AI at Work: From Hype to Impact with WNS Analytics

[00:00:04] - [Speaker 0]
Welcome to AI at Work, a podcast which is part of the Tech Talks Network. Now you may know me from the Tech Talks Daily podcast. My name's Neil C. Hughes. And every day on that show, I cover a different story around how technology is impacting our life, our work, and even world.

[00:00:24] - [Speaker 0]
The Tech Talks Network is a series of shows that drills down on unique subjects and showcases the voices at the heart of enterprise technology. And in this podcast, AI at Work, we're gonna venture into the transformative influence of artificial intelligence inside the workplace. And our discussions will focus on both the remarkable breakthroughs, but also the complex challenges of integrating AI into our everyday business functions and workflows. And, yes, we will do our best to avoid hype. Because this year, it was revealed that there is a big ROI problem with many AI projects in organizations of all sizes.

[00:01:09] - [Speaker 0]
So it's my hope by engaging with experts at the very forefront of innovation, we can explore exactly how AI is redefining productivity, decision making, and human interaction in the business environment, and learn more about how we can measure success and deliver on ROI of those AI projects, all by offering a balanced view of the technology's promising potential and some of those difficult and critical questions that it often raises. But enough for me. It's time to begin our first episode. And with decades of experience in analytics and a career spanning engineering, consulting, and entrepreneurship, my guest today brings a unique perspective on the transformative power of data analytics. So today, we're gonna explore how WNS is helping global enterprises across multiple industries and leveraging analytics for agility, resilience, and growth.

[00:02:08] - [Speaker 0]
So from understanding how to align a data strategy with organizational goals to addressing roadblocks in AI adoption, my guest is gonna be sharing his actionable insights and success stories that hopefully will help demystify the path to digital transformation. So what role does regulation play in shaping AI's future? How can businesses navigate these challenges without compromising innovation? To find out the answers to these questions, I need to introduce you to today's guest. So enough from me.

[00:02:42] - [Speaker 0]
Let's get him on now. So a massive warm welcome to the show. Can you just briefly tell everyone listening a little about who you are and what you do?

[00:02:52] - [Speaker 1]
Okay. So my name is Gautam Singh. I'm an engineer by background. Ended up in The US. I'm originally from India, I should add.

[00:03:00] - [Speaker 1]
I ended up in The US post engineering school, went to B School and then joined a consulting firm called AT Carney, which is now called Carney on its own, and spent ten years in management consulting in The US and Europe, and then took the plunge to start my own business in the research and analytics space, which I built over twenty years before WNS acquired me. I have then spent the last eighteen months or so integrating the acquired business, my business, with WNS' organic analytics business. And post post that integration, I'm now sort of heading up the combined entity of WNS Analytics and the acquired bit of the Smart Cube.

[00:03:41] - [Speaker 0]
Wow. You've been on an incredible journey there. And going from your journey cofounding the Smart Cube to leading WNS analytics, what lessons have have maybe helped shaped your approach to leadership and innovation in the the tech industry? I suspect you've you've learned quite a few things over throughout your career.

[00:04:01] - [Speaker 1]
It's been it's been a hell of a journey, but it's all been very good fun. And it's fun in particular when you are in a sort of growth environment and somehow or the other, you manage to find your end of the growth equation in there as well. But, of course, there's been many learnings. I think the biggest learning for me has been clarity of vision. So the vision and strategy needs to be there.

[00:04:24] - [Speaker 1]
It may evolve as you go along, but you need to be clear that you have a North Star that you're trying to get to. And the journey may be convoluted ups and downs, etc. But you can't lose sight of the North Star. Related to the North Star concept from an approach perspective is the fact that you need to provide clarity of that journey and that vision from a communication perspective to the team, because you're not getting there on your own. It's always a team effort and in particular in this space, it's a people business.

[00:04:54] - [Speaker 1]
So that clarity of thought and clarity of communication to keep everybody motivated, encouraged and help deal with all the obstacles that are going to fall our way. And by the way, from a learnings perspective, you learn the world isn't always fair. You don't always get what you want. People don't always deliver on the promises they make, etcetera, etcetera. So there's there's a lot of stuff that gets challenged in terms of basic, you know, thinking of how the world works, but you gotta keep at it.

[00:05:24] - [Speaker 0]
A 100% with you. And I suspect there's people listening all over the world nodding their head in agreement with everything that you just said there. And, of course, WNS is famous for supporting more than 600 clients across multiple industries around the world. So can you maybe give me a a real world example of of how your analytics practices has helped businesses unlock the potential of its data to drive measurable results? Because I think very often, right now, it's AI that gets all the headlines.

[00:05:54] - [Speaker 0]
But, of course, AI is next to useless without data too. Everything is in, the data is the lifeblood of every organization. But can you give me a a real world example of the kind of value that you're offering here?

[00:06:05] - [Speaker 1]
Sure. So first of all, there's a lot of hype around the space. And if I look at all 400 clients or 600 clients or whatever number of clients WNS has, and frankly, if I just look wider than that, the one common problem that everybody is faced with is what do I do with this technology? Where all can I apply it? How does it impact me?

[00:06:25] - [Speaker 1]
How do I make the most of it? It isn't as straightforward as what perhaps would have been the case twenty years ago saying, look, I've got to outsource some parts of my business. Let me call WNS. I'm clear on what I need. I'm clear on what WNS can do, and let me just get it done.

[00:06:43] - [Speaker 1]
This technology and the opportunities are far more dynamic and far more unclear. So one of the largest areas of value add from our side, from my team and from WNS as a larger organisation, is to help clients sift through all of this noise and this hype to really identify areas where we can make a difference and they can get benefit from a value perspective. So let me give you an example. For large retailer, global retailer, they wanted to start this journey. This is going back about ten years.

[00:07:18] - [Speaker 1]
And at the start of their journey, all they did is they said, Okay, we're going to hire somebody from outside who's going to head up this function. And their job is to build a team that's going to basically make the most of the power of data and analytics for the entire organization. So we were part of that journey. And essentially, what we became is a part of that team onshore, offshore, working with our clients to truly co create or co answer the question, what can I do with this technology, data analytics, etcetera, across my business? And to just jump to sort of an endpoint, for every dollar that the company spent on this bit of the business, they got a return of £13.5 or $20 roughly for every dollar invested.

[00:08:10] - [Speaker 1]
And the reason was it was very structured but relatively undefined because we didn't know what was possible until we started digging into it. And as we dug into it, we prioritized use cases where analytics and data can make a difference. We built solutions around it. We then implemented those and executed it all the way through to realizing that dollar return. And that's just giving you one sort of data point.

[00:08:36] - [Speaker 1]
But the reality is everywhere across the board, the opportunities are immense. There are many competing priorities. And the first step really is in making sure that we are identifying where we can bring the greatest value with the with the greatest return on investment. And focus on those first before you chase the rest of the tail where there is still a lot of value to be delivered.

[00:09:01] - [Speaker 0]
And, of course, at the moment with digital transformation accelerating across just about every industry, and there's so many different regulatory changes influencing business strategies. What are you seeing here, and what role does WNS play in in helping clients navigate these shifts effectively? Because it seems to be that there's more and more regulations appearing with deadlines, with months to go. What are you seeing here, and how are you helping?

[00:09:27] - [Speaker 1]
So this technology is actually very different from anything else the world has seen before. And I'm talking about AI, Geniei, and analytics in a in a wider sense. And this technology is different for two simple reasons. In the past, when you got new technologies, they were adopted, adapted and incorporated into the corporate world at a relatively slow pace, which allowed both the understanding of a technology, its impact and the ability to adapt to that technology coming in. And I'm talking about everything from, you know, the birth of a computer to the Internet to, you know, any other revolutionary change in technology that came about.

[00:10:11] - [Speaker 1]
It was adapted and adopted over decades. The difference here is that this technology is dynamic and so fast changing that it's going to get adopted and adapted in years, if not in months, which therefore means that everybody is very scared of what this means. My view is very optimistic. This is a positive change and a positive technology. It's going to help humankind in a big way.

[00:10:38] - [Speaker 1]
But yes, it is scary because of the speed at which things can happen, which is why I would say that regulation is going to come in very fast and regulation needs to come in as well because it can be misused. So regulation is going to happen and it should happen. What WNS is doing and what we can do is to help: a) make sense of the regulation and b) help companies and frankly humankind users adapt to the technology within the regulation that is going to come. And again, rightly so. So that's point number one.

[00:11:14] - [Speaker 1]
The second point I'd make on regulation is because this technology is built on AI and by just in layman terms, AI doesn't work in a very logical way in the sense I mean, it is logical, but it's not traceable or explainable in the sense that if you want to look at the output of what AI is saying, you know, a ChatGPT output says here's what you should do you can't trace it back to figure out how did ChatGPT come up with an answer? Where did that recommendation come from? And the reason is because it's all based on neural networks as the underlying technology or approach behind coming up with those recommendations. So traceability and explainability are missing in this new technology which means that it can be easily misused. And not only can it be misused, if the underlying data that's being used to train the models that are coming up with the recommendations have biases built into them or are not clean, then the output is maverick and dangerous.

[00:12:22] - [Speaker 1]
And without the traceability and explainability, it can be truly problematic. So a, regulation is gonna come into control of this. And B) the role of companies like us will also be to ensure that what these models are producing and how this technology is being used can be audited, quality controlled and frankly validated before it's really put to full use. And that brings me to the most important aspect that we are making a difference on. We are not letting the technology drive decisions on its own.

[00:12:57] - [Speaker 1]
Our proposition is a combination of the human and the AI. So we call it maker, checker, consumer. So the maker is the model builder that comes up with the models that are producing all these recommendations or forecasts or whatever else. The checker is checking and validating that the output from the model is correct, usable and relevant before it goes to the consumer who's actually acting on whatever the models are producing. And I think this comes we call it AI plus HI the human intelligence on top of the artificial intelligence.

[00:13:38] - [Speaker 1]
And this is, from our perspective, my perspective, a very important intervention to ensure that rogue technology or rogue output from very good technology doesn't cause mayhem but actually is used properly. So let me put this to a practical sort of example. You look at driverless cars. We've been promised driverless cars for a long time. The final hurdle to get to driverless cars I'm sure we will get there.

[00:14:06] - [Speaker 1]
But that final hurdle is quite difficult and quite expensive. And we will get there. But in the meantime, highly autonomous driving machine car with a human that's doing something to make sure that if the machine is going rogue, I can still take over It's quite important. It just says you have pilots on airplanes. Most aircraft are self self flying, but there is still a pilot in the in the in the cockpit to, a, make sure that the autopilot works and b, for critical situations, there is a trained pilot to maneuver and manage the aircraft.

[00:14:47] - [Speaker 1]
So I think at least for the kind of use cases that WNS and 80% to 90% of the world needs to use, a combination of AI plus HI is the right answer rather than trying to get to the AI alone, which also means that we have enough humans on the planet. Why do we need to replace all of them with AI?

[00:15:07] - [Speaker 0]
Completely agree with you. And I think data intelligence is also right at the heart of everything that we're talking about here and widely regarded as the cornerstone for agility and growth within an organization. So how do you see empowering organizations to better innovate and also stay resilient in this constantly evolving, not only digital first world that we find ourselves, but an AI first world as well increasingly?

[00:15:34] - [Speaker 1]
The way I would say it and place it is data is the is the oil. Yeah. On its own, it's worth a hell of a lot, but it's only truly worth something when it's refined and made usable. So getting the oil to the data in this case to be truly usable requires getting that data foundation built correctly and cleanly. There's a lot of data out there on this planet.

[00:15:59] - [Speaker 1]
It's increasing exponentially. The amount of data that we've got generated in the last ten years or twenty years is the equivalent of what we've generated since humankind existed. And that is exponentially increasing as we move forward. So there is no shortage of data. And hence, putting getting your arms around the data to make it usable becomes more and more complex, but more and more necessary.

[00:16:23] - [Speaker 1]
So getting your data strategy right, getting your data infrastructure right is absolutely critical to everything before you get it even to AI or even basic analytics. So it's, you know, rubbish in, rubbish out is is is still true today as it was, you know, when you and I were were in school.

[00:16:42] - [Speaker 0]
Yeah. It's always been that way, for sure. And some industries are almost leading the pack. They're ahead of the curve in data and AI adoption, while others are still lagging behind, maybe trying to work out how they get involved, where to start, etcetera. So from your perspective, the conversations you're having with your clients out there, what are the key barriers that are holding certain industries back?

[00:17:07] - [Speaker 0]
And and how can they overcome some of these challenges to ensure that they they turn this around in 2025?

[00:17:14] - [Speaker 1]
So first of all, I'd say it slightly differently. Yeah. I don't think there are leaders and laggards from a industry perspective. I think there are leaders and laggards from a company perspective. So you will find leaders in every industry, and you'll find laggards in every industry as well.

[00:17:30] - [Speaker 1]
Yes. There is some more hype in certain industries, in particular, the b to c ones because, you know, when you're interacting directly with a consumer, hype is this is where AI and everything else is going come in. But the reality is it's equally applicable to a B2B organization. So it is at a company level that you'll find differences. There are leaders and laggards everywhere.

[00:17:51] - [Speaker 1]
Those who are leading are typically I'm saying it's again not I don't want to generalize but they're typically relatively newer companies who don't have the history baggage of old school or doing things the way it has been done for the last hundred years. So it's easier for them to be nimble, new age, new generation and therefore start with a clean sheet of paper in terms of leveraging all this technology. The older guys actually have a greater opportunity and that's where your leaders and laggards are down to the people running the companies, I. E. The management teams.

[00:18:30] - [Speaker 1]
And therefore you will see a lot more happening in those companies where there is a much greater opportunity and frankly a necessity to adapt and leverage, these new technologies.

[00:18:44] - [Speaker 0]
And I think traditionally, many businesses have struggled to integrate analytics into their operations due to concerns from data silos or maybe talent shortages. Is that still the case? And if it is, what are or are there any actionable steps that companies could maybe take to address some of these traditional roadblocks that that keep coming back?

[00:19:05] - [Speaker 1]
Yeah. Data silos is is a favorite topic of mine. Yeah. And a lot of our clients have been spend have spent millions of dollars and many years to try and get to the holy grail of having all their data talk to each other and be interconnected and so on and so forth. And that's, to some extent, a pipe dream because by the time you get all of this done, the technology has moved on and you need to start again in order to try and get a data universe sorted.

[00:19:35] - [Speaker 1]
It's called data lakes in technical jargon. And my view on it is, instead of trying to build the data lake from ground up, so bottom up saying, look, we need to connect all our silos together, we need to cleanse the data, we need to combine the data, the data so that it's usable, my perspective on this and WNS' perspective on this is to start with the use case. Let's prioritize those use cases where data and analytics can make a step difference to our organizations and then work backwards. And if you work backwards from those use cases, you will see that while the data lake is something that the IT department is building and will take five years to build and so on, you can identify a pond, a data pond that will meet your needs to address that use case. So that's the benefit of starting with the use case and working backwards to what data do we need rather than connecting all the data and saying, now what do I do with this data?

[00:20:34] - [Speaker 1]
What use cases can I influence? Do you see what I'm saying? So by starting with the use case working backwards, it becomes a more agile operation where you build a pond and you join up the ponds later to build the lake, but the pond starts delivering value to those use cases straight away. So that's the approach that we recommend with our clients. Let's start small, build the ponds.

[00:20:58] - [Speaker 1]
As long as your pond thinking is in line with connecting the ponds to build one big lake over time, then you're not wasting your effort. You're actually delivering value as you go along. And, ultimately, the lake will be built.

[00:21:12] - [Speaker 0]
And with AI integration becoming increasingly commonplace, I'm curious. What what do you

[00:21:17] - [Speaker 1]
see as the the next frontier in enterprise data and AI, and and how should business leaders be preparing for the next emerging trends? So, honestly, I think even the world's experts don't really know how this technology is gonna evolve and what new avenues of opportunity are going to arise from it. And I still call it opportunity rather than sort of problems coming from it. So I think the mindset we must adopt is that change is constant. And in this case, the change is coming much faster than ever before.

[00:21:55] - [Speaker 1]
So the mindset has to be one where we basically are ready to adapt as the technology evolves and develops because nobody knows the power of what we are going to be able to achieve through this technology even in the next five years. It is rapid, it is constant, and it is unpredictable. So that that and that's essentially, in a word, let's just be open minded and adaptable, to this technology rather than try and predict what it's gonna achieve over the next five to ten years.

[00:22:31] - [Speaker 0]
It's so refreshing to hear you say that, and I completely agree. Nobody knows where it's going. I think, also, it's a path that nobody should be taking on their own. And one of the key words here that I often hear is not about the technology, but about collaboration. So how do you at WNS, how do you foster collaboration with your clients to co create tailored solutions that not only solve operational challenges, but also support things like long term strategic growth?

[00:23:00] - [Speaker 1]
Yes. So collaboration is absolutely the right word here. The benefits of a company like Dublin is frankly, one of the reasons why I was very attractive to attracted to exit my business is because I also realized as a small business entrepreneur, that the market was moving on and this technology and its ability to be leveraged was fast changing. What Gablo and S was bringing to the table for me as a small analytics business was the size and scale, almost 100 times bigger than me, to leverage this capability. So from the perspective of collaboration, what our clients are able to benefit from is the size and scale of Dubliners working across so many different companies, across so many different domains.

[00:23:54] - [Speaker 1]
Because the real opportunity is in leveraging what may be happening in one domain, picking up the learning and applying it across a much wider space. And let me give you an example again. You may be aware, you may not, but many of the listeners will be that life sciences companies are actually going through major changes as well. And they are separating their pharma business, true pharma business, from their consumer health business. So let me give an example.

[00:24:21] - [Speaker 1]
Johnson and Johnson have created a company called Kenview, which is a consumer health business, and it's been spun out. Others like them, the big majors, are thinking of and going down the same path. And the reason is that when you look at the consumer health side, historically, is when they all clubbed together, they acted as a pharma company. The consumer health side has realized we need to act more like a CPG, a consumer packaged goods company, Because frankly, what we are selling is toothpaste. There may be a medical element to it.

[00:24:53] - [Speaker 1]
In other words, a pharma element to it. But frankly, it's no different from selling, I don't know, Adidas shoes or, you know, eggs or what shall I say? You may be manufacturing. The point is it's it's it's FMCG, US versus European language, FMCG or CPG that drives that business. So especially when it comes to analytics, what CPG industry is doing with analytics is very applicable to what a relatively old fashioned pharma company's consumer health business needs to be doing with analytics.

[00:25:29] - [Speaker 1]
And those are the kind of cross fertilization that you get through collaboration and through companies like Governors and work across domains and can have the scale and experience to leverage learnings from one to the other. And by the way, we are also fostering this not only through our internal connect, but also through connecting our clients together through events, workshops, sharing of best practices, meet and greets, etcetera, where we get our clients together and show them what we're doing in one industry versus another and bring them to meet stakeholders or peers from other industries, other companies. Because frankly, we have far more to gain through collaboration than through competition. And there is a lot more that everybody can leverage from each other's learnings, even though you may be, in many respects, competitors.

[00:26:28] - [Speaker 0]
And one of the things I always try and do on this podcast is give my guests an opportunity to boost a few myths and misconceptions that maybe frustrate them. So in your experience, are there any big misconceptions businesses have about data and analytics? And how can organizations shift their mindset to embrace that more data driven culture that's required now?

[00:26:49] - [Speaker 1]
Yeah. I think there are many myths around this space, and there's a lot of hype as well as I mentioned upfront. So one of the myths is for everything we where is my Gen AI answer? Yeah. The other myth is why am I not getting more value out of analytics considering I'm spending so much money and throwing so much money down this hole?

[00:27:11] - [Speaker 1]
And let me just address these two big myths to start with. So the first thing is I would say not everything needs Gen AI or AI or even advanced analytics. Sometimes an Excel spreadsheet is all you need. So let's start with the use case that we're trying to solve and then put in the appropriate answer from an analytics data perspective to address that use case. So that's the first perspective on the first myth hype.

[00:27:39] - [Speaker 1]
The second is around the whole how much money is this thing taking and what's my ROI? And that again goes back to a lot of money is being thrown down this rabbit hole because of the hype around it. And what we I would encourage and recommend is to start with use cases where this technology can make a big difference and then work backwards so that you have a much narrower hole through which you're investing and are much more able to measure and track the ROI on it. And I say that because a lot of companies are going down the horizontal route of saying, let me just create a new role, head of data analytics or whatever, and let me put money down that hole to say, Can you fix analytics across my whole business? Maybe so.

[00:28:30] - [Speaker 1]
Maybe that is something we do need to do. But in parallel, start with looking at it vertically and saying, What use cases in in each function can I influence so that you start getting ROI through the pond concept rather than the lake concept?

[00:28:46] - [Speaker 0]
And finally, as someone that's right at the forefront of analytics innovation, I've got to ask, what is it that excites you most about the future of this field and where it's heading? And how do you see technologies like GenAI reshaping the way businesses operate? What excites you here?

[00:29:03] - [Speaker 1]
So for me, the excitement, perhaps it's coming from, having sort of built my own business and stuff, is the opportunity in a very fast changing dynamic technology. And it's just mind boggling as to what this can potentially achieve. It's unknown. Right? And it's fast evolving.

[00:29:27] - [Speaker 1]
So that makes it very exciting. It's not it's not known, matured, where it's BAU and therefore, you're just sort of on a treadmill that, you know, that's running at a constant speed. This whole thing is much more of a roller coaster than anything else. So that's the dynamic nature of it is very exciting. But I think the other bit that keeps me very excited is the fact that there's a lot of hype around this.

[00:29:57] - [Speaker 1]
And people are both worried and concerned about this technology, whereas I feel, perhaps as somebody deeper into in the space, that there's a lot of positive to be heard from this. So I'm quite excited, perhaps at my age as well, to help evangelize the benefits rather than worry about the pitfalls. And the pitfalls are all controllable. So if we focus on the benefits and we as human beings can be positive and optimistic that the, you know, that the gains are far more end of the hour from a focus perspective than the threats, then we're all gonna be in a better place.

[00:30:35] - [Speaker 0]
And I think that is a beautiful, powerful, optimistic moment to end on. And before I do let you go, for anyone listening wanting to find out more information about anything we talked about today, check out the website, contact you or your team. Where would you like to point everyone listening as a starting point?

[00:30:51] - [Speaker 1]
Sure. So firstly, my name is Gautam Singh. You can reach me on my LinkedIn profile. Secondly, please, I'd encourage you to go to the WNS analytics page on the WNS website, or just go to the WNS website. It's WNS.

[00:31:08] - [Speaker 1]
And, frankly, even otherwise, if you wanna find out even more, please follow me. Follow WNS Analytics, and you will get a lot more intelligence perspective and points of view from us on this topic.

[00:31:24] - [Speaker 0]
Well, again, thank you for joining me today. We covered a lot there from the importance of data intelligence in driving agility, resilience, innovation, and rapid growth to some of the big concerns and roadblocks that, businesses might be having integrating data intelligence or data analytics, how they can overcome them, and that positive outlook on enterprise data and AI and what's waiting ahead. There's so so much exciting things on the horizon. I'd love to stay in touch with you, maybe get you on next year, see how that is evolving. But more than anything, just thank you for taking the time to sit down with me and share your story.

[00:31:58] - [Speaker 0]
Thank you.

[00:31:59] - [Speaker 1]
Thank you, very much from my side as well, Neil, and I look forward to further further conversations, as and when appropriate.

[00:32:07] - [Speaker 0]
What role do you see analytics and AI playing in shaping the future of your industry? I think my guest insights underscore the importance of starting with clear goals, leveraging data strategically, and embracing collaboration to stay ahead in the rapidly evolving space. But as we continue to see the convergence of AI and human intelligence, How do you plan to integrate these tools into your business while also maintaining that focus on ROI on your innovation and tech projects? Please share your thoughts. Let's keep this conversation going.

[00:32:43] - [Speaker 0]
Remember, you can connect with me on LinkedIn just at Neil C Hughes. Love to hear your thoughts on that. But we're out of time today, so please stay curious. Keep pushing the boundaries of what technology can achieve in your life, in your business, and indeed this world. Bye for now.