not going to ever say from the center though, you know, you must go with a specific vendor. But we will talk about the broad architectural principles and standards and how you can start to build out that whole stuff.
They have to interoperable, minimum they have to interoperable. Must be interoperable. There's a, there's a, there's a theme there though that'd be quite nice about a government marketplace with, Things that have been created for the interaction where you can share and sometimes it's difficult to get the government departments to share because of the, you know, the way they operate in the way they're structured this whole operating model conversation in there.
But there's this bit about maybe a marketplace would be a good place to begin to be able to share components and widgets and ideas and that can be idea marketplace as much as it is. technical marketplace. Also sharing exposed data and sharing locally developed, uh, small language models. I built something, it's probably useful in this use case.
Is that conversation rising as well [00:47:00] about the, how do we get a collaboration between them so we can reuse what Yeah. You know, others have created. There's a few initiatives that are underway right now. We'll be able to share more in the coming weeks, but, um, one of the ambitions and a manifesto commitment that came out of the labor government was to create a national data library.
And what that national data library is all around is creating broad accessibility to public sector data assets and sharing and exchange across the system. The wide ambition here, and I use the term data loosely here, is that it's more than just, um, You know, the structured and unstructured data sets that sit across research and operational data, but it's also the ability to share in AI models and inference over time as well.
And how do you incentivize the departments to want to share this with the other departments? So this is a complex topic. It's a simple question with a really difficult answer. I was trying to bring us to a close. We're going to be here for the next 15 minutes now. Let's talk a little bit about this. So There are some [00:48:00] barriers that we are trying to break down and remove in this space.
One of those is risk, fear of risk. Um, if a department is going to make their data or their AI model accessible to others, there is a fear that they still own the risk. Where others use their data, which might be in an appropriate way. So we're thinking about how do we think about transfer and seed of risk to try and remove that as a barrier.
Because we know that that is a big barrier risk. The other is cost. Government is not even. Some departments have more data density, will have more interesting AI models over time. That others will want to have access to. And that will not be an equal equation for all departments. So how do we enable those departments that will inherit more cost on the benefit for all to be able to justify how they build capability where they are not delivering that for their own department?
The first point you make [00:49:00] is quite a fascinating one actually because I think the government might well be a microcosm of what data sharing is going to look like, you know, industry wide. Right. Because I, I, Sort of think what's likely to be emergent, especially into the sort of marketplaces that are being built into some of the cloud service provider products that are bringing together sort of LLMs is bringing together data and data sources.
And you would imagine that organizations, particularly things like Research organizations or industry centers are going to be one of me are going to be want to expose their data in a way that can be consumed by AI and integrated. I wonder what your perspective on that is, because like I said, I think you could you could see that just within in public sector that could there could there's a there's a there's a mechanism there that needs to get invented and where you think private public collaboration might come into that.
I think there's a huge opportunity for public and [00:50:00] private collaboration. I think it will be essential as well. Um, as part of the National Data Library, the ambition is that we make more of our public sector data accessible out into the economy. To allow, and again, it's always with the provision of secure, trusted, ethical, where relevant as well.
Because a lot of this has to have very strong public trust around it. Um, but to your point, we see the opportunity to accelerate things like research on AI by using that data more. directly into research institutions. Um, we see the ability to deliver on better attractive conditions for start ups to build AI products.
With access to the appropriate data that might sit within public sectors as well. So this is an area where I'm very keen for us to have more collaboration and that's vice versa as well. I think there's a lot of private sector data that would also be very valuable to have better uniformity and thinking across government as well.
We do a lot of acquisition of data today into government and siloed. Right. So I think that there's a lot of work to be done around how we think about uniform datasets as [00:51:00] well that are used. And that's going to be important in any ways, because what we're talking about here is the basis. If we think about data coming into government, that's the basis of creating common standard citizen experiences.
If we have fragmented data and if we have sub uniform data that's not interoperable, then the experiences that that drives will be the same as well. So do you think we're finally at a point where data becomes a new oil? Okay. No, I don't, I don't, well, I mean that phrase of oil, so I don't know how much you guys have like heard some of the analogies on this, so like the big difference is that, you know, data is not a use once, it self propagates value, so I don't think the analogy is perfect.
Um, I think, you know, data is going to be highly valuable. Um, and, you know, critical for how we start to scale AI. Um, I think, I don't, I don't see the right analogy. I don't, I don't think we can compare it to a temporal resource [00:52:00] like oil. Right. Um, I'm trying to think about what might be a better analogy.
What would that be? I'll give you one I I've used in the past. Yeah. Um, uh, around more like, like a space analogy. Like if we are to start to create these galaxies of exchange and like these areas of, you know, solar systems of, of AI data is the black matter that glues it all together. Mm-hmm. Um, often unseen, but every imperative to everything that sits behind the universe, essentially. So maybe data's the dark matter. There's this interesting thing, just going, looping back to your value statement, what you consider, it's not a monetary value, it's a human value that you're trying to extract. There is a move that, because people understand that data is now much more valuable than it's ever been, they'll start to close the ecosystems up.
Yes. Because that's just the monetization point, we'll kick in, it's a natural capitalistic narrative. And I think it's right going back to your point as well that it's that you [00:53:00] lead on information interchange because you don't perceive the monetary value side, you see the value to society. Right. And you taking a lead on that, I think will help promote better collaboration because if you just leave it to the private sector, you might just get walled gardens of data and to get access, it's going to be very complicated and commercial.
It will, it will, it will cost you. Yeah, exactly. I think that that's where the, so oil is a metaphor for me. Is is less about the, the matter, you know, the physical stuff. Yeah. More about, you know, its, its commercial value, right. And the fact that it was, you know, under underpinned markets for donkeys years.
Yes. And it, it sort of does feel like the data, I mean, might be, you know, kind of, we, we might be in a place to say the commercialization of data is gonna go through the roof in the next, I think, I think you're right in that positioning, you know, data as an asset has been a thing for a number of years, right.
It's not been cracked. And I think that, I think the challenge comes back to what we were just discussing. So you 100 percent agree, like, data will become an [00:54:00] increasingly valuable commodity. Um, the challenge we have right now is I feel like we are measuring that based on traditional commodities. And I actually think we need to think about different accounting standards.
Yeah, that's actually a good point. Twist on it as if you would just take the narrative of the 80s and 90s or the thinking of the 80s and 90s and it won't work, will it? I think it's also probably translates into energy as something that changed the world we live in because we harnessed energy to become useful to us in all sorts of ways.
I think we are certainly probably going to be looking at data in the same paradigm. As we can see different values from it because it underpins new things that we will be doing. But it'll be, it'll be the data collaboration and those who create the collaboration that will win. If you think you sit on it, it's mine, I don't want to give you access to it, you'll probably lose.
Well, this isn't, again, like, these are, like, the answers are not there yet. Because the other aspect of the future of data is increasingly AI will be [00:55:00] creating data. Who owns that data? Where does that sit? How do we regulate and drive structure around AI generated data? Because a lot more of the time, especially as we continue to interact with AI, it's creating new data.
It's generative in nature. And the quality of it is another concern, isn't it? Not all AI systems are even in the quality and the biases, et cetera, within it. Although I do think there's a human point to it. there, which is humans do make mistakes as well. And we've lived with that in the past. So, you know, we're able to compensate for that as humans.
Somebody tells you something that's not perfectly correct, that might propagate. We know how to deal with that. So I think that's just a part of the learning cycle. Sometimes a bit too harsh on AI. If you listen to your mate down the pub tell you a fact about something, you might not be the best quality of information.
I like the way of thinking about it, it's like, you know, AI is just like having a bunch of grads or children. Right. You know, they're, they're gonna get some things wildly wrong and it's the [00:56:00] judgment of, I liked your analogy from earlier in the conversation about everyone needs to move in a direction. You know, like making critical judgments about it and things like that.
But maybe to bring our conversation though today to a little bit of a close, I'm just interested in your sort of, your sort of lived experience of the role that you have. How does it feel as a leader being in the middle of all of the noise, and needing to sort of cut away through it and set direction?
Yeah, I mean it's incredibly exciting. I think of myself as being really fortunate and, and, and honored to be able to play a role where I can hopefully help to make some meaningful differences around how we think about citizen and business experiences across the UK. Um, It's a role where there is so much opportunity that you have to focus on the big strokes and identify the areas where you can make the most of a difference, you know, with impact quickly.
So that does require continual pretty ruthless prioritization as well. Um, it's [00:57:00] also a role which has to be one of community and coordination. Um, we've been speaking a lot today about like, we cannot create central, like, you know, dictator drives across transformation. The only way we can be successful in this is if we win the hearts and minds and collaborate and partner broadly across the public sector to get this right.
So it is a role of community. Well, look, we wish you a great deal of success with all of that. That's about as big an agenda as you're going to get, but I think it's very cool to be in the middle of all of that. One, because you're dealing with some of the more kind of tech interacts with society aspects of work, which I always find very exciting, but also I think in the world of AI, government has got a real proper leadership role to execute on.
So, Craig, thanks for coming to share some of your thoughts and experiences with us today. It's been a real pleasure talking to you. Thank you very much. Now we end every episode of this podcast by asking our guests what they're excited about doing next, and that could [00:58:00] be something in your personal life.
Um, I've got a great restaurant booked at the weekend, or it could be something coming up in your professional life or a little bit of both. So Craig, what you excited about doing next? Oh, wow. Because Christmas is around the corner, I'm definitely excited about taking some time off and spending some time with my boys.
Um, so I have a six and an eight year old. Oh, what an exciting Christmas. And it's going to be exciting time. I missed that. I mean, you can see the enthusiasm in Robert there. Oh, I love that. Mine are a lot older now, but I miss that excitement. If I was to like, Fess up to a guilty pleasure. Like I bought them a lot of Lego, which I really enjoy.
That's for you. What's your favorite set so far? Oh man, so like there's a lot of like Star Wars themes on the leg of the very nice as well So yeah, have you have you dabbled with some of those big sets? My son is an avid builder really? [00:59:00] Yeah and uh and Spends his time Well, he actually does a very good job of saving up because he likes the usc set but like things like the 8080s are a little bit out of his range recently built the uh, The big republic warship, uh protostar destroyer type.
Oh, man, that sounds amazing. It's very cool That's very cool. I think, like, I mean, to geek out on this a bit, like, there's something really nice about, like, the logical kind of, like, thinking and planning or building or something like that, which kind of lends it into, like, at least, Current programming. Yeah, I think that's right.
Um, in a future world it'll be a lot more natural language based programming, but it's good, it's good building blocks of skills to think about how you logically think about, think into software. I think even when I agree and I think even when you are, even when you're natural language building, gotta have the end in mind.
You gotta sort of think your way through the problem. Yeah. You gotta build in sections, you know? Yeah. My, my, my kids. prompt me, I'm the, they prompt engineer me to build on their behalf as well.
Minecraft as well, because it feels [01:00:00] like the natural progression from, from Lego. Have you seen what some people have built? Somebody bought an 8086, somebody built, sorry, an 8086 processor in Minecraft. Really? Yeah, yeah, yeah. It was, it was quite impressive that they built a working processor inside the Minecraft engine.
That's good. Just think about the creativity that needs to be able to do that. It could calculate numbers and digit, you know, like it would work like a, the very basic one that first came out, but it was a, it was a working processor. I thought it was amazing. One of the funny things I saw in Minecraft recently, just like on a real, on a, on a somewhere and it was one of those, you know, the kids do like live streaming of what they've built and showing people around their like Minecraft houses and stuff like that.
And this kid was showing somebody around their Minecraft house and you know there's like, um, fire torches that they have in the world of Minecraft. He dropped one of those fire torches and burnt down his Minecraft house. Oh no. And it's a funny building because the fire gets out of control and there's nothing you can do about it.
Wow. Too much [01:01:00] reality in the virtual world. So anyway, if you would like to discuss any of the issues on this week's show and how they might impact you and your business, please get in touch with us. Cloudrealities at Capgemini. com. We're all on X and LinkedIn, and we'd love to hear from you. So feel free to connect and DM if you have any questions for us to tackle.
And of course, please rate and subscribe to our podcast. It helps us improve the show. A huge thanks to Craig, our sound and editing wizards, Ben and Louis, our producer, Marcel, and of course, to all our listeners, and see you in another one. of the reality next [01:02:00] week.