← The Digital Executive
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- [00:00]
A
Foreign. Welcome to Coruscant Technologies, home of the Digital Executive podcast. Do you work in emerging tech? Working on something innovative? Maybe an entrepreneur? Apply to be a guest at www.corazon.com brand welcome to the Digital Executive. Today's guest is Santosh Kivetti. With over 18 years of experience as a technologist, entrepreneur, investor and advisor. Santosh Kvetti is the CEO and founder of ProArt, a purpose driven enterprise that accelerates value and increases resilience for its clients. With consulting and technology services enabled by cloud, guided by data, fueled by apps and secured by design, Santosh's vision and leadership have propelled proart to become a dominant force in key industry verticals such as energy, healthcare and life sciences, and manufacturing, where he leverages his expertise in manufacturing process improvement, mentoring and consulting. Well, good afternoon, Santos. Welcome to the show.
- [01:06]
B
Thank you so much. Thanks for having me, Brian.
- [01:08]
A
Absolutely, my friend. I appreciate it. I'm in Kansas City, you're in Atlanta. Today, we're just an hour apart, so I appreciate you making the time, traversing one time zone. And Santos, let's jump right into your first question. With 18 plus years as a technologist, entrepreneur, investor and advisor, you've undoubtedly faced failures. Could you share one failure or a lesson learned that changed your approach to building technology services or leading transformation?
- [01:37]
B
Sure. As an entrepreneur, I always felt like I could have taken more risks, I could have experimented more. But it's been a fantastic journey for me. Now the biggest lesson I've learned in this journey is that technology is a tool, it's not a complete solution. See, early in my career, I was often too focused on the elegance of the technology itself and not enough on the people and the business processes it was meant to serve. For example, I remember one specific instance that really brought this home for me. In the early days of ProArt, we were working with a large manufacturing client. We built this incredibly sophisticated data analytics platform for them. On paper, it was perfect, technically, very elegant, powerful, and it could provide all these amazing insights if you know how to use it. We were really proud of it, but when we deployed it, that option was almost zero. And the plant floor managers, the very people we thought would benefit the most, saw it as a threat or a huge obstacle. Definitely it was overly complex. They didn't have the skill set to use it. It was a distraction from their core world and they weren't consulted enough in the process. And that's the key. We had built this powerful engine where we had completely forgotten to build the roads and even teach anyone how to drive if they can use that analogy to building a stadium and not having the roads and infrastructure for people to really get there. That failure was a pivotal moment for me and my team. It taught us that our job isn't just to be technologists, it's to be change agents. We have to be part psychologists, we have to be part business strategist, we have to be part ethnographers. So now our approach is completely different. We start, we always start with why the business problem. And we always build with our clients and not for them. We embed our teams with theirs and co create. The way we measure our success is not by the software we shape or the consumption or billable hours, but the business outcomes we deliver for our customers. And that's what really means a lot to us in our teams. See, ultimately this lesson is one of humility.
- [04:02]
A
Right?
- [04:02]
B
I mean, I believe honestly that smartest people in the room are usually the ones who live and breathe the business every day. And our job should be and it is to empower them. And that's the lesson, you know, we as a team carry with us in everything we do.
- [04:20]
A
Thank you. Love the story. Really do. As an entrepreneur, you shared one of the biggest lessons. A couple really. But the big one I took away is technology is a tool, not a complete solution. And your story on deploying that solution kind of fell flat. And you explained a part of that. You can't just be a technologist. You need to be a change agent. There's a lot of communication prepar and you have to start with the why the business problem. Right. And really love that story. And Santosh, at ProArch, you describe your services as enabled by cloud, guided by data, fueled by apps and secured by design. How do you coordinate these four dimensions in practice, especially in verticals like energy or manufacturing, where legacy tech maybe ot right. And it converge.
- [05:08]
B
Yeah, Brian. Oh my God. That's our core DNA. That's the core of our philosophy. We see those four dimensions as the key components of a digital transformation engine. So the cloud is the horsepower it provides the scalable, resilient foundation. And data is like the fuel. It's a raw material that we turn into intelligence. And our job is to deliver trusted outcomes through data. And as you know, apps are the vehicles. That's how people interact with the data and the processes. And last but not the least security is the steering and the braking system. Right. It cannot be bolt on. It has to be engineered right from the beginning with everything else. So you can't just have one. These are not silos. You can't just bolt on stuff. It's like building a powerful engine with no fuel is useless. So for us, all of these are integrated and one part enables the other. That's how we see it. And now in industries like energy and manufacturing, this gets really, really interesting because you have the convergence of IT and ot. See, for decades, these two worlds were completely separate. The IT world was about application, business applications and Data. And the OT world was about physical machinery, industrial controls, IoT systems that were never designed to be connected to the Internet. Bringing them together is a huge challenge, but it's also where the biggest opportunity lies. I mean, our approach is to act as the bridge between these two worlds. First, we are a security first mindset. It's amazing how over the time we were able to bring our teams together and weave them into one single thinking mindset. We apply a zero trust security model to the OT environments. We assume that a breach is always possible. So we verify every connection and limit access at every point. Second, our job is to help really turn the OT data into a strategic asset. That's where there's a real value. Simple. Once you are able to connect these two systems, you are actually able to provide true predictive maintenance or analytics, even prescriptive, I may say, if you ask a question, hey, this particular turbine, based on the data I'm getting from all the sensors, needs maintenance. Well, that's one part of the question. But to go back and look at what kind of insurance does it have, what kind of warranty do we have, do we have parts in the inventory system? You're really crossing OT and IT worlds. So that's what we do is bridge these two. To be able to take it all the way and say, yep, you need to schedule a maintenance window, there's an excuse day or whatever it is. And yes, we have inventories and this is what we need to happen. You can go all the way there. That's what our job is, quite frankly. Hope it makes sense.
- [08:17]
A
Absolutely. Thank you so much. Really appreciate Your four core services are the tenets of the digital transformation engine, so to speak. As you mentioned, the convergence of ot and it is certainly a challenge. I've lived that as well as you know, and you went through a couple of those examples, but many opportunities to make things better with this convergence. And your expertise certainly is helping businesses achieve their goals. So I appreciate that. Santosh and Santosh, in your recent conversations, you've warned about shadow AI adoption and the risk of IT OT convergence, particularly in critical infrastructure. What Are the most surprising vulnerable vulnerabilities you see? And how should organizations prepare to guard against them?
- [09:03]
B
That's a really interesting question because it's really emerging, especially with AI, as new vulnerabilities are coming up. Most recently I was reading a research paper from Microsoft where you discovered that even though the chats, the encryption between LLMs is the data between the flows through the LLMs is completely encrypted. Just by looking at the metadata, you can fairly accurately guess what you know the user is talking about. So it's an emerging landscape. But look, I think we need to start with basics. The most surprising one for me actually isn't a piece of malware or a technical flaw. It's a cultural blind spot. We're so focused on external threats that a lot of times we're missing massive risks that are hiding in plain sight right inside our own organizations. One, as I've been talking about shadow AI, it's like a Trojan horse. Remember a recent IBM study found that 80% of the workforce use AI. Employees are smart, they're using public AI tools to be more productive. We have to ask the question why? But in the process, of course they're pasting sensitive corporate data, strategic documents, financial information. And it's massive unintentional data leak driven by good intentions by the way. So well, you can just simply unblock it. There are ways to restrict it, there are ways to monitor. Now I think we now especially we live in the Microsoft, we are Microsoft partner. Microsoft does provide really good tools to be able to detect shadow AI usage. But you have to learn how to embrace and educate. And honestly, you have to not look at your corporate policies or security governance as a blocker, but as an enabler. Make the secure path, the easy path. Educate employees on why it's important to use enterprise grade AI tools that are properly governed. And that goes long way. The second is just unblocked backdoor of it OT convergence. IP systems are typically, typically modernized. They are regularly patched. But in our experience what we've seen is OT systems are not. A lot of times they're decades old proprietary and they are not patched. And that means they are inherently vulnerable. And now that companies are beginning to connect them together, you provide a direct pathway for an attacker from these connections between IP and OT systems. So you really need to look at your OT infrastructure. Again with the zero trust mindset, you have to segment your networks and continuously monitor for any unusual activity. And there are a good number of security tools now available to do that. We believe Particular, for example, we use Microsoft Defender suite and Purview suite extensively. But where you should start with is first to have the visibility. What I've learned is that most manufacturing companies or energy companies lack visibility into their OT networks. True visibility. What kind of footprint do I really have and where are they in terms of their security maturity? And just from being able to say, am I ready to even connect and use the data? There's a baseline security that you need to bring everything to and just that itself goes a long way. And finally, all of this will come down to building a culture of security. And it just can be IT department or security department, everyone from CEO to somebody on the plant floor, they have to feel a sense of shared responsibility for protecting the organization. And that's the only way to truly be resilient, in my opinion.
- [12:52]
A
Thank you. Yeah, Absolutely. And just to highlight a couple things here, this new emerging landscape is this mass adoption and use of AI, but not securing the data or not looking at those security gaps. Malware you mentioned is a cultural blind spot. There's massive risks in plain sight, obviously. So what I really liked about what you said is make the path easy, but make it secure. Right. I think that was important. And the importance of compliance, security, patching, monitoring, that's so important in this day and age for sure. So I appreciate the insights. And Santosh, the last question of the day. Looking ahead maybe five or 10 years, how do you envision enterprise technology evolving, especially around AI, data, cloud and human talent? What should leaders be doing now to prepare their organizations, their talent and their cultures for what's next?
- [13:46]
B
Oh, we're witnessing transformational paradigm shift across the world. Every role, every function from top to bottom is changing and will that change will be the catalyst and the pace of change will increase and will be drastically different. Every role will be drastically different. But I believe the next decade will be defined by the shift from experimental AI to embedded AI. AI is going to become invisible, woven into very fabric of the enterprise. And the biggest challenge in my opinion won't be the technology itself. It will be about the talent and culture and the adaptability. I think we'll move from generative AI, this is already happening to agentic and to autonomous AI agent. The next step I believe will be to give AI the keys and let it drive. The second rise in my opinion will be vertical AI, think generic, one size fits all models will be replaced by highly specialized AI that is trained on industry specific data. Just as an example, we built a healthcare compliance AI engine that healthcare companies can Use proactively and reactively all the way from CISO to somebody who is a practitioner to say hey, where did the violation happen? Almost in the near real time from compliance perspective. And there's a change in the compliance. How to propagate that through the entire workflows and entire applications, entire IT footprint. That's what's going to happen. Vertical AI. And the third, the biggest transformation that I'm expecting is to move to the edge and also use SLMs as opposed to LLMs. We call this artificial intelligence of things. Instead of sending all the data to the cloud for analysis, the analysis will happen right on the device. I think the hardware evolution and the AI evolution will lead to this. A camera, for example, a camera on the factory floor that spots defects in real time as opposed to spotting the defects happening somewhere else as part of the analysis. Right. So how do we prepare for all of this? Well, first, get your data house in the order. Second, shift your talent strategy from just credentials to skills. Third, develop a strong AI governance framework. If you don't have governance framework, that's going to be very big problem for you in my opinion. I believe the future belongs to leaders who can build a symbiotic relationship between the human talent and AI. We will end up having, for example, every agent or every AI workflow is given an ID just as a human and will be given permissions just as humans. We have to try to work with AI agents with of course, preferably with the human in the loop concept. And that's where the symbiotic relationship comes. Which means that all of us will have to retrain ourselves. Our roles will change, the workflows will be reimagined. I know right now many industries are trying to bolt on AI to their current workflows. In my opinion, that's not the right way to do it. AI offers an opportunity to reimagine your workflow across your people, functions, apps. And that's what I call as AI native. AI enabled is you're just trying to use AI to gain some efficiency. Gaze AI native is way new. I really see the opportunity to transform the way really work and that's where I think all of this technology will take us to. So I'm super excited. At the same time, I'm super nervous as well because all this means that security is going to be a big deal. Compliance will become a big deal for most organizations.
- [18:06]
A
Thank you, appreciate that. And I like talking to guests about what they predict in the future. Nobody has a crystal ball, but based on your experience and what you're seeing. I just love to hear that you talked about this transformation paradigm shift. This massive change will be a catalyst launching us into that next decade, which will be moving into really embedded AI. There'll be everything will be agentic. But the key, the talent, the culture and adaptability is important. And as you mentioned, you're going to need to have good leaders that can build those symbiotic relationships between human talent and AI. And I think that's so important. So I really appreciate the insights. And Santosh, it was such a pleasure having you on today, and I look forward to speaking with you real soon.
- [18:52]
B
Sam here. Thank you so much for having me.
- [18:55]
A
Bye for now.