The Data Foundation for AI: Daniel Cohen-Dumani of Experio AI on Balancing Centralization, Governance, and Agility

Speaker A: Welcome to Evolving the Enterprise, a podcast that brings together thought leaders from the

Speaker B: worlds of data automation, AI integration and more.

Speaker A: Join snaplogic's Chief Marketing Officer, Dale hall, as we dive into the captivating stories of enterprise technology successes and failures through lively discussions with industry leading executives and experts.

Speaker B: Together we'll explore real world challenges and

Speaker A: opportunities that companies face as, uh, they

Speaker B: reshape the future of work. Hi out there. Welcome to the latest episode of Evolving the Enterprise. I'm your host, Dale hall, the CMO of snaplogic. This is the show where we explore how organizations are, uh, transforming with AI and multiple aspects of innovation. This season we've heard repeatedly that data is the foundation of every AI strategy. You may have heard that yourself. There are a few brands out there claiming that snaplogic too. But the big question remains, how should that data actually be structured and governed in a world where AI and agents are now heavily reliant on that data? Particularly in environments where there might be regulatory compliance or constant changes trying to get access to this type of data? Should an enterprise centralize everything? Decentralize? Should they have data lakes, data mesh? How do they actually make all these data sources work together to help us navigate this today I'm joined by Daniel Cohen Dumani, who is the CEO and founder of Experio. They're basically a recognized leader in enterprise transformation. And Daniel has guided global organizations through complex digital strategies and today he'll share how leaders can make the right choices to future proof their data and AI ecosystems. A hot topic. Daniel, welcome to the show.

Speaker A: Indeed. Thanks for having me. It's a pleasure to be here today.

Speaker B: Yeah, it's going to be a great one. Obviously it's a big topic. Before we actually kick in on some of the questions, give us a little bit of background on yourself, a little bit about experio and how you came to be at uh, this inflection point in our industry where AI is pretty much taken off.

Speaker A: Uh, absolutely. And for matter background, I've been in technology consulting for three decades, starting my career in consulting. I'm from originally Switzerland and moved to the US in 98. And in 2002 I uh, started a consulting business on my own focusing on knowledge and data management solution.

Speaker B: Right.

Speaker A: And that's what I got engaged into, strategy around, um, how to manage data at scale and knowledge at scale. And in 2023 I got the bug of AI and decided consulting was in the past and I wanted to focus on solving a problem that I've seen prevalent in the entire consulting industry, which is the inability to Find content and information. So that gave the birth to Experia with the first a gentic AI solution for the consulting industry. So I'm super excited to talk about data strategy, which is really at the core of what we do.

Speaker B: Yeah. When you say things like, you've been doing this for three decades. Me too, mate. So we're both old hands at this, but it's always good to see some of this new technology take off. And it's crazy because I talk to my kids, both in high school now, and I just think about what they're going to be experiencing, what I'm experiencing now, and how exciting it is, but what they're going to experience when they come out of college and what the work environment's going to be. It's exciting.

Speaker A: Exciting. It's scary too, right?

Speaker B: It definitely is. All right, let's start with some of the fundamentals before we get really into the AI piece and leveraging it and building ownership and governance and so on. Let's start with the basics around data architecture. So it's still very important and probably more important than ever. So in your opinion, why is data structure so critical? Just for the enterprise in general, but obviously, as we start to look at leveraging AI, give us the basics.

Speaker A: It's a great question to start and set the stage about the importance of data. When you think about AI, all the outcomes are truly gated by the data structure. Right. So if you don't have clean way to capture the data at its source, have rich metadata and relationship between those data sets, any AI model will start elucidating and the value that you're trying to derive, uh, from AI will evaporate, literally. Architecture is the foundation of, uh, giving AI some context and value that can provide better outcomes.

Speaker B: Yeah. Do you find when you talk to customers, clients, or the people in and around the industry, it feels like because we're in it all, we're in it every day, we're thinking about these kind of things. We talk to customers, clients. Is that generally understood? Do people understand that that is still critical? And do people have a sense that, oh, uh, structure's fined? Do they really know what's going on under the covers?

Speaker A: No. Okay.

Speaker B: All right, good. That's what I was hoping you'd say.

Speaker A: Yeah. There's a common understanding that data is important. I think that has become prevalent over the last five to 10 years, but they don't understand why. And I think it's bridging that gap. That's been a challenge over the last two to Three years.

Speaker B: And so I think again, as I talk to people on these podcasts, as I talk to customers and prospects, there's generally an exc acceptance that they know they need to improve their systems, their architecture for data. A lot of the challenge is just that there still have most enterprises. Okay, maybe some of the newer startups don't have as much of a legacy setup, but a lot of these bigger enterprises have a lot of legacy systems, legacy data source. Sometimes they can't make that connection. What would you say? Is that the biggest pain point in the enterprise right now or is it something else?

Speaker A: Yeah, one of the biggest pain points is truly what I call this fragmented organizational memory, which is where's the data and why is it so siloed across different system? Right. So, uh, I think we're still following that paradigm of I buy a CRM system, the data will be in the CRM system by an ERP system. So it's highly siloed and fragmented. And we've seen a trend over the last 10 years of, okay, we need to start integrating all of those systems. Right. But it's still very difficult to find the true source of record. Right. So if you're dealing with customer, I'm sure you have customer data everywhere.

Speaker B: Yeah.

Speaker A: Where's the true one source of record? How can you tell AI this is really that place where you should look for customer. Most organizations have no idea.

Speaker B: Yeah. When you talk to these organizations, is that the baseline that you start with? Like, we just need to know all the pieces of data where you have it. That's the only way we can get going. And I can imagine if you're a client or a customer and you think about that, holy crap, we got to define all this before we can do anything. And then there's this discussion around if you want to use AI, uh, you start with the quick wins, you get a quick win, and it grows from there. How do you balance the quick win with? Holy crap, we have to figure out where all our data is before we know what we can actually use. How do you balance that?

Speaker A: Yeah, it's a great question. And I think when you've talked to executive and say to get better artificial intelligence in your organization, you need to clean up your data, they will look at you and say, it's going to take us 10 years. It's a massive task. And it is totally. And I think instead of that approach has to be, all right, let's focus on one specific use case. And I said, let's identify what are the source of the data or the data sets involved in answering good questions for that specific use case. And start small. Right. And use that as a baseline to expand further.

Speaker B: Which side of the fence do most customers or uh, most clients currently sit on? Do they sit on? We really need to get going because they're just trying to get some kind of project up and running. And how hard is it to persuade someone that you really need to understand all the pieces of the data? Like if you were going in to advise someone today and they're conflicted.

Speaker A: Yeah, I would say do both because you know that the long term you need a unified data architecture, but knowing how hard it is starts small. So think about your long term goal, unified data architecture. But maybe start with one small use cases at the same time.

Speaker B: Right.

Speaker A: Because you're going to learn a lot from there and maybe you're going to realize that maybe we can decentralize those domain of data and knowledge in like small pieces and mesh them together as opposed to try to solve it all at uh, once. Which is scary for a lot of organization.

Speaker B: Yeah, for sure. One of the things that we hear more about with AI is the business units, the functions, the business users, ah, are driving some of that requirement. So because the data is so critically important, do you see the businesses driving some of these initiatives? But obviously IT and the technical side of the house have to be involved because of the data or are you seeing more of the it know that they have to make some of those changes around the data so they're still driving the bigger initiatives. Where, who's coming to you first? Who are you talking to more? Is it the business side or the technology side?

Speaker A: We're talking more to the business side of course, because they have challenges and business problems to solve.

Speaker B: Right.

Speaker A: They could care less where IT is, they just want them solved asap. Right?

Speaker B: Yeah.

Speaker A: But then, yes, then their conversations with shift to okay, where's your data set? How are we going to access them? What does that mean? What's the meaning of that data? And that's where I think there's constant conflict. Right. Of uh, back and forth. It will tend to slow it down. They think they can do it themselves. They don't want outside, uh, vendor to come in. So I think we see that conflict appear almost every time.

Speaker B: Yeah. If you listen to this podcast, what I try and think about is what could someone else take from this and doing their regular job. So my question to you around that specifically, and if someone's listening to this, they may be faced with this challenge. How do you help the business users get closer to it and have them move faster? And then how do you persuade or influence it to say, look, it's not about whether you build this yourself, it's about moving fast, it's about better way to solve business challenges. How can you help or how do you think about helping those two pieces come together so you can actually get a better outcome?

Speaker A: It's a great question Dale. And I think that it really hasn't changed over the last few decades I've been in this business, but I think AI has brought it up um, to a certain degree and I think the critical aspect is quantifying the pain of the those business user and what is the return on investment in solving that pain? We've always talked about ROI on technical projects. Right. But when you think about AI is that much more critical because the return on investment can be dramatically higher than anything else you've ever implemented before. Right. Just of the nature of what AI can solve. So I think the business folks have to focus on qualifying how painful it is and how much better can our life be, how much money can we save, how much revenue can you deliver or how much more revenue can you deliver by having this solution as a means to put some pressure in urgency to solve it now?

Speaker B: Yeah, it's interesting because I'm sure if you can scope a project like this focused on the business outcomes and how it will improve, I don't know, could be efficiency, it could be top line, could be bottom line, it doesn't really matter. But I feel like that's where the business is more unique. To help the senior level execs make decisions on funding where I think sometimes it is seen more of, they actually want to improve things too. But it's a little bit harder for that organization typically to directly link it to business value. So actually the two teams working together is probably a unique and mutually beneficial relationship, wouldn't you say?

Speaker A: No doubt.

Speaker B: Yeah.

Speaker A: Yeah. And I've seen a client come to me and say we love your product. It sounds like it's going to solve value, but we can't do anything until we solve our data a problem. We don't know what the data is. And I said that may be true but I feel like always start small with anything you do. Don't try to bow the ocean because you are never going to get there. You win a world that's moving so fast that making five year plan to fix your data architecture is becoming meaningless. Right. I think you have to move fast, find a way to Be agile and move at a fast speed, otherwise your business won't be here five years from now.

Speaker B: Yeah, and I like that because again, you and I have been around a long time. We've been through tons of m, moving to the cloud, CRM, erp, all these technologies and typically those projects have been massive multi year projects. And now like you said with AI, if you don't start making changes now, don't make a five year plan, everything's going to be different next year. So. Oh yeah, yeah. But how can a business really balance that? How can they literally sit down and say, okay, we're going to make this plan. It's going to be a six to nine months project. But, but they still have to have a roadmap. How do they prioritize and resource that internally in your experience?

Speaker A: I've seen that change truly over the last probably five years, but accurately over the last two years is the willingness to try and experiment.

Speaker B: Okay.

Speaker A: You know, back in the days you were doing proof of concept and I think that those tend to take a long time. But with AI technology and agents and products, you can do a proof of concept or even approve of value in two weeks. Week.

Speaker B: Yeah.

Speaker A: So you can deploy something that will show value extremely quickly. And I think the business that are being successful are the one that are willing to take a risk of. Uh, let's experiment. You know, we're going to continue with planning, but in the meantime let's try to see if we can solve this problem and see if there's new way of doing this. Specifically in the age of AI that we all live in. Yeah, you have to be willing to take the risk and experiment.

Speaker B: Yeah, I feel like across the enterprises that I've talked to, because AI is such a big impact and influence, there's still some concern obviously and again this is why it's going to come back to that data side. But there's definitely a willingness to experiment where I think moving, changing ERP applications or implementing a new marketing automation tool felt like there was a lot of like assessments and thinking and planning and strategy. And now because of AI there's a little bit more. If we can control some of the access to the data, we can do a proof of concept way quicker.

Speaker A: Definitely. And I think the IT projects, uh, often in the time we've been around top, bottom, right, someone at the top say we need to fix this and then it flows down the bottom to the bottom of the organization that have to say, okay, now we're going to have a new ERP System. Yeah. AI is bringing sort of this split upside down approach where people at the bottom say, you know what, I'm going to experiment and make my life easier today.

Speaker B: Yeah.

Speaker A: And I'm going to push it up.

Speaker B: Yeah.

Speaker A: Because we can do that. And you've seen the adoption of AI. Uh, there was a survey I think came out about three weeks ago that was showing that 68% of employees are uh, using AI without telling their boss that they are. Right. So it's staggering when you think about it. So people have found ways to go and overcome all the blocks you can do to actually leverage AI because they realize it's going to make their life easier. We never seen that before. Right. I think before it was always, you know, we talk about shadow it for a long time but it's taking promotion like we've never seen before.

Speaker B: Yeah. It feels like uh, and I have heard the term shadow AI too built on the concept of the shadow it, but it feels like there's definitely a bigger uh, more groundswell of support for people wanting to use this because I think across the business they can see value. So the 68%, that's a really big number and we can get on to like governance and so on in a second. But how do you balance the need to move quickly and try things with that, uh, putting controls in place? Because sometimes if you've got controls or like a council or a board that makes decisions that can stifle innovation and which seems like the antithesis of what AI should, should be. So how are enterprises managing that and what have you seen work?

Speaker A: So the way I've seen it, and I think that lay out well to deploying Xperia AI into organization is every organization think about, okay, if AI is going to access our data, how do we going to manage security and access to that content that's being exposed to AI? And that's true. And that's complicated. Right. Because we used to come from a world where in a document management system you will have access to the top folder and then so forth or like a hierarchical type security. Right. Those kind of security models don't work well for AI because maybe you can see the summary but not the detail. So it's turning upside down. How do you think about accessing data? So our recommendation has always been let's create an initial version of this solution or let's deploy where everyone can see everything, store the value and then we're going to come back and think about what's the best approach to secure access. Right. Are you going to use the same antiquated method you use in the past where it's folder based or are you going to think it through about we have so much great content we can provide our organization and yet before it was behind a security wall. Now we're going to be more thoughtful about what we can let people access to. So you have to rethink completely about this data security what can see what in the age of AI for sure. And that's a big job, that is.

Speaker B: Yeah, we're going to get into that a little bit more in a second. But just coming back to the data side and where to find the information. You said that you have initial projects where you know where the data is to get going and then really try and figure out your overall architecture for bigger initiatives and moving forward. Is there a difference between organizations that maybe have a very centralized data structure or that may be using more decentralized. They have a data mesh M setup where different departments can access different data at different times. Less centralized. Does that impact how successful AI can be or does it just mean you just use it in a different way? Like how are enterprises managing the two.

Speaker A: Yeah, interesting question, Dale. And so when you think about the different architecture, a perfect world is everything centralized and ready to go and this clear definition and no data redundancy and everything is great. Right. But the reality is never that pretty. I think it depends very much of the maturity of the organization and I think the size of the organization. The bigger you get, the harder it becomes to be fully centralized. Right. Because there is so much data being consumed and created every day. And what I mean by data, I just don't mean database. Right. Or data like documents, image, videos, all of that is data. Right. So I think that we're definitely seeing both approach and I think the data mesh is becoming more and more prevalent nowadays just because of the sheer amount of complexity of centralizing everything and the time it takes to centralize everything. And smart organization understand that we are going to create specific, specific domain and create a mesh of domain specific. So we have the ordering department here, we have the marketing department here and they all can be responsible to all their data. Uh, yeah, we're going to define a contract between those different data sources and some rules on how you can communicate. We're seeing more and more of that and I think AI is driving that move because you want data and you want data fast.

Speaker B: Yeah. Again, I think while there may have been some reticence to allow access to other parts of the business, they're going want Anyone else messing with it or whatever, if they've already set up a decentralized function AI, the opportunity, the only way we'll really get benefit is to allow access to it. So I think that is helping. Do you think that building agents or LLM driven applications, is that changing? That's essentially what you're saying is that's changing the debate around data architecture because people know that they need to have a different setup or they need more access to data to really benefit it.

Speaker A: Yeah, agentic AI is definitely taking a step back and not in a bad way. You know, in the beginning of gen AI, if you wanted to do some rag or some way of grounding a model on your data, you needed a centralized place and we needed to tap into a well organized, hopefully semantically organized data source. Right. And at the heart of xpo we use knowledge graphs. Right. So there's been a lot of talk about how do you ground a model with good data and knowledge graph as emerge as this driven technology. Now what's interesting is agents are allowed to go on their own autonomously, go in your ERP system and get the information it needs.

Speaker B: Yeah.

Speaker A: And as long as the agent is capable, uh, of determine with high probability or the right approach where to go get that data. Now we remove the need for this completely centralized data. What we need is good semantic representation of where your data is. Yeah, maybe some common data sets that have been brought together, but you don't need to bring everything when you want to get the detail. The agent will know on how to connect to this erp, CRM, whatever system to be able to go grab the detail of the data that was not available in the big repositories. Yeah. So agent is taking us a step back for the good way or relieving the pain or the challenge of having this big data warehouse. And now we can have small data sets, well organized, and then agents going and taking what they need when they need it.

Speaker B: Yeah, I felt like it wasn't that long ago, a year ago, when generative AI really started to take hold. There was obviously some concern about access to data, uh, hallucinations and so on. Feel like we've moved past that a little bit. There's a little bit more trust. Do you think most enterprises will fully embrace this agent model where particularly a lot of these autonomous agents that are going to not only just go get the data and give you some insights, but actually make decisions. Is there an openness for enterprises to do that? You just mentioned yourself like it's giving us a Step back to make sure that we're setting this up in the right way, the security and governance. But are people embracing autonomous agents as much as they did with gen AI, or is it a little bit more skepticism?

Speaker A: There's definitely skepticism, but there's also hype on agent in that we've seen in 2025. I think there's still a misunderstanding of what an AI agent is. For some people, it's just a workflow. Automation. Right. And an AI agent is not just a trigger that gets triggered when you receive an email and do something in your behalf. That is a workflow that's been doing that for 15, 20 years. Right?

Speaker B: Yeah.

Speaker A: I think an AI agent is more specific. It can define a plan, it can achieve the plan autonomously, and it can determine what tool to use to achieve the plan. And that's including calling other agents to do things on its behalf.

Speaker B: Yeah.

Speaker A: So agents are really more complicated than most people understand it.

Speaker B: Yeah.

Speaker A: And for that matter, the debate is still on. Right. Most enterprises are now saying they're experimenting with agents. I would say most of them are still trying to automate more than agent.

Speaker B: So that's my point. Look, I'm in tech, I'm a marketer, Right. So we love creating a story and we love saying all the things that's going right. But like, when I see a big CRM vendor say they've got 10,000 agents in production, I just don't buy it.

Speaker A: I don't.

Speaker B: You know, that guy Mark Benioff is the best marketer on the planet. I give him that. But it's not the reality of when we talk to customers and prospects, when we talk to our partners. Yes, people are experimenting, but it's like there is so much hype. And I feel like those kind of statements don't actually help people to actually trust what's going on. It's almost like they do it to just create fomo. Everyone's got to jump in and create something. But I feel like that could have a negative effect if they don't get these wins. If they buy into the hype, they don't see business success. It actually could be really detrimental.

Speaker A: It is. And I think we're seeing it. It start with this semantic misunderstanding of what an agent is. Right. In the hype driven by Salesforce and everyone that's doing agent, that's claiming usage of agent. And when you drill down and look at those agents. Yeah, not agent, they are workflow with LLM enabled, which are, uh, just. Okay, we use an LLM as Part of a workflow process, fine, that's not an agent, but they call it that way. So I think that we're still in that, that year of transition. I think we're going to see in 2026 and beyond real agentic use cases that solve real business problem that are truly implementing the concept of an agent. And yes, some organization are, uh, doing it very successfully, but a lot of failing to, failing to realize value.

Speaker B: Again, I'm not mentioning the company names, but there's a CEO that came out recently and said that first of all he said was going to get rid of himself for a generative AI market, but said that it was getting rid of a lot of people because they were going to have agents to do it and then six weeks later had to backtrack because you know, you can't just throw this into the mix.

Speaker A: Yeah.

Speaker B: And I think this comes back to then the kind of like output and balancing risk and governance. So as we've talked about, who is interested in the AI models? It's both the business side and the IT side. And in general it usually is responsible for the data. But with AI being more prevalent in the business, in the functions, who's the best person to own data, uh, particularly in say in a decentralized model. And how does that really affect the accountability of the business unit or the IT team to make AI successful? Who is ultimately responsible to make this work?

Speaker A: It's definitely the business side that has to be responsible. In my point of view, it's rarely the case because they're the closest one that's actually producing that data. Right. They are entering data in a CRM system or they're creating documents or videos or whatever, you name it, in term of data. Right. And they have to govern that data generation. Right. And they have to own where and how it's being stored and leveraged. Which means now you're seeing more organization that has blended IT and business function, where they have IT people embedded in the business, helping them achieve certain things like owning the business rule on decentralized data.

Speaker B: Yeah.

Speaker A: Governance around that data for sure.

Speaker B: Sure, yeah. In your experience as you've talked to people, what are you seeing as the biggest challenge, risk, issue with that? With a distributed model, when you're thinking about governing the data to make sure it's feeding the right models, you don't have to name any companies. But what are the biggest challenges or what fails are you seeing across some of these customers mistakes they're potentially making?

Speaker A: Yeah, uh, it gets back to balancing the centralized versus Decentralized. I think if you decentralize without a contract or underlying governance then that's going to fail because every department is going to do whatever they want. Everyone's going to start holding their customer data and start adding fields and not talking to anybody else. So that is sort of the risk of decentralization is things get vogue and then um, when you try to put it together you're going to have duplicate customer data to ah, address for customer. And I've seen that with very large organization I met with three months ago that says we're struggling just because we have all those system. We're not able to reconcile customers. We know we have customer in those three system but we don't have any unique identifier on those three systems. That's an example of decentralizers. Decentralization went wrong. The business owner implemented a system without talking with anyone.

Speaker B: Yeah.

Speaker A: And now they're left with algorithm bring it back together. So if you decentralize you have to put a clear definition of uh, roles and responsibility, rules of the game.

Speaker B: Yeah.

Speaker A: Because it all has to mesh back together at some point. So think about the meshing in the world of uh, wi fi or wireless. Right. If people didn't have a standard, couldn't mesh. You can have your own access point like serving 10 client but it couldn't mesh with the other one. And why they can mesh because there is a standard that was defined um, on how to connect. So it's the same thing.

Speaker B: Yeah, it's a good way of thinking of it. I came from wireless technology in my past so I definitely understand that.

Speaker A: Yes.

Speaker B: Yeah. Moving away from the technology side and the data structure, how it's architected. Let's talk a little bit about how organizations are they culturally ready for some of these changes? Because I feel like it's a hot technology, we want to take advantage of it. But sometimes it doesn't necessarily comes down to certain groups that are more risk averse. But it's definitely a cultural shift to allow access to certain pieces of data to get it and business functions more collaborative for uh, the ultimate goal of the business to leverage this latest technology like AI or agents. Do you feel that because of the potential the organizations will just naturally allow their culture to allow this to happen? Or are there things that you see that an organization needs to change their mindset or change some of those cultural values values to really take advantage? And can they actually do that? Would that hold them back from truly taking advantage?

Speaker A: Yeah, I think it's not as much as Culture as, uh, IT is readiness of an organization to embark in more decentralization. I think there have to be a willingness. I think you find some organization where the IT is large and very powerful and very gated in terms of keeping a, uh, hold of this. Right. And preventing other folks to even do anything. Creating major roadblock. Right. So I think the culture has to change with the IT right at the top saying, we know that we can handle everything. We know that data and software is deployable now, uh, faster than ever, including building your own. Right. You don't need to buy, sometimes you just want to build it. You. We have to enable this innovation coming from the business side as fast as we can. And it becomes the godwin. We're going to set up the role, we're going to set up everything. This is a major cultural shift. I would say more on the IT side now, on the business side, of course, that people have to be trained and receptive. Right. So think about literacy on data and AI. So I think you need to have some of that literacy and understanding within the business. Right. So that they can be involved in understanding. Are they going to connect with the centralized function as well?

Speaker B: Yeah. Do you have examples? What does good look like? What does making sure that there's accountability across the business, that they have the right systems. Like, if someone was to ask you, like, what are the three to five things that an organization should do? And again, it was great if it's a readiness and not really a cultural thing, but what are the three readiness things that the organization really needs to think about? Again, if you're listening to this, what are the three things that the person's going to go, Yep, I'm going to go and ask my business now. These are the three things.

Speaker A: I think the culture is still important, Dale. And if you're a control and command organization, it's going to be really hard to shift to a decentralized model where we're going to let people experiment, right? Yes. If you control and command organization, you're going to have to adjust to allow some level of autonomy across your business unit and departments and division. The second thing is it has to start with empowering business folks to be more like IT people to be trained. It used to be a clear separation. Your IT or your business. I think that I've seen smart organizations say we're now putting IT people inside the business organization, reporting to the business owner in this dual relationship. We're it, but we're in the business. We know these things and we know how to do it well now, uh, this is where people are moving. Right. They're nimble team with some IT folks in it so uh, that they can do their own things and move fast. And then the third thing is overall governance and control and training along the organization to be able to handle this paradigm shift. But we're seeing it with AI. We are seeing it. I've been in countless organization where we've seen this team of three business users went on and created an app using this local no code tool. And it's awesome and it's mission critical and it's working. They did it without it, which was the problem because now there's no governance, there's no control, there's no availability. Right. But you've seen other organization allow that and I think that's what we're seeing more and more.

Speaker B: Yeah, that's good. Okay, we're coming towards the end of the podcast and what I want to finish with is something that I generally like to finish with most of these podcasts, which is really getting your insights on what you think, looking ahead, what's coming down the pipe, what are the things we're going to be excited about, but in general, like what are enterprises going to be thinking about? How is it if you're a leader of an enterprise, like, how would you advise them right now in terms of structuring not just their data, but the governance, how you bring together these functional and IT expertise? Like what would you say to them around preparing for the future to really be able to take advantage of this AI wave?

Speaker A: Yeah. So I think you have to think it in several ways. We talk a lot about centralized versus decentralized model. I think enterprise have to look at this decentralization option, look at meshing data moving fast past. Right. And making that shift of embedding IT folks inside of their organization, inside the business organization. I think that's going to become critical as we start embracing agents and agentic AI to have a good balance on agility, speed with governance. And I think it will become sort of a governing, uh, body of decentralized capability across the organization. But of course it's hard to say. Right. I think it's going to matter to see enterprise adjusting to the demand of their business.

Speaker B: Right. Yeah. No, that's interesting. And is there something, I don't want to say a myth, but is there something that you hear from talking to clients, customers in the industry? Is there a myth out there that you want to, uh, debunk? We talked about like agents are everywhere already and I think the reality is there, there's a lot of testing. There's some, but it's not as ubiquitous as we think. Is there anything el that put someone's mind at ease? Don't worry, you're not way behind because everyone's still testing. Like, what is it that you would advise leaders as? Don't worry, you're not behind the eight ball yet.

Speaker A: Yeah, yeah. There's a few myths. I think the first one will be, it's okay, we can wait, you know, we have time. It's all going to be okay. I think that that is a myth that we see a lot of organization thinking that they have time to embrace and that is something that they need to, to debunk quickly. They don't have time. They need to get on the bandwagon and move along. The second myth is for those people embracing AI, that the power of those large language models will keep exponentially evolving. I think this year was very interesting. We've seen new models, but I cannot tell myself as a user that is so much better. Better. I think agents have become better. I think agentic AI and deep research have gotten better, but the model themselves are just not exponentially better. From GPT nothing to 3.5, 3.5 to 44 to 410 or whatever. I think it's. We've seen a slowdown. I think we're gonna, I think until there's a new brand new architecture that goes beyond the transformer, I think we're going to see just a slowdown in exponentially new breakthrough in models. Uh, although, who knows, right? Nobody can predict that. But sort of my point of view, that's what I've been seeing over the last year, which I think gives a little bit of time. So don't think things will just get that much better faster. It will get easier, that's for sure. But there's a myth that things exponentially is going to get better like that. And I think that that's a mistake to look at it that way.

Speaker B: Yeah. And is there something that, like you're really excited about, something that you're looking forward to seeing become more prevalent, more ubiquitous, something that you know within even your own experience, your own industry that you're excited to see, like where AI agents could go in the future?

Speaker A: Yeah, I think, you know, when you think about any business is this ability to become a true co pilot or coworker of any unique human. Right. Where you can delegate truly tasks that you don't want to deal with. And I don't think we're quite there yet. I think it's still manual, but the fact that you could just have an agent that says, you know what, take care of this for me and give me the final results when you're done and they can understand everything that you say in a very short period of time and with voice, hopefully. I think I'm excited about this. I think we're going to see more and more of very small autonomous agent that works really, really well emerge. And I think people are going to get excited and start building on this to get to a point where hopefully, uh, everyone has an army and agents at their disposal to really do the busy, boring work and focus on the creativity and interesting type of work.

Speaker B: Yeah, uh, that's great. Well, look, I appreciate your time. It's been super insightful. A lot of the podcasts that I've done recently, very much AI and agent focused, but I think sometimes you, you underestimate the impact on the data side and how it's set up. So it's absolutely a, uh, uh, mission critical part of making this success. So Daniel, thanks for joining us on the podcast today.

Speaker A: Thanks for having me, Dale. It was, it was a pleasure and great conversation session.

Speaker B: Great. Let's reconnect in six months time or 12 months time, something like that and let's see what's changed. I have ever feeling this is moving so fast. We're going to see a lot of change.

Speaker A: I would love to.

Speaker B: Yeah, great. Thanks for being part of the podcast and for everyone out there, thanks for tuning in to this episode. We'll see you on the next one.

Speaker A: As we wrap up this episode of Evolving the Enterprise, we want to extend our gratitude for joining us on this exploration of enterprise technology. Keep the conversation going. By subscribing, rating and sharing our podcast together, we'll shape the future of work. Until our next episode, stay innovative and stay tuned.