Engineering Experiences: Ciklum’s AI Growth Playbook

Pari: Hi everyone, welcome to another episode of the Zinnov Podcast Business Resilience Series. In this episode, I have Rajaram Radhakrishnan, CEO and Board Member, Ciklum, a leader in digital engineering and experience engineering. Raj has over three decades of experience in technology and is known for his innovative approach to using AI and data analytics to drive business transformation.

Pari: Prior to this, Raj held leadership roles at Cognizant and GlobalLogic. He led global strategy, delivery, marketing, sales, as well as operations. It’s an honor to have Raj join us on this podcast. Thank you, Raj. Thanks for making the time.

Raj: Thank you, thank you, I appreciate being here.

Pari: Raj, tell me a little bit about your fascinating career. You’ve been in technology for the last three decades. What are some of the pivotal moments in your career that shaped your leadership?

Raj: Wow, it’s a long career. As I reflect, maybe I’ll point out a couple of pivotal deployments. Very early on in my career, I had a leader who was tasked with transforming the telecom industry in India, the way the telecom industry worked.

I was a rookie, and he took me under his wing. There was tremendous political pressure, internal pressure, and external stakeholders who weren’t allowing things to happen. What stood out in that whole experience and learning for me was how he shut out all the noise and created an ecosystem while he dealt with everything.

His belief was that his team needed to be shielded from everything in order to achieve a vision. While he was very clear in his vision and what he wanted to achieve, he let the team do what they were good at. I was good at technology, someone else was good at building the product materials, someone else was good at marketing and approaching the market once the product was ready. That product actually transformed the industry in a year or two after it was launched.

That gave me an early lesson, that you need to be clear in what you want to do as a leader, communicate it clearly to your teams, and most importantly, give them the room to perform their roles rather than getting distracted by everything happening around them.

So that was probably a very early lesson about transformation, and the reality that you’ll often be impeded by multiple stakeholders.

The second pivotal moment would be when I was the head of delivery for the Europe business, based in London. I had a Belgian Managing Director. A lot of people were complaining, “This is hard,” “That isn’t working,” and then he stood up and said, “Guys, all that’s great, but you need to realize that we’re measured by what we do today and tomorrow. Our clients aren’t asking about what we did in the past. They care about the future.”

What I learned from that is, it’s your duty as a leader to always guide the team to not dwell on the past, but to focus on what impact they can make today, tomorrow, and into the future, because that’s what the world wants.

Pari: Great, and so having a clear vision and reducing the noise for your team, and then focusing on today and tomorrow rather than relying on the past, I think those are great leadership principles, Raj. CEOs of companies, on a day to day basis you have clients across the world. What are you hearing as some of the key trends that are shaping their strategy, and what are some of the challenges they’re facing in driving those?

Raj: Very interesting question. The past few months have been incredibly dynamic. The market was upbeat from the middle to the end of last year, and then the macro dynamics shifted. The US, of course, led the pack with conversations around a potential economic recession. Consumers, especially in the retail sector, began showing a decline in spending. That’s the macro picture. Then you have geopolitical shifts and uncertainty in different parts of the world.

The one big tool or saving grace that has emerged for all of us is artificial intelligence. You might ask how that’s connected. Well, it’s provided us with a major lever to automate processes and improve efficiencies. AI, GenAI, ChatGPT, Agentic AI, these are the buzzwords of today, but they are also the trends that keep us optimistic about the future.

Now, on to the challenges. Let me offer a client lens here. I interact with many clients. Of course, I can’t solve world hunger or geopolitical instability, but from a business perspective, most clients are very curious about AI and Agentic AI. The mistake many are making is rushing into it without truly understanding the real impact, whether internally or externally.

A lot of companies are looking at AI from a cost reduction perspective, rather than its potential to drive revenue. But that’s the true promise of AI. Just to give you some context, if I were to take a pure business view of return on investment, the AI investments happening today would need to generate around 500 to 600 billion dollars. That’s equivalent to the GDP of a country like Sweden or Portugal. It’s like creating five movie or video streaming industries combined. That’s the scale of revenue the market will need to make all this worthwhile.

That’s the challenge companies are grappling with today. And that’s where companies like Ciklum come in. We help our clients use AI to create new experiences that either enhance their existing revenue streams or unlock completely new ones. Of course, it’s a long journey.

Pari: A great insight. It’s not really about just cost savings and automation, it’s about revenue generation. One of the things you touched upon is experience. Can you tell us a little more about what experience engineering means in your definition?

Raj: Absolutely, it’s a fascinating concept and one we’ve mastered over time. I take a little bit of pride in that. Everyone understands application development, product engineering, data, and AI. We also talk a lot about customer experience. But it’s not just about the graphical interface. It’s about how someone feels while using a product.

Take Uber Eats, for example. If the app was complicated, we wouldn’t use it. But it’s one tap and done. That’s what user experience means. It has very little to do with the backend architecture. Now imagine combining three things: seamless user experience, the constantly evolving landscape of AI, and solid product engineering. We call that combination experience engineering.

At Ciklum, we approach every project with an experience first mindset. We begin with the end user, because ultimately, everything we build needs to drive revenue. We conduct psychological and sociological user research first, and only then do we go into design and architecture. This approach helps shift the client mindset from cost saving to revenue creation.

Pari: So you’re continuing to focus on creating revenue for customers. That really seems to be the DNA of Ciklum, thinking about customer revenue and growth.

Raj: A hundred percent. Like any services company, we do get requests like “Can you provide five people with this skillset” or “ten people with that skillset.” But because we lead with experience, our customer experience team steps in right from the first conversation. They try to understand what the client is truly trying to build and often uncover much more potential than what the client initially thought.

These days, we get called in very early, even when the idea is still in the CEO’s mind. That gives us a chance to shape the product from scratch and turn a basic idea into a high touch, frictionless, experience driven solution.

Let me give you an example. One of the world’s largest language learning apps approached us with a standard resourcing ask. But our team saw an opportunity to improve the user experience dramatically. Could it be voice driven? Could it be action oriented? On the backend, could we bring in AI for translation services?

It used to be a manual process. We transformed it using AI and modern customer experience design, creating a product that users didn’t just use when they needed it, but one they enjoyed engaging with. Revenue streams grew significantly as a result.

Pari: Got it. So you’re integrating AI wherever it’s needed, and bringing together UI, experience design, and engineering to create smarter solutions.

Raj: Exactly. Another example, we worked with a large travel and entertainment company that serves airlines and offers vacation packages. They had multiple systems of engagement: web, mobile, agents, customer service, and each one led to different systems of record. It was fragmented.

They asked us to unify all that. So we used our experience first approach to think from the end user’s perspective. People like you and me who go on vacations maybe once or twice a year. How could this company stay engaged with us the other 300 days?

We built that insight into the experience layer and created a platform where users had a single interface across services like airlines, hotels, and travel packages. This not only improved user satisfaction but also enabled the company to cross sell more effectively. For example, if someone booked a hotel, they could now also be offered a spa service or a city tour. They couldn’t do that before. This increased their top line by 17 percent.

Pari: Wow.

Raj: And their user ratings also went up significantly because people loved having everything in one place.

Pari: Raj, those are great examples of experience engineering. And there’s also this whole concept that Bjoern Jensen talks about—physical AI. How is AI being integrated into physical products that many of us, men and women alike, use every day, like razors? This is for a big FMCG company that we’re working with. It’s a regular razor, which they wanted to turn into a heated razor to provide a better experience. Now we had the ability, and we built that product.

Raj: As we were building the product, we actually went back to the customer and said, here’s an opportunity. What if we enable sensors in the product to pick up data on how it’s being used? That data could be sent to the cloud. We could then use AI to analyze the data and provide feedback through an app.

The user would have an app that could say, “You are using it in a certain way, Mr. User or Ms. User. If you adjust your stroke, you could get an even better experience or improved performance from the product.”

This is a simple example of taking a physical product, adding sensors, capturing data, using AI to analyze that data, and then providing personalized suggestions through an app. It transformed a simple day to day product into a user experience driven innovation. And that led to multiple millions in additional revenue for the company.

Pari: And this is a very interesting example of integrating AI into a physical product, which we use on a daily basis. What is your advice to enterprises that are just getting started on the experience engineering journey? What are some of the pitfalls to avoid?

Raj: As I mentioned earlier, one of the common issues is that people rush into AI. They rush into transformation because that’s what they’re seeing all around them, and they expect quick returns as well. But from what I see, best practices begin with a customer first approach. Think deeply about your end customers. What are their likes and dislikes? While you may think you know your product well, that’s often not the full picture.

The best example I often refer to, even though it’s from a different category, is Tesla. When the rest of the auto industry was talking about how many miles a car could go or how much maintenance it would need, Tesla shifted the conversation entirely. They moved to a user experience mindset. You’re not buying the car just for the mileage or maintenance benefits—you’re buying it for the entire experience.

So, if you can shift your narrative to think from the user’s perspective and what your end users will need in the future, that’s the first big step.

Pari: So it’s really about the jobs to be done by the end user. What are they trying to get?

Raj: Exactly. What are they trying to achieve? In the language learning case, they’re trying to learn a language. In the food delivery example, they’re trying to get a meal quickly. Or how easy can your payment flow be? That’s the mindset.

The second thing I would say is to look at your data and data infrastructure. Many times, scalability becomes a problem because people haven’t thought through their data setup. You need to modernize your data infrastructure. Everything is data driven today, and if your foundation is weak, it limits what you can do.

The third thing is to have a clear view of how much agency you are willing to give to AI. In the example I gave about creating a travel itinerary, AI can do a fantastic job. Will there be errors? Yes, but those can be fine-tuned. If you’re overly conservative and want to do it all yourself for safety, it may take far longer than necessary and reduce your competitive edge.

You absolutely need governance, security, and some clear guardrails for your AI systems. But the balance lies in defining how much control you are willing to hand over to AI.

So to summarize, the three key principles are: first, put the user at the center of your universe; second, focus on your data and how you can monetize it; and third, while paying attention to governance and security, be deliberate about how much agency you give to AI.

Pari: Love that, agency to the Agentic AI as a concept.

Right. So Raj, you spoke about experience engineering. What are the key differentiators for experience engineers from a client’s perspective when they look at Ciklum? How are you uniquely positioned to solve customer challenges?

Raj: That’s a good question, and I’ll answer it from my clients’ perspective.

What they see in us is a company with a customer first mindset. And by that, I don’t mean just the client company—we mean the client’s end customer. That’s the mindset we bring to every engagement and every product we co-build with our clients.

Earlier, I mentioned the integration of customer experience, engineering, AI, and data. That’s the second key aspect. There are many companies that do each of those areas well. Some excel at customer experience, others are strong in engineering, and some are leaders in AI. But clients see us as a company that integrates all of that seamlessly. It’s not just that we give an idea and walk away—we see it through, all the way to implementation.

That seamless experience we promise to their end customers comes to life because our teams are engaged from start to finish.

The third differentiator, which is equally important, is our global presence. We have deep engineering talent in Eastern Europe, where we are very strong. We have robust data capabilities in India. And for customer experience, clients often need nearshore support—whether it’s in the UK, US, or Canada—because those interactions are typically handled within the same geography as the end customer.

So, we bring a blended, multi-geography team that works across the client’s entire ecosystem. And that works very well for them. That’s why clients continue to come back to us.

Pari: Got it. Customer first, strong engineering execution, and global delivery with the right talent in the right place.

Pari: You lead thousands of engineers. For students or new graduates entering the workforce, there’s anxiety about AI. What mindset and skillset should they build to future-proof their careers?

Raj: Great question. I have teams across the world—as I said, in Eastern Europe, India, and the US. What I see is that many of them want results quickly, which is understandable. It’s a fast-moving world.

What also fascinates me is their strong desire to make a real impact. They care about the world, they care about the environment, and they’re more conscious of the world they live in. That’s a great blend—people who want to drive transformation and also bring empathy to the table.

What I tell the young professionals I meet is this: don’t think of AI as a doomsday scenario. Think of it as your company, your partner. If you can learn to ride alongside AI, then you have double the power to drive transformation. You’ll have more opportunities to lead meaningful change.

You also have the chance to be a change agent. The previous generation will stick to what they know best, but you are entering the workforce with a fresh mindset. If you’re willing to learn AI, use it as a companion, and take on the role of a change agent, you’ll be extremely successful. The world is looking for newer, richer experiences, and that’s your vote, your opportunity.

Pari: Great. You seem to be very optimistic about the future of the next generation of engineers. Thank you, Raj, for sharing your valuable insights and experience. And all the best in helping your customers think about their customers as they build their own experiences. A big thank you to our listeners for tuning into this episode.

I’m your host, Pari Natarajan, CEO of Zinnov. Till the next episode, goodbye.