AI Means “Knowledge Work Is Cooked”, How Should Leaders Respond | Mark Rotheram, Rowan Gill

Mark Rotheram: So it went from zero to a hundred thousand GitHub stars, which basically means it's the fastest growing open source project in history. So huge, huge adoption. And yeah, it can pretty much do anything you let it, which is really interesting. Sinéad Hammond: Interesting, exciting, terrifying, I think is probably the words I'd describe that.

Rowan Gill: Things are changing so rapidly, as Mark has highlighted. It is really difficult to keep up, and it's having an open mind, being aware of what's happening, but then really bringing it back down to evaluate what will make the big difference and is it safe. Sinéad Hammond: Welcome to the AI for business podcast with BCN, where we put AI and automation at the centre of every episode. This is a regular debrief for business leaders to digest the business news and the biggest stories and development in AI as they hit the headlines.

And when it feels like there's a new story and technology pretty much daily, we're cutting through all of the noise and we're giving you a rundown of what's important to businesses, why it matters, and what you can do to keep up with the pace. So I'm your host, Sinéad Hammond, and today I'm joined by CTO of BCN, Mark Rotherham, and Head of Marketing at BCN, Rowan Gill. Thank you both for joining us today. Rowan Gill: Looking forward to it.

Mark Rotheram: Thank you Sinéad Hammond: The first thing we want to cover is the headlines that are new in the news for AI. So let's get into those. Mark, if you had to pick three developments this month that UK business leaders should actually have on our radar, what do you think they should be? Mark Rotheram: Yeah, first thing we should definitely cover off is all the recent stuff coming out around knowledge work.

You know, the phrase knowledge work is cooked is definitely something we should be digging into. We couldn't go ahead today without talking about um OpenClaw and permissionless AI and what's going on there and the whole ecosystem that sprung up in the last few weeks. And then it's probably worth touching on things like the latest AI models and what it means and how the different kinds of AI providers are approaching it in different ways and what it means for us. Sinéad Hammond: Nice.

Lots to dive into there. And then Rowan, from a marketing leader's point of view, what sort of things are on your radar this month around AI? Rowan Gill: Um, so this month, Google has launched a new AI mode uh that replaces traditional search results with a conversational, fully reasoned AI answer that's all powered by Gemini. So we're obviously looking to that and that what it means for us and our and our marketing.

It's yet another change to the search landscape. And as zero-click behaviour becomes a norm, brands have to optimise to be cited with inside the AI answer, not just ranked in the traditional way. Sinéad Hammond: Yeah, absolutely. I think there's so many individual little changes going on with every change that happens with AI at the moment.

So just these small things and how they're changing big time in terms of the strategy and the way that marketing departments are looking at their AI is really interesting. And it's a good job you're here, because that's what we'll be talking about a little bit later in the episode. Um, so Mark, you mentioned knowledge work is cooked. What does that mean?

In plain English, what's changed, what's still very human, and what change has come on that has made this such an important topic? Mark Rotheram: Yeah, so I think if we go back a week or so, there was a really interesting article that kind of summarised the key message. Uh and it it was Microsoft's AI CEO that in the Financial Times was talking about how we are reaching the point where AI has already got human-level performance for a lot of professional tasks. So anything, you know, where you're sitting down at a computer doing something, AI is already very capable in that.

And and the prediction that he made was in the next 12 to 18 months, AI will be doing it all. So really interesting article. But I think it's come off the back of capabilities that have emerged, you know, in the last not just six months, but the last few weeks as well. And what I mean by that is let's look at benchmarks, the ability that the agents are demonstrating is pretty much doubling every four months.

So if you rock back four months, we had some awesome models, Opus 4.5 came out, and it could do five hours of fully autonomous work, and the quality was very, very impressive. A few weeks ago, 4.6 came out, and it's quicker, it's faster, it's better, and it you know, the ability and capability has doubled.

You know, we're ending up with this really, really smart brain that's able to do things as good, if not better, than people that are doing those jobs today. So, yeah, it's the ability that is being demonstrated, basically being able to do really complex cognitive work. So that that's the disruption that we're seeing. And I think it it's really interesting to see where that's gonna take us as we look at the adoption of that and what it means for the roles and the tasks that people are doing today.

Sinéad Hammond: Absolutely. And I mean, from your perspective, I think we talked about this in the last episode, you know, jobs and tasks are gonna change massively with this. What's gonna be the differentiator now? What is that thing that people are going to need to kind of have that's going to require that human intervention?

Mark Rotheram: Yeah, so we we always talk about human in the loop, and and I think that is gonna be the main theme here around where the the human actually adds value, because we we've got an awful lot of value to bring to this conversation. It's figuring out where that is and and why it is. Um the word that I'm gonna use is judgment. So we can use our AI agents, our little AI friends, to do an awful lot of wonderful work for us, but it comes down to judgment at the beginning of that process to articulate what you want it to do and why you want it to do it and how you want it to feed back.

And then it's at the end of the process, again, exercising that judgment to say, has it actually done something useful? Um the AIs are super intelligent, but if you ask it to do something and it makes the wrong assumption, you will end up with an awesome amount of stuff that's not very useful. And and I think it's that judgment that that we're gonna bring, and and that's gonna come from people that that can think about you know, critical thinking of what it is we're trying to get this thing to do that's really gonna make the difference.

You know, it's the difference between getting an AI to create a a wonderful 40-page PowerPoint presentation that is drivel, uh, versus three slides that absolutely nail it. You know, the AI can do that 40-page deck and it'll love it and it'll be look awesome. But that that you know, that judgment bit that's gonna come in is really where we're gonna see the human value come through and control these agents that that are emerging. Sinéad Hammond: Amazing.

And um, Rowan, I'm gonna come to you now because I think this is something that you're doing a lot in your team and making sure that we aren't just coming out with drivel as Mark puts it there. So talk us through on a practical level for business leaders what you're doing as a department leads to make sure that you're using AI and you're using it for that, you know, vast amount of work that's coming out, but keeping that quality, keeping that relevancy, tell talk us through a little bit of that.

Rowan Gill: Yeah, sure. And I'm really happy to hear obviously Mark talk about judgment and where bringing value through having the human in the loop and people with experience and expertise. AI is enabling us as a team to do more faster, but where we're really focused is ensuring that we are using AI in the right place and also that the output is actually a great quality. There's lots and lots of um fatigue around AI and having AI slop and people being able to produce lots and lots of content, but for us as a team, it's really important for us to always remain on brand, keep the thing that makes BCN different and special, make sure that that is retained in all of our communications across all our channels and any marketing that we do.

So, from a practical perspective, that's around using our best judgment to decide what we're adopting and what's going to make the difference. But it's also then when we are adopting a new tool and utilising AI to help us with things, it's also making sure that we have the right skill set to put the right prompts in, to put the right brand guidelines in. So, for example, everything in terms of our brand guidelines, our tone of voice, we will align it to our strategic priorities, key messages we we are wanting as a business to convey within our marketing.

And then we will also layer on things that we know people instantly spot as AI. So, what we don't want it to tell us, we don't want it to come back with our terms that we we want to put in room 101. So, you know, those long M dashes, those Americanisms, you know, all of that. We we have a list of phrases that we constantly update, making sure that we've got all of the right prompts in place and giving the AI the right knowledge it needs to produce great quality content that aligns with our brand.

And that's really important because more than ever, AI is doing a lot more of the groundwork for marketing. So bringing the human back into the loop to look at that 40-page PowerPoint or look at that blog post or look at the social posts or an entire campaign and know what will and what won't work is really where people can use their um use their experience to add value around AI. Sinéad Hammond: I like what you said there about using your judgment. I think that's really important.

And I think going on to the next topic, Mark, that you mentioned there was around the open claw news that happened last week. You're gonna have to start from the beginning and tell me first, what is open claw? Describe to me what happened last week, why is this so important, and what does this mean for businesses? Why should they be listening?

Mark Rotheram: Yeah, so open claw is a really interesting development. So it's probably been about a couple of weeks now since this hit. So effectively, what happened? There was a single guy, a little developer, who'd been push publishing software for a while, and he published this thing called it wasn't called OpenClaw, there's been a few names, but we'll leave it as OpenClaw now.

But effectively, it was a scaffold that allows you to install a piece of software on a computer. This is where we had lots of fun around, uh a rush for MacBooks and Mac Minis and most recently Raspberry Pis, all around people wanting to host this um this bot. And the best way to describe the bot is if you think about Iron Man and Jarvis, where you've got your personal assistant. You know, we're not talking uh Siri or Alexa or um that those kind of things.

We're talking actually a real AI that can do things for you. That's kind of what this was all about. So it went from zero to a hundred thousand GitHub stars, which basically means it's the fastest growing open source project in history. So huge, huge adoption.

And yeah, it it launched, it was on your device, you communicate to it using WhatsApp or Telegram or iMessage, and yeah, it can pretty much do anything you let it, uh, which is really interesting. Sinéad Hammond: It sounds interesting, exciting. All of a sudden, I'm thinking, I assume it has access to everything. Is there like a level of permissions that you might already my head's thinking, what can't it access?

Does it have access to bank details and all of that stuff? Is that all part of it? Mark Rotheram: Yeah, again, this is this is where we we end up with a a very interesting take from in the corporate world where we operate and we advise all of our customers. Um the first thing I said, but let's not do this quite yet.

You know, we're we're on a journey to agentic, this is looking awesome, but let let's not quite do that because at its heart it is a permissionless deployment of AI. And what I mean by that is, yeah, you deploy it on a device and you give it access to a lot of things, yeah. And you can control what you give it access to, but the out-of-the-box deployments that people were doing were, you know, you can access my bank, you can access Amazon, you can access um my iMessage, you can access all these things.

Now, the the first few days were a nightmare, if I'm honest, for um the community because the original name had to change, they've they changed the name a few times, and in between those name changes, mainly because Anthropic weren't happy with it being called ClaudeBot to start off with. We had hackers get involved, they managed to get domain names and inject malicious code. Even just deploying it um for the first few days was a bit fraught with people getting hacked. And then we've got this really interesting kind of disclaimer that says, you shouldn't do this if you're not technical, which is like saying, well, it's it's one of those ridiculous disclaimers that says, you know, nuts might contain traces of nuts.

People are going to do it anyway. So you've got all these people that are installing it that don't understand how to secure it, which was leaving lots and lots of holes for people to get in and hack and steal keys, accounts, all that kind of stuff. So yeah, we we went from a an awesome thing to actually this is quite insecure and scary, to kind of actually a huge evolution of kind of what's happening now. Sinéad Hammond: Interesting, exciting, terrifying, I think is probably the words I'd describe that.

And I think for us at BCM, we are quite aware of how much AI is getting access to. And I know you mentioned that when you were talking about what businesses that we work with, how we kind of help them with adoption there and enable it to be something that is innovative and supportive, but also doesn't have those gaps that enable hackers and threats to kind of infiltrate. Sounds like Open Claw last week was an example of that happening on scale in a very bad way. Um it's gonna be exciting to see what this next stage of AI looks like off the back of this.

What do businesses need to think about or business leaders need to think about with the announcement of something like this? What does that bring up again to the surface? Mark Rotheram: Yeah, so I guess this was done by an individual. There were no new models, there was nothing groundbreaking, it was literally a framework of how to use current gen AI.

And it the the concept has exploded. So if if you take just that concept of current gen AI with a whole ecosystem that was thought out by one guy in his bedroom just knocking things together and making such an impact, bringing that concept to the business is really interesting. We've got this really wonderful AI capability. What are the use cases that we could deploy in a safe way?

What are the outcomes we could be targeting that you haven't thought of yet? A lot of the the most interesting things that we end up doing are new things. Yeah, we spend an awful lot of time talking about how can we use AI to solve an existing outcome or an existing process. But the fun and the real exciting thing is when we we actually enable something we've not been able to do before in a way that AI is allowing us to.

If you look at all the new things that have come off the back of this, you know, we ended up with MaltBook, which was a place for all these agents that they're called Maltis to go and chat with each other on a social platform. We ended up with a whole array of other social things being created by the agents for the agents. We've got Coinbase who enabled them to be banked now. So there is an AI wallet for the agents, so they've got money that can do things.

We have examples where people have used AI to negotiate a car price and make, you know, they've they've got dealers pacing off with the AI in the middle to get a better deal. Uh we've got them going rogue, where these bots are taking over iMessage. We had one guy that he he left it open and his his little bot sent 1,500 messages to his wife on iMessage, you know, before he could stop it. So you've got some some really interesting stuff going on.

But I guess to take it back to the business context, it's a demonstration of what we can do today. Yeah. All those use cases went through didn't exist a couple of weeks ago. They exist now.

They're ideas of things we can do. We can build safer versions of OpenClaw or Moltbot. We can build these little agents, and it's really now the art of the possible. What what is the the thing that's really going to make that outcome and bring it to life?

Sinéad Hammond: And it's a what would then happen in three weeks from now, six weeks, twelve weeks? Like there is just so much, so fast happening. I think it's incredible. It's so hard to keep up with.

Um, Rowan, from your side, I can imagine your kind of alarm bells going off a little bit with this. Because I know that when you're deploying or implementing tools, there's always that consideration of the permission level. What do you think on a practical level we can do or the marketing teams can do that enable us to have this capability that Mark's talking about? It's just really exciting, cool, innovative things that we were never able to do before, but in a way that's not going to expose us to threats.

Rowan Gill: Yeah, security is absolutely high on the agenda for us as a marketing function, but also as a business. We've got really, really strong security foundation and an amazing internal cybersecurity team. So, as well as all of the like the lead-in-age tools that are protecting us in the background and running every day that we don't realise, we've also got a really robust cybersecurity awareness training program that happens throughout our business, which is making us aware of the risks that AI can bring if we are not using it correctly, safely, and securely.

So for us, those security considerations are baked into the thought process when we're evaluating any new tool that we might want to bring in to help us solve some issues or some problems or make us do more faster. So we will always check in with our cybersecurity team to check: does that new tool have the permissions? Does it have MFA? Where is our data going?

Are we potentially putting the business at risk, allowing somebody to come in via these tools? And if if there's any risk at all, we will not use that tool, even though sometimes it's really disappointing because they could be really shiny, wonderful new tools that could help. But for us, security is far more important. In terms of what we do know, what is secure, which does help us massively from a really practical level.

We utilize Microsoft Copilot all the time, obviously, because it sits within our business, it's safe and it's secure, and we know that is a great place for our team to adopt and move forwards with AI. And then because we're not stopping the adoption, but what we're doing here is doing it safely and securely. Sinéad Hammond: Yeah, I think it sounds like there's a huge amount that we can do. It's just making sure you put those steps in place in advance because I think a lot of people will be wanting to use and test out some of these cool things.

I mean, I'm not I'm not sure how much I want a bot to send 1500 messages to a contact. I'm not feeling that's quite what I'm gonna ask AI to do or I'm gonna want it to do. But there's all this capability which is really exciting and things that I, you know, we really want to explore, and I as an employee want to explore. But I think it's quite good to have almost having that second guess stage when you're thinking, okay, I want to be able to do this, but what are the things that I need to consider before I'm pulling it in?

And that's a business-wide thing, that's a culture thing, which I think is really important. Rowan Gill: Businesses want their employees to utilize AI and to experiment. And any business that thinks their employees aren't doing that are probably wrong. They probably are utilising AI tools, but they're doing it without anybody knowing about it.

So this we've seen this massive rise in in shadow AI. So utilising tools that aren't approved, that aren't safe, that aren't secure. Business leaders need to empower their teams to utilize AI in a safe way rather than stop them or have them do it behind the scenes, which could be a potential security risk. Sinéad Hammond: Is that what you mean?

Is that what shadow AI is? Do you want to just explain that term a little bit more what that actually means? Mark Rotheram: It it's basically where we we try and encourage people to use the right AI, but adoption's really hard. Yeah.

So everyone that's in the Microsoft ecosystem has free co-pilot chat. But if they don't know that it's there or know what it can do, then they go to what they know. Yeah. So that's where you start seeing people go and use Gemini, Chat GPT, Claude, all these different ones, but they use the public versions.

Now, the public versions are fine from a public perspective, but there's a reason why we have enterprise grade security and enterprise AI tools. So the shadow IT is where typically we've not done the best job in articulating what the AI tools are that we want to be using and why and the capabilities, or they've not been made available at all. And people are just left to go and find their own way. And when they do that, and you know, we've got lots of curious people, they will go and find tools that do the job for them because they're there.

So um this ties really well into how you manage adoption and make sure you have got an AI policy because when you thought about it and you write your policy and you think, actually, right, how do I encourage people to do this? You know, it's carrot and stick. You know, you want to stop people doing the bad stuff where data might go out of control, but you really want to encourage people to use the ones that that are safe and explain. Them, what you can do with them.

To go to the point, shadow AI is where people are basically left for whatever reason to go and use AI that you don't really want them to use instead of the tools that you're making available for them. Sinéad Hammond: That makes sense. And it leads us on to the third point that you talked about, the third headline that's come out, which is around some of the new updates and moderates, loads going on with open AI, more agentic AI, I think is what's kind of happening. Again, tell us a little bit about kind of what's new, what's happened, what's going on, and which updates businesses should be paying attention to.

Mark Rotheram: Yeah, so there's a lot of tech in there, and I'm not going to go through all of the finer details. I mean, just suffice it to say, there's been some massive changes in the ability in the models, you know, from a numbers perspective, you know, we've seen benchmarks continuously improved upon. Um, the context windows are increasing, and that means basically that AI can think for for longer and uh cover more topics. Imagine you can throw more books worth of data into it before it runs out of of memory.

Sinéad Hammond: Nice. Mark Rotheram: Um, but what we're also seeing is new skills arrive within these models. And the skills are things that we might have been doing manually with the AI models previously. So some really good examples are where we're seeing agent teams come into play.

And this is where instead of saying AI, go and do this job, and it goes and does that job, it will then spin up a team of agents that will coordinate the work, they'll split it up, they'll work together, and they'll all run in parallel and they'll come back at the end. And this means that you you can offload with the right judgment and planning even more work. And that ties into another kind of really interesting feature, which is the length of time these models can run. Previously they'd run out of grunt after seconds, then minutes, then hours.

We're now getting into the point where they can run for a long time, and and that is increasing all the time as well. So again, feeding in the right input becomes paramount to make sure that the output after that length of time is what you expected. So, yeah, we we're seeing these kind of skills and abilities creep up and you know the intelligence levels just tick up as well. So uh I guess they're not just smarter chatbots, they're models that are designed to run for hours, they're designed to coordinate work, split it up, run in parallel, bring it all back together again, and they've got the ability to have much broader context than they've ever had before.

And we're seeing you know these things happen across the different vendors, you know. Anthropic are very much focused on bringing more and more intelligence and capability at roughly the same price point that the previous model was. OpenAI have have done some amazing stuff with GPT 5.2 and most recently 5.

3, where their approach is, you know, we're going to make everything cheaper as well as nudging up the intelligence, I'd say. And then we start to get into what's happening in the open source world, which again is is really interesting. You know, Microsoft initially partner with OpenAI for co-pilot that was powered by their models. They then brought in some anthropic, um, so they're they're looking at the enterprise grade, and now we're seeing the emergence of these permissionless, again, we use that word open source models, which are are lagging behind for the moment that the big players, but they're open source.

And this this brings some really interesting perspectives around data sovereignty and AI sovereignty and what you trust and what you don't trust. So give a really good example, the best models always come out at a global level, which means that we will have to use global processing for the model to work. Whereas the open source models, we can run them locally, you can run them on your laptop if you wanted to, which means the data goes nowhere. But the interesting bit is how comfortable we are using models that have originated from different countries.

So uh you end up with all these different perspectives of um governance and control that you've got to grapple with to make sure you're making the right decisions at the right time to kind of get the most out of these models as they as they come online. Sinéad Hammond: Just as a bit of a definition thing there, because I'm you know that I'm not technical at all. So just a few quick ones. First of all, open source.

What's open source? What was an example of that? What's what does that look like? Mark Rotheram: Yeah, so we've we've used the term a couple of times today.

So open source is where people develop something and they make it publicly accessible. So people can take it, they can rewrite it in some cases. So it's it's kind of like the Komodo is open, yeah? They show you exactly how it works.

The open AI and anthropic approach is private, so we can access the model, but how the model is built, how it operates, how it works is their secret source. Right. Um, so the the big West players have all gone down this route of we are going to keep our secrets to ourselves. Whereas China and a few of the other open source kind of leaders have gone the other way and said, actually, we think we will be able to go faster, quicker by making all of our models open source because we'll get everyone in the world to potentially contribute and improve them over time.

So two very different approaches to um how AI is being developed and published. Now, like I said, for the moment, the private kind of approach appears to be leading the way. The models from OpenAI and Anthropic and Google are superior to the open source ones, but it's definitely one to watch as the open source ones just keep gaining momentum, you know. If you go back probably six months, they were further behind than they are now.

So there's an element of them catching up. And it's it's part of a global AI arms race that's going on across, you know, the the main US labs and China mainly, around who can ship the best models quickest and get the adoption going. Sinéad Hammond: I've been looking at the news and seeing the the difference in the AI robots in China from 2025 to 2026. I don't know if you saw it was a it was a festival they did and it is light and night and day, um, which is quite interesting.

Mark Rotheram: So Yeah, the the robots bit is is extremely interesting because a few years ago all of the robots were designed to run using code effectively, and then most of them have pivoted to using neural nets. So effectively you've got an AI model in the brain of these robots now that's taking all those inputs and doing all the outputs. And yeah, it's it's really interesting to see that you know, once you've taught one robot one trick, all the robots get that trick. So you get instant scalability.

So yeah, the whole robotics being led by AI is really interesting to watch, and that'll be definitely a the next disruption curve to come in the next uh probably two years or so. Sinéad Hammond: Gosh, lots and lots going on. Um, for businesses, I suppose, Rowan, probably not talking about robots here, I'm not sure we're quite there yet with that. But what are you doing in your team to kind of enable them and help them to kind of embrace all of this new stuff?

I know that you work a lot with Mark on this, and Mark, you have so much access to all of this knowledge and all of this technical expertise and all of these different models and information and all of that sort of thing. Rowan, on your side, how as a department lead are you enabling your team to adopt this and to bring this technology in whilst you've got all of the other work that's going on? Rowan Gill: One of the um really big bonuses of working for a company like BCN is we do, as a marketing function, have access to amazing technical experts like Mark and the rest of our AI and innovation team.

And that is absolutely brilliant. And we can see the art of the possible and everything that AI can help us do and how it can impact us as a marketing team to really accelerate. What I try and do is make sure that my team has time to look and evaluate the tools and then investigate AI and work out what's going to make the biggest difference. And having that space to evaluate everything is really, really key.

And it allows us to sit in that room together and talk about well, actually, what are our challenges now? What are the everyday things that we're actually, if we could solve, that would make things better for us as a team. So it's really having the strategy in place and knowing what we're going to adopt when and why, but also we never want to stop hearing about these amazing things that are happening and the latest and greatest things that we can bring into the way that we work, but we have to bring some sort of calm to the chaos because things are changing so rapidly, as March has highlighted.

It is really difficult to keep up, and especially within the marketing world. So it's it's having an open mind, being aware of what's happening, but then really bringing it back down to evaluate what will make the big difference and back to again the other point is it safe and is it secure, and can we move forward with it? Sinéad Hammond: Yeah, yes. I think if business leaders want to know what's happening in AI and how it applies to businesses, is this the right space for them then going forward?

Just really quickly to finish off, Mark and Marin, both of you, I'm gonna ask, I'm gonna put you on the spot. Um, what is the one priority for an organization technology in terms of AI this sort of next 30 days that businesses should really be focusing on? I'm gonna start with you first, Mark. Mark Rotheram: I think it's ironic that you asked from a technical perspective, but the the best takeaway is the technology barrier is gone.

Yeah. So we can do these things. The the here, right now, the ability is there. The barrier is organizational readiness.

Are you ready? And if you're not ready, how are you going to get ready? So focus on that. Focus on do you have an AI policy?

If not, get someone thinking about how to do that. And then that will lead you into adoption, and then that'll lead you into right, actually, what outcomes should we be focusing on that we do today? And then we get to the fun stuff of ah, I didn't know those were outcomes, I could even go after, let's go and work on those. But I think you know, just think about the technology barrier is effectively gone.

How do you organise around the capability that's available to you today? Sinéad Hammond: And Rowan? Rowan Gill: Yeah, I think going back to the um to get to that place, you need the time to actually work out what you want to implement and how you're going to do it. So I would say sometimes it's hard to say stop, especially within some teams that are running quite quickly.

But sometimes you need to stop and evaluate things to then move forward in the in the best way. Sinéad Hammond: Thank you very much. Really interesting conversation. Loads going on, as always.

There's always new releases, news about AI. So if you want to get your regular debrief for AI news and business updates, then this is the place to be. We will be doing episodes like this regularly. So make sure you subscribe and visit bcn.

co.uk to find out more about the services we offer around data and AI as well. Thank you very much, Rome, for joining us and for Mark joining us as well. It's been really nice chatting.

I've learned a lot of new things about AI and new terms that I didn't know about and some exciting stuff that's coming up in the news. So, really, really good conversation. Thank you. Thanks for watching.

Thanks for listening to AI for business. If you are interested in finding more out about our AI services, you're worried about shadow AI, you're trying to understand how to adopt AI better across your business, then get in touch at our website. We're at bcn.co.

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