Google Cloud Summit Special: John Abel, MD, Office of the CTO at Google Cloud, and Alex Rutter, EMEA MD for AI at Google Cloud

Speaker A: In this era, speed doesn't mean at the cost of security, doesn't mean at the cost of scale, doesn't mean at the cost of having poor Data.

Speaker B: Today, over 75% of Google Cloud customers utilize one or more of our, uh, AI products in their business. So we're starting to see real widespread adoption across the platform as a whole.

Speaker C: This week's episode is a little different. The Google Cloud Summit 2026 in London has just taken place, bringing together tech leaders from all over the world to discuss the latest developments in AI cloud and data from one of the major players. The event featured a number of significant announcements and there was a clear theme running through the event that the conversation has clearly moved on. We're no longer talking about whether organizations should adopt AI, but how they deploy it at scale and turn it into real value. So with that in mind, I had the opportunity, I was very privileged to speak with two senior executive leaders at Google Cloud to get their perspective on where the market is today and, um, where it's heading next. First up, the Managing Director of the office of the CTO at Google Cloud UK in Ireland. Without further ado, this is Mr. John Abel. How's today gone?

Speaker A: Do you know what? Brilliant. Lots of conversations showing the latest technology and also helping people transition to the new generation of this technology that will hopefully help people a lot in the world going forward.

Speaker C: Absolutely. I think it's some of the most transformative technology we've certainly ever seen. But for the listeners who, uh, are unfamiliar with yourself, John, can you give us a little bit of introduction, um, of your journey with Google Cloud and your current role?

Speaker A: Yeah, so I've been in the tech industry since the 80s, uh, so. And then seven years ago, I joined Google Cloud. I'm in the office of the cto, the managing director of the EMEA team. So, uh, and we are an engineering group that works into Google Cloud. We do emerging technology and experiments that hopefully will end up in a product.

Speaker C: Fantastic. So in terms of, Obviously, you know, we know about the importance, uh, of some of the announcements today, but from your perspective, can you talk us through the key announcements, the key themes and the things that, you know, that have been announced so far that are, that are really important and profound.

Speaker A: Yeah, for me, uh, first of all, the biggest one and the one that I'll probably home in the most is the stack of technology that is actually operating as one. One of the things that we already know is a lot of people struggle, uh, with this very large, uh, ecosystem that's been born in A short time period. And one of the things that we have as a duty of care is making sure that people get the value they need out of it in the fastest route they can. So when you look at like security with agentic defense, or you look at the agentic data cloud where we're accessing data, or even the ability of using anti gravity to vibe code, now having one stack where it's integrated and actually provided is super essential. You know, in this era, speed doesn't mean at the cost of security, doesn't mean at the cost of scale, doesn't mean at the cost of having poor data. So taking that responsible route to making sure that we have the best technology that's integrated, that provides the best value, allowing the users to focus on what they want to be famous for is essential.

Speaker C: Yeah, absolutely. I think the seamless transition from doing what they've always done to using these incredible tools to provide to produce better outcomes is that's the main barrier to adoption, isn't it? From an individual standpoint?

Speaker A: Correct. And if you think about this era, machine learning has been around for multiple decades, and we've used it in financial services in many industries, healthcare example, this generative AI era that's now moved into the agent era, is about actually knowledge experts getting their hands on the technology to be incredible knowledge experts. One of the things I'm really fascinated by is that we're in the era where we're going beyond human scale limits. So I can scale myself much easier in this world with agents and this agentic world we're moving to than I could ever do in the past.

Speaker C: Yeah, absolutely. So we often hear that sort of AI success starts with data, of course. In your view, John, are organizations generally further behind on data readiness than they realize?

Speaker A: That's a great question. I think the first thing is what data do I need to answer the question? And what I mean by that is that it's less about do I have data? It's more about do I understand the contextual benefit it gives me. And one of the things that you need to look at is actually, can I what do I need to do the job in hand? So when I look at, like agents, for example, I look at the skills, for example, the things that they need as skills to be able to perform the job. I look at the tasks that they need to perform. When you look at the skills, you then quickly look at APIs and data that's required. Once I understand the data that's required for that I understand is the data of the Right. Quality to help my agent achieve it with the smallest amount of human in the loop. Once you go through that, you quickly find out how good or bad your data is.

Speaker C: Yeah, no, absolutely. Very well said. So I think we've spent the last couple of years talking about AI models essentially and extracting value from models. But this, the announcements today and recent discourse and product releases from the major technology players, uh, it's all shifted towards agentic AI. So can you just talk us through that development and what that shift means from first generation AI, if you like, to agentic AI?

Speaker A: Yeah, I'll actually give you an example. Today I did a keynote where I showed an agentic uh, board running. So I took a, I created a simulated annual report. I gave it to board members that were agentic agents that ran in autonomy against the Persona style that I gave them like coo, cmo, CEO and I asked them to evaluate the annual report. Now imagine if I gave them the profile of my actual execs or if I gave them the profile of how those people should operate. It's very easy to find information about how a CFO should work. It's very easy to find out how CEO should work. My agents can quickly process this large array of data, hundreds uh, of pages and go through scenario and they can go through the ability to give me guidance of the things I need to worry about. Well that's game changing because today uh, getting uh, a board execs together is quite an undertasking. I can get a first pass. Then when my real people meet, I can have them not only use their own intelligence and their own creativity to look at problems, I can enrich it with the agent's view. So the agent becomes like a team member of the board.

Speaker C: Yeah, that's incredible, isn't it? I think we are increasingly starting to perceive agents in that way. But I think it's got a long way to go. So in terms of pitfalls from an organizational and a governance standpoint, John, in terms of adopting AI agents to deliver real time tasks across the business, what kind of challenges are you seeing for companies from a governance standpoint?

Speaker A: First of all, there's always the discussion about security data. They're not going to go. And that's something that we've been really homing in on. And obviously with the recent acquisition of Wiz, you would have seen that we've really reinforced our security portfolio. I think the bit I'm most interested in is not the technical aspects, it's the human aspects. And what I mean by that is Is that I typically see three aspects of human character that are important and you should never forget. One is the culture of the company because you want to work with the culture and add to it, not replace it. Two is biased. People have their own journey with technology. Everybody's got a different skill level. If you asked your listeners today how did they find your wonderful podcast, Everybody would find it differently. We didn't all go through the same room. And then finally, is the beliefs, the belief is one of the most strongest elements of any company or organization. Our belief we do this for this job. So what I say to people when you're dealing with agents, first of all, the knowledge expert is the most important person because they know the exam question they need to have answered and they know if the answer is correct. Second is invest in the right simulation and also the right evaluation. You want to evaluate your agents and simulate your agents. But if you do this, you can work in a different way and never make it a downwards mandate. Make it organic. Strategists from the corporate or the leadership need to be. We're investing in this for your future. Then empower people. The stack that we presented and we're now producing for our customers allows the right safety rails to be put in place to allow executive leaders and the directional strategies of the company to empower the people that need the tools because they're going to be the biggest benefit for this.

Speaker C: Absolutely. Would you be able to unpack the stack a little bit in a bit more detail, John? So the listeners are, uh, familiar with exactly what you're talking about?

Speaker A: Yeah. So there's many elements. I'll call out the most important ones because they're there. I'll start with the bottom. The AgentIQ Data Cloud is a fundamental building block. We need data for AI. Then you have AgentIQ defense, which allows you to have areas. Example would be model armor. So I know that my models are working in their correct way. Then you work up the stack and one of the biggest areas of announcement and as a deep technologist, I was like, wow, this is impressive. Is the split in our own, um, TPU processors. We've got our, uh, AI for inference, the execution of AI and AI for model, the training of AI because they're different tasks and having that split aspect. Probably for me, the one that I really like is Agent Garden because it gives me agents that I can already use out of the box and then having that single Surface Gemini enterprise that allows me to interact with my agents, allowing me to interact with. So today, for example, I have My Gemini age, my Gemini Enterprise has summarized all my meetings today. Make sure I have all the references to my briefings and now I'm actually ready to go and do the job I need to do. And the other thing that's really important is I'm currently missing emails because I'm talking to your wonderful listeners, which is at the moment is my. What I want to be famous for is help them on their journey. And that means that I need to have people get know that I'm out of office and the out of offices that I had in the past I'm out of the office for today is not acceptable. Some of these can just be directed to my team colleagues and they'll let them work on it when I'm not there in the office. So my agent's helping me. So these are the ones we're in this world and that's why the stack is so important is that.

Speaker C: Do you have to have a, ah, G. Do you have to have G suite to have access to this stack? Do you know what I mean? Can an Outlook customer use.

Speaker A: We've even got Gemini Enterprise Integrate with Office 365 so you can do different surfaces, but also you can have a developer experience. You could have a no code experience. You know, one of the tools that I've been showcasing a lot today is Anti Gravity. I uh, love Vibe in creating solutions and I do that with Anti Gravity. So if you're a developer, there's a developer experience. If you're just a business user, there's a business user experience.

Speaker C: Yeah, absolutely. That's amazing. Thanks for elaborating on that, John. It sounds really exciting and there's all sorts of questions I have coming to mind. But I know we're a bit pushed on time, but there is one thing I wanted to ask you about. So businesses are evolving quickly as a result of this technology and comparable technology. What skills do you think will become more valuable as organizations increasingly adopt AI and AI agents? What skills do the individuals in those companies need to work on to keep up with this pace of change?

Speaker A: Every year I give a Christmas lecture to uh, students. And one of the things I say is there's three skills that a human has to have, uh, in this era. 1. Agility. 2. Flexibility. 3. Creativity. And the reason I say that is when I first started in my career m, my skill as an engineer was my forte. To make people's dreams come true. Now my skill is being a creator of my own ideas and allowing my ability to have AI offered code make My dreams come true. The key here is the dream that I had. The creative moment is my key asset. So what I say to people is in your career you're going to have many pivots. You know, if I go back to when I started in the 80, many of those roles today don't exist. This has been the same for human over time. You know, there was no concept of, you know, web designer, there was no concept of influencer, uh, there was no concept of digital marketeer. Well, they've all been born in the cloud era and there's going to be new roles born in the cloud era. But if you've got the ability to pivot and be agile and be inquisitive and be a learning mindset and be a creator, you can pivot to many industries of your choice.

Speaker C: Yeah, absolutely. No, Very well said. That's a great answer. So, uh, uh, in terms of the increasingly AI competitive AI marketplace, okay, you got a lot of competition popping up everywhere. Um, Google has been investing in a, in AI for many, many years. What, what do you believe differentiates Google from the competition?

Speaker A: Um, the first of all is it's a healthy market of competitors, you know, which I think is really rich for customers. Sure, I suppose one that I take away. There's three things that I take away and I don't call it unique by the way, I call it a value to the. Everything is about what value and outcomes can we give a customer. Well, first of all, we have a frontier model from our wonderful team in Research and DeepMind. And for us we're lucky they're based in the UK, uh, where I live. So we have a good understanding. And why is this important? We understand safety training, the ethical usage. The second bit is the amount of open source that we create. If you look at the amazing, um, technologies like Kubernetes that allow clustering, uh, we got the Gemma open models means that we are okay in open sourcing our technology. And then finally I just, I love being at the company that makes me driven to learn. And it's not a technology, it's a mindset. When I joined Google I just found it that I got the urge to learn again. And I'm not saying my previous employer was amazing. They were amazing. I loved being at Oracle for 25 years, but it really made me hungry to learn and that learning keeps me excited every day.

Speaker C: Yeah, that's a great answer as well. I think, uh, you're the fourth executive we've had on from Google and they've all said the Same thing. And I know from, in terms of this, uh, in coming up with your own project and being, being encouraged to innovate is where Google Maps, Google Wallet and all of these incredible products were born from, from that, that culture, weren't they? So um, so that's really interesting John. So let's finish on this one then. So we're an IT consultancy firm and I speak to ctos quite a lot. Okay. And what I'm seeing is that uh, a lot of companies are stuck in pilot mode. Okay. So my question to you is what separates organizations that are generating real business value from AI and those who are still stuck in limbo and pilot mode, what are the ones that are creating the value? What are they doing?

Speaker A: Right, following five steps. First step is they have an autonomous agent that can run in its own context with a well grounded problem to solve.

Speaker C: Yeah.

Speaker A: Two, they use a framework that they're not building and they're just consuming. In our case that's the stack of AI technology. Three, they understand their cognitive architectures they're trying to build is it an act and use is it problem solving. Four, they have clarity in the evaluation and also how they do grounding and they understand the qualities they need. And five, they've understood when human in the loop is required.

Speaker C: Absolutely brilliant. And on that note John, what a, what a perfect way to end.

Speaker A: Brilliant. Lovely talking to you Garel.

Speaker C: Likewise John. Thank you so much. This episode was brought to you by B Digital B Digital support leadership teams to optimize cost and get more out of technology investments. B Digital and the team have unrivaled expertise with technology license management and data remediation and um, are therefore perfectly positioned to help prepare organizations for AI technology capability. And on the last point, B Digital have developed a cutting edge AI readiness assessment which provides tech leaders with a platform they need to make well informed decisions about AI adoption strategy. Go to bdigital uk.com to find out more and get in touch. I love that one. That was a great conversation. Thanks to John Ably didn't have much time, um, but needless to say we covered a lot of ground in just 12:12 or so minutes. And what particularly stood out was his emphasis I think above everything else on the foundations required, uh, for successful AI adoption, uh, especially for sort of big complex enterprise organizations. Uh, whilst much of the attention at the event and at the moment is focused on agentic AI, AI agents and the latest technology around that, the organizations seeing the greatest success uh, quite clearly are the ones getting the basics right at A foundational level with their data and with their IT asset management. John spoke about the importance uh, of the five pillars as he called it, which I thought was great. Just to break that down, that's data governance, security, technology and of course people having a structured approach to moving from experimentation to real business outcomes. I thought that was great. Of course, you know, understanding this framework is one thing, but implementing it is another. So next I sat down with the EMEA Managing Director for AI at Google Cloud, who needless to say is right at the center, uh, of Google's push around agentic AI and enterprise scale deployment. So this one was a, was a real treat, a slightly longer one than last time, but again packed full of value and insight. This is Mr. Alex Rutter.

Speaker B: We've had a fabulous day here. You know we are packed the rafters in terms of clients and partners that are here with us. The level of insight we've got from people has been really fascinating. Kind of about many things AI related, as you'd expect. But also just I think the sharing of learnings I think is my favorite part of these days, which is customers informing other customers how they're using technology in a way that's relevant to their business. And just seeing those creative spots come to life is just a great way to spend a day really.

Speaker C: Yeah, absolutely. For those listeners who may not be familiar with your role and what you do at Google Cloud, could you give us a little introduction?

Speaker B: Sure.

Speaker C: Right.

Speaker B: Look, Sephora. So Alex Rutter, uh, I'm the managing director for Google's AI organization. That is our cloud AI business. Uh, and I'm responsible for that operational territory across the unit, the uk, Europe, Middle East, Africa and the Gulf. So quite a significant part of the globe from an operations perspective. Um, and I look after our, uh, go to market, uh, AI specialists. So people that are very deeply focused in AI both as applications of business, um, and this utilization. We have a deep customer engineering function that looks at kind of technical capability for pre sales and we have our full deployed engineering teams. Well that all ultimately roll up into what is our cloud AI business globally.

Speaker C: Wow, you got a lot to keep you busy then. So, uh, definitely. So a lot going on. Massive announcements today. What are the biggest takeaways from the summit this year, Alex, that you want people to remember when they leave at the end of the end of the day.

Speaker B: End of the day. Well, I think there's a couple of us. I think the first of all, the first one primarily has to be from a technology footprint is that we have made an announcement today that uh, Gemini 3.5 flash will be localized to the UK for both processing, for data residency and for machine learning. Which is a really strong announcement for the UK economy as a whole because we'll have that model running locally here um, which is great for sovereignty and for those organizations we've got great increased levels of data sensitivity. That's a really strong statement for us. The other bits that are really important that we found is you know we have got a significant investment going into UK infrastructure. £5 billion going into building uh, brand new infrastructure data center of Waltham Cross um, which will bring next generation services uh to date um, across the UK again serving the UK market from inside the UK territory. Um they were the two that were massive takeaways. I think we are so close in proximity to Google I o There's probably another couple of ones. If I think back to my own keynote this morning. The other couple of things that people want to leave here thinking about. The first one is Gemini Spark, uh our first personal assistant agent that runs 24 7. It is optimized for long running tasks directly integrated into the Gemini interface. Um, I've been using it for the last week and I was super impressed by it. The other one which is Gemini Omni. So you'll be familiar with nanobanana, the brilliant image editing capability that came out last year. Gemini and Omni is that but for video. So the ability to create anything from anything. So we've seen some amazing initial creative from uh, influencers and tech influencers around the world that are playing within a prega fashion. So I'm really excited to see what people do with that when that finally goes live on the market.

Speaker C: When does that go live Alex? Do you know?

Speaker B: So we haven't got a definitive date for it at this point in time graph we're working through a GA probably sometime in Q3 uh but we're not there on that yet. But we can follow up with further details when we know more closely to the time.

Speaker C: Absolutely. Well one, one of the dominant themes from, from the summit was agentic AI of course. And the industry is obviously shifting beyond AI assistance and chatbots and large language models towards agendic AI uh and you know sort of AI basically taking action, executing workflows, operating across business processes. Could you, could you talk us through what the technology and the product releases and how this impacts how your customers can basically benefit from agentic AI?

Speaker B: Of course. So I think the first thing we need to do is need to just break that down into two levels so when you think about Google and for those people that have listened to Chimney before and they know love about the Google stack, we are the only hyperscaler within the organization. Just forget hyperscaler that effectively enables a fully integrated stack from an AI delivery perspective, that is from the AI hypercomputer at the very foundational level where we offer our tensor processing units and our access to world class GPU accelerators. We then have access to our own research and models with Google DeepMind here in London, which is great, especially a big part of the London summit. We then have our agentic data cloud which I'd like to start thinking as kind of the context layer for agents. We then got our cyber layer which effectively looks at the new acquisition by Wiz that brings into it multi cloud stability and multi cloud sensory behavior for cyber. We then have our platforms that sit on top and then we have our agents or our uh, agentic solutions that accelerate business values. That's kind of the stack as we see it. When it comes to delivering value it comes in two key areas. The first one is around the platform, so Gemini Enterprise Agent platform and this is the governance layer that sits across an organization that enables you to build, govern, scale and optimize across the business. And we made a few announcements today, or we reiterated a few announcements so that came from IO that are really important for business people to think about when they think about deploying AI agents across the business. So when we think about build, we're fundamentally rewriting the way that software is created today. We use now use agents and agency capability to govern, manage, deploy and iterate on software. And as part of our announcements at I O which are directly integrated into this platform, we launched, we announced antigravity um 2.0. So the first fully agentic IDE environment that customers can build, code, create in. But crucially we also rationalized our harnesses down to one single harness across all three surfaces. Whether that is AI Studio, whether it is Gemini, uh, Anti Gravity 2.0 from the desktop environment or through the CLI environment all in one place to provide a easy to create natural progression for low code agents, natural languages all the way to full code all in the same place. The second area is around what I would call agent orchestration, the ability for agents or teams of agents to be able to be seamlessly able to pass tasks and workflows between agents inside the dynamic process called agent agent orchestration. Probably the most important ones, where we've had the most conversation today is around the governance of AI agents. And two Particular things in importance. One is agent identity. So think of that like a cryptographic ID or a Mac address. If you're old school enough to think about something unique for each agent that effectively allows you to trace, track, understand what that agent is doing, why it's doing it and where it's going. Just to make sure that you've got that level of governance inside the organization. Yeah, secondarily is the agent gateway. And um, not all agents will run inside gcp, not agents will run inside every cloud. So being able to manage, govern and optimize third party services inside one place is crucial. And then finally at the optimized layer. In order to optimize on a platform you need to have the greatest amounts of observability. And we have built an observability entity with inside the platform that effectively has OTEL compliant telemetry to look at all the aspects of agent behavior across the business whether they are inside Google or outside the Google cloud environment. So that's the platform. That platform serves a variety of models, notably first party services for Gemini and for Gemma, our open source model as well as the anthropic models all from within inside gcp. And so we're seeing that happen quite extensively. When you then look at that on top you have the Gemini enterprise app and the app is the front door to AI inside an organization. It provides a holistic environment where we are getting uh, a um, platform for people to connect to first party data services like Google BigQuery or Google or Looker or our uh, new data Lakehouse. It connects to third party applications like Slack, Jira, Confluence and probably most notably the Microsoft 365 suite. Kind of putting Google AI on top of that legacy productivity suite and then the ability to build agents on top of that in a safe governed way, crucially with quotas, fine grained cost controls. Really that's kind of the way that we're helping businesses bring agentic capability to the organizations they're working for.

Speaker C: So one thing you mentioned there, which I think is probably one of the main barriers for large enterprise organizations certainly is obviously governance and getting the right governance in place. So as, as a uh, as AI agents are given greater autonomy and access to enterprise systems, where should organizations draw the line between automation and human oversight? Can you just give us your thoughts on how organizations can govern this so they can roll it out quicker?

Speaker B: So I think the first thing is we need to have you know, every organ. This is first of all we should be good. This is going to be different for every Organization. Every organization's approach to risk is going to be different. Every approach to automation is going to be different. Yeah, but there are some guiding principles. So earlier on today, during our live demo, as an example, we showed people kind of a walkthrough of something that happens every day in every organization. Payments, invoice, reconciliation, um, something that we see usually very human capital intensive, but also, you know, has the ability to provide great levels of efficiency for a business in that environment. What we demonstrated and what we're able to show is how Gemini effectively, through using a set of standard protocols, is able to increase the relevant confidence of being able to process that invoice without human in the loop intervention. Now that took an accuracy rating from 34% likelihood to about above 98%. That's great. But it still depends on every business. So what we end up seeing is first of all, optimizing the process to minimize human in the loop involvement. And that is not by, that is not by principle, that is by design. What we're also starting to see now in the more advantageous organizations or the ones that are really forward thinking, is human over the loop oversight. So rather than looking at parts of an individual process, actually having the AI agents pass an entire process themselves with a human oversight over the entire agent process just to make sure that it is operating within the parameters it would be expected to, I do think in the future we'll get to a place and we're starting to see this with our agent designer version 2, which is agentic based reinforcement learning. So effectively the agents starting to learn from themselves how they can further optimize processes where we might be able to take steps out or circumvent or shortcut processes, still complete the same output. But ultimately that's driving greater levels of token efficiency, which is driving less cost per process, which is also a highly, ah, topical conversation right now.

Speaker C: Yeah, for sure. I mean what, what struck me across, you know, in terms of what's come come up, what I've heard about the event, I'm not there in person, but obviously I've been watching it quite closely is obviously the customer announcements at the summit. There seems to be. And um, um, the language that they're using and the things that emphasizing. I think if we went to an AI enterprise, IT AI event last year or the year before, it was very much about technical capability. Now they're talking about business outcomes. Okay, so is that a sign that AI adoption is maturing? And how far, you know, how far into the journey are organizations in terms of adopting and benefiting from agentic AI.

Speaker B: So I'm going to pick up on a couple of those announcements that we talked about today. But to address your first point, um, it is maturing unbelievably quickly. Um, so today over 75% of Google Cloud customers utilize one or more of our AI products in their business. So we're starting to see real widespread adoption across the platform as a whole. I think if you do look at the customer announcements today, both the ones that were made on the keynote and the ones that were used later on during some of the press analyst summits, um, we are definitely seeing a shift to more business centric focused outcomes. So the conversations that Sam Kinney from Unilever shared were very much about optimizing the business for next generation capabilities and really leveraging supply chain information. Stuart Riley, who joined us as a global CIO of hsbc, talked about how they are looking at building AI agents for safety and security across the bank, enabling them to do more on a global footprint. And I had the opportunity to introduce into interview Tara Lewins, who is the Chief Operating Officer for rightmove. And they've just launched their um, upgraded search capability for helping people find homes. 18 billion searches a year for homes across the UK alone. Helping people find that magical place they call home is ever more increasingly difficult. So we're definitely seeing the translation this year from technology capability highly focused on business outcome, which helps drive that ROI conversation with inside organizations.

Speaker C: Yeah, yeah, absolutely. So, I mean, in your view then Alex, what separates? Because Most of the CTOs I'm talking to, we're an IT consultancy firm, they're stuck in pilot mode with this stuff. In terms of the agentic adoption at least, you know what I mean, they're benefiting from generative AI in various functions. But I mean, um, agentic AI seems to be stuck in pilot mode. What do you think separates organizations that are generating genuine business value from agentic AI to the ones who are not, who are stuck in pilot mode, who have just not rolled it out yet. What are the biggest barriers?

Speaker B: So there's a couple, uh, if I can be so bold, I think the first one, and uh, I'm going to try and cover the spectrum of different conversations. The first one is, I think for organizations that are still in pilot mode is primarily because they are still kind of, um, I would say kind of testing multiple sets of technologies they haven't really made. They're still in evaluation mode, not pilot mode. And rather than actually picking a technology and sticking to it, they're continuing to try things with different platforms. So one of the things that we've seen is a huge increase in adoption of our Gemini enterprise platform. Not necessarily all of those customers use Gemini first party models. They're busy using um, Gemini. They're using open source models using Anthropic or Mistral or others. But they have one platform for governance that helps them really start to scale agentic AI inside the business. Because building is not hard today. Design is hard. And therefore having a single layer where you can do governance, orchestration and delivery without having to worry about the underlying technology for a minute actually helps accelerate. The second thing that we're seeing very consistently is a cultural shift both at uh, the bottom of the organization and the top, which is around it being okay to test and a new set of learnings around the AI world which is, you know, some of those that we've seen today and I've talked about in the past are things like, we need to get better at uh, uh, measuring the impact, not the result. Right. The result of a test might mean that it's 5% better, but if the impact is significant then we need to start looking at that. The second factor is really driving, um, a greater level of understanding around measurement for agentic AI. Be very clear on what you want the outcome to be. If it happens to be that we want to evaluate 10 times the amount of documents per hour, then that is the metric for productivity and success. If it's that we want to be able to answer 20 times the amount of customers in a day, that's the measurement for success, which is not necessarily an orthodox measurement test technique that we've had. The final thing, and probably the most prolific difference between organizations that have pushed agentic wide and are using it aggressively and to business benefit is where they have deployed the capability across the organization. So for us that is the Gemini enterprise app. And we've got organizations like Signa Induna Insurance or Bosch who have deployed it to their entire employee base. That gives not just the ability to have agentic, uh, AI running, but for them to build custom business processes and low code that, low code or no code, agents at the edge that really define or change your business process in every single organization or step that they're working through. And that's when we're really starting to see people run a scale.

Speaker C: Yeah, fantastic. So I want to talk about talent then. Okay. Because um, I mean obviously this is one of the biggest shifts in business it we've ever seen. Obviously, as organizations begin to building AI agents governing AI agents. What are the key sort of principles? Like what are the key skills that they need to acquire, uh, in order to be effective in an agentic AI world? What skills do we need to be teaching our employees, um, and uh, their talent coming into the workforce? What skills are going to become more important over the next couple of years, Alex?

Speaker B: So I think it's a really good question. I think there's still very much a, uh, I'm not gonna say a movable feast, but definitely something that we're continuing evaluating because the technology shifts so quickly. Um, but there are some guiding, but there are some guiding principles. I think, you know, anyone in today's world can use this technology. It's one of the beauty, one of the things that is beautiful about it, right? The. There's someone was very famously quoted on Twitter that English is now the most prolific coding language as an example.

Speaker C: Right.

Speaker B: So if you have an idea you can now create. But I think when you look at the skills, there are still a couple of fundamentals. I think critical thinking is going to be a skill that we're going to see even more desirable amongst businesses. But ultimately it's two kind of key traits. It is around the ability to thrive in ambiguity because the world is going to continue to shift a little bit. And it is also able to have a understanding of how a business process inherently works. Ages and agentic systems still need human input. We are a long way away from being able to ask an AI system to go and deliver a set of capabilities without any business context. So deep industry understanding, the ability to understand how technology works at a fundamentals level, but crucially the ability to try, um, I think about my own daughter and, um, when she would enter the workforce, there's going to be two types of people entering. We are going to have AI literate people entering the workforce and I would classify us in that. But also those people kind of going through higher education and further education now. But if you look at the age bracket of people in the kind of 13 to 16 category, you know, we are now going to get into AI, AI natives, people that have always had a Gemini to uh, you know, use as part of a learning aid or part of their digital experience, and I think that's going to be a really profound shift. One of the questions I often ask senior leaders is what skills and talents do you. Would you most likely look for in someone replacing you today? When you do succession planning and you look outside of the organization, especially an unknown individual, what skills would you want them to have? And it's quite a good soul searching exercise that we do as part of our executive engagement plans.

Speaker C: Yeah, no, absolutely. So um, I wanted to ask you about data. Okay. So obviously, ah, you know, if your data is not there then you're not going to get much value from these tools. What advice are you giving to CIOs in terms of putting their data in a condition that they can benefit from the stacks and the product releases you're talking about today, what can organizations do to improve their data?

Speaker B: I think the first thing is we said two different things. If they haven't got access to it and how can they improve it? So I want to just address both of those things. So I think when we say haven't got access to, I think that is a challenge that we are actively and continuously trying to solve here at Google. And um, again in today's keynote you heard from myself and from Andy Goodman's, our uh, general Manager and VP of data. And we address this across two different ways if I look at it from an AI perspective. So we're inside the Gemini Enterprise Agent. We have worked very extensively to create first party integrations into Google's suite of technologies, whether that is our data portfolio across the whole software lookup, BigQuery, our uh, global data lakehouse, et cetera and our conversational analytics native integrations into the platform itself as well as our Google workspace environment. We're also very clear that the majority of customers don't work in a single ecosystem. So we have also created third party federated connectors for most of the top, I'm going to say 15 applications uh, that are pervasive across enterprises today like ServiceNow, Slack, Jira, Confluence, SAP and crucially the Microsoft Office 365 and SharePoint environments so they can connect to all of those underlying data sources and then still use the Gemini Agent app to access that data in a holistic way. Outside of that, the other challenge we're solving for is in our data is in our knowledge, our knowledge catalog which gives you the ability to access information regardless of whichever hyperscaler it sits on today. So today we have support for Google and most of the on premise uh, service providers from a SaaS perspective. We have Amazon Web Services, uh, can interconnect high speed interconnectivity. Now Azure is coming later on and then we've also started to build out connectivity so we should really be removing that problem from CIOs. My conversation with is very much about allow us to remove that barrier for you and focus on Using data as a context layer for the agents you want to build inside your business.

Speaker C: Yeah, no, fantastic, fantastic, great answer. So I want to talk to you about the, the uk. Several announcements today involve major UK organizations and public sector initiatives. How do you assess, Alex, uh, the UK's position or you know, how do you assess the UK's position in the global AI race? Uh, how, how well are we doing on a globe, on the global stage?

Speaker B: So look, I mean I think we, I think we are doing incredibly well on the global stage. Right. I think if you look at where we are we have got some fabulous um, innovation that happens. Right? I mean we shouldn't forget that, you know the frontier lab that is driving the majority of research around the world in Google, DeepMind is based here in London, Kings Cross. We also have a huge and thriving eco and tech startup systems. You saw the announcement today about ineffable, um, that have chosen to build their superintelligence platform on Google. Um, I spend an awful lot of time, in fact tomorrow morning I'm actually hosting another breakfast with an entire room full of UK startups that are building out on uh, AI capabilities to really transform and reshape the economy. I think there is more that we can do. I uh, think as an economy, as a country we've always led in the form of innovation. Um, and we need to make sure that we continue to put the policies and process around them both from a tech perspective but also from a uh, wider government perspective that we continue to invest in those high potential organizations and high potential industries that can lead to further economic growth.

Speaker C: Fantastic. Alex, uh, where can people find out more about the product releases, the major announcements today and more about what you guys are doing in the AI space?

Speaker B: So first of all I would say there's a couple of interesting ways and we have some great um, great social channels. So um, by all means there is a Google Cloud, ah, showcase page on LinkedIn and on X that is useful if um, you want something a little more customized based on the markets that you're in. If you're tuning in by, feel free to uh, follow me and I'll make sure that we post stuff fairly regularly around what's relevant. However, the bigger platforms are really on Google itself. Obviously we can Google Search use Gemini for latest announcements. We should just go and ask it what are the latest announcements and how do they compare to your existing technology stack for example? But primarily google.AI.com would be the place that I would go to get the majority of information. Latest and greatest, um, on everything we're doing in the corner portfolio.

Speaker C: Fantastic. Well, we covered everything. Your, your answers were so comprehensive. Yeah, brilliant. Thank you so much, G. It's been an absolute pleasure.

Speaker B: Thank you very much for the time and the opportunity to talk to you as well.

Speaker C: Really enjoyed that. Massive thanks to Alex Rutter for joining us. He was so busy but uh, we, we managed to get him for the best part of half an hour, which was very much appreciated. Uh, really great guy, I think. So much to talk about here, but what really stood out from, from this conversation and actually both conversations was just how quickly the discussion has evolved. You know, for the last couple of years the focus has been on generative AI and what technology can do, you know, for organizations from a generative AI standpoint. But increasingly the conversation over the last 18 months especially is shifting towards agentic AI and what happens when AI starts taking action, automating processes and becoming embedded within the way organizations operate on a daily basis. And if that transition plays out, as many expect, and I'm sure it will, the implications for businesses could be really significant, to put it lightly. But all in all, the Google Cloud Summit 2026 provided a fascinating insight into where the market is heading. The message was clear from organizations like thg, HSBC bank and UK Government as well. Actually, AI is moving beyond experimentation and starting to really become deployed at scale. It's certainly going to be an interesting couple of years ahead, but as always, thank you so much for listening. We have some incredible guests lined up through the summer, so please don't forget to subscribe and I shall hopefully see you on the next one. This episode was brought to you by B Digital B Digital support leadership teams to optimize cost and get more out of technology investments. B Digital and the team have unrivaled expertise with technology license management and data remediation and are therefore perfectly positioned to help prepare organizations for AI technology capability. And on the last point, B Digital have developed a cutting edge AI readiness assessment which provides tech leaders with a platform they need to make well informed decisions about AI, uh, adoption strategy. Go to B Digital UK to find out more and get in touch.