How Barracuda’s CIO Sees Gen AI as a Paradigm Shift for Leadership and Scale

Speaker A: Gen AI has transformed the way that we think about the work that we need to do. We can both remember times in our careers when we were really excited about something that we knew would be a game changer for our environment and we were the only ones holding that mantle. Now everybody's holding the mantle and everybody's involved. Everybody has a way that they can benefit. We want our customers and our partners to get answers as quickly as possible. We certainly have the bevy of data that can get served up at a split second notice if we just have the right types of agents created in order to do it. Scale mindset is so essential. As a business leader I have to ask myself every day, if we do this this way, will this scale? Am I creating a single point of failure? Am I adding too many snowflakes? Is my process not standardized? But I believe that there is just a complete paradigm shift with Genai and the concept of scaling it could, if you use it for all the right reasons, could really make a massive difference for your company.

Speaker B: Welcome to the new automation mindset where AI automation and integration come together. I'm your host, Markus Zern, and as chief Strategy Officer and part of the founding executive team at Workado, it is my mission to find these top innovators in AI automation and integration and share their journeys with all of you. So today I have with me, uh, Sirui Mushegian. Uh, she's the CIO of uh, Barracuda, the cybersecurity company. And actually looking at your LinkedIn, Sirui, I mean you had a lot of different IT leadership, uh, positions actually VPIT, Blackline, that's still technology but then CTO at WNAT Group, that's broadcasting, VP of IT, NBA, that's maybe the most exciting one. And then IT leader at Ralph Lauren. So look, I'm getting curious here and so many different industries and we're all looking forward to learn from these uh, diverse experiences. So first of all welcome and I would say let's dive right in. So um, Siri, we have lots of uh, CIOs here, uh, um, in the audience and when I talk to CIOs these days there's really just one topic. Everyone talks about AI transformation. So I think we gotta, let's start off on that one and just get a little bit an idea like how do you think about AI transformation? And maybe explain also given your experience, like how's it different from maybe previous transformations we've seen like digital transformation, cloud transformation, that kind of uh, thing.

Speaker A: Yeah, well those are great questions. Markus, first of all, thank you for having me here. AI is a topic that is at the tip of everyone's tongue. It is, uh, the topic that I talk about the most every single day. It's also one of the most exciting times that I can recall in recent history in my own career. I've been around for a while. We discussed offline that you and I are near the same vintage. So we have a lot of similar experiences through the advent of, uh, the Internet age. Um, I'm old enough to remember what it was like to go from, uh, not having a computer and being completely analog to having one. So there have been many milestones in, uh, the world of technology that have changed our way of being, but nothing like I have seen, uh, the way Gen AI has transformed the way that we think about the work that we need to do. And so to your question, how is it different for me or I think in general, really the difference between how we think about Gen AI, transformation over cloud or digital transformations, the difference for me is that it's pervading every area of our company. So it's not just me that's trying to roll this out. It's. Everybody's got a stake in it and an interest in it, and it's coming down from the top. I think we can both remember times in our careers when we were really excited about something that we knew would be a game changer for our environment, and we were the only ones holding that mantle. Now everybody's holding the mantle and they're all very driven to see how it can make a change for their business processes, for things that should be automated, for ways that they can take work out of the system, reduce friction, allow people to do things that, uh, you know, spend less time on working on things manually and change their whole mindset to things that can be strategic from tactical. That's how I'm seeing things so markedly different from a regular traditional digital transformation or some other transformation. The whole family's there, everybody's involved, everybody has, um, a way that they can benefit and they're all excited about it too. So it's just a whole different. It's a whole different ballgame. So, yeah, very exciting times and I

Speaker B: think exciting times specifically for a cio, Right, Because I think you're right. I've never seen. It's been a long time that I've seen something so strongly promoted and desired from the top where you, uh, know everyone is really pitching in. You're absolutely right. Um, and it's super exciting, you know, I had this experience just the other day. Um, I ordered some clothes online, you know, uh, and one of the clothing pieces was delivered wrong. It was just like, you know, wrong item, wrong size, everything wrong. Some mistake must have happened and I dreaded like what would happen now. You know, when you call the call center, you know how that goes, right? I mean Amazon made it a little better, you know, doing the returns with the press of a button on the website and so on. But I didn't know what to do, it wasn't Amazon. And so I sent an email back uh, to their support number with my order confirmation and just explained quickly know what was wrong. And I got one wrong item, two right items and 60 seconds later I got an email back going like oh, we're so sorry, uh, we actually checked your item that you didn't receive is actually available. We could send it to you right now and uh, could you please take a photo of the wrong item and we'll uh, take care of it. And I was like really surprised. I was like how's that even possible? And then down below it said your photo friendly AI support agent and uh, you know, took the photo, got an email back another 60 seconds, the order got submitted. I mean it was just incredible. And I think that's where what we have out there happening right now, it's somewhat magical even. I'm curious, do you have any examples like AI projects that uh, you guys have done something that really, you know, made a big difference? Maybe, maybe something that, maybe there's something that you tried and struggled with. I'm curious, I'm sure the whole audience here wants to, wants to learn from what every one of us is doing.

Speaker A: Yeah, I uh, also am enjoying sharing these experiences with my peers and even with my family. Anybody that is using Genai day in and day out is able to provide some sort of feedback loop for, for me as I'm thinking about what we can do at a corporate level. Going to your example of having a customer service experience with the provider of the thing that you bought and how your friendly gen AI bot helped you get your answer immediately. That is a template that we are actively using at Barracuda. It is work that is almost come to fruition, um, almost in a production state where we are going to be transforming our customer support organization based on the exact same template that you just described. We want our customers and our partners to get answers as quickly as possible. We certainly have the bevy of data that can get served up at a split second notice if we just have the right types of agents created in order to do it. So to me that is one of the. If you think about the marquee of all of our AI initiatives, I would say that one sits at a high place on the marquee of our AI initiatives because that is something that not only is going to help us internally with our processes, but it will be immediately evident for the people who use our products and our partners. So that, that's something I'm super proud of. Then we are going levels deeper. So one thing I wanted to say is these initiatives are sitting in a framework like we have a roadmap for every single executive pillar and we are tracking these very carefully because we're very proud of them. We know that they're going to make a difference. We want to make sure that we measure that difference. Um, and so some of the other areas that we're working on is like with our HR partners, for example, we've developed an agent that helps our HR business partners when they uh, are going through, let's say an onboarding or an off boarding with an employee. They're capturing information in this agent and that agent is providing sentiment analysis about all of the people that have recently onboarded or off boarded. So that's an example of something that we're using internally. The list is great. Uh, it is just super long. Um, but it could involve ways that we're improving the lives of our accounts payable department, ways that we're helping improve, uh, finance in general, FP and a get to answers quicker. Um, there are agents that we're looking at that are going to help transform the way that we have surfaced data as an enterprise asset. Being able to get insights quickly and easily with absolute trust is another area that we are exploring avidly with Gen AI. Every part of the company is getting some sort of benefit. So very excited about all of these things, especially the ones that are able to be customer facing.

Speaker B: Yeah, no, it's um, it is uh, it is exciting. You know we have a very large automotive supplier as a customer here and they had this process where you know, it's the aftermarkets division so they sell these spare parts and then they get big orders, uh, like 10,000 parts of this for example. And then now they have to price that item. And as you might imagine, different channel, like different volume. It's complicated. Different countries. I mean it's really complicated. Maybe different service level agreements. Uh, and they were trying the old way like you and I did. Uh, you know, the BPR business process redesign like Drawing these, these processes back in the days, um, they tried that and they totally failed every single time because we're too complicated. Too many manual steps involved. There was uh, an edge case like left and right and so on. And so finally said, hey, let's do it with an agent. And you know, let's just basically just put the requirements doc in and, and have the agent figure it out. And it was a huge success. So the, I mean these kind of things are. There are interesting. Now one question because you did bring up finance. Uh, so we see, um, customer support, the marketing people, they seem to be all over it. It's my personal uh, experience. Finance people is a little different. So I was at a conference recently which was all finance people, technology and finance. And if you think about it, uh, finance people are, I think by definition they're very accurate. They have to be type A, they have to be in control and every single number counts, which is the right thing to think about. And so that uh, stochastic behavior of the agents where you can't exactly say what they're going to do next, it seems to frighten them. And there was a huge discussion around that there is a humongous opportunity in finance to do things better with these agents, but that the finance people themselves may be in their own way almost a little bit because they just can't get to grips too. Does that make sense? I'm curious if you run into certain people that you feel, feel like they're going to be more easily on the journey and other ones are maybe a little more hesitant.

Speaker A: Yes, that's absolutely true. That's a great summarization of how I see the entire enterprise full of employees. There's the spectrum on which we all sit. Some people are very precocious and capable. We have engineers and product designers who are geniuses, genius level people. And they do not need my help. They have figured it all out. The ideas that they have are unbelievably creative and very cool. You have the other end of the spectrum where some people who are on the business side of our company, like uh, you know, hr, finance, legal, other areas where they have folks that are AI champions that we have, you know, hand selected to work with us. They have hand selected themselves to work with us. But then the rest of the uh, group might not be so far along and they're definitely nervous. They are questioning the efficacy of what's produced by an agent, whether or not they can trust it completely. The way that I've seen it help to get over that is to dip the toe. So one area that we're very focused on is education. We know, as I mentioned, that people exist on a spectrum of understanding and learning related to Gen AI. We want to make it approachable so people understand what it can do. And while the product and engineering teams are often running, doing the most amazing things with Gen AI, some other teams are not there yet. And that's okay because this is new to all of us. We want people to grow comfort levels by learning, by going through these educational experiences that could be through learning paths, through, you know, different tools that are out there that provide, uh, training. But we also want to work closely with those people specifically and help work with them closely so that they see what is happening, what goes into building the agent. What have you helped surface as a need to be automated? We could even get as deep as, you know, like, what do you spend all day doing that is a very manual, arduous task for you? What can you dream up as being something that you would prefer not to do in favor of doing something else that has a higher value? And just having those human discussions with people who are afraid that we've got robots taking over the earth, um, you know, helps them realize that this is just another way that we can help grow and scale our company. And we, we can do it in, you know, like, um, an area of a safe framework and through governance that we've been setting up. So those are, um, you know, like all the areas that I'm seeing where people who have reticence about exploring the boundaries of Gen AI can get a little bit more comfortable. But it's a work in progress. People have questions about it all day, every day. And we are availing ourselves to make sure that we help grow that comfort level across all areas.

Speaker B: And it is, I mean, I totally get it. I mean, it's, uh, it's quite different. It's maybe the first time I feel, at least in my career where, you know, when we talked about always seemed like, you know, the analogy was like, you know, their applications, their system that kind of were system. They were like machines. You were thinking of computer, uh, as a machine, right? Or this application is, you know, some kind of, um, machine. Now you're almost feeling like these AI agents, they're like colleagues. They're like not. I think, I think we're crossing that borderline between like, is it a machine or is it. Do I have to think about this just like a, uh, colleague, like an employee where, you know, I got to do one on ones I Got to do performance review, you know, all of that kind of stuff. I mean, it can be frightening, but it's also, uh, just unbelievable.

Speaker A: They're very smart.

Speaker B: They're very smart. I mean, in certain cases they're. And they're very smart at so many different things, which is, uh, uh, which is crazy. You know, the one thing that blew my mind in the olden days, you know, you mentioned accounts payable or accounts receivable. Right? Um, you know, we had this document processing software that would detect like, what, what it, what that is, like from a PDF or even from back in the olden days from a written document. Um, and you know, it would work in English. And then of course you had to like, do it for every single other language separately. Now comes Genai. And you build a detection based on Genai. And it, you put a Korean document in and it works, and you go. You put in a Hebrew document and it works and you go, like, how is that even possible? But it's crazy. It's crazy. Uh, also the, I mean, you mentioned legal. Legal. I think there is. I, uh, mean, this feels like a breakthrough because, uh, you know, these contracts, like 25, 30 page. I mean, you know, we used to do this on invoices, which is like one piece of paper, right? That with a table and some. It seems like nothing compared to like a 25 page legal document that's written. And now, you know, we, we tried it out with Gemini 2.5. It actually understood the whole contract. It extracted the right structured data that needed to know. And so you can automate things. You could just never could have automated before. It's, it's incredible. Um, but, but I'm, I'm actually curious. So, so there's all this hype and we're getting all this, and I think there's a really exciting things happening. And then at the same time, I wake up the other week and there's this new study that says, hey, 95% of all AI pilots are failing. And you go like, okay, so that's, that's weird. Uh, I, I'm curious what's, what's your take on that? Is that, I mean, does that make sense to you? And then maybe also if it, if, if it does, like, how, how can we get past these kind of hurdles?

Speaker A: It does make sense to me and I've seen that happen. So there's a, there's a couple different aspects that could contribute to, um, a failure. That's um, you know, a very absolute term, but it's true. And the way that I have observed this not working well or not being able to see to completion has to do with a couple of areas. One of them is because we don't have the right kind of alignment with our business partners that we're trying to help. So let's say we, um, you know, we'll use accounts payable as an example. Uh, we're supposed to be working with them on helping them build an agent that is going to automate the work that they do for, um, you know, you mentioned ocr. Um, you know, so very similar to that, where there's work that's done out of a mailbox, receiving, uh, documents, opening those documents, transferring data into another system, all of those types of things. We want to try to automate as much of that as we possibly can to help that group out there, you know, like struggling with the amount of work that comes in. And we can certainly lighten the load now that could, uh, that automation could fail if we don't take the time to understand all of the aspects of their requirements. And while we talked about a little while ago that there are groups that are very capable, the groups that are less understanding of what's needed. We owe it to them to sit there and, you know, and understand the alignment that we have to have. What is it? What's the problem they're trying to solve? Um, you know, there you can use AI to create AI, like, you know, you have power automate, sit in the background of what somebody's doing all day and you could have it run based on certain, uh, you know, parameters and tasks that they're doing. But if we automate all of those things, then we're not doing the right job. We need to make sure that we call through all of the elements that they're looking, all of the pieces of the puzzle they're trying to solve. Um, and, and that could be one area where it's not going to work out completely. You may have automated the wrong thing. You may not have understood what was required. You know, the human element exists. You still have to work closely with different groups and make sure that you're providing the guidance and the understanding. So it's one area. Another area where I have seen things not go completely well is if folks are not operating within the structured framework and governance that we are so carefully crafting. And that could mean that a group doesn't understand that they should come to. We've built an AI council. We have a, uh, clearly communicated acceptable use policy for AI and we're developing every day new processes to help make sure that people can get what they need and that they have the right support and they can experiment to their heart's content. But it's in a place that we feel confident and is safe and secure. So it's, uh, you know, we're not trying to add more work to the system by creating all of these extra processes and frameworks and things like that. We're actually trying to create, uh, you know, a smooth transition for people that want to test. But if they're doing that outside of the boundaries of the framework that we're setting up, then they could be going off on some sort of path to testing out a product that we wouldn't necessarily feel comfortable with implementing. Or it could be that we have another answer to an AI agent that they're trying to solve for. And it's with tooling that we have already sanctioned and feel confident about. And in that case they could be spending a lot of extra time trying to test, um, some sort of tooling or application or solution that would not pass muster with the governance and the practices that we've set. So those are two areas where I've seen things not go so well. And then, you know, like the alternative to that would be people are following the guidance that we're setting out, the parameters that we're setting and we're working closely to make sure that we are, number one, understanding what folks want to automate or create agents around. Um, and we're showing up and paying careful attention and asking all the questions that we believe should be asked. And we're also making sure to communicate the frameworks that people should operate in when they want to test new things and be creative.

Speaker B: Yeah, it's really interesting. So governance, security in general. I'm actually curious, I mean, you're Barracuda, the cybersecurity, uh, company. I'm thinking of these agents and I do get worried. I have elderly parents. I think you can do things today where you can swindle people out of money with these almost real life agents that, uh, are criminals. I mean, I think it's very, I'm actually curious what you guys think, uh, at Barracuda, like what if there's new cyber threats that these kind of agents are going to launch at us? I mean, I can see that very clearly. You almost, I mean, I don't know how long it's going to take, but it's going to be really, really hard to differentiate. Is that someone real? Is that someone not real? Right? Is that.

Speaker A: That is real?

Speaker B: Mhm.

Speaker A: Markus, that is very, very real. So if you think about the speed with which Gen AI is providing a company that is operating inside of all of the ethical boundaries they should be that making them able to go faster, to scale more quickly, to adopt things more readily. Think about the speed with which it's allowing threat actors to use Gen AI to create all kinds of additional vulnerabilities, to create social engineering scams that are difficult to detect. As you said, think about the ways that spam, um, and phishing has changed dramatically in the last year alone. The fact that there are almost no misspelled words any longer because you can use Gen AI to make sure that the sentiment, the voice that you have in your email sounds exactly like what the person would expect receiving that email. We have to be more vigilant now than ever because of all of these reasons. And so yes we are seeing that. That is absolutely a trend.

Speaker B: I can, I can totally see that with the uh, with the emails. Uh, because you know I had a positive example just so people don't get too scared. But positive uh, example was a sales email. You know, so we had this situation where our CEO had to write a very high stake email to actually a chairman of a prospect. Uh, and so you got, I mean it's got to be perfect, right? Can't be too long, it's going to hit exactly the topics and so on. And it was hilarious because we were all, I mean me included a lot of execs, our CRO, uh, we were all on discussing on email like what to write but nobody would give it a try because we're all scared, you know, to do it wrong way. And actually one of my team members took that email thread and put it into AI and then said like hey, um, now can you take that discussion and actually create that email that the CEO should write? Um, it's got to be that long to the chairman and so on. And it was incredible. I mean I would argue, I mean we are all sitting there and going like well none of us obviously figured uh, out how to do that. And here in 10 seconds it was there and it was really, I mean we all looked at it and we're like that's exactly how our CEO would write it. It hits the style, it hits all the topics. It's, it was perfect. Uh, but yeah you can, I mean that perfection. You can, if you're a bad actor you can use uh, in really bad ways. I can, I can totally see that. Tell me a little, I mean so one of others so we were touching that one limit of, you know, maybe not it fails because it's not in the limits of governance, or maybe people get scared about it. I think we at Workado, we see sometimes the other side also, which is, um, people got inspired. What Everybody knows is ChatGPT, right? And ChatGPT is like you ask it questions and you get an answer back. So it's English in, English out. And then somehow we feel that the real value may not be in that paradigm. We feel the real value really is when the agent is interacting in the actual business process, meaning doing things, putting some information into the SAP system, or looking it up from the salesforce CRM system. That connectedness, we call it being an agent at the core rather than an agent at the edge. Uh, I don't know, does that make sense? I mean, I'm just curious also, just in general about business processes, how do you think about that? I mean, given that we both worked on that decades back, how do you see that changing with the agents when you do this in practice in your company? Are, uh, people thinking differently about processes now?

Speaker A: I know I'm thinking differently about processes now, and I'm seeing people who are getting more familiar with the concept of automation enabled by these AI solutions change as well. So just to set the stage, um, not that long ago, if you wanted to use, um, robotic process automation, you yourself had to develop the Level 3 to Level 5, to whatever process diagram, step by step so that you could take that particular task and automate it using rpa. Now you don't have to do that. As much automation to the core as, uh, I love that term, I would like to borrow it. I think that's so interesting to think about it in that way. But think about how getting everybody who's part of a process to divulge what it is they do. It may not be that they want to keep it a secret. It may be that just so natural to them, they don't even think about it. And if you were using these older tools to do this, you are missing out on so many parts of the process because, you know, said person kind of didn't think it was important to put that into the, to the process diagram or to automate or, you know, like they just forgot about it. Now you have AI running on the back background of what somebody's doing. They're observing what those processes are and they are creating the suggestions for you of what you can and should automate. It is an incredible paradigm shift. Now, do you want to have business Processes that you're automating that are, uh, look like a pile of spaghetti. Hopefully not. And hopefully you're using some of this as a forcing function to simplify your processes. That should always be a pursuit. I think when companies get of a certain age and vintage, they will inevitably have processes that are a little more arduous than they should or could be. But nevertheless, you no longer have to sit there and ask every single person that's involved in a process to document that and then turn that into something that is automated. It's quite incredible. It's. I, I love seeing it to some of the other points you were making. It is absolutely amazing because you, you know, you just can't believe that it's getting all of the details. But it is, I mean, cross check it, you know, check it a few times to make sure it didn't hallucinate or, or get something incorrect. But for, by and large it's really capturing stuff.

Speaker B: And actually, I mean, I, I have to say people talk a lot about hallucinations. Right? Uh, and, and Yeah, I mean, LLMs lie probably just like a lot of people do too. But the, um, the reality is if you have these agents in the process and you give it a couple of things they can do, like look, find those, we call them skills like what they can do, actions they can take, and, and, and information they can gather and you keep that constraint. It's actually quite different from the chatgpt where it can be anything in English that comes out. So, so our experience has been hallucinations actually much less of a problem, uh, in, in the, in the process world. Um, it is so funny that you mention that. I mean, you were trying to be really careful about it. Um, but this is totally my experience that I think people are fundamentally bad at processes. Maybe I'm very pessimistic on that, but I've done this for a while and I was always surprised how hard it was to actually figure out this process. Maybe people don't want to think about processes. Maybe. I think at the end of the day, I do believe the writing down these processes, or it's actually not even writing down. It's like drawing these diagrams of the processes in a way is kind of more analogous to a programming task than anything else. Right. It's kind of like visual programming. That's kind of what it is.

Speaker A: It really is.

Speaker B: And I mean not everybody's a programmer, so I think the people who don't have that part of the brain so. Well, I think, I think it probably doesn't resonate. But to your point, now with these agents, they don't have to be programmers anymore. They, they can write it down in English, they can write a little essay about what they do every day, and then the agent will figure out and create that process from it. And I think that is. That is fascinating. I. The way I describe it to people is that instead of doing the step by step of a process, a business process, before, right now all you do is you describe the actors in a process. It's almost like you got a little play and you characterize what each actor has to do, and then they'll just figure it out together. So the process kind of emerges without you describing it, uh, like explicitly. And that is. That's huge. I mean, I haven't seen anything like that in, honestly, the 30 years that we're talking about here. That, that is, uh, crazy. So, actually, last question. Um, and that goes back to the, you know, we talked a little bit about the book that we put out and the podcast called the New Automation Mindset and so on. And obviously we wrote that book before Gen AI happened. Uh, but there are a couple things. So we talked about how there's a process mindset. We talked about that, you know, innovation mindset, I think is just always rethinking how to do that. But I want to specifically talk about the scale mindset, um, because scales mindset to us was really, really important when we started, uh, what we did. Right. And it was that we felt, hey, it's really not good if only like three or maybe five people at max can do something in a company. And if you think about business process, if you think about how the business operates and so on, the more people you can get into the boat, the more results and outcomes you will get. And, you know, we tried in the old world to simplify wherever we could. Right. Uh, but I think now what we just talked about is the fact that you don't have to even lay out this process diagram that you can just write down, you know, your thoughts in an essay or, or maybe even you can just give it all transactions and say, like, hey, input output and like, you know, figure out the process so that when this goes in, this comes out, uh, and maybe write just a little, a little paragraph on it, um, and give the agent a job description like, you know, how to do in hr. Uh, you can get there. What do you think? I mean, I'm actually really excited about how this will be the next step in scaling technology to everybody in a company. And Also getting a, a dialogue sometimes it's not like the business is doing this all themselves, but I think getting to that dialogue between the business and the IT organization and working together, I think that's very powerful. Which, which in the 90s, I don't think that was there. It's like me, I was drawing the visual diagrams and then the IT organization, I had some customers like that, they then programmed it in cobol. I mean these people would never talk because they live in completely different worlds. Um, I don't know. Are you seeing any of that? Do you see that, uh, getting more pervasive? And do you see that scale mindset happening? Uh, now with AI, I'm seeing so

Speaker A: scale mindset is so essential in life. Just uh, as a human being able to see things in terms of scale or being able to grow something and scale something as a leader is your stewardship to the company that you work for and yourself as a person and as a business leader. I have to ask myself every day for every, you know, like as I context switch all day long from one opportunity to another potential issue that we're trying to resolve and you know, an innovation that we're trying to consume any type of thing, I say to myself, if we do this this way, will this scale? Am I creating a single point of failure? Am I adding too many snowflakes? Is my process, uh, not standardized? But I say that to myself every single day. That resonates with me and incredibly because I just think it's, it's something that is inside of me that I just care about so deeply because I've seen in all of the places that I work it, uh, you know, no matter where I am, this concept of doing things in a non scalable way can stymie the growth of a company that could apply to the smallest of tasks. It could apply to the way that you onboard your employees, to the way that our product and engineering team launches their products. Um, it could apply to the way that we consider compliance or even the way that we want to roll out Gen AI solutions to our environment. The interesting thing about scale is and Genai and the way that they sort of coalesce with each other is that Genai is creating this like democratization of being able to help your environment, your team, your company scale. You're putting the tools and the solutions in the hands of everybody that is there to give them the opportunities to help. Um, and so scale, you know, I see that certain people don't have it in their minds to consider it as they're going through their day to day. But I believe that there is just a complete paradigm shift with Gen AI and the concept of scaling it could, if you use it for all the right reasons, could really make a massive difference for your company.

Speaker B: Mhm. And I think it's a trajectory. It seems to me like if I go back again, if we talk about the old times, um, I was thinking about CRM. Right. I don't know if you remember the world when Siebel was there, for example. I mean, everything was on prem. It was so complicated. Right. I mean, you needed like so many people to make this work and you paid a lot of money and, and, and, uh, you know, then Salesforce came about and it took the infrastructure away as a concern. It. It kind of simplified things. It got better. And. And I do. I think you're right. I think AI is another dimension that we're leaping forward in that. In that journey to taking away the complexity of it, uh, which is, um. I mean, to me, it feels like that that's what needs to happen.

Speaker A: Yeah, I agree.

Speaker B: Cool. Uh, listen, this was fascinating, Siri. I really, really enjoyed it. Uh, I hope the audience also learns a lot of it. I think you said a lot of things. Uh, well, a lot of things resonated with me. You know, I'm on that journey to kind of figure out AI transformation, um, and, you know, how people should think about it. And I believe there was a lot of gold here in this conversation today, so thank you so much.

Speaker A: Well, thank you, Marcus. It was an absolute pleasure to be talking about a topic that is growing with intensity and curiosity every single moment. So it was a pleasure to be with you today.

Speaker B: Thank you. Thank you so much for tuning into today's episode. Please make sure to rate today's episode. Leave us a comment with your thoughts and subscribe to the show so you will never miss an episode. I'll see you next time.