Agentic AI and the Future of Enterprise Software with ServiceNow’s Amit Zavery | Utilizing AI Ep. 13

There's no doubt that AI will deeply impact enterprise software, especially as agentic AI rises in importance. But it's really an additive process. This episode of utilizing AI features Amit Aari of ServiceNow, Nick, patients of the Futurum Group, and Jon Swartz of Textron, considering the future of Enterprise Software.

Welcome to utilizing ai, the podcast focused on practical applications of artificial intelligence from the Futurum Group. Every Wednesday, we explore news and use cases of the ways in which AI is transforming enterprise IT and the industries it serves. I'm your host, Stephen Foskett, president of the Tech Field Day Business Unit here at the Futurum Group.

Before we dive into this discussion, let's meet who's on the panel today. Hi, I'm Nick Patience on the AI platforms lead at, at Futurum Research. I focus on, um, enterprise ai, uh, both at the application layer and, and down on the infrastructure layer as well.

Hey, I'm Jon Swartz. I'm the West Coast Bureau Chief for Techstrong and, uh, which is part of the future in group. Um, I write about ai, I write about it and write about digital CXO, and it's great to be here again.

Hi, this is amit zavery. I'm president, chief Product Officer and COO of ServiceNow. Uh, I've been working in a software space for 30 years and really excited about having a conversation about AI and software.

Well, let's dive right in then. Um, we have heard many people say that AI will be the end of enterprise software, that somehow AI is going to be an all singing, all dancing platform for basically everything that enterprises use. I disagree with that.

And, um, Ahmed, I think you probably disagree with that too. Let's start off by just, uh, kicking off. What is your reaction to that whole sentiment?

Yeah, no, I mean, uh, there's definitely a lot of this conversation about what AI does to enterprise software, and I've spent 30 plus years in enterprise software, and I know how complicated and what it takes to run a business. And there's a huge amount of context, huge amount of understanding of a business, uh, processes, as well as, uh, kind of governance and security required. So there's a decent amount of understanding, uh, which you need to really bring in when you're running enterprise business out there.

AI is a very good, uh, technology enabler as well as opportunity for us to redefine and rearchitect the products which we built today for enterprise software because it adds a lot of value. But having AI replace everything enterprise software does, it's really not possible. And we've seen this on a day-to-day basis.

I mean, LLMs have come a long way today, uh, but today not a single company runs just on standard A LLM. Uh, they do require a huge amount of context and huge amount of capabilities, which is built around those, uh, AI building blocks. But there's, uh, work to be done and that continues to happen, and enterprise software is will con continue evolving to really make sure we deliver what customers need.

Uh, without just saying that, AI will take over everything out there. So, Amit, you've, um, you've led the transition to the kind of agent AI era at, um, at at ServiceNow. So how do we move, um, from AI that, um, simply suggests actions, um, to AI that has agency to actually execute multi-step workflows, um, across the, the enterprise without a constant human handholding?

Yeah, no, I think you're right. I mean, there is, there is an opportunity now with a gentech to automate a lot of the business processes. There's always been this interest in enterprise software to automate and remove as much human interaction as possible.

And AgTech and AI turbocharges that for sure. Right? So the way to think about this is you think about a business process, what are the pieces of business process, which you can now automate using ai?

Uh, but there are a lot of data capabilities also required because what AI does is gives you a probabilistic answer. Now you wrap that around with the data you might have for years in terms of running workflows, like what we've been doing at ServiceNow for 20 years. What we do with this probabilistic kind of workflows or workflows is add the data element and say, Hey, is the outcome accurate?

This is what you're supposed to do or not. And if it's not, you go back and redo things or bring in a human into the loop as needed. Uh, but that has to be done end to end not doing pieces of it.

So when you look at a business process, uh, any of the standard ones, right, order to cash or procure to pay or things you might be doing for it, service management and other things like that, you require the combination of what a context is, what the data associated with a particular outcome is, and then AI enabling and kind of making some part of the decisions, but it cannot take over the whole thing without having some data associated with what, what the outcome should be, uh, with which we should have expected from that. So that's really what we're seeing now. And ag agent, uh, uh, processes are very powerful.

It does reduce your cost, it gives you, makes you more agile, uh, as, as well as it gives you some, uh, ability to improve how companies run. But you really to wrap it around in many, many other things as well. So, Amit, your appearance is pretty fortuitous.

I mean, today, the day we are recording, you announced this expanded strategic collaboration with OpenAI. I'm wonder if you could go a little bit into it. I guess the, ultimately what you want to do is deliver advanced agent AI to enterprise customers.

Um, part of this mad scramble among all the companies in terms of their workflows. Can you maybe discuss a little bit about what specifically this addresses and how impactful you think it may be? Yeah.

So we've been working with all the Frontier Model company, uh, providers for many years now. And, uh, we've been always an open ecosystem. So the work we're gonna do with OpenAI, which we've been using them as the one of the large language model choices for our customers, uh, in the first phase, we were doing a lot of summarization and giving ability for people to comprehend what happened as an action.

Uh, the next phase as we evolve some of those capabilities is to really now use large language models for voice capabilities. For example, multimodal, multilingual, so that we can understand what the cus what the user is trying to say, but the intent in terms of knowing what the context is and then be able to action that. That's really the IP ServiceNow has been building for many, many years.

So we still use these large language models to kind of take out some of the mundane pieces of our work and understanding a lot more of, uh, user data, uh, faster. And that's the relationship we've been building with OpenAI. We do the same thing with anthropic in some cases, and we do that with Gemini, but our goal always has been to use whatever technologies out there and the advancements in there, but then build a lot of IP around it to make those things much more efficient, much more context driven, and much more valuable to a, a customer.

So we're doing this for now, say employees, right? Understanding that employees are requesting help. What does that help mean?

Uh, if they requires require requiring a fixing of VPN issue or they're requiring a new laptop, or they want it to be onboarded, once we know what the request is, we know what that needs to happen because we've been doing that from the backend, from an agent perspective with human agents before now with AI agents. And OpenAI helps us now take that data and wrap it around to integrate with various different systems, but then our, uh, agent processes and things we build in our AI platform is now doing the actioning part of it as well. So that's the relationship and we keep on evolving that, but there's a lot of good technologies coming out, and we use that as a building block, and I would say five to 10% is IP from them, and 90 plus percent IP has been built by ServiceNow to really get you the outcome customers want, because customers shouldn't care what you're using underneath the covers.

It's really the outcome driven kind of mindset. Yeah, that's really important with a solution like ServiceNow because you are reaching far beyond the technical IT staff. And I mean, really this is a solution for the entire business.

So having it be able to handle multimodal interaction, which, you know, as you said, uh, voice, uh, video, you know, things beyond typing and certainly well beyond coding is really consistent with the goals that the company has had since the very beginning, which is to make this sort of process automation, uh, applicable beyond the typical enterprise IT staff and, and really, uh, useful in in the entire, uh, in the entire staff. And that's something I think that, uh, generative AI is particularly good at is, uh, you know, acting as sort of a user interface. Nick, this is something that we've talked about quite a lot on the podcast.

What's your reaction to the direction that ServiceNow is taking? Yeah, I think it, I think it makes, um, it makes a lot of sense. I mean, it's, it's, it's an interesting to watch a, a fairly mature, um, software company like ServiceNow's, obviously, yeah, there's other companies that have been around longer, but, but you very mature now, um, adopt AI in a way that, um, actually speaks to some of the problems, um, that, uh, the, that companies face.

And also you obviously, you're not trying to, you know, boil the proverb ocean from day one. It, it's very much, you know, you understand your use cases, uh, extremely well. Um, but your use cases also expanding a little bit, aren't they, beyond, beyond the kind of, you know, the it operations into, into HR and into, into other, into our other areas as as well.

So I think it's, it struck me as a, as a a fairly pragmatic approach. And I guess, I mean, I mean, um, I mean, you come, you came from, you had long time at at Google and, and, um, and, uh, Oracle as well. I mean, how does that, that kind of experience, how has that informed what you've been doing at ServiceNow for the past, um, I guess almost two years now?

Yeah, no, I think as you point out, I mean, we have expanded our use cases in quite a few areas, right? So what we did when I joined, I made sure that one, we are very clear about the customer outcomes, what we trying to solve for, what are the areas where we can be differentiated as well as value add, uh, to our customers. So we did gotta pre-packaged agent flows and say, you know what customers, if you're doing this currently deterministic workflows, can you replace that with while I guarantee an outcome?

And that resonated a lot with customer. Second thing we wanted to do was really think about controls, be it around governance, security, auditing, and giving a customer's visibility in terms of what they're doing with ai. Every customer I spoke to and every C-suite, uh, member I spoke to, they were worried about proliferation of AI technologies without any idea what happened, who did what and who's running what.

Right? And that doesn't work in enterprise software. This is not like you can just click to a different website and start using something else.

You have to make sure a business doesn't come down or also anything which is outcome, which is wrong, and it really impacts your financial standing and everything else. So we had to make sure that we are building that controls, providing governance, providing all the scaffolding required, and guaranteeing customer confidence as soon as we gave them a lot of this thing. And we launched this product called AI Control Tower, which allowed you to get visibility, uh, lifecycle management of AI technologies, ours and third party, as well as the ability to now audit and do risk management.

Once we gave that to a customer, they started doing a lot more agentic use cases after that because they got that worry outta the way. So my experience at Oracle, my experience at Google, we really understood that ai, uh, at Oracle, of course, understood what enterprise software means, what it means to really run large companies business without having any kind of, uh, wrong outcomes. Uh, Google gave me a very good grounding in terms of what AI is capable of, what he can do with it, and it's really amazing amount of IP being built there.

Uh, but combining the two is very tricky, and that's what I think ServiceNow has been able to do a good job of is really taking AI building blocks, using that a technology provider and using it where it makes sense, but then really building the frameworks around it and making it easier for customers to get value outta it. So our AI control tower, the workflow, the connectivity with third party systems using AI agents, what we are doing from the perspective of, uh, understanding intent, uh, and it could be NA domain, right? We're doing it for hr, we're doing it for finance, supply chain procurement, and now expanding it to CRM and then security and risk.

Those things made customers much more comfortable that we're looking at it end-to-end. We, we call ourself, uh, the enterprise operating system because of that, like going east to west and not to south, we're just not a verticalized tech, but really looking at business processes, which cut across. And that has been the, the goal, uh, game changer for us in most of the customer conversations.

And that's what we bring to the table to our customers, and they still feel very excited about what we can do for that. So if, if you could indulge me for a second. So I, there's no question AI agents are gonna be integral.

They're, they're absolute necessity within enterprises, but, um, we've kind of gone through this, this timeline where we are, there were a ton of announcements, including many from ServiceNow in 2025, and I, I'm kind first kind of an observation what I, that I'd like you to address, and then I have a question. The observation is, where do you think the adoption of AI agents are now and what are the real risks for enterprise that enterprises that try to adopt AI without modernizing or integrating their core systems? Yeah, no, I think there's been, of course a lot of promises out there.

I mean, as you said, there's a lot of announcements. What we've been very happy about and very excited about is the adoption we've been having, uh, with our now assist product. Uh, we have delivered that early last year.

And as soon as, as I was talking about earlier, this AI control tower, once we gave that to customers, they started feeling more comfortable gonna use cases. And I'll give you an example of companies like Bell, uh, where they've been able to now use all of the customer service, our AI automated flows for doing deflection, but also understanding intent and resolving issues. So I'll tell you the common use cases we see in adoption wise, incident management, uh, resolution of, uh, any kind of, uh, request, uh, things like triaging, uh, coordination of any kind of, uh, issue they might have run with, uh, disputed disputes.

Like things we are doing with Visa. When a customer requests an issue with their credit card bill, having that been automated between a merchant, a credit card issuer, uh, the consumer and Visa in an automated fashion, and reducing the friction around that has been a game changer for them because they're reducing cost using AI agents. But it's an agentic flow, not just an AI agent talking to each other.

Right? Uh, what we have introduced recently is this idea of taking out, uh, L one support for any IT related requests, right? So autonomous it, we call it.

And that really changes the game for a lot of the companies because a lot of these questions and requests they get, we can resolve it as an, uh, employee in a way who's doing all the work for the request and resolving it thing without having to really file a ticket, even file a ticket. We resolve that without having human interaction. So that are all use cases we're seeing live being used, and we have customers across the board who are adopting it.

Last quarter we announced the adoption rate went up 55 x in terms of the amount of in calls we getting back and forth between our AI systems and the Gentech use cases. So adoption is there, there's gonna be turbo shot this year, I think. Uh, but there's a lot more comfort in, in this area from our customers than probably previously, uh, out there.

So mi, there was a, um, late, uh, 2025 service MA ServiceNow made its biggest acquisition ever with Amiss and also bought to Visa, or I guess that's right. So this kind of, I assume signals that you see security as a foundation layer of, um, of, for a AI and AI in particular. So why, why was it necessary to, to make those acquisitions, um, now especially, especially Army?

So what does it say about the kind of future product direction for, for ServiceNow? Yeah, I think, uh, we do believe, uh, security. And so we, I don't know how many people know ServiceNow has a billion dollar plus business in security already.

Uh, we've been building out a security stack and portfolio, especially in post breach. Anytime an incident happens, all the life cycles, CISOs depend on ServiceNow to manage that. We integrate with Palo, we integrate with CrowdStrike, we integrate with of the world to lifecycle of any incident and resolving and managing and everything around it.

We see a lot of customers asking us for now, one, how do you manage the identity of the user? Uh, especially now we go into non-human identities, be it, uh, AI agents or, or, or devices. So the basically does identity governance, which has been a big, big kind of use case for ServiceNow already, because all the employee, when they join a company or an object you add, or asset you add, the lifecycle goes through ServiceNow typically.

So was a very natural extension for us to giving this identity governance for especially non-human identity. Second thing with, uh, arm, what we saw was IT and OT starting to come together. What ARM does is provides OT security, the operational technologies, so any kind of manufacturing, be IT devices, robotics, uh, be it, it, uh, IOT devices, how do you secure them?

So when a lot of the, what what ServiceNow has had is a product called cmdb, which tracks all the assets inside the company, hardware and software assets. That's becoming kind of the gold standard for companies to know what's inside their enterprise and how to manage them. So security around that is becoming very critical.

So customers been coming to us saying, Hey, you already know our assets. Can you really make sure that there's no breaches associated with that? So vulnerability management, as soon as you get signals, and again, the exposure management around that is what ARM does.

Combining it and taking it to the OT environment, making it end to end, because IT teams are really managing the OT environments for manufacturing, uh, or any devices they might have inside the company. Uh, give you an example. JP Morgan Chase, the new building they put in, they have so many devices, iot, wireless, other things like that, that the security associated with that was all been done by arm.

So we manage that asset, then a lifecycle and incident happens, goes to ServiceNow again. So completing that lifecycle was very critical for us because the customer's asking for it, we're giving pieces of solution, not end to end. And as we move to Gentech, you have to think end to end.

You can't just do pieces of it again, uh, without, and it has to be some partnership and some things we have to build ourselves. So our mission closes the gap for us and really expands our capabilities, and it really plays into the idea of having an open ecosystem with third party systems while we, we help them manage that lifecycle for any asset associated with that. So that's the reason why we bought those companies.

Uh, it adds a lot of domain, uh, and really expands into the security space, which we believe we've been doing very well, and our customers are pushing us to do more and more. Yeah, I'm, I'm glad Nick asked about cybersecurity because, and I didn't realize how large your security business is also. So I'm glad you pointed that out because that's something that kind of goes underneath the radar.

And in a sense, I always think there's a gap and, and maybe this is one of the reasons why there is kind of a slow adoption of genic ai or a reluctance among some companies because there's that whole security issue. And that's something that when we do, Textron gang, and Steven can vouch for this, that point is hammered home that security slowly evolves RA rather than is revolutionary. And AI definitely is revolutionary.

So there's this gap between the adoption of AI and the urgency of now versus the consequences of a security issue. And I'm wondering, do you think that's still, uh, a significant issue or will continue to be a significant issue for enterprises that are thinking of diving whole hog into, into genetic ai? Yeah, you're right.

Uh, John, I think, uh, without security, without risk, having a confidence in security, confidence and risk management and tracking what AI is doing, there will be reluctance to adopt AI in in enterprises. I think the other parts of the world might be, okay, so if you don't sell the make, solve the foundation, have AI built, AI built with cybersecurity in mind and the ability to control it, I think customers will be very wary about not knowing what is AI doing into their environments, right? And that's why we are doubling down on building a stack our platform.

We still have this concept of one platform with one data model, one user experience, as well as one kind of end to architecture. Bringing ai, bringing security built into the platform, not a bolt or not something on the side. And that has to happen.

I think whoever wants to be succeed in this space has to really think about that as ground up as a P zero and then build everything around it to really make our customers successful. And that's the foundation we've laid down, and it has resonated with our customers, and they're getting a little more confident as they continue down the journey. AI is gonna stay here, it's gonna be a game changer, but you have to do it thoughtfully, and you'll do it in a way where enterprises can benefit, uh, and not really get into risky environments, which could really, uh, crumble their businesses.

So Amit, we talked earlier about, uh, the OpenAI partnership, and uh, that's obviously, um, yeah, fairly hot off the press, but you, you also have this, um, this, uh, partnership with, um, with Nvidia, um, with, uh, with its, uh, you know, the, the models, the AOL emron, um, um, models. I'm just curious about why you, why, um, and, and, you know, why domain specific models and why reasoning models like, like that one, um, are better for a kind of service desk environment than, um, say, um, a general purpose LLM? Yeah, I think a few things.

One, the OpenAI and other large language models don't run in sovereign environments. They don't run in, uh, on premise customers. We have customers which, which deploy software in many ways, public cloud in our data center, as well as in private cloud environments as well, sovereign cloud environments.

So we have to cater to all those different kind of deployment models, and that's why we wanna give customers choices in terms of what they can use as a building block, which might meet their require secure requirement, but also deployment requirements. Well, so what we're doing with Nvidia and April, it's pretty impressive in terms of what we've been able to build, uh, from, uh, those, uh, uh, domain specific models is to kind of take some use cases, Hey, we can use this in this particular, uh, use case without needing a large language model or a frontier model in a way. Uh, and also it'll meet the so sovereign or any kind of deployment needs customers have.

So that's why keep on, we keep on having that investment going to ensure that customer choice is prevailing, uh, but also we understand what is the auto possible in many, many different cases, and can we reduce cost? Can we give you better outcome? Can we compare to different, different, uh, scenarios?

Can we do different pro, do a better prompt engineering? So that also helps engineering team get better when they kind of mix and match things and really get the best, uh, outcome for our customers as well. So we'll continue that part, and I'll ensure that customer choice remains, uh, and, uh, it prevails long term.

That makes sense. So just a quick one on, um, sovereignty. You mentioned it as one of the reasons for that partnership.

So are you seeing that, um, as an increasing concern for, for CIOs, um, across your customer base? Is it, is it a geographic specific thing still, or do you see it across the world? No, we're seeing this globally, right?

And it depends on the industry as well. So it's more industry driven than a particular region. But I think as you've seen with some of the, uh, geopolitical tough stuff going on in, in the world right now, there is a lot of, uh, requests now coming from countries outside us who wanna run their own specific environments, and they don't wanna be completely be, uh, on a public cloud some cases, or they don't wanna be outside the particular country.

Uh, so that requirements are pretty common. It's gone up over time than it was probably a few years ago. Uh, we're seeing it by regulated industries as well.

Uh, so if you look at public sector for sure, but, uh, banking, uh, we think we see this, uh, in, uh, some of the things around manufacturing and other areas, uh, where it could be very specific use cases. They don't want that data to be outside their particular domain, uh, so that, that, that use cases keep on emerging. There are a lot of local providers.

We work with a particular country because, uh, those customers in this country wanna depend on the local providers as the infrastructure providers, and we wanna make sure we support that as well. So we run on all hyper cloud, uh, uh, hyper cloud providers. Uh, we run on our private data centers, we run on customer data centers.

Uh, we run on a lot of those, uh, now, uh, I would say it's sovereign providers in some countries as well. So we've been always thoughtful about what our customers need and where we wanna be to meet them there. You probably already touched on this, but I kind of want you to go a little bit deeper on sovereignty.

I can't pronounce that word for some reason. Um, but Deb, can you maybe talk a little bit more about that? Yeah, the sovereignty, I think the way to think about is, one, there could be multiple, there are multiple levels, and we can spend hours talking about it because it starts initially.

First is data residency. You wanna make sure data doesn't leave a particular en a particular region or a country, right? That is usually, uh, p zero for many customers.

Second is who's touching the data? So once it's le not leaving the country, who's running and operating that environment. So it could be citizenship of the people who are involved with that particular data center or the infrastructure.

Uh, third is that, uh, whatcha interacting with, uh, which systems, who is the provider? Uh, so that could be another, uh, thing. Fourth is, are you going to connect to anything outside?

Is it completely, uh, uh, disconnected more in a way, right? So that there's no other, other than the customer's environment. So there's so many different levels of sovereignty requirements, and we have to cater to very different, different needs depending on what the use case of a customer is.

In some of the government and work we do, uh, those ones are much more stringent. Some of the industry specific, like banking and all may be regulatory driven. So you need to kind of, uh, address those requirements.

They come up with. Uh, it's usually around data access, people, uh, systems, connectivity, so various things. And they get more and more complicated as you go down those conversations.

And that's why you need a lot, a lot of good local partners, as well as you need software which is flexible, so you don't wanna keep on rewriting it. So you have to architect it in a way that is one, uh, very flexible in terms of how it's deployed, but second is very secure and it gives you controls. So you have to have visibility.

And this is where I think when you talk about ai, a lot of those things are always missing. A large language models and things like that have no way to give you controls. It does what it does, and there's no split brain, right?

This is one thing and it does everything. And you land up not having idea what happened behind, and you cannot have black box also in sovereignty. You need to know what was touched, when was this touched, who made changes, all that whole attribution needs to be required as well.

So those are the sovereign requirements. So they're, they're getting more and more complicated. Uh, and that's why we get, uh, companies like ServiceNow are liked by our customers because we understand that complexity and building enterprise software requires a lot of mundane stuff to be done, but complexity is always there.

And, uh, you can't ignore those things. Yeah, it, it's really kind of a refreshing, um, and realistic, uh, message. And I think that enterprises are going to, uh, be more, uh, they, they will be rushing to embrace this sort of approach because it's, it's more mature.

I mean, sometimes in the AI space, there's a bit of a sort of fast and loose break things and see what happens, uh, attitude. And that is not at all compatible with the kind of customers that major companies like ServiceNow have. And frankly, with most of the customers that I spoke speak to as well, I think a lot of them are very wary of AI and the messaging that you're putting out here that essentially this is a powerful tool.

It enhances the abilities that we already have, but we recognize that there's a lot more than just throw AI at the problem that needs to be done. So I guess to sum up here, uh, again, this is, uh, utilizing ai. We're supposed to be very practical here.

And I, and that's what I'm hearing from you. Um, let's sum up your message here. Message.

I mean, um, if a customer comes to you and says, oh boy, that AI stuff, how's that going to impact me? What's your elevator pitch to that customer? I would tell customers to be unrealistic, right?

Uh, AI will impact you. AI is going to be important. Get used to it, get learn it, but learn it in the context of your business.

Don't just do it AI for the sake of ai, right? It has to be part of your solution, cannot be the solution. So it's a building block, uh, re it's reinventing software stack.

Uh, just like a lot of other technology changes have had done. This is probably more powerful than other technology changes, but pay attention to how it runs and operates your business. Don't just take a product out there and deploy it and then realize that your business will fail.

So bring it as in, as part of the overall solution, not the only solution. You know, it's gonna be, it's gonna be interesting to see how companies think about this relationship between AI models, agents, and the enterprise architecture they sit on and, and kind of what the rise of agen AI in particular means for CIOs and tech leaders in practical terms. And it, it's gonna be, uh, fascinating to see how this path or this path leads us, um, and if we fall into the same traps we have before with early cloud and SaaS adoption, that's kind of my takeaway.

And I'll, I'll pass this on to Nick. Yeah, it's been really interesting conversation. A mean, I mean, we, we touched upon the, you know, the, the idea that, you know, we talking about the OpenAI partnership, the NVIDIA partnership, and it just sort of Rams home that, that point to me, that models are, I find them, you know, super interesting and they are, they are incredibly integral to what we're doing in enterprise ai, but they're not the application.

Yeah, the application sits on top and its ServiceNow. ServiceNow. Well said, Nick.

I completely agree. And, um, thank you very much for joining us for this episode of, uh, the utilizing AI podcast from the Futurum Group. Uh, before we wrap up, though, uh, let's give you all a chance to let us know where we can connect with you and continue this conversation.

Uh, I mean, let's start with you. Yeah. We have a big, uh, customer conference coming up called Knowledge, uh, in May, and please join us there.

We'll have 20,000 plus people, uh, practitioners who you can learn from and, uh, kind of share ideas. Uh, it's gonna be a great time. Oh, yeah.

You know, I, I'm gonna vouch for what, uh, Amit just said. I, I've been to that show several times and it is fantastic. It's usually at the Venetian, I believe, um, and it's early May, right?

Um, well, you can see I'm working on tech AI stuff. I'm on Techstrong gang, I'm on this show. I, I use LinkedIn inordinate amount.

Uh, I, I kind of shy away from X and I, I've kind of given up on Facebook, sorry, Facebook. Um, but that's where I am. And I'm gonna be writing about Davos and some of the political repercussions or the, the landscape there in the next couple of days.

And I'll be, I'll be focusing a lot on sovereign ai, um, and over the, over the coming months, as well as digging into the, um, the output from our decision maker survey, which is in the field at the moment. You can find everything I do on Rum Group do com, and I'm on LinkedIn, um, and, and X as well. And as for me, uh, I'm very excited to have, be leading our AI Field Day event here in, uh, may.

So, uh, tune in for that. But in the meantime, uh, thank you very much for tuning in for this episode of the Utilizing AI podcast. If you enjoyed this discussion, please do subscribe on YouTube or your favorite podcast application and consider giving us a rating and review.

This podcast is brought to you by the analysts and experts from the Futurum Group, where Insights meet ai. ai, the utilizing AI YouTube channel or the Text TV app on television or.