Techstrong TV August 15, 2025

Hey, everyone. We're back here at Black Hat, still back off the floor in our suite here, uh, studio Suite, doing videos. I'm really happy to have my next friend up.

I, I think I mentioned it on a few videos today. One of the nice things about coming to Black Hat RSA industry gatherings, unfortunately it's only twice a year, but you get to meet with your friends. You get to meet with people who, you know, you've seen come up through the ranks with you and, and their careers have, have, you know, gone in really interesting places and they've done interesting things.

My next guest, here's one of those, his name's Fred Wilmot. I know Fred. Geez, Fred.

We, we know each other at least 15 years. 15 Years, yeah. Yeah.

At least. Yeah, I think maybe more, to tell you the truth. 'cause I was still, it's still secure.

That's right. And I left still secure in oh eight. Oh yeah.

Okay. It might be closer, closer. 18, 20 years.

Yeah. Yeah. But anyway, Fred is the founder of a company called Detect Team, and we're gonna find out all about Detect Team.

But let's first let Fred, lemme embarrass Fred a little bit and tell Fred, tell, share the, your story, your journey with the team here. Uh, so, uh, I started off, uh, after I got out of the, uh, the Navy. I went to go work for IBM and, uh, moved out to Seattle, uh, where there was this burgeoning, uh, scene of startups.

com was there and all these things. Yeah. And I hadn't done any security work at all before.

Um, and I started working at this company called Rabine, which at that time was $600 million in venture funding. Dan Hassey, the CEO of at t Wireless, like whole thing. And I ran into some really interesting, uh, characters over there that were probably all on, you know, CIA work release programs.

But, uh, some of the best security guys that I've ever met. And, uh, I mean, sort of have one of those moments of clarity. First time you could follow a colonel, first time you figure out how to do something and exploited system, you know, all of these types of things.

And it was just intoxicating, uh, from that perspective. So, you know, from there on, I, um, spent a bunch of time doing that. Uh, that's managed services stuff and, you know, forensics and, and incident response and, and detection engineering.

Uh, and then I went to go work for, um, sort of like this weird curve of work for a vendor work for, you know, a commercial entity or something like that. That's a, that's a well-rounded career right. Going on both sides of the street.

Yeah. And I think that actually really serves you well when you try to figure out, you know, am I building something that's purposeful? And if I were a person using it, do I even care?

Right. What is that person? What's that person's experience like?

What is, what's their point of view? That's right. So, love a few of those curves.

Uh, spent some time at Symantec and Disney, and then I, uh, I went to Splunk on unwillingly, went to Splunk when it was super small, and I was probably the first security person, uh, there that they hired in the field. And, uh, we started doing a bunch of work there, building products and building services. And eventually, uh, this thing enterprise security came to be.

And that was a lot of fun. And, um, we spent a lot of time with customers actually solving problems. And that was really intriguing.

Um, and then I, I sort of got hooked on this, uh, machine learning, automated data science platform thing. I did some of that. Um, and I spent some time with, uh, Richard Clark and my zko, and we tried to build a, you know, sort of this, uh, ensemble model that would help find adversaries, uh, of the advanced persistent threat nature.

Mm-hmm. Um, and eventually went to Packet Sled. And, uh, which was, uh, if you could drop a packet sled box in any environment, in as an instant response tool, we could find bad guys in four, eight hours.

Right. And take action. And, um, that was used widely by, you know, uh, even the CrowdStrike guys and some other, uh, Cylance guys on their forensics and, and, uh, and programs for customers.

So a lot of fun there. Um, I then went to Devo to build something to take out the Splunk product, enterprise security called SecOps. And that was an awful lot of fun.

Um, it was a great opportunity. I also was CISO there too. I had the luxury of going back and forth to Spain every couple of weeks.

Poor you, poor Me. But Ivo was Madrid, wasn't it? Diva was Madrid.

And I was the only engineering leader in the us. And so, um, awesome opportunity to go build rapport, you know, get culturally, you know, embedded of Some, uh, you know, black Acorn Hamon. Oh, you know, It Hamon the top shelf.

Oh, yeah. Spectacular. And really olive oil, the culture of the people there.

It's a great, it's a, it is a spectacular country. Yeah. Spectacular people.

Yeah. Agreed. One of my favorite places.

Uh, and then after that, I, I wound up at, um, a little startup, uh, uh, that's an I, uh, identity provider called JumpCloud. Um, think Inherited that. Yeah.

Not too tiny anymore, but, uh, a lot of really, uh, cool, interesting problem space there. And I thought, oh, I haven't done identity yet in my career, and it's so critical. I mean, and we know today, well, It just came out this week.

Right? 25 billion worth of critical right there. That's right.

Um, of course not JumpCloud. I wish my friend Raj the best with that. Yeah.

But, uh, in this case it was CyberArk and, and the folks at Palo Alto. Um, so after JumpCloud, tell Down, tell us. Yeah.

So JumpCloud then, I, I, I was doing sort of just CISO work, so kind of a, in my mind, that was a part-time job, not because the CISO job is part-time, but because I've always done products and engineering and see something. So for me, it was, um, great. But I wanted to build something.

So I went to go work with, uh, Nick Lanta and some guys to sort of build the, we, we, we built the first, uh, um, CEM platform mm-hmm. Uh, as it were. And, uh, wanted to create this risk index and so on.

And, um, that was really preparatory work. 'cause I felt there's a bunch of things that I needed to learn, even though I, you know, sort of got battlefield promoted as a CEO while at packets, there were a bunch of things I thought I needed to learn. I thought that would be a great mentor for me there.

So after leaving there, I came to, uh, to, to start this company with my buddy Sebastian called the tech team. And this was born out of the problem that a lot of customers asked us over time. And, you know, I, I felt very guilty that we had been building rules and, you know, correlation rules and these searches and that over all these years, you know, to know that they don't actually really work all that well, or you're not sure whether they do work.

And, you know, customers would ask us regularly, like, what, what does this thing do? And I could show you how we tested it. I could show you the things, but I couldn't tell you.

You would actually find something with that. So we started the ideology of the tech team because of that problem. And we got to put it in the water, uh, through the 16th Air Force at the time.

We were doing something to help the help build their cyber weapons platform, uh, capabilities so that, uh, cyber weapons officers could find badass. So we started creating scenarios that would help, you know, identify different areas where this characteristic or these behaviors and this order of operations would help signal what this would look like if this were an advanced persistent threat over time, over these behaviors and systems. And as it scales out, um, including building, you know, hydrating the same organic size of an environment as a base or a network or, you know, JPMC or you know, whatever.

And through that, um, we realized there's a path here to not only help figure out how to write great detections, but also how to make sure your responders know what to do when that happens, and that your responses work the way it's supposed to. So we found a detecting and, uh, What a great story. It's been, it's been a lot of fun.

It's been a great ride. You know, you're a humble man, Fred, right? There was a lot of, uh, so having not walked in your shoes, but walked alongside you, let's say during all these adventures you've been on, I, I think you, uh, you underplayed your role in the success and, and kind of, uh, groundbreaking kinda work, right?

When it comes to security and looking at things differently in new ways and making it better. But here we are. Here we are, um, understood What detect team is, is about how, you know, what was the idea behind it?

Did you think you'd be sitting here, black hat 2025, and you go down to that floor, you've been down to that floor, can't, you can't say a sentence without the word AI in it. Um, people are talking about replacing security people with, with AI things and, and, uh, agents. I, I was just, yeah, I was interviewing the CEO of Qualys, my friend Ed Yep.

About their ai, uh, agentic ai, rock risk operation center. And basically, it's funny, you bring up a page of, okay, what do you want an agent to do? You need an agent for Patch Tuesday?

You press a button, it says employ like that. You really Absolutely. Hiring someone.

Yeah. You want an agent to do this? Boom.

You hit the employee button for that. They have names. The agent, Sarah, for Patch Tuesday.

Debbie for this, and Tom for that. How does that vision and where this is taking us, where's that lead tech team? Is that something you guys piggyback ride?

Is it something, Hey, when you're done playing with that, come over here. What, what, how does this all fit in? That's a great question.

There's a lot of promise in all of the capabilities that AI can help materialize. Mm-hmm. Some of those things that we see and we talk about all the time.

Yep. If you've done it 15 times and 15 minutes, then that's an automation problem that should be solved. Right.

If you have something that you spend the same logic, uh, uh, filters on the understanding of this problem space, and you do that over and over again, also automatable, right? Uh, soar automatable. Okay.

Those types of things that are process automation problems. Great. Some of the inference, uh, things also great.

However, uh, the challenge is today, the bar is pretty low. Uh, to do something impactful, I think to optimize, uh, a low bar of process achievement, to operationalize a better way to look at intelligence data or to grasp more information and more context or more semantic analysis of a set of data we didn't have access to before. But fundamentally, that doesn't make you any more secure.

And part of the challenge that, you know, we look at here, we, we use AI too. Sure. Uh, we've got a very small team.

If we're not using ai, that's not very smart of us. However, validation, confirmation, and transparency, all those problems we always used to talk about governance and providence have to be front row seats. If you're going to say anything is evidence.

Yeah. So if we're going to prevent, you know, certain things from happening or detect certain things that are happening, we have to have. Cause otherwise, how do you know what you did when you did it and when it changes again?

Yeah. The context window of promise is not that big. And the number of tokens you spend to do certain things right, has an effect on how high quality it is, which model you're using, how many parameters, all the things.

But the bottom line is there's an awful lot of value in helping automate that process to reduce time, increase expertise, apply to the problem. But it's pretty critical to make sure you have smart security people doing the real work. And, and I don't think, excuse me, I don't think that's changing anytime soon.

I don't think so either. And I think pretty smart security people will leverage ai 'cause they're smart enough to do that, not run from it. So I, I, I think that's gonna make a big change.

Um, as you sit here though, let's, okay. We look back, we looked at where we are. Let's look ahead, how do you see the mission at detect team changing, morphing, evolving, short term, six months, 12 months, longer term?

Can't go too long. 'cause we can't see more than 24, but 18, there's a horizon. 24 months.

That's the horizon. Where do you, where do you see detective? So We are really interested in becoming sort of the arbiter of truth around detection, engineering.

We have a platform that we think everyone can use. We understand everyone's languages, a Rosetta Stone. Mm-hmm.

And while it's interesting to say those things, it's more interesting to say, help me understand accuracy. Help me understand quality. Help me understand coverage.

And if an industry can rally around a single way of scoring, measuring, evaluating, and deploying a rising tide will raise all boats. So if we thought about it, we would say every SIM should use us internally, all customers, their ability detection should use us. It doesn't have to be built in ai.

You can use text objects, you can write your own things. Yeah. You can craft anything you want.

But most importantly, it's a harness. It's a testing harness. And so when we decide that we wanna build a detection, or I wanna take a, some finished national intelligence froma or something, I turn that into a scenario.

I generate all the data from every class and type of data that it, it should generate based on the TTPs. I can send those anywhere they need to go. I can send those everywhere they need to go.

I can send that from multiple clouds into every place. It needs to go in every place on the planet. And then we can generate detections that are illustrative of the context of your environment, or don't turn a context on.

'cause you want none of that information to be mm-hmm. Involved. So when we think about that, uh, there's a lot of, I think, hyperbole around generate me a thing, right?

Go get me some cloud code action here and generate a bunch of detections, or generate a bunch of data. And there's a lot of challenges in making sure you have the right answers, not just answers. So we think if we give the community an opportunity to build right answers, right, they'll, they'll g go onto that and take charge.

And that will, you know, that will help the industry in that sense. And so we think that's a great opportunity. Love it.

Hey, I'm gonna pivot a little bit. I, this is not necessarily your security com, uh, security question where they're gonna give you a CEO question. Sure.

It's a crowded floor out there at Black Hat Security. You know, the last time I looked, I think it was 6,000 or 6,500 venture backed security Companies, right? Or public, you know, commercial security companies, AI changing the game in marketing, go to market, not just in how we're using it for better security.

How do you as a CEO make sure that the tech team gets its fair share? That's a great question. I believe, uh, at this stage of the company, um, and maybe my whole career, the proof is in the pudding to start with.

So you have to be able to operate with your friends, your peers. You've established credibility over the course of your career by doing the right thing and doing something purposeful, meaningful that you can stay in mind. Uh, we've been fortunate to have a lot of communication with folks that have seen that have done that and participate.

Um, that indirectly answers your question because all of the AI first companies that are now using AI to build their marketing, right? Uh, I did an exercise the other day where I took five marketing messages and put them next to each other and asked people which products they were for. And of course, the most of them are wrong.

Um, and the rationale is because everybody does, uh, sort of a good quick through on on that, builds their slides this way, right? They're building websites in the same way, uh, hiring people with resumes. Same problem.

And so what do you do? It's actually rolled the clock back. And so just like a handshake, just like a phone call, right?

These are the ways that, you know, we know people are starting to back To business. Relationships matter. Relationships matter.

Turns out. Yeah. It's always true.

And I think that's the thing that we've, we've sort of leaned into heavily, um, down the road. I think this is going to become a little bit more chaotic and obviously more of the traffic that's been generated around marketing, messaging, uh, communication on the internet. You know, as more and more bots, you know, continue to communicate with one another.

I I believe there's gonna be sort of a turnoff moment here for some of those behaviors. And people are gonna have to figure out how to sell something that matters of value to people that they know and that they don't know in new ways. Yeah.

So it is a challenge. I don't, uh, I don't see anybody's, no. I I I, I think, I think there's a lot of marketing people in security that're just kind of pulling their hair out if they have hair, you know, that, that said, how do you, how do you play in this field?

I mean, it's a different, it's a different stadium. Anyway, Fred, we're gonna wrap up, but for people who want to get more about detect team, where should they go? com.

Uh, feel free to reach us out there. You can get us on, uh, on Twitter at, uh, detect Team Inc. Or feel free to send me an email, Fred, at you, uh, wrap with you for sure.

Absolutely. And if you like listening to Fred, he's usually on Friday mornings. Friday mornings?

Well, we record Friday mornings, but it's Monday. He's usually on Monday mornings. It's, we record Friday.

Oh, by the way, we record Thursday. Thursday. It's on Friday.

Yeah. But you could see him on the text on gang. Hi.

Good. Who are you? Housekeeping.

Housekeeping. Oh, can you come back? We're just recording something?

Yeah. Oh, okay. Nothing.

We're good. It's okay. Bye-Bye.

What The f**k was that? Housekeeping. You wanna wrap up Again?

Land, land Shark? Yeah. Let me, let me just do, just do the last round.

Alright, let me wrap it up. And we're done. So you are on Fridays, the place Mondays or you're on Thursdays?

The place Friday. I'm on Thursday. Because usually you're on with Ira.

That's right. Yeah. I love it.

That's my good security day. We're The, we're the tandem, right? The yin And yang.

I, Iris, Iris of, uh, of Yeah, he's, he's, he's here. I saw him yesterday. I, all right, gimme come back.

Counts him back. Oh, I thought that was your wrap. No, no.

Well, but yeah, but housekeeping came the Land shark, you know what I'm saying? But you just Hit, oh, no, no, go ahead. Okay.

Three, Two. All right. And, you know, and if you like listening to what Fred says, and Fred, Fred always has a lot of good stuff to say.

He's a regular on Textron gang. You could catch him most on our Friday morning shows. And he is usually on with Ira Winkler, which makes for a real powerhouse cyber team on, on the gang that day.

So check that out on wherever you're watching. Text On Gang on. Until then, though, this is Alan Shimel.

We're gonna head one more time back to the floor at Black Hat. Uh, we'll wrap up from there. And, uh, we'll, we'll call it a day on our Black Hat 2025 coverage.

Hope you've enjoyed it. Take care, everyone. Bye-bye.

Hey everyone, it's Alan Shimel for Tech Stunk tv. We're back here continuing our coverage of Black Hat on the show floor. It's early in the morning, keynotes are going on, so it's not as crazy here.

And it's a little better quiet. And we can do some talking. I am at the harness booth.

Of course, it's traceable by harness as well. Um, but we're here to talk harness with Sudir Pati Sudir. First of all, welcome to Text Drug tv.

It's great to have you on. Hey, Allen. Uh, thank you so much for having me.

Uh, it's great to be talking to you today, Alrightyy. So Sudir, as I mentioned, it's quiet right now, so we can talk without all of the stuff going on. Why don't we start with a little bit about you Sudir?

Sure. Uh, I work as a Senior Director of product management, uh, at Harness, uh, with a focus on, uh, runtime protection, uh, products. Okay.

And also on the platform capabilities. Very good. And, um, before Harness, kind of what's your background?

Uh, my background has been mostly in the application security space. Uh, worked at several, uh, large enterprise organizations like F five and Akamai. Okay.

Uh, before joining Harness. Uh, so, so that's So but on the more on the vendor than the practitioner kind of thing? Exactly.

Yeah. I've been, I, I started as an engineer in my career. Uh, and then, uh, post MBAI actually moved into product management.

Very cool. com. Right.

And so we obviously cover Harness. I have covered Harness from the day it was launched, but we're here at Black Hat, a security show. And of course, look, DevOps is DevSecOps, right?

Hello? You can't do security or you can't do DevOps without security. Yep.

However, let's focus in on security. I mentioned traceable, traceable ai of course, was a kind of a sister company founded by the same team and, and investors as harnessed. They've recently merged.

I guess that's gotta be six or eight or more months ago now, nine, 10 months ago. But there's more to security at harness than just traceable. So assuming our audience knows Harness, they may not really know the harness security side of things.

If you wouldn't mind, let's start there. Give us sort of an overview of harnesses security capabilities. Yeah.

So, so we all know Harness as an AI native DevOps platform, but now we are an AI, native DevSecOps platform. Yep. So we help developers ship secure code faster and in a reliable way.

Right? That means we help embed security in every phase of the software development lifecycle, all the way from design to runtime, right. From the time when developers are coding to when the applications are running in production.

So we have tools at every phase of the SDLC that helps secure the applications and APIs, uh, as they go from code to production. Perfect. And let's get specific about the, the offerings, right?

com, we're Security Boulevard. com. These are your people.

Let's peel that onion back a few layers. What specific security ai, security DevSecOps, whatever you wanna call it, what are the specific things that Harness is offering? So, uh, with the merger of Traceable, uh, we have like five modules now within harness, within the security pillar.

Uh, I'll go through one by one. Okay. The, maybe from left to right, all the way from Code to Runtime.

So the first module is called Security Testing Orchestration. Okay. Uh, The goal of this product, uh, is to help developers prioritize vulnerabilities and to focus on the right vulnerabilities that they need to fix.

So the challenge that we see right now is there's so many tools out there, uh, for different types of scanning, like SAS or SCA or secret scanning, so Container Security das. So there's so many tools and each tool has its own format. So what we do with security testing orchestration is we bring, uh, the outputs of all these tools in the CI ICD pipeline.

We de redo put the findings from the tools and create that list of vulnerabilities based on a specific criteria prioritized in an order. So developers can fix those vulnerabilities in an easy way by leveraging ai. So, so we have AI embedded in that platform.

So we order only tell you what are the important vulnerabilities you need to fix, but with the help of ai, we also show what you need to do in the code specifically and help developers create pull requests automatically with ai. And when you say AI is doing this thing, is it more of a kind of a chat dots with suggestions or is it more of an agent AI that's autonomously doing these things? Or maybe both?

So we have a combination of both. Uh, we have different agents, uh, in the platform. There's a DevOps agent, there's a security apps agent.

So there are agent flows where you can give an outcome, uh, to the ai, and AI will do all the steps for you. Or you can also interact with the AI in a chat bot style conversation, uh, to, to kind of question and answer format. Very good.

Alright. io. Yes.

Harness io. Okay. Let's talk black hat day two here, Thursday of the, of the expo floor.

Of course, the conference and training's been going on now for four or five days. What, is this your first black hat? Have you been here before, or No?

I think, uh, if I remember, I think this my fifth black hat. Fifth, okay. Yeah.

Fifth. Yeah. What do you think about this year's Black hat?

It's, It's, uh, great, uh, to be here. Uh, first of all, you, you get to learn so much, uh, from practitioners. Mm-hmm.

Uh, also from different vendors. Yes. Uh, and there's a separate area for AI innovation.

Uh, yeah, there is. I checked out yesterday, which is really cool. Uh, and, uh, it's great to see all the innovation happening in the security space, uh, with respect to ai.

It, it, there is certainly a lot of AI here, especially when you go both booth to booth. Um, what are you hearing from real life practitioners who come by here and talk? Are they so bought into ai?

Are they just all AI too? Or is it, I I often wonder, are we as practitioners, not as practitioners, as vendors pushing AI on practitioners? Are they as eager to take that AI and use it as vendors are to sell it?

I would say if it was maybe one year ago, uh, practitioners were cautious about ai, but now they're realizing that it's, it's a real thing with, uh, for example, with AI now with the concept of vibe coding. Yes. Now it's so easy to write code, and everyone started realizing that it's a real thing.

And you see in the news that like 30 or 40% of the code will be written by ai. So there stats like that, which is a real thing. And, and not only vendors, but practitioners have started adopting ai.

So it's a combination of human and ai. So how AI can help you automate a lot of your day-to-day tasks and free you up with a lot of, uh, manual tedious work so that you can focus on the more important things. And AI can do all the, uh, the grant work or the, the cu cumbersome work for you.

Sure. So as I, you know, bring this full circle blackouts of security show, we think of Harness DevOps, DevSecOps, of course, there's much more to security that even just AppSec or, or DevSecOps. What percentage of the attendees that you speak to you think are interested in DevSecOps or even AppSec versus some of the other things we're seeing?

Cloud security, endpoint security, threat modeling, you know, all, all the different flavors of cyber today? No. We see a good traction at our booth.

Uh, as I said, with more and more code being produced now, AppSec is gonna become even more important. Mm-hmm. Uh, it's equally important, like the other pillars of security, like cloud security or endpoint security, because it all starts with applications.

And applications are growing day by day. Uh, so, so yeah. I mean, like, it's a, it's a 50 50 split, I would say.

Uh, and AppSec is gonna get bigger and bigger. Alright. Last question for you.

Was there any news or any kind of thing that harness announced around the show that we can tell our audience back home about Very soon? We are gonna bring out new announcements, uh, maybe to give you just a, Don't say anything that's gonna get us in trouble. Okay.

But give us a little Sneak. Yeah. Sneak peek into, uh, it's gonna be about ai.

Okay. Uh, so that's a sneak peek. Fair enough.

Yeah. So you gonna see a lot of cool, uh, new innovation and products from Harness. Fantastic.

Hey, I want to thank you for coming in early before the floor opened to do this with us. Continue to success to you and Jody and the whole harness team. Of course.

We'll always be following it along here at Techstrong, but we're gonna let you get back to it. 'cause I think they're about to open the floor. Thank you so much, Alan.

Uh, great talking to you. And you have a good time. Have The conference.

Thank you. Thank you. All right.

We're here at Black Hat. We'll be continuing our, just waiting for some trucks to go by here. It sounds like we'll be continuing our coverage of Black Hat throughout the day and you'll be seeing it on Tech Trunk tv.

But until then, this is Alan Shimel. We're out. Hello and welcome to the latest edition of the Techstrong AI Leadership Insight series.

I'm your host, Mike Vizard. Today we're with Jay Haus Sun, and he's the CEO of Flocker io. And we're talking about the implications of AI and data privacy, because, well, a lot of data's being collected, but we don't always know by whom and for what jl Welcome to show.

Yeah. Nice to be here. A lot of companies are collecting a lot of data because they're using that to train their AI models.

But theoretically, there's supposed to be these, um, end user agreements that we're supposed to read. But somewhere on page 422, it says that they can use that data to train their models. But we don't always know how that's gonna manifest itself.

'cause it might be months or even years later before it pops out. In a way we didn't intend it. Do you think that the rise of AI is eventually gonna force a, a deeper conversation about what privacy means and especially when it comes to data?

Yeah, yeah, of course. 'cause, um, there are not only, I mean, we, we will see model, we see how much convenience they can bring us. Right?

But then not every, uh, there are, there are places, there are applications where it just cannot be, um, um, applied. Um, when it comes to, for example, sensitive, sensitive industries or regulated industries, banks, hospitals, right. Naturally, not even, not even between businesses.

Even the data within the same business cannot be shared between different desks. Right. So in such scenarios, like even today, some of the banks or regulated business, they can't, they can't use IGBT just simply because there are so many concerns over this private privacy issues of data of, of, of all this models that can actually, you know, uh, took away your, your, your, your information and with it, uh, and expose your, your, your users, uh, privacy and everything.

So, um, that, that's exactly, that, the point that you mentioned, uh, I think it's, it's gonna raise a lot of concerns in the future. How long do you think it will take for that to come to a head? Because today, it seems to me at least that I see people using these tools in their copying and pasting data into them with little or no regard for who actually owns that data or whether they're just the custodians for it, or it's just loaded with personally identifiable information from customers or whatever.

So do we have to wait for some sort of catastrophic event, or is there some way to get ahead of it? Oh, I think, uh, those type of things, well, no, of course. No.

We, we shouldn't wait for a CATA catastrophic event. Or, I think in privacy everything's so important that even, even if it's, um, a small event that's already catastrophic. Um, um, and, and I think we already see lots of, uh, technologies, lots of companies trying to build this, you know, build models as open source build models that can be, you know, tuned on your local devices, private devices.

I think it's, um, yeah, it's kind of things that we, we've been super, um, um, um, uh, positive about and trying to, you know, tackle this specific scenario, you know, versus those centralized companies, you know, building those closed source ai. Mm-hmm. Will this get addressed as a set of regulations, therefore, and we're gonna have to just wait for various, uh, legal bodies around the world that kind of come to terms of what data privacy means?

Or is this gonna be maybe addressed more holistically from the top down at some point? I, I, I would say, yeah. Yeah.

I would say, um, especially because the definition of data privacy, right? How, how, how that can be defined. Whether your data itself of course is private, of course, but then whether the derivable your data are also private or whether the derivative that's not even readable by any anything else are also your PRI privacy, right?

For example, your data, uh, transform the model. The model had a, had lots of weights, and when those weights are still private because it's a derivative of your data, and also it's actually a collective di derivative of lots of people's data, whether that's kind of like the collective IP then, uh, when it's being trend. So there's lots of grounds need to be discussed, but so far, under GDPR and many of the local, uh, data privacy, uh, legislations, um, the, the, the very obvious first priority is to make sure the raw data never got exposed.

So that's at least the first steps every company trying to secure. I feel like this issue's been kicking around for a long time, even before AI became up. So, is AI just really, um, forcing a discussion around this topic that's, we've kind of been postponing for many years?

Yeah, I would say so because AI is now, you know, uh, um, absorbing all the data across the world, right? Then people starting to realize, ah, it, it actually understands everything around me. It actually understands, uh, all the context I'm talking about.

How, how that happened. Like, I just bought a bottle of wine earlier this morning, then I actually received some marketing materials this afternoon. How come?

Right. It's, uh, lots of things happening around us. Well, thanks to ai Of course.

And also, you know, due to ai, right? We, we we're starting to realize there are certain privacy concerns that's already, you know, being noticed by us. Mm-hmm.

Do you think the general population will start to push harder on this? Because, well, I think everybody's having that same experience lately. No matter what you do, if you're talking about a topic at your family dinner, the next thing you know, there's an email about it, right?

Yeah. Yeah. Yeah.

I think people will do, and many people will, will raise awareness of it. And that's actually back to the point I, I wanna I bring up, uh, earlier. Um, so there are scenarios where convenience really being bought by ai, right?

But then the concerns only by certain people who are concerning their privacy got leaked. There are other scenarios where, um, it's only privacy. AI can be deployed, as I mentioned, uh, regulated businesses, or for example, privacy, uh, uh, personal assistance, privacy companions, where you do you actually share data with, um, with an ai share everything about yourself, because if not there, you will not have a good assistant for yourself, right?

Um, then that's a dilemma. Whether you wanna share everything with them or with the ai or you, you wanna just share some service information about yourself, then your AI wouldn't be good enough. So there are always concerns and, and, and balances between the two.

And eventually, I think eventually the kid application or the, or a killer solution for this, it's a total private AI solution where you don't need to worry about it where you are safe and confidently happy to share all your data with that AI that's actually helping your day-to-day life. So how do we go about building that? 'cause so much of what we're using today are public tools that go off to some sort of cloud service.

What would be required to get to that private kinda architecture you're talking about? I'm thinking, uh, I'll say there are many decentralized AI companies starting to build solutions or different solutions around different, uh, um, tech stacks, right? There are people trying to do encryption, meaning that they can encrypt your data while the receiver receive your data is still encrypted data.

Meaning that your role data has never been, um, exposed. But Sam, that as, um, uh, the thing I mentioned, right? Whether your data director is still, still, still your data.

It's a, it's a thing to, to, to discuss in the future, maybe by the legislation, but at least at the least for now, we know data is secured. And there are other ways, for example, federated learning. That's part of what, uh, flock is doing better learning and blockchain.

That's why we call us ourself flock. So, um, it's also one of the solutions where, well, we keep every data local, we deploy local models to your local devices, makes the training, makes the changes, and then update model makes the general model better. Um, that's, that's, um, that's also one of the solutions, um, um, that's been deployed so many years between different companies and centralized solutions.

For example, Google, apple nowadays, if they, if you install their, um, uh, typing, uh, well typing software, uh, input software, right? They're actually predicting your next words by using Federation Learning J just to make sure your local data's local, um, um, like, because otherwise they're gonna have everything you type down, including your passwords, your secrets, everything. So there are, yeah, there, there are so many of such, uh, text stacks and people being, uh, uh, exploring and they all leads to the solution where we keep the raw data as close as possible to the user.

We don't actually submit them over to any, um, other, uh, other servers or other, other clouds. Mm-hmm. That's generally how the solution works.

So it is more or less a decentralized approach. Um, do you think that, uh, oddly enough that the combination of privacy and AI might pull more, um, blockchain applications into the enterprise be rather than just, you know, everybody thinks about blockchain more or less as Bitcoin, but, uh, are we gonna see applications that go well beyond just cryptocurrency? Oh yeah, of course.

Of course. Like, like, like for us, the, the, the, the own chain structure is one of the governance, uh, protocol that's being used to govern the whole failure federated learning training mechanism. 'cause if you're only leaving this training mechanism to a centralized company, right?

They can be evil. They can just send back the raw data because just easier and more efficient for them to train the model, right? You want a public governance over the training model.

So that's how Flock works as a mechanism that we proposed. So I would say, yeah, there are lots of such innovations. Not only just treating crypto as a cryptocurrency, but also treating crypto as one of the governance methodologies to help, um, to facilitate the, the, the transparency of the whole AI model training process.

Do we have the expertise to execute on that? 'cause I think one of the issues you hear a lot from enterprises is they barely understand how to make AI work and their understanding of blockchain is probably even less. So, um, what would it take to kinda realize that from an expertise perspective?

I would say you can, you, you can, uh, think of this as, for example, uh, Bitcoin mining process and, and then other vendors who create machines to mine Bitcoin, right? So there are, so, so there will, there will definitely be power users like AI engineers in the world who can actually help facilitate the whole training process, who can help, uh, um, evaluate the training models, the, the, the outcomes of the models, right? But there are also the general public who actually, uh, who wanna invest or dedicate into the training process to make sure that okay, they can put their weights on more legit governors into the network.

So the networks is safer with more stakes on legit, on legit governance. So similarly as a, like a QS process nowadays for, for Israel, meaning that yeah, you have power users, you have general users that they all can participate to secure network, What is your sense therefore of, um, who's gonna take the lead on this within an organization? Is it gonna be driven by somebody who's a CIO?

Or is there, um, the security folks or who's kind of gonna stand up and say, Hey, we need to rethink our approach to privacy and ai? Uh, I don't have a question. Uh, you mean, you mean in our clients, right?

Who gonna be the person to raise awareness of this? Ah, I guess that's CIO position. Yeah, that's the CIO position for it.

Um, and, uh, but for us, it's not just the, uh, just pitching to the traditional, for us, it's not just pitching to the traditional businesses who have their CIO to raise awareness for us, it's more of those companies who already had a pinpoint of this. So we are providing a solution, right? It, it's always hard to just, just pitch to a company saying, oh, I need to figure, find your CIO and let you know that you have a concern of your privacy.

It's always the other way around. While in hospitals, banks, they come to us and ask, okay, whether we can provide a private AI solution because they can't just use the centralized solutions now. Mm-hmm.

Do you think at some point companies themselves may come around and say, we need to drive some sort of decentralized approach? Because if I look around the world, there's gonna be different data privacy regulations everywhere, and at some point, maybe everybody will just get tired of trying to figure all that out and just look for a more technical solution to the problem. Yeah.

Um, tech, um, I think, I think, um, the, the, they're right. Uh, I think during the whole process, especially for all this, um, um, developments, right? Um, if it's actually generating more, um, um, okay, so it's actually if it's actually creating more business opportunities and generating more benefits for their own business, right?

So that would be a, like, like a smooth proposal for the internal business to actually, oh yeah, we should, we should focus or, or at least lean more on the private AI solutions versus we buy the wholesale solution maybe from a centralized provider that might cost us a huge fortune. And then still all our business insights are on health of another company. So, Hey folks, we've been having this argument about centralized versus decentralized for a few years now.

Maybe even more than longer than anybody cares to admit, but it looks like maybe it's all gonna come to a head in the age of AI and data privacy, because we're gonna have to make some fundamental decisions about just who do we trust with that data and what are they doing with it? Hey buddy, thanks being on the show. Thank you.

Thanks so much. Cheers. And thank you all for watching the latest edition of the Textron AI Leadership Insight series.

You can find this episode and others on our website. We invite you to check them all out. Until then, we'll see you next time.

Hey everyone, welcome back here to Tech Drunk tv. I'm really happy to have our next guest on because, well, I love to talk football anyway, but we're gonna talk about more than football. Let me introduce you to Catherine Johnson.

Katherine is the field CTO of a company called Hydraulics. Katherine, welcome to Textron tv. It's great to have you on here.

Thank you. It's great to be here. Absolutely.

Um, I just gotta ask, that's a really nice room you're in. It doesn't look like one of these fake backgrounds either, so It is not, this is my actual office. I like it.

I like it. It looks like a nice place to work. Um, speaking of which, Catherine, tell us a little bit about your work, how you got to be field, CTO, a little bit of your journey.

Yeah, sure. So I have been, uh, doing stuff in data, working for data vendors for about 25 years now. Um, I started working with, uh, high speed in memory data grids in the early two thousands.

So I think like trading platforms. Um, spent some time at Oracle doing more integration work. Uh, ended up back in high speed computing at VMware and Pivotal.

Um, so again, working with trading companies, uh, things like the Brazilian stock market, for example. So being able to recalculate risk really, really quickly on lots of fast data coming in. Um, from there, uh, I had a good friend who was one of the first employees at Elastic.

Um, he convinced me to come over to Elastic, and that's where I really got into observability and mm-hmm. The thing that was interesting there, elastic started as a search platform. Yes, It did.

Um, Yeah, but the, the bigger, larger, more interesting deals were around observability data. So if you think about something like Wikipedia, that was like 10 nodes when we used to talk nodes of Elasticsearch versus somebody who's doing a really big observability platform would be hundreds if not thousands. And so what we were seeing at Elastic was that just kept getting bigger and bigger, and it was getting to the point where it was kind of untenable.

Like, we would talk to customers who would say things like, I have 135 terabytes of data that I need to analyze every day. And we were, you know, it's like, we're not really sure how we're gonna do that, and not even how we're going to do it. But if you just added up the cost of running something like that, you needed to be really clear where the value was coming in, that expense you were gonna pay, it was going to be millions of dollars.

And if you didn't know what you were looking for in that data, right? That's, that's not Great. A lot of money.

That's a lot Of money. Um, so from the, go Ahead. Go ahead.

No, no. I was gonna say from, you know, to me that was always a lesson of just because you can't, doesn't mean you should, right? We, we tried so hard to be able to get our ha hands around that big data issue.

Mm-hmm. And then when we started to all of a sudden, like for instance, I remember this with Splunk and security, like we want in security, we always wanted to know everything, right? Because we can never tell how far back we had to go and when the breach happened, but who had the money to pay for that?

And so you had to start making trade offs, and some of them weren't always easy, but I I, I digress. Go ahead with your story. Well, It, it's, it's part of how hydraulics came into being.

Um, so I, I spent time at different observability companies at Grafana, at New Relic, and while I was at New Relic, uh, Marty, our CEO had looked up, he's in Portland, Oregon. I'm in Portland, Oregon. He was looking for, you know, people that had this background.

He's like, you popped up and you're in Portland. And so he came with this story of this database he wanted to build. That sounded way too good to be true.

It was like, we can store all the data in S3, we get this great compression. You get, uh, you know, real time response, like human real time response in terms of query times. And I, my brain was just like that.

That's not possible. That, that sounds great. That's not possible.

And it's another database. There's so many databases out there and different ways that people are already looking at data. So it's really hard to sell into that, right?

Like, why do I need to buy another database if I already have 10? So, fast forward a couple years, um, I'm already started the company, uh, I came on really early after he had hired a couple of my friends who are engineers. And so one of them was someone I had gone to grad school with a long time ago.

Uh, and they were both like, no, it works like it, it's real. This, this isn't like some over stated marketing shtick. Like it is very real.

Um, so I came on board, uh, I left briefly did some of my own stuff, but, um, when I came back, I came back in as field CTO because of my depth of knowledge in both the hydraulics product and, uh, observability. But the thing that was unique about hydraulics was Marty's background, and Hassan, his co-founder, were in massive observability data. So application logs and things like you mentioned Splunk, like that data keeps getting bigger and bigger and bigger, the world of CDN and media, the observability data, and that was even larger by orders of magnitude, right?

So if you're a media, you already know that. And so that, while that was our first use case, what we really went after is how do you all the things that you just said, how do I keep my data without it costing me a ton, without having the trade-offs of am I sampling? Am I throwing stuff away?

Like what am I giving up to keep all this data? Like how do we give the ability of somebody to keep all that raw data at a price that's reasonable and be able to query it quickly in, in real time, like human real time? So that's what Hydraulics is really about, is taking massive amounts of data, storing it compressed, which is one of the things that we're really, really good at, compressed in a way that it's not costing you a ton of money, and you're able to query it without it being hard, without having to rehydrate it, without having to take a long period of time.

Um, so that's what we really do is we're a massively scalable database built on stateless microservices, um, with using object storage, which is the cheapest storage available as the backend. So you clearly consider yourselves a database company, not an observability company. We're both, um, so, Okay, that's what I Asked.

So our first product market fit that we found was in CDN observability. Um, and observability data is really the largest data set that's out there right now. You can use this for any data problem you have that has large data associated with it.

It just so happens that that observability data does tend to be a large bulk, right? io, and hydraulics is spelled H-Y-D-R-O-L-I-X. Excellent.

Now, in your role is Field CTO, Catherine, what do you, you're out on the road talking to customers? I talk to customers, and a lot of them are the more complex use cases. So something where we haven't done that before.

The customer has not been able to solve this problem before. Um, so massive observability use cases for, you know, a lot of live streaming events, which we're going to talk about, um, and are really important customers. So where is it that we, we know that we really need to succeed in order to gain more traction as a company.

So those are the places where I tend to get involved. Excellent. All right.

Let's, you mentioned massive sporting, live sporting events. None, you know, the granddaddy of live sporting events, well at least in the US maybe the World Cup and the rest of the world. Mm-hmm.

But here in the US the Super Bowl, is it? Yeah. Right.

Doesn't get any bigger. Hydraulics had the, I don't know if I'd call it the privilege, but the opportunity to work on the, uh, super Bowl this past year. Why don't you give us, set it, you know, set the table.

Tell us what, what the story is here, Catherine. So we had done the Super Bowl the prior year with Paramount. We had done the observability for all their CD, CDN data coming in.

And what that means is every single request that viewers are making, so you don't down, you don't get the whole video at once, right? It's not finished yet. So it's coming down in segments.

So it, it was about gathering all of the raw data about all of the users and all of the segments that were being downloaded to view in order to get to, in order to maximize the customer experience, right? So we, or a lot of us saw the Mike Tyson fight a couple of months ago where, you know, there was a lot of buffering or, you know, things were coming late or you couldn't see the video at all. And so for companies that were streaming Super Bowl, it is really important to them that customers have a fantastic viewing experience, that there weren't complaints afterwards.

You didn't see any negative social media on Twitter or any of the other platforms about, you know, how terrible the quality of the stream was. So for Super Bowl last year, uh, that was with Fox, and we started from a place with them of, yeah, we've tried to do this before and we can't, like we've, we've broken everything that we've tried in order to do this level of observability. So we started with a proof of concept with them, uh, and this was nine months before the game, right?

So it, it had, we had to sort of build up to it. So we started by looking at the data, um, and understanding what data sources they were going to have. Uh, we started building that part out, um, because we needed to normalize the data we needed, you know, if it's coming from a bunch of different sources, you wanna be able to query across them.

And so that means like naming things the same, you know, normalizing units of things. Are you talking bites? Are you talking seconds, milliseconds, being able to normalize all of that.

I think it was around August last year, they said, Hey, things are changing a little bit. And we went to live stream this through Tubi, and Tubi doesn't have a paywall. Um, it does have a login wall that doesn't have a paywall.

So suddenly, like, you don't know how big that traffic is, right? Like, you, you have no idea. And depending on how the game goes, that can change really dramatically, right?

So we were told things, I'm, I don't watch a ton of football, but it was, depends on where things are at halftime as to what the viewership is going to be in the second half. Makes sense? Yep.

Um, so we started building from this perspective of, we don't know how big this is, right? We, we have some guesses. Uh, so we had built out and, and assumed that the traffic was going to be three times as large as what it, it eventually ended up being.

Um, but it's really hard to scale test that right it, it's really hard to generate traffic that would be representative of global traffic, really spiky, sporadic requests coming in. Um, so we had, there were several games that we monitored for them leading up to Super Bowl. And the two that were really, the big tests were the playoff games and so on.

Both, during both of the playoff games were like the first real live scale tests that we had, uh, of the platform. So during the first playoff game, everything went great. No major problems.

During the second playoff game, however, things changed quite a bit for us. And one of the, uh, one of the things that changed was, um, how their paths were constructed. It sounds kind of like a detail, uh, but they were embedding, uh, basically user level information into the paths that, uh, customers were requesting.

So to get like a, you know, one of the game segments as you're watching it without all this information embedded in it. And that really changed things for us because they wanted to query on that data that was embedded in the string at really high volumes that is hard to do. So if you're talking about like searching in a string over a couple terabytes, no big deal.

You're talking a couple hundred terabytes or petabytes string matching, suddenly big deal. Big deal. Really big deal.

Um, so after that game, we made changes in order to accommodate those queries. And so a lot of the work from the second playoff game until the Super Bowl was about what are all the things that we can anticipate that might change during that game that we hadn't already thought about? We got a hint from this change between the two playoff games.

Um, so the next several weeks we're really focused on how do we optimize as much as possible for all the things we think might happen. Um, and then we also implemented some things on our side. Uh, so we have the ability to have different, um, sets of compute dedicated for queries.

So we set up a query pool that was just for the operational folks. We set up a query pool that was for the executive dashboard so that the operational queries weren't, uh, contending with the, what the execs were looking at. So we all know like that that's the most important thing.

That's what they're looking at. And then the final one was for ad hoc queries. So anything that would go wrong during the game, we needed a separate set of query compute so we could run, you know, gnarly queries basically without impacting either of those other two groups that still had, you know, things that they needed to observe.

Got it. Now the, the playoff games are, I'm just trying to, if I read, you know, I remember, 'cause I always read the articles about audience size and everything. The playoff games are like a quarter of the size of the audience that the Super Bowl is, if I'm not mistaken.

Right. Something like that. And, um, I mean, I guess the question is, look, it's a tremendous engineering feat, no doubt about it.

I mean, the scalability here is truly scale, and they must find tremendous value in it if they're doing it year in and year out. Yes. As we look to, I don't know if it's too early, but looking the next year now, right?

What changes had, does AI help for instance, or I mean, what, you know, how, how do we improve on this? Yeah. So the, the way that our platform is built, we depend so much we built for the cloud.

So we depend so much on the scalability of the object storage, of the compute that's available in whichever cloud we're in. And so what we had seen to date before the Super Bowl was we hadn't really, and truly we haven't, still haven't, we haven't found like a top end limitation other than the underlying storage limitations of the amount of data we can't handle or the amount. We, we just haven't hit that in terms of cardinality, in terms of volume.

What we did this past year for Super Bowl, which is sort of like a prep for the next year, is I mentioned we had to do, we anticipated three x the traffic, right? So we went ahead this past year and we're able to deploy, uh, our technology in a multi-region way in order to make sure that we weren't saturating any given region. This is running in AWS we weren't saturating the compute in any given region, so we did a lot of work up front before the game to prepare for a much larger load than what we had.

So that's already in place. The things that are going to be additional are, like you mentioned, um, we started offering, uh, an MCP server with our technology. So there's ways to integrate and do more natural language types of queries.

Um, we're looking at, uh, supporting other query languages as well. We've already been doing that. So for example, you can, uh, query our data from Splunk, from Spark, from, uh, elastic search query languages.

So if there's more real time analytics are going into it, we can look at stuff like using Spark, like for larger amounts of the data to do that analytics in real time and look for anomalies. Um, if somebody is already using Splunk and they're used to that tooling, we can plug directly into that as well. So we're really looking at how do we make, for us the magic in what we do is the statelessness, uh, the cloud, the leverage of the cloud technologies.

And then we are really good at compressing that data down, uh, 20 to 50 XA lot of times. Um, and that's part of our secret sauce. Uh, so all of that combined together is, you know, will, we'll help us move forward here.

So we're looking at, we focus on our secret sauce, basically, and then we try to figure out how do we plug into things that people are already using, either the pipelines they're using to get data in, or the things that they're already using to query. So for us, the expansion is really in supporting more tools around the edge. Um, we're feeling really solid about our scalability and our ability to handle those bigger and bigger loads.

Got it. I love it. I love it.

Um, you don't get tickets to the Super Bowl for this, do you? No, I, I was actually a little disappointed because they're like, we're gonna need you at Super Bowl, and I'm from New Orleans, and I was like, yes, yes. And, uh, no, it, uh, they box had a, a war room, his big amphitheater set up at their headquarters in, uh, a Tempe, Arizona, and they had all of the different vendors that were involved in delivering super in the room together.

Um, so everybody's sitting in one room. We had, uh, two days of like, you know, basically game days, right? So we did dry runs, um, Fox went through, tested all the, you know, possible things that they could of that could go wrong, made sure that we could see all the way through.

They tested things like, you know, being able to switch between CDNs. Um, so we had two full days of practice before the game itself. Very cool.

Very cool. Now, next year you mentioned Tuby was this year, I think I actually watched it on Tuby this year, next year is AWS is the partner for it. Is that the story?

Or? Uh, it is actually whoever the, uh, the whoever's delivering the Super Bowl that year. So, uh, 2024 was paramount this past year was Fox, right?

I believe this NBC Oh, peacock. Sure. And I, if I'm not mistaken, P Peacock might also work with Pluto.

You know, all of the, the relationships on the backend among the streaming providers now are just crazy. Anyway. Well, We, we did do, we've already been working with NBC, so we helped them deliver the Olympics, and, uh, yeah, That's a big one too.

Absolutely. Catherine, what a great niche. And, you know, to, to, it must be fun for you, but I'm sure you have other customers you talk to, but keep up the great work at Hydraulics and, and come back and keep us posted here.

I mean, you know, the scale and then their scale. Yeah. Yes.

That, that's true. Absolutely. Catherine Johnson Field, CTO Hydraulics, that's H-Y-D-R-O-L-I-X do io you said, right?

Sweet. All right. You're watching Text Drunk tv.

We'll be right back. Hey, everyone, it's Thursday. Welcome to another Shimmy.

Says, I'm really glad you're joining us today. You know, in addition to going live on LinkedIn, we added something new to the mix. We're live on X.

com on X, you could check this out, live there. And, uh, happy to have you in here. Um, I'm actually really happy to be joined by a guest who's gonna be joining us today on Shimmy Says, and we're bringing them in.

We've never done this before, so bear with us. But we're actually gonna bring a guest into the, uh, into the live stream with me. It's Futurum, COO President, Dan O'Brien, dan o as we call him, Dan.

O Welcome. Is this what, we've never done this before, Dan, is it working? It's working for me.

Yeah. Appreciate your help. All right, man.

We, we brought you in after we went on, so that's, that's good to know that this works. Dan, welcome to Shimmy. Says, man, it's great to have you on.

Yeah, awesome to be here. Thank you, Alan. Thank you.

So, Dan, look, yesterday was a big day. We announced Future Signal. Of course, it goes live August 20th.

So everything we're saying here is a bit of a preview, you know, but we'll do our best. And look, August 20th is next week. com.

com, tech strong, AI tech, strong IT, security Boulevard, everywhere we could think of. So I encourage people go out there, read about it. I don't have enough time in nine or 10 minutes to tell you the whole signal story.

Dan, I wanted to focus today, not on the great way. We're collecting data using ai, and, and I would deal with G two for exclusive G two A, uh, data access as well as the renowned FUT analyst mm-hmm. Input.

Right? So we've got three great data sources, but I want to talk about what, what's the end user experience like in FU Signal, right? Why should people, to me, I, you know, the way I wrote about it is, look, we moved from, from Shaken PBF reports and emailing them every quarter or two to almost like an app, like always on experience.

Yeah, Absolutely. What g give, uh, without giving away too much, 'cause it's not the 20th, I get it, but give a, give share with us, what, what's the end user experience like here? What are they seeing?

What are they getting? What's in it? Let's see, th again, thank you for having me all, Alan.

Um, you know, when I think about the end user experience, I think we're really trying to improve upon kind of what else is out there in a few meaningful ways. You know, one, I think we're trying to be much more forward looking, right? You know, if you're just starting a buying decision right now, as a technology decision maker, you're probably happy to be up and running in production in six months, right?

So, you know, we really need something that's predictive, that's looking at the momentum, the roadmaps of the vendor community against the market need, and really telegraphing for the end user, you know, based on when you might be installing this software. You know, here's who we expect to be able to meet your needs at that future state, right? So really trying to turn this from a rear view mirror, uh, you know, buying guide, you know, buying, uh, advisor into something that's much more predictive on where the market's going, and much more forward looking.

Um, as a buyer, we really want people to feel like the data that they're getting from the futurum signal is gonna make them look smart two to three years from now in terms of the decision they make today, right? Yeah. So I think that's one.

Um, I think two, we're really trying to zoom out from the kind of micro product evaluations that really exist out there in the market. Um, it's no secret, you know, buyers are buying at a platform level now. Every decision they make is made in the context of the platform decisions that they've made.

Um, but we really don't see the platform, uh, market well served by, you know, other, you know, existing evaluations out there. It's very down in the weeds at a tool level without much context as to what other, you know, what other major decisions have been made in the IT stack that that next technology is going to be embedded into. Right?

Absolutely. I think we're, we're trying to be much more dynamic, much more real time in, you know, in terms of really adjusting the view to the reality of what's going on in the world around us, right? Um, I don't need to tell you how fast the innovation is happening in an AI right now, right?

You know, the, the leaders of today were not leaders six months ago, the leaders of six months ago, were not leaders today in many cases. And, uh, we really need something that takes that data in from the market in real time, um, and is keeping up with the pace of change and the pace of innovation in the market. So, you know, something that refreshes on an annual cycle, you know, is maybe somewhat accurate the day it publishes though, probably still already out of date.

Um, and by the time, you know, uh, you know, by the time we're almost a year later and they're thinking about refreshing it, the market that they evaluated doesn't exist anymore. The market that people are living in is, is, is very different and much more dynamic. Um, so I think those are a few of the things from an end user perspective that we can, you know, really expect out of this is, you know, something that's much more predictive, much more reactive to the world around us in real time, uh, mergers and acquisitions, major roadmap changes, um, you know, kind of key, you know, technology evolving, right?

I mean, who would've thought 18 months ago that, you know, designing for, you know, agent to agent and MCP servers were critical decisions you were gonna make in your stack, right? Well, you know, turns out that that's, that's actually latched pretty quickly. Um, and it's become really an industry standard quite quickly.

Um, so anyway, a a few bits of color there on kind of what the end user can expect. Sure. But we think we're solving a lot of what, you know, a lot of what's wrong without what, what's out there, Dan?

I, I'm, to me, I analogize this to why I love DevOps. Mm-hmm. Right?

When I was building companies and we were building software, I mean, look, we did a release a year, maybe if we was a good year, we did two releases, and of course we had the old roadmap slide, right? Show me your roadmap, and I show 'em my roadmap, and then DevOps came and, and, you know, uh, the 10 XA day mm-hmm. Release cycle, right?

The very famous presentation that ignited this whole thing. Why can't we release software 10 times a day? If not, if you are released to, if you are used to relieving, releasing once a year, release once a quarter, that's still four times better than once a year.

If you do once a quarter, do once a month, that's three x you do once a month, do once a week, it's four x and now it's everybody release. We don't even keep track of releases. They, it just, the updates happen automatically.

Continuous, It's continuous, continuous updates. Well, to me, this is continuous coverage. Mm-hmm.

Right? I I I wanted to ask that, is this the kind of thing where people should leave their dashboard open on the, on the desktop? Should they check it weekly, biweekly, monthly?

Because this is getting up to the minute data, isn't it? It Is. Yeah.

I mean, do I expect the view of the market landscape to change day in, day out? No. And, you know, I don't think it would be serving anybody.

Well, if it did, right? You know, um, you know, if our analysis is accurate, it should really change when material market news happens, right? So, you know, take some of the recent acquisitions that have been announced, right?

Salesforce buying Informatica, Palo Alto buying CyberArk, right? Those are transformational, you know, changes to that market landscape, right? And, you know, we feel like, you know, buyers don't ignore that news until, you know, kind of the next annual report cycle updates, right?

Yeah. They're, they're working on that news in real time. And, you know, that's, that's really where we're driving futureum signal is when something materially changes in the market, new products are released, more roadmap information is available, uh, m and a, you know, whatever it might be, um, when momentum shifts in the market, we wanna reflect that, you know, in real time.

Absolutely. So, quarterly updates, you know, would seem very reasonable, I think, at minimum. Um, but you know, it's dynamic to the point where if tomorrow morning some major news gets announced, there's no reason to think by tomorrow afternoon that we couldn't have an updated view, you know, based on what that means for the market.

And, you know, what a buyer needs to consider You. In my mind, Dan also is this is a transition from the advisory market being a services orientated kind of thing, or, you know, you gotta Services software, right? Yeah.

To, to software. It's a, it's a huge thing. Hey, I, I got a couple questions that I prepared though, and I, I know the answers, you know the answers, but I want our audience to know if you just wanna track a single company, I just wanna see how Salesforce is doing, or Google or whatever does that sort of single company view, right?

And, and I, I, I do have some graphics here. I don't know how it'll play on, on our camera to tell you the truth. They're way too small.

But, you know, it's a little More dynamic in the future of intelligence platform. But yeah, it still shows a all paper View. Yes.

Um, but, you know, like a business value index kind of shot, right? Where we, we have one company and, and how it plays in, in, in, you know, the business value, strategic vision go to market ecosystem, business value index innovation and solution capabilities, right? Just one company at a time.

We could look there. Then we have sort of this bubble sort of view if, if you, I think you know what I'm talking about. Yeah, Absolutely.

Would that, what is, if I could explain kind of the three kind of main Yeah, go Ahead. You have Alan. Uh, so for every vendor we evaluate, you know, we've got kind of five key dimensions we think a buyer really needs to consider.

Um, and we really, you know, rate them on each of those dimensions. So you've got a bit of a spider chart that kind of shows, you know, relative strengths and weaknesses at a vendor level, right? So, you know, if interoperability and, you know, partnering really well within the rest of your IT stack ecosystem is really important to you.

You can lean into the vendor who has the most open standards, the most integrations, whatever it might be. If you're incredibly cost conscious, don't need a ton of feature functionality, you may lean into the business value side. Um, you know, go for somebody who may not be as fully featured, but meets all of your requirements and, you know, has a really easily, you know, easy digest pricing scale, um, and scales up really well.

Right? So, you know, I think most of the other alternatives in the market are effectively two dimensional. You know, we're coming out with something that's five dimensional.

Um, so in addition to rating every vendor on those five dimensions, we also kinda show how the market landscape, you know, appears against those five dimensions, is what we call the heat map. And so you'll see mm-hmm. Across vendor comparison where on each of those five dimensions that we're evaluating, you know, kind of how each vendor scores relative to one or another, which again, you know, everybody cares about something a little bit different, right?

We need this contextual awareness when it comes to helping a buyer find the right technology partner. Uh, the, the, there's not one right answer for everybody, right? We know that every company who's out there competing wins for one reason or another.

And what we're really trying to do is to help the buyer to understand here's where each of these companies win, so that they can kind of find a supplier who they sit there and say to themselves, well, it seems like they do well with people who look and feel like me, right? So that's where kind of the heat map comes in. And then Sure.

You know, ultimately we kind of lay all this out on, you know, kind of the podium, the hierarchy chart where, you know, you've got kind of bands of performance, you know, from elite all the way down to aspiring vendors within the platform. And then, you know, in addition to that, you know, we also list off a, a very large number of disruptive companies that we're keeping in our eye in this space, right? Not all of those companies have kind of all of the, you know, technology requirements needed to be considered a, a full, you know, market participant there.

But, um, there are vendors who very well could be acquired by the Evaluat. Uh, the vendors that we're evaluating, they very well could be the right fit for you if you've got a more narrow need within that particular market segment. But, you know, we're also trying to really shine a light on, you know, these are the main players in the market and is the ones you should be primarily considering.

And here's kind of how to find which one best fits for you, but also here's a list of other up and comers you should really keep your eye on if this is a key part of your technology stack and, you know, a critical, critical technology to driving your business where it needs to go. Very cool. Dan, Dan, we were lucky enough yesterday, we, we, we had Daniel, Daniel Newman from German, CEO on tech, drunk gang with us.

And, you know, one of the questions I asked him, and we got into was when I was, again, building, starting, found co-founding companies, I always felt like we were too small to get the attention of the, some of the giants in the, in this space. And it was too expensive. It, they priced us out.

I, I know we're not releasing pricing here today, but Daniel Newman emphasized that, look, there's, there's packages for companies and users of all levels. Is that true? Yeah.

Important. Absolutely. Daniel and I are a hundred percent aligned on that.

Yes. Uh, I mean, at first I would say, you know, you don't need to be a customer of ours at all to be included in these reports, right? We, we are really trying to call out the vendors that we see as impactful in the market, regardless of whether we work with them or not.

That's what buyers are looking for. Um, you know, buyers don't care what they're a vendors of free return and group customer or not in their buying decision. I assure you that doesn't play into anybody's calculus, nor should it with any other analyst firm.

Um, but you know, as you said, you know, lots of different entry points, um, you know, smaller companies really require a different solution. And I think we've really tried to, you know, build our product and our offering sets so that, you know, we serve kind of all levels. We serve the technology giants, but we also serve the startup ecosystem really well.

And that's a lot of what we're trying to do with our disruptors list is really, you know, try to shine a light on the companies that are making waves in a particular space. And, um, you know, like I said, I think that, you know, the trend we see in the market is that a lot of those bigger companies that we're kind of fully evaluating as full market participants in the segments we're analyzing, um, you know, a lot of m and a where those up and coming companies are, you know, filling portfolio gaps for those larger companies. Um, so, you know, really trying to, you know, bring, shed a light onto that for our, uh, investor stakeholders as well.

Right? I mean, I think these reports are really trying to serve three primary audiences, the buyer, the vendor, and the investor. And that's important to note too, Dan, it's not just bias vendors.

I mean, they, they, it's part of the business model. Speaking of the business model, we could only cover, so, you know, Rome wasn't built in the day. We're gonna build up the coverage areas August 20th out of the shoot.

We come out with intelligent data platforms, correct? Yeah, yeah. Data intelligence platforms.

Absolutely. And then what, what are the, the next areas that we'll be following up with, you know, in fall and beyond? Yeah, so I think you'll see thematically, again, we're really trying to come at it at the platform level.

Um, so, you know, a Gentech AI platforms is on our list, software engineering platforms, uh, you know, cyber, uh, cyber operations platforms. Uh, we're looking at, uh, cloud marketplace platforms. We're looking at sales, marketing and service platforms.

Um, and honestly taking a lot of client feedback along the way, I think there's a lot of interest in potentially looking at observability platforms. Mm-hmm. Um, hearing from a lot of, you know, a lot of customers and a lot of market participants in that space.

So we've got, uh, like, like you said, about six more planned between, uh, you know, kind of middle and end of September, and, you know, aggressively planning out our Q4 roadmap as to where we go next. And, you know, encourage any of the viewers on the program here. You know, if you've got ideas on where we should go, markets that are, you know, undercovered or aren't being covered, well, you know, let us know.

Love to inform Absolute. And that, that's a good segue. Nice show job there for me.

Dan, if you want to get more information, you wanna sign up early access, give us where you'd like to see us go. It's futurum group slash signal, right? Is is kind of the homepage for Signal com slash signal.

Exactly. You'll see, right. You know, kind of a preview of what it looks like there.

You'll get some info on the methodology, our process, and, you know, the opportunity to sign up and, you know, be amongst the first people to, uh, receive the reports. Excellent. Hey, Dan.

Oh, thanks for joining. Shimmy says today, man, we appreciate your, we're live on YouTube and, and, uh, x we hope you've enjoyed this. Hey, if you didn't catch the whole thing, we'll be on you.

Excuse me, not YouTube, we're on LinkedIn in X Live, but we'll be on YouTube within a few hours. com. Dan.

Oh, I'm excited. I know you are. You are rocking over.

It. Can't wait for August 20th where we could show you all of this until then. Hey, man, this Shimmy says we'll see you next week.

Says, says, says, Hey everyone. Welcome to the six five Summit AI unleashed, Daniel Newman. Here we are in beautiful Bellevue, Washington.

I'm very excited for this next guest. I have Brenda Bone, Brenda SAP's, CMO of their enterprise AI business overall. Um, I couldn't be more excited to sit down with you.

It's been a few years since I've actually had the chance to do a video with you. Mm-hmm. But what a big moment.

First of all, welcome to the summit. Thank you, Dan. It's great to have you here in Seattle.

Thank you for visiting us. And it's sunny, you brought the sun with you. It is great.

Now, I, I think that's a bit of a misnomer, by the way, sometimes. I know Seattle does have many cloudy days, but it's not always cloudy all the time. Right?

You've been here for a few years. Yes. Over 20 years here in Seattle.

Yes. That's where we met. You Refuse to leave.

Um, no, it's, it's really, really great here. But it is good to be here. And I did bring the, bring the great weather with me, and you are gonna bring the insights for our audience.

We are very excited, as you know, we've entered this amazingly fast moving era, and here you are at SAP one of the world's most influential companies when it comes to enterprise business data and enterprise applications that run companies critical mission, critical businesses, day in and day out. Um, first and foremost, let's just start talking a little bit about, we are in this era of rapid change, rapid shift. Um, you are helping companies drive towards this, but kind of what are you seeing out there as it pertains to kind of what is going on with enterprises and moving their AI strategies forward?

Well, first of all, then I think you're right. I mean, everybody, everybody wants to do AI because it's there, it's available. Um, but I think that the major shift that I'm seeing is we're moving from like, the hype of AI to really using AI and it becoming real.

So we talk about moving from last year being like a proof of concept year to now actually deriving value from ai. So that's what I'm seeing mostly with our customers. One of the reasons that I was thrilled to join SAP is because exactly what you said, we run the most mission critical business processes in companies, right?

And we take that responsibility very seriously. Um, we have a breadth of applications from HR to supply chain to customer experience. So back to your question, we see a lot of, of, of different journeys from different customers in multiple industries, but what is common across all of them is really deriving that value and, and making it real for customers, which looks different in every customer.

Right. So I love that you pointed out the hype and mm-hmm. I never want to necessarily call 'cause I am, I am a buyer when it comes to ai.

I'm a believer. I think all the, I jokingly call the, the, the AI bubble bears all the people that think that AI is going to collapse and not happen. Now.

I think the timeline's very interesting. And so enterprise ai Yep. Brenda versus sort of the consumer experiences, right?

The, the chat GPTs and all the things that we're seeing in our apps has moved at a little bit of a different pace. Mm-hmm. But it's not hype.

No. It's very, very real. But your customers, the ones that you talk to every day mm-hmm.

They are sort of trying to navigate the journey. It sounds like they are trying to go from POC to bringing this to reality. Yeah.

Kind of. What are some of the things that the customers, let's start off with, what are some of the things that customers are learning early on in this journey Yeah. That are sort of maybe helping them get through, because it hasn't happened necessarily as fast.

Yeah. But it feels like it's gonna start to accelerate really quickly. Yes, it is accelerating.

And one, some of the realities that we see today is the fragmentation of the landscape, right? I mean, the reality that customers live is they use different tools, they use different systems. Uh, that's why getting your data in order is so important.

Right? And at SAP, we launched earlier this year, business data code, which helps you do that, right? SAP and non SAP data, bringing it together.

We have a great partnership with Databricks, uh, that, that we launched earlier in the year. So that's one aspect that heterogeneous landscape that customers are dealing with. The other thing that I see is, uh, a mind, like a, a mind a mindset shift, right?

Like customers, I think are moving from like the traditional way of doing things, things into experimenting with ai, right? And that's, to your point, that's different in enterprise, right? Like, because you, your tolerance for risk is, is much lower right.

Than when you use AI for any consumer things here. Like you got mission critical processes. So we see customers thinking about particular use cases because they gotta start somewhere, right?

So we talk about 400 use cases that we have in SAP that we're helping customers find value out of those use cases in the applications that they're already using because they're making it real. So we wanna make sure they're very mindful of where they can apply that AI that is, that is very tangible and that they can derive value from, from it. Um, and the other risk is that I see is in reality is that sometimes customers don't think that they're moving fast enough.

And, uh, and one of the things that we reinforce is that we're gonna move with them at the pace that they need, right? Because different customers are in different parts of the journey, right? Some are on-prem, some are in the cloud, and we're going to, and we're going to go through that and, and through that journey with them.

Uh, but at the same time, AI moves very fast. So we wanna make sure that they have the latest and greatest so they can derive value from that investment that they're making. Now, that's A pretty big challenge for these companies.

It feels like they have lots of options being thrown at them. Mm-hmm. You know, you mentioned the partnership, the business data cloud.

Yep. Um, having such a vast data landscape. SAP of course works with, you know, companies of all different sizes, but mm-hmm.

Bread and butter. You work with the world's largest enterprise customers. I mean, that's a, that's a lot of the heritage of the company.

These companies have built land, uh, you know, data estates, I'll call them that have thousands of business applications mm-hmm. Different database tools, operational and transactional and analytical databases. And then of course, they have different apps that have data, and then they have segmented and fragmented data mm-hmm.

That sits on people's laptops and then on their Excel spreadsheets. All the stuff like how much, uh, are you seeing, like how difficult is it to kind of bring all that and create that kind of enterprise context for these businesses so that they can actually get access? Because I hear what you're saying.

You have a lot of the core mm-hmm. A large majority of the most important enterprise mission critical data sits in, in an app, like in, in an, an environment like S-A-P-S-A-P having. Thank you.

Um, having said that, um, all the other data contextually becomes super important. Yes. Yes.

Exactly. And we talked about business data cloud. So we launched Business Data Cloud earlier this year.

We have an amazing partnership with Databricks, and that helps you bring together all the SAP and non SAP data. We also are creating that flywheel that we talk about earlier in Sapphire too. We have our applications, which is an amazing breadth and depth of applications, like I said, from customer experience, supply chain finance, business operations.

And then we also have data with business data cloud that brings in all that data is state and that heterogeneous landscape that customers are dealing with. And we also form great partnerships, like with perplexity, specifically for ai. We just launched a partnership in which is not only like the business data, but it's also contextual data, right?

So when you are actually using AI in your systems, it also derives that data from, um, from perplexity. And it suggests actions because it's not only about reasoning with ai, but it's also about acting. And we're, we're implementing that.

And then you have AI on top of that. So we have the applications, we have, uh, the data layer, which was business data cloud, and then we have ai. And the way that we have AI is we have it embedded in our applications, but we also have it with JUUL and with JUUL agents, which we can talk more about.

Right? But all of that is really integrated into the system. And that helps you not only bring all the data state together that we talk about, but it also helps you integrate with any other data that you have in any other system.

Right? But it integrates with your most mission critical data, which we know that lives in SAP, but we wanna make sure that that is all together and interoperable. And you and I have had a few conversations, uh, you know, over time about mm-hmm.

How SAP is making AI sort of pervasive. Yep. And I think this is probably worth noting here.

'cause I'm gonna get into JUUL here because I have been, uh, tracking the journey very closely. And I think the agentic opportunity is clearly arrived. Like, yes, we spent two years talking about ai, then we talked about generative AI we have earned, but some of this is basically AI needs to move with you.

Yes. Meaning as all, you know, alternatively, otherwise, we're really back in the era of if, if you have to push a button that says do something. Mm-hmm.

We're still kind of in what I would kind of call the historic software era of you go in and interact with your, your systems. Yeah. AI should be designed.

And I think, you know, you made the acquisition of WalkMe. Yes. I think part of that was kind of about making AI sort of pervasive.

Talk a little bit about how SAP's thinking about making AI more contextually aware mm-hmm. And kind of more pervasive to drive user and productivity growth and efficiencies that people maybe don't know how to get out of ai. Yes.

And I get really excited because that omnipresence of ai, it really moves us into the era of age ai, right. Um, we talked earlier about customers that are using and making AI real and SAP we have 34,000, over 34,000 of our customers that are using some form of generative AI already in their system. So we know that they're being used.

Those use cases that we talk about, we're gonna get to 400 use cases by the end of the year across all of our applications. They're real. Now, what we want to make sure is that the barrier to entry for the usage of AI and for the adoption of AI for our customers is really low.

And one of the ways that we do that, it was, is with that omni process across all the applications, across any system that you work in. And that's what we're doing with juul. So if we think about Juul as the new user interface for ai, it gives you access in a very natural way with natural language for you to be able to ask Juul any question.

And, and, and then all that, all those agents or those billions of agents were in the background to process the answer. Now with the acquisition of WalkMe, what we did that is really cool is that we launched something called, uh, the dual action bar. So the dual action bar like literally travels with you dad, like it travels with you.

It doesn't matter if you're in another system, if you're interacting in ServiceNow, for example. Like that dual action bar is there and they don't, and it doesn't wait for, for you to prompt the action bar and ask a question. What it does is that it it, it's looking at all the behavioral data, looking at all the actions that you're taking, and it's prompting suggestions based on what you're doing already with your system.

So we talk about that dual action bar enabling to you to be everywhere and to manage everything. Right. And it's truly what it's doing.

Right. And then in the background, you have all of the agents Right. Working for you, which is the agenda AI component that we talk about.

Yeah. And, and, and WalkMe had a very significant evolution since the acquisition by SAP. You know, originally it was sort of trying to help people use the technology it's invested in, which it still is, but it was before trying to use an, say a SAS or an application.

Now it's basically an enabler of helping people use consume and proactively implement AI across your entire business journey. Yes. Which is, is is fascinating, um, because I do think adoption is a gap.

And there's a reason that, you know, many of us became successful in taking something like a, a chat GPT tool and using 'cause it's like, hey, this is kind of like search, I know how to do this. I've been doing this a long time, like I asked the question and now I give more context and kind of an answer. And it, it can do it differently.

How I, I don't just get a link. Yes. But for enterprise, and a lot of the challenge has always been about adoption.

Mm-hmm. Like people have always kind of said like, I don't know that I feel I'm getting the value of all these apps that I'm investing in. Mm-hmm.

AI really does seem like a great opportunity to kind of tear that wall down Yeah. And make this everything more usable. But one thing that I do think that enterprises are challenged with, and I'd love to get your take on this, Brenda, is they're challenged by right now, kind of everyone's bringing AI at them.

Mm-hmm. Okay. Um, you know, you have been a consumer of products as a CMO for a long time, meaning that people sell you stuff businesses.

Right. People probably try to sell you a software to run things. Right.

Um, long story short is as you're, as you're sort of buying the stuff, you're trying to figure out how to optimize it and how to, how to use utilize it in agent world, it's like, oh, I wanna sell you an HR agent. I wanna sell you a CRM agent. I wanna sell you an ERP agent.

I wanna sell you a supply chain optimization tool, an agent Oh, I want you to run you an agent in your productivity suite. Yeah. You know, I know that in your Sapphire keynote was, uh, in the Sapphire keynote, there was a great, uh, sort of flywheel narrative about why mm-hmm.

Start and build with SAP. Yes. I'd love to kind of get your, you know, as agents will consolidate, I don't think you're gonna buy an agent from every software app that you have.

Mm-hmm. How is SAP sort of driving kind of a narrative and, and helping the market understand why maybe SAP is the right place to start build and orchestrate a lot of their agentic solutions? Yeah.

And, and we talked about it at Sapphire, like you mentioned, but one of the things that when we think about agent AI and about agents, I always first start with what do we consider an agent? Because there's so many definitions of AI agents, Albertan, that people sometimes get confused and, and you talk about billions of agents. So people sometimes get a little bit, it's daunting, right?

To think about, oh, do I need to use billions of agents? And if I'm not using all of them, then I'm behind. And the reality is at SAP, what we consider an AI agent is an agent that can help solve a complex business process.

And why a complex business process? Because we can do that, right? With all the data state that we have, with all the breadth and depth, depth, depth of applications that we have.

And we know that there's going to be smaller tasks that are going to be done in the background, like sending an email, some vendors out there called that task an agent. So we don't do that. We call an AI agent that we ship out of the box like an agent that solves a complex business task that sometimes to sense all of those, um, all of those business processes, right?

Sometimes it goes beyond the finance function and it speaks to a supply chain agent and it speaks to a customer service agent. And then that is the agent that we ship out of the box. 'cause we know that customers are dealing with that complex business process.

So we have over 40 agents that we, that we have out the door, um, that we talked about Sapphire, and I can give you an example. We have an agent that, um, that standard charter bank is using specifically for goal setting in hr. So what they do is they rolled out that agent to 84,000 of their employees globally.

So that's truly at scale. And that agent is helping, is helping the employees build goals specifically based on their performance base, based on their business goals, based on the conversations that they've had with their managers. It helps them build goals that they can then enter into the system, right?

All that is automatically done. So before what it took, what it used to take like two hours for an employee to do, and now it's taking them 10 minutes, right? So it is dramatically improving the productivity of that person within a business process.

Um, we also have out of the box, like a customer experience agent, right? Bosch Power Tools is using the customer experience agent is not only really replying to emails, it's actually reasoning all the inquiries that are coming in is redirecting some of those inquiries, but it's also drafting responses already and triggering other actions, right? So it's, it's the act of reasoning and then, and then acting, right?

And then triggering another action that is going to, that is going to always have an oversight from a human right. But, but it's already doing that action for you. Um, in addition to that, we have, uh, the capacity to build your own agent.

So those are agents that we ship out of the box, right? But then we have the ability for you to build your own agent with JUUL studio, right? And that is, and that is, there's, we have customers like Du Lei that we have had great partnership over the years with, they are build building their own agent to be able to manage accounts payable across all of their shows.

So each show that CDU Soleil has is specifically a p and l and they create an agent for them to deal with accounts payable for each one of those shows. And the agent deals with all the translation across because those shows travel across the world, deals with all the translation of languages, deals with the action of paying to the suppliers, et cetera. So, so that's the variety of the agents and the agent AI system that we're building for customers to use.

And in addition to that, like you said, there's so many, so many vendors throwing AI agents out there that we wanna make sure not only that you have a strong foundation to use out of the box agents with USAP system, build your own agents that we know that you're gonna, that you're gonna have to use, right? Because you have unique business processes that you're running, but also the ability for you to work with other agents, right? So we have joint, uh, interoperability protocol of agent to agent collaboration with Google and with Microsoft.

And we're also building like very strong integrations with, for example, Microsoft Copilot, that they have a strong productivity angle, of course, because you can use, we know that our customers are using copilot in productivity with the office suite, with teams. So the integration that we're building with juul, you can use JUUL within Microsoft copilot, or you can use copilot within Jul. So you don't have to do that kind of toggling between your system depending on where you are.

Like you can actually get all that goodness from copilot as well if you're in your SAP system. So we're very mindful of all that heterogeneous landscape that we, that that is out there, and we wanna make sure that we can integrate with that. But at the same time, we know that the core of mission critical business process is it's run an SAP.

So we wanna make sure that we have that very well established for our customers. First of all, really appreciate you bringing some of these customer examples. Mm-hmm.

Because so often I think right now it kind of starts with the hype and making it real. Yeah. Um, we sort of hear that, oh, they're doing and they like, oh, it's a POC and it's being done in some very small vacuum in a very small number of times.

Mm-hmm. Part of the reason we're building out this massive AI infrastructure is because in when this gets done at scale Yes. When you have companies like the ones that you mentioned, large banks and large manufacturers that have many of these agents all working by the way, 24 7, they can work all day Yeah.

Of you will have trillions, these tokens everyone talks about. But I like that you brought those examples. I also like though that you sort of explained agent, because I do think there's kind of, well what is a, uh, generative AI tool?

What is a bot? What is an agent? Because I think some people are kind of conflating a bot, which have been around a long time answering an FAQ, you know, it kind of could maybe be in somewhat of an agent sometimes.

But what you're saying about a complex business process, in my mind, it's all about when it can kind of orchestrate and coordinate. Yes. Meaning if it's just going back to a knowledge graph and saying, oh, we're open nine to 6:00 PM that's a bot.

But when it can actually say it's open, give you express directions, maybe coordinate a meeting for you. Yeah. You, when all those things can happen, now we are actually experiencing mm-hmm.

What is a gentech. Yes. And so you mentioned JUUL studio, and I just wanna ask this question because right now I think a lot of businesses are gonna probably go with a lot of off the shelf, even some of the examples, these are huge companies that are taking what you've built off the shelf.

Mm-hmm. But how hard, from what you're hearing from your customers, is it for them, because every company has a few custom processes. Every and SAP knows this mm-hmm.

It's built, its on these massive custom deployments, which you're, you've moved away a little bit from what as you've gone to cloud and ai, but you're trying to deliver more outta the box. But when a customer does need to go custom JUUL studio, are they finding it something they can really work with? Yes.

I think that the, the beauty of Juul studio is that you can define the skills that you wanted to use. You can also define the models that you want to pull from. You can also define the other agents that you want that agent to work with.

And not only you can build your own agent, but you can also customize. So when we shape out of the box, you can take it and say, Hey, I wanna tweak a few things here. And you have all those variables there.

And, um, and then you can, you can create the own agent. I think that because of the custom business process, but also because the complexity of our customers sometimes, like globally, I mean, we talked about du la it, what, what it's like mind blowing. It's like the, the amount of shows that they have and the amount of countries and the scale that they go with.

So they have, it's the same function of the agent, but they have to do it in different, in different contexts. Right. Because they have different suppliers.

And deploying it is super seamless. They said, we build it once, and then we build, we deploy it for each one of the shows, and it's beautiful. It adopts the same language that the, the same language of the show.

It, it recognizes the suppliers that we're using. And sometimes those suppliers are big ones. Sometimes our like mom and pop shops that are in the city and, and they're like, it's super seamless.

Right? So that speaks to the scalability and the adaptability of those custom agents, depending on the process that you want to build. But they based it off something that we had already talked with them about and that we had already built.

So it's that, that beauty of like, you can extend, you can customize, but it's depending on what you need. And it's a really neat way of doing it. If you go into dual studio, the, the really neat way of doing it because it outlines all the variables that you can do.

Yeah. It seems like there's going to be a really significant opportunity for companies like SAP that have the data and insights of so many enterprises and that mission critical data to be able to build a lot of out of the box. Yeah.

I think that's gonna be part of this whole going fast and scaling, but we just know history has proven that there's infinitely more variables and options that different companies could end up having. So being able to build is gonna be really important. Yeah.

As well. So as we sort of pull this all together, we started the conversation talking about, you know, bringing enterprise AI to life, that taking away the hype, making it all work SAP in order to build a future where it's gonna continue to be as successful and growing and grow at the, at the rate it has and even faster. Mm-hmm.

You have to be the enabler, right? All these companies are gonna have to evolve and they're gonna have to evolve with you. Just kind of in a, in a more simple sort of contextual way, how do you think SAP is going to be most influential in driving and helping companies be successful in the era of enterprise ai?

I think that there's, there's a, a few ways that we're doing it right now with our customers. We talked about, depending on where customers are in the journey. And I think that that's very important, right?

We talked about, uh, like tens of thousands of our customers using AI that is going to just accelerate, right? I mean, once they start using, once they start finding those use cases is going to accelerate even more. Um, AI goes very fast, like you said, Daniel.

So, so we are shipping innovation on a monthly basis. So I think being with those customers on their journey and helping them make it real and deriving value from it, that is very critical. And that is how we're going to continue to evolve into, into this, this era of enterprise ai.

Um, the other, the other thing is with, with making, we're making a seamless experience. We talk about low barrier to entry, right? I mean, these are mission critical processes.

So risk, uh, risk aversion is common, right? And we want to make sure that we lower that barrier to entry, right? So really establishing a strong AI foundation, which we have, right, with our AI foundation, which we talked about Sapphire, uh, that gives you the reliability, the security, making sure that your data estate that we have built with business data cloud is secure, is reliable, and is trusted.

Um, and having that foundation with our customers, it's going to be really important because that's from where they build, right? The other thing that I would say is we continue to build very, very strong partnerships in the ecosystem, right? I mean, we talked about partnerships that we build with other vendors like Microsoft and Google, et cetera.

We also build partnerships with pure AI companies, right? With Mistral, for example, we started building a finance agent specifically that we can ship out of the box. We build a partnership with Nut Diamond, we built a prompt optimizer.

And when you hear the expression like prompt engineering is status. Because, because we know the developers are spending so much time, right? Building this prompts and with this prompt optimizer with not diamond, really the developer is going to out outline the outcome that they want to have.

And all that optimization is gonna be done in the background, right? So those are just two examples of like AI companies that we partner with. And we are going to build the future of enterprise AI with those partnerships as well.

But we know that all the gravitas of your mission critical data, your mission critical process starts with SAP. And we want to make sure that you continue to feel confident and that the trust that we have built with our customers continues as we build the future with them. And of course, you mentioned per complexity as well.

Another, you know, quickly rising, uh, ai, pure AI play that, uh, is now part of, uh, the SAP ecosystem. It's always been part of the S-A-P-D-N-A to have partnerships. So that's not surprising at all to me.

I'm also really glad that you did mention, uh, trust and security. We didn't talk about it a lot, but I think part of the reason enterprise has also gone a little bit slower is, is 'cause there is a, a meticulousness that's required to make sure that, you know, enterprise data stays in the right place. And you'll being a company with European descent too sovereign is going to be another big opportunity to expect SAP to be able to capitalize on as well.

Alright, so final just where can the audience go and learn a little bit more about what you're doing in enterprise ai? We just had Sapphire done. So it's our biggest customer event.

It's our flagship event. Uh, we had it just in May in Orlando, and then in Madrid, uh, we have many more announcements coming. Uh, so definitely stay tuned.

Uh, we have Veeva Tech coming up. We have SAP Connect coming up in October as well. But I think customers can go and watch our Sapphire keynote.

It was a very, very popular keynote. We have a lot of announcements there. Uh, we have our, our innovation guide as well.

Uh, but if you watch the keynote, you'll hear all the momentum that we're building, all the things that we have available today, which is we're making it real with customers. And then obviously we're gonna, we're gonna continue to deliver the rest of the year. Brenda, so great to sit down with you.

Great to see you Dan. Thank you. And thank you everybody for being here with us at the six five Summit AI Unleashed.

That was a great conversation with SAP. I'm gonna kick it back to the studio more from us soon. Hi everyone, welcome to the six five Summit AI Unleashed.

I'm joined today by Kush Punch by SVP of Product Management for ServiceNow's AI platform and this enterprise app spotlight session. We're gonna be talking about building strategic vision to real world impact with your AI Kush, thanks so much for joining. Welcome to the summit, first time.

Glad to have you here. Thank you Daniel. Thanks for the opportunity.

Looking forward to the discussion today. Absolutely. Um, look, you have no, uh, you don't have big shoes to fill.

Uh, your CEO Bill McDermott opened our entire event last year and now we brought you here to keep this conversation going. Last year we were talking about ai, we were talking about sort of how it's gonna shift and change the future. Uh, you know, but gosh, even in a one year time push, I can't even tell you how much innovation, disruption, and change has taken place.

Even the things we talked about one year ago versus now. It's an incredible time, exponential time. So I want to hear from you get a lot more of that.

But one of the things that I've really enjoyed in following the journey of ServiceNow was just not only how the company has really changed and disrupted evolved itself, but seeing the customers getting value. You know, you like, you like to talk a lot about kinda the future of what the software industrial complex looks like. You talk about reducing chair swivels kind of new ways to bring systems together and make work more productive.

So we know organizations are moving towards agentic platforms. Yeah. You know, as you're kind of watching these companies and these enterprises move this direction, what are some of the common challenges that you're seeing them face in terms of creating this coherent AI agent strategy?

And kinda how do you recommend, what do you think they should do to balance innovation while keeping the operation on the rails and, you know, in, in minimizing complexity? So that's an amazing question. I think one of the common themes, which, uh, I'm observing is there is an organizational tension happening in inside the enterprise.

Let me explain it to you. So what happens is you have product teams, engineers, designers, PMs, who wanna go 10 x, they want to innovate, they wanna embrace this amazing new technology. At the same time you have another side of the organization.

It could be the compliance team, it could be the legal team, it could be the privacy team. They wanna make sure that each and every step they're taking is measured. And when these two sides are colliding, that enterprise is kind of stalling.

And one of the things which we encourage customers and we solve at the ground level in our platform is embedding governance in AI platform. And that's why our governance is not bolt on, on top of it. In each and every layer.

We are adding that governance and we are built tools for it. And it's not just governance, which is giving you visibility, it's workflows embedded in it. So risk and compliance workflows for which we are an industry leader is embedded in the products at Knowledge.

This year we launched AI control tower. Now this is a single pane of glass in which you can see your entire AI footprint from AI agents to AI skills, to AI models. You can govern them, you can see the performance of them, you can see the value it's creating so that both sides of the organizations are running in the same direction for the business strategy.

We truly believe that governance is an accelerator rather than a break in the system. Yeah, I, I tend to agree because the big difference and you know, we've seen how quickly consumer AI is proliferated and it's basically because most of what we're using has come from open internet data. So the compliance risk was different.

Like this data was already out there, it was already available. Yep. And so we know that kind of in the early instantiations of trying to do AI and the enterprise, there was all the, okay, when we start commingling data, right, you remember kind of even some of the early LLM use cases where like people were putting proprietary data in and it was causing all this problems because you know, now you're feeding something that's constantly learning something that maybe wasn't supposed to know.

Yes. Then now once it knows it, it's like how does that, so if you're, you know, with when ServiceNow and you're looking at companies, they're saying we're feeding, you know, critical HR data, we're feeding critical CRM data, we're feeding critical, uh, infrastructure systems data about our company. Um, that company that is using your tools has to know that the data is safe.

You know, we hear about it whether it's through compliance and governance, through sovereignty. These are different angles and of course trust layers and securities, these are big things in the enterprise probably has a lot to do why we can't go faster. Now I wanna talk a little bit about hype here with you too, K, because you know, we've had this kind of theory of AI and we hear, you know, AI is gonna, everything from, you know, it's gonna take over the world.

It's going to uh, take our jobs, but also it's going to create amazing growth, productivity, efficiency. Um, but we're also sort of hearing that like not everything that is theoretically happening is actually being deployed or even being deployed at scale. Mm-hmm.

So how do you think about sort of going from that kind of, we want AI to make us 10 times more productive. So like going to an enterprise and saying here's a practical scalable approach to start getting outcomes, um, in a timely manner. 'cause how fast things are going but in a realistic capacity.

So you used an amazing word outcome. Yeah. I think one of the most important things enterprise needs to do is align their business strategy to the AI outcomes.

And that's one of the things which is not happening naturally. Okay. You would see enterprises are like looking at how many models they're deploying, how many skills they're deploying.

I guess the real question is what value that model brought to your business? What value does skill got to your business so that we move from experimentation to aligning everyone to the business strategy and tackling those outcomes with ai. And I think that's when the real magic happens because you are measuring each and every uh, AI investment.

You're figuring out are you going in the right direction? You are governing that AI and seeing the value of that ai. And then you are course correcting it accordingly.

And when you are aligning at the top level business strategy, you are solving it east west, each and every department is coming in and figuring out, okay, for this business strategy, how am I aligning, how am I pitching in to make sure that this AI is an accelerator and helps us move forward compared to the competition? And, and I'll give you an amazing example actually. Uh, so, so we have a customer called AstraZeneca.

I have everyone knows them like AstraZeneca was spending 30 minutes per person on procurement. Now think about the amount of hours gone into procurement rather than researching and building lifesaving drugs. So when AstraZeneca standardized on ServiceNow AI platform and automated those workflows, they saved 30,000 hours per year.

Now that's real measurable impact because that is all the time gone into researching new diseases, coming up with new drugs and essentially aligning to the mission of AstraZeneca and creating outcomes which are measurable. Yeah, and I like that you bring like a real world example. You know, companies in fields like healthcare kind of hit on both the things we've talked about in terms of the real world scale challenges Kush, but they also face the real governance challenges.

They deal with a lot of very sensitive data. So they're trying to scale and move these workflows very, very quickly, but they also have a lot of sensitivities, whether it's trial data or customer data, you know, so you've got really balance all that. Um, talk to me a little bit about how, you know, enterprises should think about, you know, building a system of intelligence.

Now that's a little bit of a different word. We've often heard system of record in the AI era, you know, system of intelligence. It needs to be able to scale AI but it has to really embed what we just talked about, you know, trust and transparency into every layer or else you know it, it's just not gonna work in this particular era.

So a hundred percent spot on, I think scaling AI is super important. And if you're not doing it right, it's like building a skyscraper on sand, it may look amazing, but as soon as you do little bit of scrutiny, that skyscraper falls up. And that's why we believe in providing governance and trust in each and every layer.

When you can trust anything your adoption increases with that. Like if a developer is producing something and you can trust it, you would adopt that features. If regulators can go on that, they will be more comfortable releasing it out there.

So that's why with AI Control Tower, it's not just a system of record like you called out, it's a system of intelligence because we are embedding workflows into it. Compliance workflows, legal workflows, security workflows, risk workflows. And they are not just something which you treat as a department.

We are taking it east, west, north, south approach because that's how you're going able to scale ai. Now if you are just gonna think about AI as a point solution and not the platform, then you are not gonna be able to do complete business transformation with this amazing technology. So when we use AI control Tower internally, we see how much impact it's creating even for ServiceNow.

At ServiceNow we have a program called now and now where we drink our own champion. So we have seen that our self-service deflection has gone 14% up in just one 20 days. When we deployed Agentic AI internally we saw $10 million of savings.

That's like having 50 employees throughput saved with ai, which is super, super amazing. So we see the results internally, we see the results with our customers here. One external, uh, example, which I'll give you is Bell Canada used a ServiceNow AI platform for their customer service and in one year they deflected 3 million customer calls, which is huge.

And they did that with compliance embedded in it. So they ran fast with compliance baked in. Yeah, no, I think that's a really important, and I like your analogy 'cause I use the east west analogy a lot too.

Um, you know, we've talked a good bit about North South with the governments governance and such. They kind of have to be built into the systems, you know, in this era we love talking about kind of fast paced infrastructure. So today you and I have kind of talked about all the things that you should take a breath and the caution and think about as you're kind of building the stack.

But then the second part of this, as you build the stack and then you start to take it across systems, because yes, we are seeing companies like ServiceNow with what you're building, the control tower and the now platform, they can sort of aggregate, you know, in the agent era, companies probably are not gonna want to have agents running on hundreds of different softwares. What they're gonna wanna do is build agents centrally, deploy them. And then like I said, some things become databases.

Um, you know, some are databases with logic, uh, others is literally just going to be like, you know, middlewares and connectors and all the things that really need to happen to connect all the software in, in a real enterprise state. Um, so you gotta do that and then you gotta govern it and then the data has, you know, has to be, you know, really well thought out how it moves because you are responsible and you know, in different regions of the world, data leaks, its big risk not just to your customers but to you as well. Um, you know, how are you sort of approaching the challenge, you know, approaching this challenge of governing the fact that you're going to increasingly be pulling data, you know, you're doing Raptor, but I mean eventually everything's gonna become the data and ideally if, if if if you succeed at ServiceNow, ServiceNow agents are going to scale and span across all the productivity and applications and databases and it is going to become this single pane of glass that we talk about.

How do you sort of approach that challenge and what do you, what can you kinda share from your current experience about what it's gonna take to get that done? So another amazing question. I think one of the things which you will see, uh, from the ServiceNow AI platform is we bring three key ingredients on the platform.

AI plus data plus workflows. And that's the key for business transformation because the fuel for AI is data. So like you said, we need to have data connectivity and with Raptor and workflow data fabric, we get that for our customers.

On top of that, we build an amazing AI layer, which you can go on and it's an open system where you can pick and choose which LLM model, which infrastructure you wanna get it. And then we bring in 20 years of our experience in doing workflows. That's how we move work from east, west, north, south.

And when you have these three ingredients on that platform, this platform gives you everything you need to do that rewrite you are talking about because that's how you are building new outcomes, you're solving new problems. You are not only solving for operational efficiency, but you are thinking about how do I grow my top line with AI now so that I am growing from both sides of that equation. And especially with the workflows embedded into the platform, customers are able to run super fast.

I gave example of our GRC workflows. Any time you wanna run a risk and compliance workflows, you are able to do that and you are able to do that in a scalable way. Today we have an EU act for ai, but we wanna make sure that this platform is scaling to any new act which comes.

And that's, uh, how you do it with these workflows. A new act comes in, you plug it in and that's, that's where the machinery is churning behind the scenes to make sure that each and every regulation, each and everything the customer cares for is maintained on the platform. Yeah, I think there's a lot of challenges there and I appreciate you sort of trying to, trying to simplify the next couple of years 'cause you're gonna be very interesting as companies kind of try to tie all these threads together.

And that's why I said I think as much as we want enterprise to move at that sort of speed of sound, it does take the right partners, it does take the right technologies. I think there is gonna be some need for, you know, some, some uh, removal of some of the abstractions of how many layers in enterprise, you know, uh, has of software and of tools to be able to go as fast as we're gonna need to go in the AI era. But it's really compelling and of course all the things that AI creates challenges that can also be used to help us with all this stuff.

Kush, I wanna thank you so much for joining me here at this year's six five Summit. It's been great chatting to you and, uh, we'll have to do it again soon. Uh, I'll really enjoying following the, the ServiceNow journey.

Likewise. Thank you Daniel. And thank you everyone out there for joining us for this Enterprise Apps Spotlight at the six product summit.

Talking about ai, a lot about governance there, compliance, really important stuff. And while some of that stuff is more, you know, it feels like it's the nitty gritty, we are never gonna see enterprise scale if we don't consider all those important details. com/summit.

More insights coming up next.