n this special collaborative episode of Below the Fold, recorded live at the Dubai AI Festival, Ronnie Sheth, CEO of SENEN Group, joins Naveen Bharadwaj to explore what it really takes to align data, ethics, and impact at scale. From ESG to enterprise AI governance, Ronnie shares how global brands are building responsible, human-centered AI systems — and why this approach is no longer optional.
🔍 What you’ll learn:
📢 Whether you're a data leader, founder, or changemaker, this conversation is your roadmap for designing technology that matters.
[00:00:00] Hello everyone. Welcome to this episode of Date with Tech, brought to you by Recon in association with Launch Foundry and Below the Fold. I'm your host, Naveen Bharadwaj. With me I have Ronnie Sheth, the founder of Cen. Ronnie, welcome to the show. Thank you. It's great to be here, Naveen. Thank you very much. Ronnie.
We are gonna talk about, uh, what and does and emerging technology data because that seems to be the center of the business, that you do artificial intelligence, human intelligence, and a lot more. But before all of that, tell us about the Ronnie that people may or may not know. So when you're not running a business, when you're not busy running teams and perhaps spending time on programs and coding and whatnot, what do you do?
Outside of this, outside of this, um, [00:01:00] you know, I spend so much time in data and AI and I spend so much time looking at computers that I want to get away. Yeah. And my escapes, I should say, are more solitary. So I love, um, flying my single engine planes or horseback riding or, and this is a very Texan thing to say, but I love shooting guns.
Oof. So all in all to say, you know, there are some very fun activities that still remain very much human. Yeah. And that are not on the a RM still. Yeah. Wow. Flying planes, riding horseback and shooting guns. Flying planes is cool. House is really cool. Shooting is cool. But I hope, uh, you know, I don't ask you some questions that makes you train your guns against me.
Well, I, it certainly won't be me. But we won't put it past society in general. Yeah, yeah, yeah, yeah. Let's, um, let's, let's get into a C now. Does any of these hobbies or passions that you have have an impact or bearing on the [00:02:00] business and your style of running the business? I think for me, everything that I do is interconnected, as with all humans.
So the time that I spend flying, for example, you know you have this thing in flying where you basically, when you're, when you've run into an issue, you just let go. Hmm. And you let the plane kind of correct itself. Natural course. Natural course, and you make little tweaks, not gigantic twists and turns.
And I learned something from that when I really put some thought into it. I learned that when you are dealing with disruptions in the business and even in your personal life, it is much easier to make little adjustments and get back on track. Then make gigantic twists and turns and really kind of pivot to areas that you may not have [00:03:00] thought of earlier.
Yeah, and it works both ways. There are times when you need to bank left and bank sharp, right. But there are times when you just need to let go. And I think that I took that away from flying from horseback riding. It's how do you deal with an animal which has a mind of its own? How do you deal with that?
How do you work with that? How do you collaborate with somebody who has a mind of their own? And let's think ai, right? AI in reality is evolving and it really has started getting a mind of its own super intelligence. So how do you deal with that? So these are the things that I take away from my hobbies into real life, as I would call it.
Yeah. Um, in business. It's an interesting perspective. So you talk about, um, autopilot, you talk about maneuvering when needed. You talk about letting natural course corrections, but it's sometimes correcting the course by force or by will or by action. That's right. And then balancing, right? Yeah. The takeoff, the touch down, and even the hard spec writing.
It's, it's quite [00:04:00] interesting. Uh, that I think also sort of relates to data. And how, you know, you guys crunch the data, perhaps manage the data and come up with, uh, those magical decision making analytics and whatnot, right? Mm-hmm. So can you tell us a little bit more about, because data is quite broad, right?
Yes. Can you tell us specifically what is it that you guys do with data at Sentin and the different types of industries mm-hmm. That you guys have been helping? So. Uh, a lot of people ask me this question and I love to say it this way. Sendin designs and deploys human-centered experiences. With the help of disruptive enablers like big data and artificial intelligence.
And what I mean by that is when you look at data, a lot of people think, you know, we, um, in, in real terms, in business terms, [00:05:00] we design and deploy big data and artificial intelligence strategy. We really help execute that strategy, operationalize that strategy, and we actually build literacy upon that strategy so that people have an easier time adopting that technology and working with that technology and even data.
But what, um, what is really interesting is when we think about working with data itself is a lot of times people forget that data is. An addition to how we think. Mm-hmm. And how we make decisions. You know, Mo most, most of the times when I talk to people, they wanna be data driven. Yeah. They wanna be AI centric.
And this poses a problem because in reality you should be people centric and value driven. Yeah, so that's really what we do with data is really unlock critical insights to [00:06:00] help people make decisions, to help companies make decisions to become more people centric. And more value driven. Fantastic. So people, centricity seems to be the core.
It's the core of, of everything that you do. So that allies the fear of people thinking, oh, with data, would any bad happen to me? Would I lose my job? Would I lose that opportunity and whatnot, right? Yeah. You, you work with, um, lots of different industries. You work's with lots of different, uh, brands, right?
Uh, do you see that? Challenges and problems across different industries are almost similar, or you see this a stark difference between, uh, one company or one industry to another. I think there are always nuances, but every po, every company likes to think they're unique. The reality is the fundamentals and the, the foundation of what people do when they work with data and ai, they remain the same.
You know, [00:07:00] ultimately when you think about big data, you are. Working with a resource that is almost infinite at this point in time and growing eternally, and you're trying to lock down and leverage that resource, distill its essence and get to an actionable insight. It's literally that simple. Yeah. With artificial intelligence, we don't understand each other.
We're trying to understand how artificial intelligence is going to work a year, five years, 10 years down the line. There is no way. There is no way that we can even predict what AI is going to look like and what we can do with AI in the future over the next five years or 10 years. Right? Yeah. So to me, when we think about all of this, it's critical to remember that for us as humans, as companies, when we think about data and ai, they truly are things that we can use as resources and enablers.
But we don't fully understand what they can do for us [00:08:00] even today. Yeah. Yeah. So I think the answer then is it is different. You can't, you don't know. You don't know. You know you can work with it, but you dunno. You don't know. You don't know. And that's the same with every single industry that you work with.
Yeah, yeah. Across industries. True, true. Uh, everybody's of course, as you rightly said, um. I think the quantum of data is only increasing. The demand for data is increasing. The perceived understanding of data is increasing, but whether or not that's the right perception point is is secondary. Right. Great point.
Where do you, where do you, uh, fit aspects like data, serenity, data privacy, data residency, data governance. Mm-hmm. Data security and everything else that's not just associated, but also become the fundamental pillars of that enabler that is data. Yeah. I think as you pointed it out, it is a fundamental pillar of.
Our [00:09:00] approach to data, um, and how we unlock insights from data and how we not even just unlock insights, but how we truly make data work for us. I think data, if we put data sovereignty aside for a second, data privacy. Data governance, all of these are critical, critical pillars on which to, uh, on which your data strategy really depends.
And they are very much a core component of your data strategy. So when we work with customers, um, the first thing I say is we are not going to have a conversation unless you have your legal and compliance team and your cybersecurity team. Mm, very good. So you bring them in. At an earliest stage. So it's not an afterthought at at an early stage because right now the critical thing is how do we protect the privacy of individual humans?
Mm-hmm. That is critical. And remember, the data governance is. Um, centered [00:10:00] around driving behaviors. A lot of people think it's policies and procedures. No, no, no. It's centered around driving the right behaviors. It's shaping the right behaviors, um, with the right guide, guardrails, and guidelines. So when you think about governance, you can say it's offensive.
So it's designed for innovation. And really fostering innovation or it's defensive, it's designed to really protect, mitigate risk, you know, focus on privacy or, or, uh, regulation, compliance, whatever the case may be. Ultimately, governance, privacy, security. They all play a critical role in making sure that you are using data not only to the maximum capacity, but safely, securely, and in the right way.
You are doing right by your organization, by your employees, by our customers. Partners ethically by your ecosystem. Now as we move to data sovereignty, which is a very, very interesting subject and a [00:11:00] huge space and a huge space, um, we're now moving into more of the public sector narrative. Yeah. Because now we're thinking, okay, where does data actually reside?
And who owns owns it? The data. And that is a massive question, not just for the public sector and in the governments, but also in organizations. Even in organizations. In companies. When we work on data strategy, we have noticed, you know, people like to hoard data. Okay, this is my data. Yeah. We're not gonna share data because information is power, right?
Yeah. So the question then becomes, okay, who really owns the data? And I think that's the question that we need to start answering when we think about data sovereignty is really who owns the data. True. I'll tell you something, um, funny and then I'll bounce back to this, uh, this serious and, and relevant conversation.
Sure. So regarding this data ownership, right? Um, we all tend to have this habit, including me. Oh yeah. But that's my data, that's my information. I don't wanna give it because it's worth [00:12:00] millions of dollars. Right. But when you look back, am I making the millions of dollars from that? No. If not, then how do we put a value to that data?
We, as you said, we hold onto it so dearly and it is important, right? Yes. At least from a previously security compliance, governance, security standpoint, yes, it's important, but we are so quick to jump the gun and say, ah, it's worth millions of dollars. Right. I don't think many of us have actually cracked the code of how to make the millions.
That's right. And in that process, if we do end up making those millions, are we breaching some of those? Uh. Ethical aspects, the ownership aspects or the Yeah. Veen aspects. Right? Yeah. Uh, to, to go back to our, um, our, our conversation, I liked what you said, the fact that you are bringing different teams, departments, and conversations right at the early stage Yeah.
Uh, is making you actually ensure that by design, not as an afterthought, but by design, the data is being used in the right aspects. That's right. Loopholes are being [00:13:00] thought of in advance. Mm. Gaps are being addressed in advance. Mm-hmm. And any possible gray areas or issues that might happen later on, uh, are actually avoided.
That's right. Have you come across a situation where despite all these things without of course, taking the name of a customer or a project where, sure, you did your absolute best, yet something went wrong, something went Ari, for whose ever fault? I think there is always a chance, um, that things can go wrong.
And one of the things that we have learned about strategy is that it is a journey, not a destination. So when we work with our clients, we always prepare. For things going awry. You know, I'm a strategist by nature. Yeah. So I always have, okay, this is the roadmap, but if something goes wrong, this is how we flip.
This is how we pivot, this is how we adapt. So there's always that baked into our strategy. But the, the important point here is that [00:14:00] absolutely. Um, there are times when companies really go off the rails, even though they have a roadmap. And one of the examples would be when. You know, we give somebody a, a, a fantastic roadmap, but they decide to take things into their own hands and they kind of add their own flavor to the strategy, and all of a sudden you see all of these little bits showing up in strategy and all of a sudden it's sort of this mishmash of, oh, wait a second.
It's been diluted to, we own the data. Yeah, it comes back to us and you sit there and you're like, no, that was all not the original strategy. Yeah. And so you have to again, kind of sit down and realign those, uh, stakeholders, realign those departments and teams and say, guys. Let's realign on the goal, right?
Because ultimately, as I said, everything goes back to driving [00:15:00] behaviors, attitudes, mindsets, and what you very clearly pointed out and very correctly pointed out is the perceived value. Yeah. Yeah. You know, what value am I getting if I let go of this information? Right, true. So those are the questions that we end up answering when things go awry and really kind of bring people back to this common ground of there is a shared sense of value.
Even though value may mean something different for you, it may mean something different for me, but we're working towards the shared sense of value. These shared goals that create the individual value drops that we all require to make our. Um, lives beautiful. True. So to speak. I think, um, the future of digital economy is dependent on data.
Yes. And it is shared. It is shared. It is shared. So if it's not shared, and if you're putting your own boundary walls and fences and whatnot, you need to guard what needs to be guarded. But then there should also be the degree of perhaps sharing of [00:16:00] anonymous data. Yes. Or a sharing of that data, which is okay by the user or by the company or by the country.
Right. Right. Yeah. Interestingly, a lot of the countries have actually come up with their own, um, data privacy laws. Mm-hmm. Mm-hmm. You have GDPR, you have PDs, you have ND ps and whatnot. Yeah. Uh, do you guys see this as a challenge? Because every country has its own way of guarding its data and, and, and enforcing those policies and regulations.
Right. How easy or not is it for you to navigate and make sure that you are, uh, in-country compliant? I'll, I'll tell you something very interesting and this, we started this back in the day because I firmly believe in this. When we used to work with US and EU customers, um, you know, after GDPR, I always told my US clients, um, especially the ones that are more customer facing, like retail and CPG, who own PII data who have a lot of PI data in their membership database.
In their loyalty database. I [00:17:00] always say, look at EU default to their measures. Yeah. Default to their measures because it, it saves you a lot of risk. It saves you your brand reputation if something goes wrong, if you look at some of the stringent regulations and kind of default to those, at least have the mindset of.
This might be something that we might wanna look to and understand completely and maybe apply certain principles so that we are doing right by our organization in protecting ourselves and mitigating that risk of in case a data breach happens, what do we do? Yeah, right? Or maybe in some odd way, we misuse consumer information.
Yeah, what happens? Well, if we default back to some of the GDPR laws, that actually guides us a little bit. Yeah. Into how to approach these situations and how to even mitigate that risk at the forefront. [00:18:00] So my easy answer would be, look at the more stringent regulations, understand them, and try to apply the principles, at least if you can, in more relaxed economies.
Yeah. Yeah. Fair, fair, fair. Uh. Artificial intelligence, let's bring that into the mix. Awesome. Not because it's a buzzword, because it's now a reality. And that's right. We are here at the Dubai Festival where plenty of conversations have happened around challenges, opportunities, solutions, implementation, use cases.
You name it, we've talked about it, right? Yeah. Uh, for you guys in your business, where does, uh, AI fit in, in helping extract. Maximum value from data, making sure that compliance is adhere to. And uh, you mentioned earlier about, um, you know, the behavioral analytics. You also talked about, um, create, you know, decision making ability or helping in coming up with critical decisions, right?
Where do you guys use ai? How is it helping you in, uh, in fasten things or not? So we focus [00:19:00] predominantly on AI strategy. Mm-hmm. And. Follow that up with execution. The trouble today with most organizations is they're still in the exploratory phase of ai. They're still in the POC phases of ai. They're still trying to figure out, oh, maybe if we implement this AI product, maybe we can have X outcome or X value.
Which is why strategy is so critical for a lot of these organizations because strategy really defines. What use cases should you be going after? Because a lot of people have a list of thousand use cases and they think, oh, if we do these top three, we'll be good. But the problem is, what about the practical side?
If you choose this three, you feel like you're generating X amount of revenue, but in reality, you're gonna be losing money. Yeah. And no one is out there who has given them practical advice of, listen, we've been there, done that. We have deployed AI [00:20:00] and these three use cases, even though they look great, they're not gonna generate any revenue.
Instead, focus on the three simple ones that are gonna get you to the aha moments faster. You can get momentum and use that built up momentum to drive some of the harder use cases. Yeah, right. So. Instead of even using AI today to automate, I would say people are in the area in the phase that we talked about, and that's where we really help people really move the needle on AI and getting to successful AI implementations.
Right. So you, in your, uh, conversations and the type of customers you deal with. Mm. Um. As part of the strategy and then the implementation, where do you see most of the traction? Is it on the gen AI side, on the agent AI side? Is it on the a GI side or is it on the super intelligence that we are about to reach?
Where do you see the most traction or interest? So, you know what's really interesting is November, 2022 hit all of us, uh, and took the world by [00:21:00] storm with generative ai. Yeah. Um, I joke about this, but I've been in the AI space before AI went mainstream. Right. Um, I was with IBM Watson team way back in the day before really AI went mainstream.
And one of the things that, one of the things that I've learned is when people say we wanna do AI or we wanna leverage AI or implement ai, they generally mean generative AI today. And the reality is they actually don't need generative ai. They need to understand what their business use cases are and where AI truly transforms the business.
Yeah, right. It could be simple classical ai, it could be generative ai, or in the future it may be agentic ai, but when people say AI, in their minds it's generative ai. True. Chad, DPD comes to our minds first thing, AI or Right. But in [00:22:00] reality, when we. Have that conversation, we kind of take it back a little bit and say, let's understand what AI actually means.
Yeah. For this business and what that will do to transform where you are today to where you need to be your ideal state based on your strategic objectives or your goals. Yeah. For the company. Speaking of Gen ai, what's your favorite, uh, personal tool? You don't, it's not, you're not representing SEN here, but you're just of speaking for yourself.
Of course. What's your favorite gen gen AI tool? I am gonna say chat, GPT, only because it introduced generative AI to the line. Sam, you've done a great job, your second favorite generative AI tool. You know, in reality it's, um, it's a very difficult pick because, um, I love, I am. Creative, um, in more non-traditional ways.
Mm-hmm. Mostly music. Um, I am not a very artsy person when it comes to drawing, when it comes to really [00:23:00] painting and creating those beautiful landscapes. So whatever tools help me create those. I mean, from a Canva to a mid journey, I love every single one of them. Yeah. I love every single one of them be, and I, I really do play with them because they allow me to.
Really have an outlet to say, oh, this was in my mind, and now look, it's reality. Yeah. Yeah. I have never been able to do that. Yeah. And that's the only reason I love those tools. Well, no offense to, uh, the creative folks that are really good at drawing and painting and whatnot, because just like you or just like us, uh, musicians, their, uh, industry is being transformed.
Yes. Coders is, they're being transformed. Exactly. Content writers, journalists, doctors, bankers, government folks, policy makers. Every single industry, every single job out there is actually being transformed. Yeah. Thanks to the power of ai. So I think, uh, it's time that we embrace it and as you rightly put it, um, see what you need and then apply based on your [00:24:00] today's need and prepare for, uh, for tomorrow.
Exactly. Will there be an AI tool that you actually don't like? I think we can never predict anything with ai. Yeah. Yeah. Um, as long as, uh, and this is my personal definition of ai, you know, if you ask somebody what AI is. Or how to define ai. I think people have a very hard time. What is your definition of ai?
Good. I'm glad you asked. Um, the way that I define AI is a strategic partner for humanity. Nice. Because, interesting to me. To me, when AI falls outside of the realm of in-service to humanity, yeah. That is a tool I will not support. I think that's the fear that most people have. Where do you draw the line?
Where does it go Out of bounds. That's right. Self coding beyond human comprehension and, uh, detrimental things happening. I think know every time a new wave comes in, we are all, uh, trained and tuned [00:25:00] to think of all the doomsday scenarios, right? That's right. So I think, uh, with, with collective intelligence, with uh, heightened maturity, with all of us working together, I think you can.
Use those guardrails. You can make sure that that's right. We are still controlling. We are still the masters and I like to tell people that at least as of today, we say AI agents, so we don't say AI Masters. Yes. AI assistance not. AI bosses. That's right. Right. That's right. So I think as long as we are playing in that space, all looks good.
Yeah. Although, um, I don't know if you've heard of this, but there are companies that have made AI CEOs Oh yeah. So there is a trend. I'm sure my team would love to have an AI c as opposed to me. So there is a trend and I think us as business leaders, um, and there's, there's a lot of government folks here, us as truly public.
Um, and society leaders, we really have to have an open dialogue. Yeah. On, you know, how do you manage and navigate through that to make sure that we have a [00:26:00] healthy society, not only today, but in the future, in the next three years, five years, 10 years down the line. How do we create an innovative, progressive world and a futuristic world without compromising on humanity?
Yeah. Yeah. Right. To me, that's important. Fantastic people centricity. Humanity. Know your data, use your ai, but remember, everything has to revolve around people. So as long as that sentiment is strong and that's, uh, uh, seen in every single step that you take in your so-called emerging tech embracement journey, all should be well, hopefully.
Ronnie, thank you very much for joining us on that date with Tech. Thank you. Thank you for having me.