Subscribe
Share
Share
Embed
Short on time but curious about how intelligent technology and AI are reshaping the enterprise world? Fear not, my friend. The Intelligent Enterprise is your short, sharp pause from the chaos of business as usual.
Every other week, Creative Thinker and Business Strategist, Tom Stoneman, meets with a business leader for a simple, real-world activity outside of tech, to talk about what’s going on inside it. It could be a coffee, a walk, some DIY, who knows? But it’s a chance to take a break, reflect and get to the heart of a single challenge in enterprise transformation with the help of leading experts in digital transformation, change management and workplace innovation.
From AI implementation strategies to debating the next wave of tech leadership trends, this is a show for CIOs, CTOs, and anyone navigating the fast-moving world of enterprise solutions.
The Intelligent Enterprise is brought to you by Digitate, creators of Ignio, the award-winning AIOps platform that keeps tech running smarter, faster and more autonomously. Join us for a coffee, run, or maybe even a dog-walking session, and get the inside scoop on what’s next for the enterprise world, by getting outside of it.
Tom Stoneman: Hi, I'm Tom Stoneman, and this is The Intelligent Enterprise, where every two weeks we take a break from the chaos of enterprise life and get inside a big idea by getting outside of it. Each episode, we meet an industry expert who helps cut through the noise from all the updates and rollouts while exploring one of their favorite break time activities. It might be over coffee, a workout, or even a game of ping pong, something that gives them some head space when they're deep in a problem. Today, I'm joined by Karthik Ravindran, General Manager of Enterprise Data & AI at Microsoft Corporation. Karthik has spent more than two decades helping companies modernize their data foundations, scale responsible AI, and rethink how technology and people work together to drive real value. Karthik is a true practitioner and evangelist of enterprise transformation with data and AI.
Karthik Ravindr...: Today, we are living in a time where everybody's sitting and waiting for some tech leader or some thought leader will come up with the recipe for what should be next and what can be next, as opposed to looking inward to contextualize it for each of ourself and to think uniquely about what is it truly that should be the next chapter that we should be looking forward to.
Tom Stoneman: Karthik sees a future where our greatest advantage isn't the technology itself, but how we choose to use it.
Karthik Ravindr...: One that is powered and scaled by the tech, but which is never going to replace or take away the unique characteristics that only each of us as humans can perform and do.
Tom Stoneman: Let's get inside the future of enterprises by stepping outside of them. Welcome, Karthik. Thank you so much for being with us.
Karthik Ravindr...: Thank you so much, Tom. Thank you for having me on this podcast. Really excited for this conversation.
Tom Stoneman: Well, let's jump right into our topic today. We really want to talk about this whole journey we're experiencing with AI. And there's a recent article you wrote just January 1st, 2026. It was called Top of Mind Considerations to Thrive with AI. And in there, you mentioned that AI applications need to foster new opportunities, new economic opportunities for people. And conversely, we must also realize that people are core to favorable and durable AI economics. So the question I have for you is I'd like to hear more about your view on how impactful leaders can help to achieve that balance with their companies.
Karthik Ravindr...: Yeah, that's a great topic. Let's unpack this. I think we are now in an age and time when the work of leadership is extremely important. A lot of the AI narrative that is currently in the ecosystem is building more of a fear-based intuition about the tech as opposed to the energy that it needs to be promoting with people from all functions and facets of life. The challenge we have is the technology is very powerful, it's here to stay, but a lot of the narrative is anchored on how the technology can automate and potentially out-automate humans. When in reality, it's a human plus AI operating model that I believe will shape the most pronounced innovations in the periods ahead. AI is really good at being able to process signals at unparalleled scale and speed, and humans are still going to be awesome at what they do the best, which is understanding of context, judgment, foresight, empathy, and trust.
And it's the collective set of factors that need to come together to really help organizations innovate and thrive ahead. But most of the literature and most of the narratives today tend to focus a lot on the technology side of AI to the point where more often than not, the conversation is around how the technology can replace functions and take away human jobs. And leaders have to realize that to truly achieve the transformation that they're looking to achieve, they need to change the tone of the conversation. The conversation has to shift from building a fear-based outlook towards embracing and applying the technology, towards one that's more optimistic and focused on showing people how they can thrive and scale together with the technology. And the work of leadership to do that, to co-discover and co-navigate that opportunity with their people is extremely important for organizations to achieve their full potential with AI.
Tom Stoneman: So let's just unpack that even a little bit further, because I like this topic a lot too, and I like what you have to say about it. In that same article, I know you stated that while AI can help to scale restorations, rebalancing, and prevent regressions, it can't do this without people working together with AI. So I think we all know that now. I think we're past the point where we need to understand that. I think everybody gets it. But I'd like to hear you, if you could give us a scenario where you've witnessed that, where it exemplifies that kind of collaboration recently.
Karthik Ravindr...: Absolutely. So I think it's important to first unpack what humans can do the best and will continue to do the best. And to me, it comes down to the following. And it starts with the recognition of realizing that AI can do for any organization what AI can also do for the organization's competitors. The tech is there for everyone to use, but what truly differentiates the application of the tech is that unique context of each organization, context that is shaped by humans, context that will continue to be evolved by humans, combined with the foresight and the judgment that also needs to be applied contextually to guide the systems to the best business outcomes. And these factors also have to anchor on something that's more primitive and fundamental, which is trust and empathy. While AI can scale, automate, and help you do things faster, eventually the accountability of the decisions that are taken and made will always have to be borne by humans. And the humans being able to influence those outcomes through their context, judgment, and foresight sets up the overall ecosystem for greater net positive outcomes as opposed to not.
So if you took a real example, taking some of these concepts into the picture, there's almost every function in an organization where the combination of the human plus the AI can come together to create magic that just either one of them couldn't create themselves. So let's take an example of a marketing use case. In a marketing department, one of the most common goals of any modern high-tech marketing organization is to solve the problem of personalized campaigns, personalized user communications. Personalization, it's been the holy grail of marketing for like several years now. It's an extremely challenging thing to accomplish, one-to-one personalized experiences. Doing one-to-many targeted segmented campaigns is fairly straightforward, but when it gets down to being able to nurture each customer and each user within a customer's organization down journey paths that are contextual to their day-to-day work, that requires a lot more complexity to be unpacked and addressed.
The signals, the signals that comprise product usage telemetry by individual users, the journey paths that users take to discover products, to use products, the points of inflection where users either get the aha moment where they really get hooked into a product to use more of it, as opposed to also points in inflection where the users realize that, "Hey, probably this product is not doing it for me. I'm probably going to churn and not use this anymore." Every one of those signals are critical signals that need to be processed at massive scale. We're talking about at the scale of every individual users of modern products. Human strength to connect and correlate and synthesize all of those signals is a very complicated and draining task. But what humans are really, really great at is in knowing, like for instance, in this case, we are talking about the human marketer in specific.
The marketer really, really understands their product or their solution the best. They understand the customer's pain points and opportunities. They know how to connect the pain point and the opportunities with their product and solution. The marketer can provide the inputs around those outcomes that they're trying to achieve with their campaigns. They can provide the context of the personas who are their target buyers. They can articulate the top level journey paths that they would like to guide their customers through. They can lay out a repository of great creative content with messaging that can be delivered at different touch points based on the customer and user context. These are all the primitives that exist, which are human shaped based on the context of the product, the solution, and the organization that the marketer serves.
Now combine the two together. If you can take the speed of AI, the scale of AI to be able to process the various user signals that are coming in fast and furious, right from the moment a customer starts to discover a product offering on a website through to the point where the customer gets into a trial experience, goes on to use certain capabilities, churns from certain capabilities, shows signals of becoming a really active customer, show signals of potentially becoming a customer who's going to churn, all of that red signal coming in at the grain of every single individual user, being able to stitch that data, connect it, synthesize patterns from it, that is the stuff which is very complex for humans to go scale and doing by themselves. Versus the human marketer can apply AI for all of that rich synthesis, pattern matching, pattern extraction, and can then combine that richness with the inputs that a human marketer can uniquely provide.
Now you've got a very, very differentiated marketing solution, one that is contextual to each brand, one that is contextual to each brand's products and the personas that they serve, as well as personalized at scale to the signals that are representative of each of the brands' individual customers and users. That's marketing transformation right there. That's powered by humans plus AI tackling and solving this hard problem of one-to-one personalization without trying to go at it and solve it without the context, which is what most of the time AI could end up doing, as well as without trying to solve this through just pure human scale, which is almost a very, very difficult task to uptake and perform.
Tom Stoneman: What stands out in Karthik's perspective is that even as AI scales the work, the fundamentals don't change. It's still about understanding people, their needs, their intent, and bringing the creativity and judgment to act on it. The tools might be changing, but the need for human insight and strategic thinking remains. So what does that mean for individuals navigating this shift? How should we think about our responsibility and personal leadership in an AI-driven world?
Karthik Ravindr...: I'm glad you brought up the complimentary facet to leadership, which is the personal leadership of each of us as people. So yes, I mean, there's absolutely no question about the first topic we talked about, which is leaders have to set the tone, a tone that's based on trust and safety and empathy, not one that's based on fear. Right? Now, assuming that you've got that construct set up, it is upon each individual, each of us to also write our story forward. And this is the one topic that I'm very passionate about because very often when I sit down with my mentees and talk about what's on top of mind for them, a recurring topic that has started to come up, in fact, it's been coming up for quite a while right now and it never fails to come up in every conversation goes like this. "Hey, I'm really concerned about this AI thing." It is like, "My organization is encouraging me to start using it, but I'm really, really concerned whether it's going to take away my role and my job. And how should I think about this? How should I approach this? Am I even going to have this role five years from now? Should I think about a career change? Should I go do something completely different?"
And my first response is usually a smile because I'm like, "Yes, this is all natural fears." And at the same time, it's also helping the person understand that you know what, you've got more in your power than you think you do. If you've got a supportive leadership, that's great, awesomeness. But your leadership by themselves are not going to write your next chapter. You own the pen on writing that next chapter. And if you looked at this intentionally through a lens that comprises these three questions that you have to ask yourself, the first question is, what is it that only you can do uniquely in your craft? And when answering the question, try intentionally to filter out of what you come up with to exclude the activities. I want you to focus on the real impact of what you do, not just the activities.
All of us have jobs, but I'm sure if you thought hard, there'll probably be a good percentage of things that we do on a daily basis that we wish we did not have to do because they were either dull, they were draining, they were daunting, they were repetitive. And those are the things which are not the unique mode of us as people. Those are things that we can look to offload and scale within through technology like AI. So really being able to intentionally answer the what is it that only each of us can do uniquely is question one. The question two is to just be really honest about what is it that AI can do better and faster than you? Processing signals, connecting patterns, synthesizing insights from vast assets of assets. Right? There's no way you can compete with the machine scale that AI is going to bring to do all of that. In fact, you're better off leaning on AI to do that for you so that you can focus on doing what you really do the best.
And the third question is the combination of the first two, which is how can you leverage AI to scale your uniqueness? Once you've understood your uniqueness and what AI can do best, if you can combine those two and now imagine a state where you are truly elevated to a whole new altitude and level where you can do more and more of what you're uniquely positioned to do and love doing while leaning on technology to help you scale on the things that are repetitive and not truly your unique mode, now you have a magic equation that's one plus one greater than three. And helping people really unpack that in the context of each of their functions, their roles, and how they can own the pen in writing that next chapter and take those thoughts to their leadership team. Obviously, leaders have to be super open to co-innovating and co-discovering the next phases of evolution together with their people, but assuming you've got leaders who want to do that, it's as much on each of us as people to demonstrate the personal leadership, to write the next chapters for us, to write the next chapters for the functions and roles that we represent, which comprise several others like us, and then being able to take that learning and then to co-evolve together with the leadership team in defining what's next.
That facet of personal leadership, which is owning each's own destiny, is something that I highly encourage people to think intentionally about because today we are living in a time where everybody's sitting and waiting for some tech leader or some thought leader in the industry to come up with the recipe for what should be next and what can be next. As opposed to looking inward, to take all of that good input, to contextualize it for each of ourself and to think uniquely about what is it truly that should be the next chapter that we should be looking forward to, one that is powered and scaled by the tech, but which is never going to replace or take away the unique characteristics that only each of us as humans in our crafts and roles can perform and do. So that is something that I think is worthy of deep thinking and reflection as people continue to take their next steps forward.
Tom Stoneman: I'm going to go back in time quickly here. I'm curious to hear if there's anything that's really surprised you, like what's different from what you saw coming versus where we are today?
Karthik Ravindr...: Yeah, that's a great one. I mean, I think when I first started dabbling in the space, the thing that became very evident to me right off the bat was how important data is to make AI work really well. And there's a saying that your AI can only be as good as your data, and there could be nothing further from the truth. At the end of the day, AI is trained on data, public data, your unique data, your organization's data, and it's the sum total of the quality and the state of that data which determines what AI can then go on to do next for you. What I've learned though is it's not just data. There's something that is as important, in fact, I would say more important that needs to layer on top of the data, and that is context. And this was an aha moment in the learning as I got deeper and deeper into the practice of applying AI in day-to-day, I would say like opportunities, where data by itself is great, but data also is often represented in forms and formats that are not necessarily directly reflective of your day-to-day business and user contexts.
And the importance of being able to describe your data in language, in verbiage that reflects the true intent of your organization, your business functions, you as a person, your working style, your workflows, that language barrier between the technical facets of data and AI is something that needs to be bridged in a way that reflects those reality concepts. And the industry is now really talking to this as the context layer. You might have heard references to things like the context graph, the semantic layer, and other such. And one of my big aha moments is how important it is to get that right and closely connected to that, how important it is to truly activate the human in the loop workflows where humans who have a critical role to play in defining and shaping that context have to co-exist and work together with AI in order to build that rich semantic or context layer on top of the data, which then makes the collective data estate all that more powerful and useful to achieve the AI outcomes.
So that is something which has really been like an organic evolution of my experiences and learnings. And then later on, after you realize it, it's like, man, how could I not have seen this earlier? It's not just the data. It's also the context plus the data that makes this thing really powerful. So that is one thing that I think is a new passion of mine, which is trying to figure out how do we make it really, really simple for organizations to model and map their context to their data to make their AI more effective. And then closely connected to that is the human in the loop experiences, where I would say this is something that both AI tech providers as well as AI users have to think intentionally about, which is if you're ever presented an AI solution that is a hundred percent focused on agent tech automation and agents doing all the tasks on behalf of humans, I think you'll do yourself well by pausing to really reason and ask the hard questions on what happens or what to do when things don't go as expected.
And almost always the answer will be the need for a human in the loop intervention and experience to manage. And instead of the human in the loop being reactive to kick in only when problematic states arise, if the same human in the loop design concept was applied proactively as AI is trained, as AI is set up to go execute its tasks, then we could be a lot more proactive in terms of avoiding some of those reactive risks that can surface without the humans being an active part of the loop upfront. So human in the loop user experiences and the importance of getting them right, not just after the fact, but before the fact is something that I've also reflected a lot on and more intentionally than what I did at the onset.
Tom Stoneman: At the end of the day, Karthik's message is simple, but not easy. In an AI-driven world, you are the person writing your own story. No one else is writing it for you. And in part two of our interview, we explore what it takes to write your best story.
Karthik Ravindr...: That foresight and experience is something which if you truly continue to develop, is going to be a unique mode that separates human practitioners from the AI that helps them scale.
Tom Stoneman: We'll also discuss how to prepare for a future that might look a lot different than your present reality. Plus, we'll learn about Karthik's preferred way to clear his mind, even in this fast moving industry. Join us in two weeks for part two. Thank you for listening to The Intelligent Enterprise, a podcast where we get inside big ideas by getting outside of them. I've been your host, Tom Stoneman. Please remember to follow the podcast and leave a comment or review wherever you get your shows. See you next time.