Overcoming Cloud Complexity in Mid Market Operations - with Dirk Michiels of Savaco

Welcome, everyone, to the AI in Business podcast. I'm Matthew DeMello, Editorial Director here at Emerge AI Research. Today's guest is Dirk Michaels, CEO of Cevaco. Cevaco is a Belgium-based managed service provider specializing in hybrid cloud solutions, cybersecurity, and enterprise software implementation, including ERP, customer engagement, and data analytics systems. The company employs 350 professionals across Belgium and India and partners with technology providers such as Microsoft, HP, PTC, and Palo Alto Networks. Michaels joins us on today's program to explore how hybrid cloud architectures are reshaping digital transformation efforts by enabling scalability, security, and agility for modern enterprises. Our conversation also highlights practical workflow improvements, including streamlined integration of legacy systems with cloud platforms, enhanced cybersecurity protocols, and measurable ROI through faster development cycles and reduced operational risks. Today's episode is part of a special series sponsored by Zurint. But first, interested in putting your AI product in front of household names in the Fortune 500? Connect directly with enterprise leaders at market-leading companies. Emerge can position your brand where enterprise decision makers turn for insight, research, and guidance. Visit Emerge.com slash sponsor for more information. Again, that's Emerge.com slash S-P-O-N-S-O-R. Also, are you driving AI transformation at your organization? Or maybe you're guiding critical decisions on AI investment strategy or deployment? Without further ado, here's our conversation with Dirk. Dirk, welcome to the program. It's a great pleasure having you. Thanks for having me, Matthew. Absolutely. More and more often on the show, we're talking more about small and medium-sized organizations. Mid-market organizations are in a particularly challenging position when it comes to IT modernization, as we're going to talk about on today's show. These organizations face many of the same demands as large enterprises, complex service environments, rising cybersecurity expectations, and growing pressure to deliver seamless digital experiences. But they often have to do so without the same depth of resources or specialized talent. Balancing that business agility with operational resilience has become a core struggle, to say the least, especially as hybrid and multi-cloud systems expand the surface area for both innovation and risk. Just given those dynamics, what are you seeing in terms of the main operational challenges mid-market leaders are facing when modernizing IT service operations and infrastructure? Well, excellent question, Matthew. It's obviously very much about business and IT alignment. and that kind of question has been around for a long time. But there is a consistent and persistent misalignment between IT and business. And that is despite the fact that a lot of people have put efforts in modernization. It's very difficult to meet the evolving business requirements and the legacy constraints. And it's just the fact that in the mid-market, there are a lot of legacies available that they need to deal with. And therefore, business students demand rapid deployment and high service liability. But traditional ITSM organizations and toolings and infrastructure are just too slow and too rigid. And this means that the manual change approval process that goes with it is just not fit for the task. And that's something we need to deal with. And obviously, the companies also need to deal with the legacy systems itself and deal with the technical debt that they need to handle. So I think there's also the question about business constraints and ROI justifications. The reality is that mid-market leaders need to balance modernization and cost control and overbuying ITSM services and misaligned investments can really lead to an erosion of the ROI. Absolutely. Just right there, while we're talking about kind of those initial investments, that kind of seems like a catch-22, right? Because the whole process that you're going through to adopt AI is going to be based on you having the best information possible for the best insights. That's where you're going to be at the end of the process. That's really nice to have when you're at the beginning of the process, trying to make the right investments in the right places. You know, but even short of where we're seeing, you know, that sort of catch-22 scenario that I'm painting in kind of the preamble into this question, just for what we're seeing around workforce and skills challenges, how is that shaping IT service management in the mid-market? I think there's on the one hand, obviously, business IT alignment, as you're describing, that's a challenge. But there's also the aspect of technology alignment. And it's all nice talking about AI. And AI is definitely important. And we'll probably go in a bit more detail in this podcast. But the reality is that people are faced with security threats that are impacting their operational organization their IT organization There the complexity of managing on hybrid multi environments There's the aspect that tools are fragmented and that people are working with data silos in different environments, which results in the fact that there's just inconsistent reporting and poor visibility across the service operation. And I think those things need to be handled with. And I forgot to mention, for example, integration. I think that's also a very important thing. People have ITSM tools, but they are not nicely integrated with the fact that actually this leads to operational inefficiencies. And that's where I think that automation and capabilities really come in to handle this issue with dealing with ITSM. And I think therein, you know, there's what often gets called the skills gap, and that's kind of across industries. This is very much at play in the current environment where you have not only AI or AI skills needed, but AI itself is kind of a filter through the hiring process. We've had a lot of conversations where we're doing a concurrent podcast series on the impact of AI capabilities in HR workflows, in hiring workflows. So it's even more catch-22s than we've even noticed towards the beginning of today's show. But really just with these different challenges, especially what we're seeing around the need for certified professionals to manage platforms like Azure, the lack of organizational knowledge in incident problem and change management. How are organizations rethinking productivity and collaboration as AI becomes embedded across their operations? I think there is a dual shortage, actually. there is a shortage of technical skills and people, and there's also a shortage of service management skills. Obviously, we are all driven by the IT technology monsters of the world, and they want us to be certified and be technical specialists in all those new areas. But on the other hand, there's also the aspect that if you want to deal with an IT environment, you need to make sure that you actually have the right communication skills and the right processes in place to do IT service management. And if you look at the recent CIO reports and also from talking to CIOs in our organization, we definitely see that there is a knowledge gap on AI and machine learning, on cybersecurity, on cloud and infrastructure, on data analytics. And that is true in general, but it's specifically also true for ITSM and for ITSM practices. So I think the challenge there is you need to balance that and get that right. And this is actually why, as Salvaco, we have an MSP business where we serve our customers with IT service management, and we build that on the really solid Churn platform. and Xurent helps us to have an integrated environment to deal with that management problem. And obviously this removes a bit of the burden that you need to have people that really understand the ITIL processes inside out. And this allows us to have tooling that is integrated with Microsoft, Logic Monitor, with Alta and many, many others. And actually that has led this year to the fact that we have been elected by Xurant as the MSP of the year, and we're very proud of that, obviously. On the other hand, we also offer these tools as integrated tools to customers. A lot of customers who have a more, let's say, advanced organization, well, they are looking to get the benefits from AI, and once again, that's where we would typically propose Xurant and Xurant tools integrated with all the management environment that you need to manage the right infrastructure. Absolutely. Painting with a broad brush here from your last answer, but there's a lot of nuance that listeners just heard. Generally, though, we're seeing a lot more technology kind of in the place of those skills, right? We're depending a little bit more on platforms like Kindrel and Zurn, as you said, offering that faster time to value and simplified deployment. What effect is that having on go-to-market cycles? I think there's a huge pressure on time to value. I think in general, people can't afford anymore to do one-year or two-year implementations of an environment. They really want to have something that can be available as fast as possible. And this is an area where I feel that AI income is big time. we are just seeing that where in the past an ITSL environment would take like a half year to implement now we can do it in weeks and really tailor this towards the needs of the customers now especially for IT operations kind of the status quo right now is everything's on a doom loop you know everybody's so used to sort of an emergency every or periodic emergencies to the point where everybody got to get in the room and you know that status quo never really moves the needle And everybody kind of used to this cycle to the point where they think kind of that their job The more as we covered in this podcast series so far, the more that we're seeing AI work its way into these systems, the more we see a future where the doom loop is addressed in kind of the core issues that we have per industry when it comes to just being able to make sure that the pipes are flowing from an IT perspective, that's becoming far more streamlined. What strategies are helping mid-market enterprises achieve that resilience and real business value through AI and infrastructure investment? Well, I think that AI is transforming industries and economies in a really massive scale. And We can't emphasize that enough, I think. It's really revolutionizing the way we work, and it's reshaping the future before our very eyes. And we shouldn't underestimate that. I know that in the press there's a little stuff about the AI bubble, but AI is there to change, to stay. And I think the impact of it cannot be overestimated, I feel. And perhaps I can make an analogy. Like currently in Europe, we have this big new hype on recently introduced Google or Bing Maps. It's that shows you all the Roman roads. It shows you the roads as they existed 150 years before Christ. And actually, there are about 300,000 kilometers of them. So really a lot. And obviously, you can also do a direction on it. and you can, for example, determine how do I get from Cologne to Rome. And the thing will tell you that it takes you 450 hours to walk and actually you can do it in 300 hours by horse. But two years later, actually, you can do this with a car and you can do it in less than 15 hours. And I think what we are looking at with AI, it's not about making horses run faster, but it's really about introducing carshade. It's about making this huge change in our way of thinking. Simply making the horse run faster, which was done by incremental changes and approaches using new technologies. You really need to now think about reimagining the entire process, about determining how you can actually use AI to completely rethink and operate the way you want to work. And that's more than the digital transformation we have been talking about for more than a decade now. It's a pivotal change. That's what makes CI so damn difficult and also so very, very powerful for your organization. Right, right. And also really necessitates these kinds of conversations of looking at the problem differently. Because if you're just stuck in the doom loop and you're abiding by that status quo, like you're saying, all you're doing is just making a faster horse when maybe what you really need to do is think about introducing a real technological change, introducing a motor engine to the equation, which will really, really change things. Of course, a lot of organizations right now are dealing with kind of the onslaught of shadow AI, as it gets called. And that's just kind of a fancy term for, you know, you don't have official AI policy yet and your employees are using chat GPT or maybe these open source platforms to accomplish tasks. And this might make a messy situation, especially on the privacy front. Just any advice, especially for leaders advancing these systems, especially when it comes to embedding AI within organizational workflows to prevent that shadow AI? Actually, I think starting to get value out of AI starts with defining an initiative for your company. If you don't do that and you don't align transformation initiative with process improvements and goals, it's not going to work out. And people will then obviously use the shadow AI, which you are using at home as well. And actually, AI will not be the business value driver. It will just be a technology experiment. And actually, this happened for the last couple of years. A lot of people were really struggling to bring AI tools and machine learning tools to production, where they could actually make an impact on the organization. And therefore, it's important to define an initiative. It's a prioritization of the culture, the process changes, making sure that you also have the buy-in of the executives. and do this enterprise-wide. Otherwise, you will just fail. Absolutely. And we talk all the time about how in these spaces, there's a point where it becomes less about being reactive and more about being proactive. In the IT services space, at least so far in this podcast series, we've talked about the end of the doom loop as what that looks like. But I think at the end of the day, that means that we seeing AI really perform a whole enterprise service management task Tell us a little bit about what that looks like for the organization how to get there and what are the proven frameworks that leaders need to leverage towards that goal? Well, it's a very important and good question, Matthew. Actually, obviously, you can look at the AI from the perspective I got my data right, and then I'm going to find out what the use cases are and just take it from there. With tools like CERN, you already have AI built in for IT service management, but the good thing is that there is also AI built in for enterprise service management. And there's an opportunity for that with AI to apply service management principles and tools and best practices to improve service delivery and support across the enterprise and not just in IT, and it enables you basically to have consistent workflows, automation, and user experiences across departments. With AI in ESM, you can actually transform this by automating processes, improving decision making, and making sure that there is a really good user experience, not only for IT, but also for non-IT functions such as HR or finance or facilities. And this really moves ESM from the reactive service delivery to proactive and intelligence operations. And this is where the return to place. I think it's really, really vital to have a look at it because it's a low hanging fruit. You can really get good value out of it without spending too much effort on it. Also makes a really sterling case. I know we're kind of at the point where it used to be build versus buy. Now it's build and buy and what's your ratio. But it really helps, I think, in those kinds of questions where you're taking those first steps and you don't have the data in place yet to make the investments that are going to pay off in having the data that that will be in place. And, and the catch, just the catch 22 I was referring to before. But that is a really ripe situation to be partnering with folks in this space who really know what that end looks like, especially when they've worked with so many folks who are starting from the same, from the same starting line. Dirk, really, really fascinating stuff. Thank you so much for being with us this week. It's been a great pleasure having you. Thank you. All the best. wrapping up today's episode i think there were three key takeaways for enterprise leaders in data and ai from our conversation today with dirk michaels ceo of sevaco first hybrid cloud architectures are essential for enabling scalable secure and agile digital transformation initiatives that meet the demands of modern enterprises. Second, seamless integration of legacy systems with cloud platforms is critical to unlocking operational efficiencies and data fluidity across complex environments. Finally, strengthening cybersecurity protocols and optimizing deployment processes translates directly into measurable ROI by reducing risks and accelerating time to value. Are you driving AI transformation at your organization or maybe you're guiding critical decisions on AI investments, strategy, or deployment? If so, the AI in Business podcast wants to hear from you. Each year, Emerge AI Research features hundreds of executive thought leaders, everyone from the CIO of Goldman Sachs to the head of AI at Raytheon and AI pioneers like Yoshua Bengio. With nearly a million annual listeners, AI in Business is the go-to destination for enterprise leaders navigating real-world AI adoption. You don't need to be an engineer or a technical expert to be on the show. If you're involved in AI implementation, decision-making, or strategy within your company, this is your opportunity to share your insights with a global audience of your peers. If you believe you can help other leaders move the needle on AI ROI, visit Emerge.com and fill out our Thought Leader submission form. That's Emerge.com and click on Be an Expert. You can also click the link in the description of today's show on your preferred podcast platform. That's Emerge.com slash Expert One. Again, that's emerj.com slash expert1. We look forward to featuring your story. If you enjoyed or benefited from the insights of today's episode, consider leaving us a review on Apple Podcasts and let us know what you learned, found helpful, or just liked most about the show. Also, don't forget to follow us on X, formerly known as Twitter, at Emerge, and that's spelled, again, E-M-E-R-J, as well as our LinkedIn page. I'm your host, at least for today, Matthew DeMello, Editorial Director here at Emerge AI Research. On behalf of Daniel Fagella, our CEO and head of research, as well as the rest of the team here at Emerge, thanks so much for joining us today, and we'll catch you next time on the AI in Business podcast. Bye.