How Enterprise Leaders Are Building Their Agentic AI Strategy - with Matt Renner of Google Cloud
The AI in Business Podcast · 11 Jul 2026
Source: https://podcast.emerj.com/how-enterprise-leaders-are-building-their-agentic-ai-strategy-with-matt-renner-of-google-cloud
Transcript: https://podcasts.opennash.com/shows/ai-in-business/how-enterprise-leaders-are-building-their-agentic-ai-strategy-with-matt-renner-o-f17b0f/ (via OpenNash Podcasts, machine-generated)

This is Daniel Faggella, tuned into the AI in Business Podcast. We've had on countless unicorns on the show, Lord knows if it's hundreds of companies worth $1 billion or more. There's only so many companies worth over $1 1 trillion, and Google Cloud is one of them. This week, we have Matt Renner, who's the chief revenue officer for Google Cloud, one of the largest, most powerful companies in the entire world, clearly one of the most important players in AI infrastructure.

And we speak directly with Matt Renner in this episode around what infrastructure means now compared to what it meant two years ago, when enterprise leaders are saying we're leveling up our infrastructure. What the heck does that mean? Google works with all the biggest companies in different industries. He makes that definition and then gives us a sense of what it looks like to make infrastructure nimble. If we're going to have a world where we have more digital employees than we have physical employees by hiring digital workers, spinning up all kinds of workflows that are vastly more agentic in order to spark that up across the enterprise.

What's the transformation that has to happen? There's patterns that go well, and there's patterns that fail. Matt Renner talks about both, and he proactively talks a lot about security. You'll notice in this interview, I didn't ask him about security, but he had a bunch to say, and I think it's a bit of a portent of some of the considerations we're going to see in the agentic world coming up. So this was a lot of fun to be able to chat with Matt here.

This episode it was originally aired on the AI infrastructure podcast. Most of the episodes on that show are not aired in AI in business, but because AI infrastructure podcast is so new, we're featuring some of our heavy-hitting episodes from that show on AI in business. So that you, our AI in business listeners, can know about the AI in infrastructure podcast. If you go on Apple or Spotify or anywhere you listen to podcasts, you type in AI in infrastructure podcast or AI infrastructure podcast Emerj, that's m e r j.

You'll find the show. But also you can follow this convenient link to go directly to Apple. We're going to get into Matt's episode. Let me just give you the link here. E m e r j dot com slash i n f and then the number one. That's i n f like infrastructure. So e m e r j dot com slash i n f and the number one. That's going to take you straight to Apple for the AI in infrastructure podcast.

We are proud to launch this new show. We have a tremendous number of heavy hitters from multibillion dollar in some cases 1 trillion dollar companies. Uh, on the AI infrastructure show, we started off with big guests. So check out that show if you're interested in the bigger AI foundations of the enterprise of the future. Without further ado, let's fly in. This is Matt Renner, chief revenue officer of Google Cloud here on the AI in Business Podcast. So Matt, welcome to the show.

Thanks, Dan. Glad to be here and appreciate the time.

Yeah, totally. I mean, we've we've got a a topic that I was as I was just mentioning with you has never been more salient with our crowd. The idea of infrastructure is relevant to people vastly outside of IT, as we're kind of preparing for whatever the agentic enterprise looks like, and I think everybody's figuring that out in real time. I want to start with what you see as the hurdles in legacy enterprise around getting ready for leveraging AI at scale, having data silos broken down, feeling like we can leverage agentic in different parts of the the enterprise, et cetera.

Different people are different maturity levels, but when you look at the hurdles that are most common, you know, for companies that have been around fifty, one hundred years or plus, what do you see?

Yeah, that look, I think there's a lot of challenges that we've seen across the enterprise, uh, with without question. But some of the old things that that probably were always lining up, even even the last few iterations of technology come up come to play, and the big one's data, right? That that comes into play. Uh, I when we we got into this last three years, you know, working with AI, AI just hit hit the world by storm, and and companies took a a series of approaches with varying degrees of success uh on on how they were going to go after it.

I would probably say there's all kinds of numbers out there as to what the failure rate were for the original POCs, the original science projects and the things that went on. But but uh, the majority did fail, and failure being defined as it didn't end up in a production system that was driving value, right? Uh, again, a lot of cases, this the the the technology may work, the the you can go down the path of the AI, the software, the the tools, but it didn't didn't come to bear.

And and a lot of times it was just, uh, frankly, started with qualification and understanding which projects would yield the most value, would make the most sense to go forward, uh, and and and again, justify the expense, right? AI has a level of expense with it, whether it's the technology or, frankly, change management or or ultimately the investment. Uh, but would it make sense, right? So that was one area, right, where we saw a lot of different approaches, which a lot of cases missed, whether it was executive understanding, visibility, or sponsorship, right?

So so part of it was just finding the right projects. The second thing that I think, uh, hit a lot of customers was around data and understanding where their data was and how data related to the success of an AI project. That could be everything from to the comments of, you know, wasn't was the data in a silo, was it accessible? Where where was it? It was in a modern place, you know, relative to your your data estate, right?

Legacy systems coupled with with cloud systems, coupled with, you know, situations that could be on-prem or hybrid or any of the above. And what that meant was a lot of times, I don't think folks realized where how important that was for towards the success. And then the third, I would say, is just it's just the the the overall understanding and the skill sets that are required for AI, uh, and and the early stages and how that's progressed, right?

So, uh, and and ultimately setting a strategy for your organization for AI, you know, where that sits. I I think a lot of that has matured over the course of the last eighteen months. Uh, we're seeing a tremendous, tremendously higher percentage of customers that understand those things going in, uh, and are having a lot more success. So so those are the big rocks that we've seen that we're leading towards challenges, but I also think we're seeing a lot of maturity on the customer side, uh, as as we we execute with them.

yeah, I I will I'll bounce this off of you, because we we have kind of a phrase here around executive AI fluency, which is fundamentally for us, the the term could be seen as the people making decisions around AI spend and maybe overseeing the departments where it's being deployed. Understanding, like, conceptually, what AI can do, and of course, that's moving quite quickly compared to what it was a few years ago, but conceptually, what AI can do, representative of what it could do within their industry or their department or their workflows, and then ultimately kind of where that fits into their strategy.

And when those any of those three, but or those three collectively, were pretty low because we didn't have a lot of experience in this stuff, it was hard to figure out where projects could be useful. But but um, the companies that we see that are mature now, those failures, so there's those maybe three different kinds of ROI we could think about. There's like, you know, the measurable ROI that you can just see as a number. There's strategic ROI, kind of setting us ourselves up to win.

But then we think about like a capability ROI, which is like, is our is the way we work as teams and the leaders of teams and the way we think about and leverage our data. Did that get stronger even if the project didn't go through? And a lot of the companies that are farther ahead now, they got capability ROI out of the projects that may not have seen immediate financial. And that that wheel is spun in some companies to the point where they get it more.

It sounds like you're seeing something similar.

very similar. Uh, I I think the there's definitely a correlation to those executives who embraced AI and recognized uh, uh, a the power, the advantages, probably, uh, you know, frankly, the impact that AI will have on the industry, virtually every industry across the board over the course of the next next, however, period of time it is, whether you believe that's decades or or years. But that's been the magic. So so again, to your point, right?

Repetition has helped bring them to a a a greater level of awareness and experience, right? And I think I used the term earlier, they're maturing. Uh, that's part of the process, right? They've gotten a lot smarter, right? But but I also think it's a case where they've i in some cases, executives learned lessons that you had to lean in with AI. There aren't any departments or areas that are necessarily devoid of having to worry about AI, or AI isn't going to impact because your competitors will be, right?

In almost every industry, in every circumstance, they are going to be using AI, right? To ultimately hopefully do things. I I will say things have shifted a fair amount from the early days around ROI, right? So when we started this three years ago, I think you you went back to your typical business models where a CFO would look at this and and look at round numbers and eventually originally t tied this to productivity and and human beings, meaning if we could remove people, um, it'll improve our bottom line.

And they therefore, AI was good. That hasn't played out, uh, in in in masses, right? I mean, the if we look at the vast majority of the use cases, we look across it, that can occur. There's no question you can provide efficiencies. But I think the customers that start to embrace AI could also help with their key performance indicators like growth, right? Like like efficiency, frankly, customer satisfaction, right? There's areas of this that are a little bit more nebulous, but it helped them compete in a much more mature way, right?

So AI can be used for other things other than to eliminate people, right? Which is again, I don't think totally. And is a scenario.

that's great.

So, but those customers are getting smarter on how to how to do that, how to how to identify truly how growth will help them, or identify how they're whether it's a KPI associated with a customer sat metrics or or frankly, the way to to distance themselves from how quickly they can bring information to their customers, uh, in in a more modern way. It again, those are the the companies that we're seeing that that are really getting what AI's all about a and and really seeing the progress as they go forward.

Yeah, I'll touch on that a little bit with you. So, you know, and I remember seven or eight years ago, you kind of really talking to legacy enterprise, very, very clear. It's it's really it's like risk and efficiency was really the thing, and efficiency isn't always cutting out humans necessarily, although sometimes that these things happen. But it it's also just like, how can we streamline processes, reduce costs, whatever? It was almost seen as like how we would think about RPA or something.

It was very much almost identical, maybe with the caveat, although RPA may have fit in here as well of like, can we reduce, like, regulatory risk? We do a lot in the regulated industries. It's like, okay, if if we can be a little bit more compliant, or at least show that we're more compliant in some ostensible way by by having AI maybe check certain kinds of things, that's why we'll spend. We'll spend for defense motives, so the regulators don't slap us, and we'll spend to save a couple dollars.

That's it's defense motives. Um, it feels like there, that that that's still the the mindset and legacy. It's a little bit of the DNA of companies that have been around forever. But um, but there is now more people thinking more boldly of what could this maybe product line look like, or how could this actually drive revenue? And there's spaces like e-commerce that are much more digitally native than, let's say, automotive manufacturing, who who were thinking about that quite early on, uh, with the previous waves of AI.

But now we're seeing a little bit more of that tilt towards revenue openness and not just this is an extension of RPA. How how have you seen that play out in conversations?

well, I've I've seen it play out in in practice, right? As as you watch a company mature. And I I'll let me use real examples, right? So financial services, uh, and we do a tremendous amount of work in financial services, and and that industry's fun to watch because it it continues to evolve very quickly with AI, uh, in different areas. So so I can use Citibank as a good example. So when we started working with Citibank and AI, their first few use cases were exactly down the path you just mentioned, which is we've got a tremendous number of documents, right?

Whether they're regulatory associated with that or just frankly, customer data and where things will sit that are very difficult for our average employee to have access to and to track down. It, it burns a lot of time for them to figure that out, uh, and and so we created some AI solutions that would help them cut reduce search times and reduce, frankly, the ability to answer questions and whatnot, uh, and by tremendous amounts, right? And it had a lot to do with with with that, but also prepared them, uh, to ultimately comply a lot faster, right?

By combining all these, whether they were structured or unstructured data, bring that all together into one spot, analyze it, be able to produce things so they could be very quickly can operate, but also can be nimble enough when new regulations come out and other scenarios for things to move forward. And that that was easy, right? Because AI is always going to be very good at taking tons of data and and getting you access in new ways very quickly, faster than a human could do that.

But what's evolved in our relationship is they started to realize, well, wait a minute, what about, uh, our customers? Like, how are we going to change that customer experience? We, you know, our bread and butter of what we do on a daily basis. So you look at the system we built called City Sky with them, and and again, in this specific case with that product, uh, it's all around customer experience. It it's it's ultimately utilizing it's a very forward thinking scenario, everything from avatars to other individual, uh, ways to pull data together to answer qu questions on the spot.

I mean, what most of our financial services companies that we we work with will tell you is when we have that person on the phone, we have that person online, we have when we have that that customer experience, you're going to want to maximize that at that time and not have to go back and and retrench because you lose a lot of the effectiveness. Well, that's a perfect example. So started working internally to get more efficiencies, started trying to tackle problems with large data and where they sit, but then evolved to, well, wait a minute, let's change the way we have business.

Let's get more customers, let's yield higher portions of their of their investment portfolio. Let's make it so they want to come to Citi, right, when they think about their next investment or where they want to have one stop shopping when it comes to their relationship. So we watched them evolve. But I I think another one I would give you is let's let's shift gears completely, and that's Honeywell, right? So Honeywell, 130 plus year, maybe 140 by now, 130 plus year, incredible company, one hundred divisions, right?

Massive, old school manufacturing that's right. Yep, company. Uh, they started working with us to drive efficiencies that you would imagine, right? Let's let's talk about supply chain, let's talk about, uh, driving efficiencies into some of our employee related activities. But what they also figured out is, wait a minute, we've got all this data that's related to our manufacturing, our products, and all these other scenarios. What if we were to actually create businesses with AI and actually create a revenue stream around a services business that was related to a lot of these products, services that we do in all these businesses?

And they created that. So they've actually used the mentality of I've gotten to know AI a little bit. I understand some of the power and the opportunity. What if I created revenue generation opportunities here, like not just get better, but actually expand? And so, and you you couldn't find a more tried and true, incredible company with with century it it in place shifting gears with AI, right? So we're seeing pick an industry and I can give you an example.

But but they're maturing, right? They're changing their approach on what they use AI for because they understand it better.

yeah, so you're you're you're seeing the evolution of again, like, how could we be more efficient too? How could we rethink how we do X, uh, actually play out in the real world? And and and for us, that was always like, you know, if I go back seven or eight years, that maybe I would have thought, oh, it's another four years away. It was longer than that. It was more like eight years away. But but we're now really starting to see the beginnings of what feels like what could actually be more revolution than very gradual evolution because people are around now thinking upside.

You know, d defense motives are never going to fundamentally shift what you're doing. But if you think about like a whole new way to manufacture, a whole new way to serve customers, a whole new way to win customers, now all of a sudden, you know, you you will really get those outside results and be able to see bigger changes there. So, um, I I guess, uh, you know, thinking about this, this overcoming of some of these kind of core challenges that you've seen.

I mentioned one of the ways some of these companies have learned is they had to just go to the school of hard knocks, right? They they were low executive AI fluency. You do a couple projects, you might not get measurable ROI, but you get the capability ROI, which is like, we're smarter. We now know how to organize our data a little bit more. You know, we've changed a couple processes that'll help us with future AI projects.

And they kind of built on a compounding of it wasn't really failure if we got stronger in different ways and then we could kind of go from there. Are there other mechanisms you've seen that have gotten companies to that same kind of maturity? School of Hard Knocks is obviously part of it. Any other kind of trends or patterns you saw that were like, Yeah, Dan, I think this is sort of some of the people that get it now, they've gone through this, or they've done that.

Like, anything else for you that could be helpful for listeners?

yeah, look, I I think process is important, right? So so having a, uh, a at each level of of of the experience, right? When you're with from an idea to to production, right? Having a process and sponsorship is super important. So, you know, again, I it what's interesting is I can use Honeywell again. I'll go back to that. And

Love it.

their their CTO is is it actually does a really admirable job of this. And she and she had set up, you know, frankly, everything from a review panel, uh, for AI to the execution and and the monitoring of this. So it's one thing to have an idea, it's another thing to qualify it and decide you're gonna go down that path. But then seeing it through and having levels of visibility to track your projects in a certain way made it a lot easier for the organization to know exactly how this is going to come together, right?

So so putting that in place, and sometimes that's that showing up is is as at a leadership perspective. You got a chief AI officer or or, and there's a lot of different titles. That's always interesting, but but you do have somebody that everyone knows, all right, that's the AI person that that ultimately can help me and where I go, right? Or if I have an idea, this is where it's going to go, or this is the process I'm going to go through.

This could be, uh, set up from a technology perspective, whether it's it's you build an application for folks to do that and scale, or or you at least have a set process that you've thought through and have the organizational, uh, structure there. We've seen a much higher degree of success, especially large companies, when you put that in place, right? You have that scenario, uh, you also have a strategy. I go back to a comment I made earlier.

Data and data and data, and data is so important in these conversations. Where your data is, how modern it is. I mean, obviously, we spent, well, in in in Google Cloud's case, about fifteen years convincing people to move data into the cloud, but not just move it. Make sure you you're you're putting it in a modern space, right? I mean, it's one thing to lift and shift and early on in the cloud days, go twenty, twenty five years ago when this first hit and AWS hit and some of our competitors.

There's a fair number of folks who just moved it over, lift and shift and didn't really modernize where the data was. It was just basically an outsourcing scenario. And I I think the folks that are having a much easier time now put it in more modern places. They've utilized whether it was the tools that are out there, use our tools. We have a phenomenal store in BigQuery, and now we've got our our, uh, a lot of things going on with with our our agentic data lake that we're putting out there at this point.

But we're just trying to make it easier and easier for you to, oh, leave data where it is, pull it into one spot and use it for AI, right? Which I think is important. Um, the the last thing I would mention to you, what we're seeing while the success is is making sure you have an orchestration platform. When you start talking about agents, agents is definitely for the foreseeable future, I think will be, uh, you know, things have matured from chat box and and question and answer to agents and agentic flows.

Uh, so having an orchestration platform, we have one called Gemini Enterprise, which is the fastest growing product in our history to ever hit the ground. Because it has the ability to orchestrate your agents, it's easy to generate those agents, it puts you in one spot and it and it can talk with other agents, right? A lot of the the logic that's there. So I would drive towards, uh, a standard process. I would drive towards an orchestration platform that can help you for across all of your enterprise.

So you put it in one place, or you have one place where at least people can can work through this, uh, and I would continue to make sure you understand your data impact and where that data is when you go through this process.

yeah, I I think, yeah, d uh, going beyond lift and shift, I think is pretty pretty inevitable transition for the bigger companies that are tuned in. Some of them learned some of that the hard way. You touch on agentic, I so I'll just double click on it because I know in the second half of the year for us, uh, so much of the enterprise interest, uh, of what we're going to be covering, who we're going to be talking to, the the themes we're going to be doing our market research on is is really around this broader shift to agentic.

Um, and I think different people have different ideas of what that'll look like. You know, for some folks, it's like, oh, you know, I'll have digital a digital coworker in addition to my physical coworkers in the call center, or or, oh, you know, uh, we'll have AIs talking AIs to handle this use case or that use case. But I think it is a bit of a paradigm shift if we take it seriously, and I think if we zoom forward, you know, even two years, never mind five.

Um, enterprises and business generally is going to be a totally different ballgame. It's it's a lot to drink in and to try to imagine what is our business in that future. When you paint the picture of kind of what the agentic future we're stepping into, of what what enterprise and business is going to look like. How do you kind of set the frame of like, hey leader, think about it like this. A lot of things are going to shift in this way.

How do you set that frame for people to do the brainstorming? Because the idea is obviously a buzzword, but it's a paradigm shift. I think people need more clarity on. Let me know how you like to think about it.

I I I think a couple things, uh, so so when you think about agents, first of all, not all agents are the same, right? I think that's the first comment I would make. Meaning some agents do a very simple task. There are other agents that can change an entire flow, uh, a as sophisticated as a supply chain, right? I mean, so so it depends on what depth of agents are going to be involved, how many, how much of the enterprise it's going to co collide with, what systems have to talk to each other, frankly, where the data is located and all that.

So depending on what what you're going to use agents for, uh, we do think it's a massive opportunity for for folks to lean in in both cases. Uh, the second piece I would bring up, uh, around agents is where are they located and and where should you get your agents? I guess is a question that a lot of folks are asking. Because right now, I I I think you'd be hard pressed, Dan, you could p pull up every major ISV that's out there, pull up their website, and it's going to talk about agents and AI on the first page.

I mean, it's 100%, yeah, everyone, every single you you one.

better believe it.

So people ask the question, Well, should I buy my agents from them? or should I buy my agents from you? And and again, we're we're more of a Switzerland, i if you will, orchestration platform for agents as you go through it. And or should I build my agents with them? or should I build my agents with you? And and so, uh, you know, the interesting thing I think I've landed on after watching this for a little while is it, it's both, right?

I I think there are pre-built agents that are built into some of these ISV platforms, uh, that are very high value and very easy to deploy. And there's no reason why you can't do that, right? The these same ISV platforms tend to go down a path of saying, well, use me for all of your agents globally across all other areas. I don't know how many customers are going to jump in on that because you have a lot of ISVs, um, but I think you can do both, as long as those agents, I with your ISVs, are being created with the right protocols.

A two A is the is the most dominant one at this point. So it can talk to agents, uh, in your orchestration platform, and you can bring all this together. Well, then you can have one agent strategy, right? That's kind of the the piece I go back to, which is have an agentic strategy. Understand where your agents are going to be created, understand what you want to accomplish, make sure they're using protocols and standards, right? That are that are industry wide, where that goes across.

And that way you're not throwing away agents, you're not having agents on islands, you're not having agent, right? You have the ability to build on what you've built in the past. And so, um, we we love our story around Gemini Enterprise. We think it could be the it, well, we do believe it's the best window into AI that you could you could have, uh, across an entire enterprise. But the magic in it is it ties this whole thing together, right, with one platform.

Because the other element we haven't talked on talked about, yes, is security, and oh, for and security associated with it. But what this allows you to do is tie it all together in a secure platform to protect yourself.

yeah, well, and and so, uh, I I think, hopefully, you know, some of our folks will be familiar with at least some of your product set. You're talking about being able to build an agentic strategy so you can orchestrate maybe agents from different, you know, o- original sources that, to your point, some of them have very wide mandates, very, uh, small mandates. Some of them are really enmeshed with human teams, uh, all day. Some of them are quite autonomous off in a dark corner, and we haven't really, you know, done much to change them in the last two months.

Um, so, you know, big big variety here. I think the question I I may even ask is like, what are what kind of future are we imagining ourselves walking into? I I'll give you an example of, like, the level of granularity I'm thinking and just get your instincts on this. Because I don't think enterprises know what agentic strategy means. I think most of them see, hey, you know, well, I guess in the call center, I can have some AIs do some of this stuff instead of people.

Okay, maybe that's my part of my strategy. Over here, you know, I could do this, uh, maybe that's part of my strategy. It seems to me, Matt, call call me crazy, that the bigger picture here of, you know, b- business strategy writ large, of which the agentic stuff is a part, uh, i is kind of a broader picture of if we have the ability for systems that would do things only humans used to be able to do in the past at, like, some monstrous scale.

Then what's the shape of this whole damn business in two years of, like, what man does, what machine does? And this doesn't mean we sloth off all the humans, although I'm sure in some industries, in some cases, that'll happen. But but what it means is like, we may have departments that are human light and AI heavy. We may have departments that get more human heavy because we can move them out of places that we used to have people pushing paperwork, and now we can get them over here to do such and such.

But like the two, three year shift is is sort of, you know, we're going to be our supply chains are going to be increasingly run by AI, talking to other people's AI, humans will be part of that mix. The enmeshment of what's going to occur, I think, is beyond most enterprises present imagination. You guys are a bit on the cutting edge because, of course, you're developing many of the tools. We've had DeepMind people on the show before.

Uh, was just out in London with a bunch of your your colleagues out there. Like, when you tee up what we're walking into, how do you set the stage? I I did a little bit of it on on my own and very tertiary level, but anything you think leaders need to know, Matt.

look, I I I think a couple things, uh, uh, um, at least what we're seeing pretty consistently. There's there's definitely, I mean, there's, there's, there's, there's all kinds of stuff that have hit the industry around AI, right? There's there's fear, uh, there, there's, there, there's all. I mean, you can go down a list of, uh, of bubbles and all kinds of stuff that the terms that get thrown out here. Uh, I think this is a long-term generational, uh, technology.

I think agents are absolutely going to play a role in every company, in every department. It's just a matter of when. I also think, uh, to your comment, that that's where we're seeing more of, which is this. This isn't eliminating jobs, it is moving jobs around, right? Meaning folks are going to have to probably have a a thought process of of maybe changing skills and embracing AI and where where you can do more, right? So where we've seen more times than not is is the same people being able to accomplish their tasks faster, more accurately, right?

And in some cases, drive new outcomes, right from where they sit and from that perspective. So, uh, the folks that have embraced, I think, are a good thing. So human in the loop, and there's a lot of different thought processes around human in the loop. We still believe it's it's a very major, major component in virtually all AI oh, certainly, systems, by and large. So, um, but by the same token, I think it's more of a positive than a negative.

And so, uh, when you talk about, I was talking about from a strategy of more of a technology strategy, which is I just want to provide these platforms. There's a process strategy as to how we're going to build and what we're going to build and what decisions what it's we're going to

going to look like. Yeah,

make, how we'll execute it. But then when it comes to the strategy around the art of the possible and and where do you go with this? I I do think you you probably, where we've seen a lot of success is you've got varying degrees of of buckets, right? You've got top buckets which talk about the art of the possible. Think big, right? We go there. Then you've got kind of a bucket that's more of the middle ground of just operationalize some of the things that we do well today.

Let's do it better, or find an area we don't do well today and do it better. And then you've got the blocking and tackling mundane things that can just be sped up and more accurate, right? Which is down below on the lowest level. Uh, so so it depends on on having a strategy as to how many projects you're going to have going in each of these three buckets. Uh, I would ar- I would argue the best companies have all three going at once, right, relative to this.

So you have a strategy relative to how you're going to actually choose projects and how much you're going to undertake at one time. Um, by the way, huge element here on business disruption, right? When you start talking about management, you start talking about everything that's associated with that. Uh, obviously, the senior executives can figure out how much their organization can can withstand at a in a single period. But that's all part of your strategy. Like, I'd be mindful of all of the above.

yeah, yeah. So a great many considerations of what rolls under agentic, of course, but I I I do think that, um, the almost immediate ability to get a lot more out of whatever people are trying to do day to day. If they have a, you know, something on their shoulder that can, that can get a heck of a lot done. I think just even within a department, there's so much transformation in the relative near term.

I think the the the last piece, Dan, and and I brought up briefly earlier, was security, right? It's th- this is this is escalating on a daily basis at this point, right? And and sometimes a a competitor, or or actually, uh, like, I should refer to them more as a partner competitor, right? We we compete sometimes, we where they're a customer of ours. it's an interesting We

ecosystem, though.

I was, look at Anthropic, right? Which is an amazing company, incredible leadership, incredible products, right? Uh, the the buzz and the activity that's hit financial services and some of the other core infrastructure com companies around cyber and around the thought process around a model and and and some of the downside of that, which can be if the the folks on the wrong side of of the ledger here are going to attack our our our key industries and utilize models to do so.

How do you defend it without a model? Meaning the speed that they can find vulnerabilities, the the the access and the things that a human would have a hard time doing unless you have a model to battle that, right? To do the same thing and and go down a path. Um, it's going to be v- it's almost table stakes, and and and it's escalating, right? If you talk to the governments globally, you talk to our US.

government, it is way up there on concerns when it comes down to. We're really worried about these folks using these models to attack. And and and the the the first place the government worries about is our our key infrastructure industries. That's not just the government, it's shutting down financial services, shutting down energy, shutting down some of these key areas, health care, right? So so those companies, um, are are escalating how quickly they're looking at that and having to have a strategy for cyber that's associated with AI, and and it's it's, it's super important.

Now, I'll I'll give a a a two second plug for for Google Cloud just for some fun, right? Uh, just cause I happen to be there. But, uh, we saw this coming for some time. You know, we made an acquisition of Mandiant about seven years ago as we were starting entering in into this era, uh, which was the preeminent consulting organization for for cybersecurity. We made an acquisition here was the single largest acquisition in Alphabet's history in Wiz that we completed here in March.

that was a big one, huh? I Asset mean, and man, they they grew so fast before you guys gobbled them up too. That was like the wildest startup story ever, huh?

largest startup in in in I mean, history, right?

e- exactly. I mean, like, and and Israel's not exactly a place where there's a shortage of pop-off companies, but you talk about a pop-off there, that was a wild story.

the monster. But the reason we went down that path is because we were knew we were going to head down this path. We knew we had a model in Gemini, right? We knew that could be utilized, but we also realized that the model's only going to scan for the most part. It's going to find your vulnerabilities. But now what? Like, if I'm if I'm a customer and I'm ch choosing a a AI cyber strategy, who's my partner?

Well, the reason we're different, I mean, just being very candid, is is all the models are going to leapfrog each other and get better or worse on identifying and defending against cyber attacks. Uh, in fact, most customers, I think, are going to use multiple models before it's all said and done to make sure they've found all the problems, make against the scans. But I think the difference is the customer that can remediate those problems, can track them, can monitor them, has products like Wiz, products like CodeNdder for us to fix those problems.

Um, that's the difference. So we're hoping that that the the decisions we started making years ago are going to come come to fruition, much like the decisions we made in AI, right, when we got into this era. When we chose to not only offer the optionality that our competitors did of other models, other chips, other data products, other, whatever that might be, which we offer today, but also our first party stack, right? So we're trying to replay that strategy, but it it we didn't decide it yesterday.

The acquisition of Wiz, you can date it back a couple years, you can date this stuff back. We saw this coming, but we want to offer a full suite for protection. But I go back to this point, that's part of your AI strategy. If you don't have a strategy around data or security, you've got a hole in in your AI.

well. And unfortunately, I think compared to a couple years ago, I think everybody gets that now, fortunately. Now, are they all figuring out exactly what it looks like? Probably not, but they're at least aware we better damn well, uh, figure it out. Of course, It's you got a dog in the fight, but you, you, you guys also, you know, have done your darnedest to stay ahead of that side of the the coin. And a lot of our our listeners in regulated industries are tuned in kind of for that, for that part it's of what we're discussing.

it. It's incredible how fast in the last even six weeks, uh, things have spun up on this dialogue, right? And and it's it's financial services is is is out in front, to be candid, the government's

it's a useful thing.

way right. I mean, where they're just, um, a, and it's, it's, it's well, it's viable, right? I mean, it is, it should be a viable concern and is part of your strategy.

100%. I think it's whether we like it or not. There's going to be an arms race dynamic here of the the machines doing the bad stuff and the machines doing the good stuff. And I think getting on on that train earlier rather than later, there, there probably isn't going to be a way around it. So, uh, for those of you tuned in, uh, let that be reinforced with you again here. But Matt, we've got a lot of good insights about what I think the winners are doing well in terms of mindset, ways of thinking, and kind of prepping for what this agentic future is and what infrastructure actually means.

I know that's all we have for time, but it was a real pleasure to have you with us. Thanks so much.

Thank you, Dan. Absolute pleasure and I appreciate your time.

So that's all for this episode of the AI in Business podcast. A big thank you to Matt Renner for being able to hop on with us and share some of his thoughts, and a big thank you to you, our listener, for tuning in all the way through to the end of this episode. I hope you've enjoyed this one. We've got a lot of other very big infrastructure guests coming up here over the course of the next six months.

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