CRSP07: State of AI 2025 pt.2 - For the love of data with Indhira Mani, Intact Insurance

 In this second episode of the special AI mini-series, we now explore the human side of transformation, where technology meets purpose and people remain at the center. From future jobs and critical thinking to working with C-level leaders, how human intervention and high-quality data drive success in an AI-powered world.

This week, Dave, Esmee, and Rob talk to Indhira Mani, CDO at Intact Insurance UK, about the Love for data, insights on leadership, resilience, and preparing the next generation for what’s next.  

  

TLDR:

01:30 Introduction of Indhira Mani and Scotch whisky
05:45 Explaining the State of AI mini-series with Craig
07:12 Conversation with Indi about her boyfriend called Data 

38:33 Umbrella Sharing in Japan and the trust on AI
45:15 The British Insurance Award and Women in Tech finalist 

Guest
Indhira Mani: https://www.linkedin.com/in/indhira-mani-data/

Hosts

Dave Chapman: https://www.linkedin.com/in/chapmandr/

Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/

Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/

with co-host Craig Suckling: https://www.linkedin.com/in/craigsuckling/

Production

Marcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/

Dave Chapman: https://www.linkedin.com/in/chapmandr/

 

Sound

Ben Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/

Louis Corbett:  https://www.linkedin.com/in/louis-corbett-087250264/

 

'Cloud Realities' is an original podcast from Capgemini

Creators and Guests

Host

Dave Chapman

Chief Cloud Evangelist with nearly 30 years of global experience in strategic development, transformation, program delivery, and operations, I bring a wealth of expertise to the world of cloud innovation. In addition to my professional expertise, I’m the creator and main host of the Cloud Realities podcast, where we explore the transformative power of cloud technology.

Host

Esmee van de Giessen

Principal Consultant Enterprise Transformation and Cloud Realities podcast host, bridges gaps to drive impactful change. With expertise in agile, value delivery, culture, and user adoption, she empowers teams and leaders to ensure technology enhances agility, resilience, and sustainable growth across ecosystems.

Host

Rob Kernahan

VP Chief Architect for Cloud and Cloud Realities podcast host, drives digital transformation by combining deep technical expertise with exceptional client engagement. Passionate about high-performance cultures, he leverages cloud and modern operating models to create low-friction, high-velocity environments that fuel business growth and empower people to thrive.

Producer

Marcel van der Burg

VP Global Marketing and producer of the Cloud Realities podcast, is a strategic marketing leader with 33+ years of experience. He drives global cloud marketing strategies, leveraging creativity, multi-channel expertise, and problem-solving to deliver impactful business growth in complex environments.

What is Cloud Realities?

Exploring the practical and exciting alternate realities that can be unleashed through cloud driven transformation and cloud native living and working.

Each episode, our hosts Dave, Esmee & Rob talk to Cloud leaders and practitioners to understand how previously untapped business value can be released, how to deal with the challenges and risks that come with bold ventures and how does human experience factor into all of this?

They cover Intelligent Industry, Customer Experience, Sustainability, AI, Data and Insight, Cyber, Cost, Leadership, Talent and, of course, Tech.

Together, Dave, Esmee & Rob have over 80 years of cloud and transformation experience and act as our guides though a new reality each week.

Web - https://www.capgemini.com/insights/research-library/cloud-realities-podcast/
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CR115: For the love of data with Indhira Mani, RSA Insurance
[00:00:00] Before we let you go though, we ask every episode of our guest on this podcast. We ask at the end.
Welcome to Cloud Realities, an original podcast from Capgemini, and this week, the first of our state of AI mini series. [00:00:30] And we're gonna talk about the love of data. I'm Dave Chapman, I'm Esmee van de Giessen and I'm afraid we haven't got Rob with us this week. But joining us, I am delighted to say, is a guest host for these state of the AI shows Craig Suckling, who is head of AI for Europe here at Cap. Craig, how are you doing? Nice to see you.
Doing really well. Thanks. It's great to have this mini series kicking off Dave and looking forward to some really great in-depth discussion on ai. Likewise mate. And you are somewhat portrait turn gamekeeper, I [00:01:00] think slightly.You've been on one of these as a guest, haven't you?
I've enjoyed both sides of this and I've loved being a guest. I've loved giving you a hard time. That's right. And now it's great to be on the other side helping to understand like how others are exploring it. I, I think that might be a unique pivot, you know, like I, none of us have had that experience, so you're like one step ahead of us as a team.Go, go Easy on us. I, I've seen behind the curtain. That's right. And seen the magic. This is the challenge now. That's right.
That's right. Well, we'll come back to the miniseries in a second. Let us just [00:01:30] introduce our amazing guest for the day. I am delighted to say we've got Indhira Mani or Indie, who's the CDO at Intact Insurance UK. Intact formally being Royal and Sun Alliance Insurance, Indie. How are you doing today?
Oh, I'm doing very well. Thank you for inviting me to this wonderful podcast. It is our pleasure. It's lovely to see you. And whereabouts in the world are you today? So I am in the Sunny Scotland. I love Scotland. So where do you, are you a whiskey [00:02:00] drinker Indy. Scotch. Scotch. We call it Scotch.
Scotch. There you go. Is that, is that a yes or have you just tried to duck the question? Yes. I did a tour of Scotland in like a camper van, oh God. A long time ago. Probably 15, 16 years ago. And just went round to all the different bits. No, it wasn't, it wasn't intended to be a whiskey toe, but it turned into one such an absolutely fabulous experience, like really sort of visceral because the whiskeys like [00:02:30] represent the You know the world around them to a certain extent, don't they? 'cause they're all based on the water and the peat and stuff like that. Craig, are you a whiskey drinker?
I am a whiskey drinker. Oh. Um, but I am currently torn between where the scotch and s Scottish whiskey is better than Indian whiskey 'cause I think there's a really big boom in very good Indian whiskey right now. Yeah, I know, right? Yeah. Yeah. That's very controversial. I know. Very. Yes. You know, I've never tried it. I've tried Japanese that, you know, the [00:03:00] Japanese do a, an extremely good whiskey. So what, what differentiates the, uh, Indian whiskeys do you think?
So what I heard, um, is that the maturity process is accelerated because of the humidity and the climate that you have in India, which helps to make a much better roundup, good flavored tasting whiskey in a shorter period of time. Very
interesting. One fact, if you wanna try a whiskey, if you want, if when you remember my name, there is a whiskey called Indri, [00:03:30] I-N-D-R-A-R-I. It's one of the single molds which are, um, most sold in India. Amazing. The whiskey is called Indri, I-N-D-R-A-I-I. Try it at single malt. If I am right, I think half the Indian population who drink whiskey drinks that I've taken a note of it. I am going to check that out 'cause I'd not, I'd not heard of this. Boom. So that's an excellent takeaway. We haven't even got going yet. That's one takeaway we haven't, we just on the intros, ez, how you doing? Oh, and let's not forget that every [00:04:00] tour you make a whiskey tour, so, um hmm.
Well, they didn't eat a whiskey tour around India, is that what you're suggesting? No, no. Doesn't matter where you do the tour. You make it a a whiskey tour. Marcel, can we organize that? Is there any way we can couple that to the show or something? No problem. Yeah. Excellent. That, that's the kind of facilitation that I like. So as you Well, yes, I am. I actually, I, I'm very excited. I just received my plateau with the iPhone 17 Pro features. So it's a plateau with a phone attached to it. It's a [00:04:30] pro. Yes. It's the plateau with the phone attached to it. Yeah. Very exciting. What's your, what's your first touch review of it?
I love it. Yeah. I don't know it, but it's also the buzzing feeling of getting a new phone. To be honest, I do have to, I think the, the most common feedback that I've heard so far is that everyone really is struggling with the liquid, uh uh Oh, the ga The the cooling thing? Yeah. The, no, yeah, the liquid. The, the, the way the, the apps are now, you know how they're show.
[00:05:00] Oh, liquid glass. Liquid glass, liquid. Yes. Do you still find new phones exciting because the last. Three, four years, it's become so commoditized like. Yeah, it's true. I, I've, I've actually not upgraded in the last few years. I used to be a huge upgrade fan stock. I'm still on the 13 and I'm actually considering a 17. I, I like, like, a bit like you Craig, I've, I've just opted out for a little while, but I dunno, there's something about the, there's something about the 17 I find quite interesting.
Hmm. I might just wait for the chip in my brain with AI to help control [00:05:30] things. Terrific thought. So I, I think I'm working a controversial bomb here. I I'm so glad I have a phone. I think I have a phone. Yeah, yeah, yeah. That's how I work. Right? Well look on that note, let's frame up Craig, the state of the ai. We're gonna do, we're gonna do four episodes or so talking to leaders in the field and just take a check-in on where they are as individuals on their journey.
What's going on. [00:06:00] Their organizations with it perhaps, and then also maybe just ask some bigger market questions As an expert in the field yourself, what are, what are you looking forward to and, and how does it shape up for you?
Super excited to be kicking off this mini series. I think it, it offers a real opportunity for us to speak with some groundbreaking leaders in engagement ai.
We've covered a bunch of really exciting profiles starting with indie. Right, that really look at the different aspects of AI from both an Indus industrial or an industry perspective, [00:06:30] as well as thinking about all of the different. Topics or subtopics that cover a topics as, uh, a topic as complex as ai.
And so we're gonna be diving into how we think about scaling, driving business value and impact. We're gonna be talking about how to think about adoption and change and what this means for us as a society culturally. What this means for talent and future generations. We're gonna really look at this from different angles, and importantly, we're gonna hear the voice of experience, the voice of [00:07:00] people who are on the coal face of this driving change in their organization.
So really excited for it. Really excited to learn and engage in really good conversation throughout this series and just super amped to get started.
Well, on that note, let's get started. Indie. Let's start with just a sense of how you. Got to your role. So why don't you just sort of describe your journey a little bit and how are you, where you are? My journey to RSA hasn't been like straight line, right? Like anybody else, many of the good [00:07:30] journeys are not straight lines.
Uh, it's, it's not a straight line just like anybody else. I've had my. Parallel, perpendicular, rectangular, squire journey. Mm-hmm. Right. To a shape now. Right. That's what I call it, a journey, but it's always been about curiosity, resilience, and courage. Hmm. I, I didn't know that I will end up as a chief data officer and I, that wasn't something I was always aiming at. Like I Right. But I always knew that, [00:08:00] um, I want to do something where I will be solving. Problems creatively, because that's who I am. Mm-hmm. But also I know that I had this silent romance towards data.Right. Right. What first attracted you to data? Yeah. Is what you wanted to say.
Literally, I was, I was working on a big regulatory program and there was nobody to take the data work stream, and it just best got bestowed upon me. Right. And then when you [00:08:30] started working on data and then you found that one thing which gave you the mojo to go and do more, that was data for me.
That's why I call it a silent romance. Mm-hmm. Mm-hmm. Because. I think data is a best boyfriend I've had. It doesn't ask me questions back. Well, that worked really well, isn't it? And I also solved it really well. So when I knew answers, when I solved problems, data became. My thing, you know? [00:09:00] And uh, from there on it was always about new challenges in data and technology and trying to build teams from scratch who could work together to build business outputs, which made sure that organizations were successful.
So I just found out that this is the one thing I could do really well and I got, got enthusiastic about it and I just. Stick to it. And then now I found an opportunity with RSA where I could actually use a Greenfield [00:09:30] to build upon and make sure that we turn the organization into a data-driven organization and build the basics. And explore, scale up, innovate. So that's the journey to RC.
Well, just before we dive into some of these notions of data driven and things like that, I'm quite interested as somebody who has had this sort of career long romance with data that you describe, how do you conceptualize it? And, [00:10:00] and the way that, the reason I'm asking that is because so many say data architects and maybe even data scientists, they see it as a Architectural exercise to get it right, or they see it as a very mathematical thing. How would you describe your relationship to it? How do you see it?
So for me, data is not about an ivory tower. Right. I, I always say this to my team. I cannot make anybody successful [00:10:30] by building my data landscape, but the business need to determine what their problem today is and data will solve their problem.
So I see that, I see myself as an enabler. Data is an enabling function rather than data being on its own can, you know, bring your COR down or build your profits up. So I see myself as a business friend, right? An enabler, right? A keeper of secrets, and then making [00:11:00] sure I make everybody happy.
India. I, I love the analogy of data being your best boyfriend, and if, if I try and stretch that a bit, h how do you keep the relationship fresh and exciting for your business, who might fall out of love of data?
Mm-hmm. How do you keep them seeing the value and the interest and the excitement in that relationship?
I seriously think there is a notion that business don't like data, but. What the [00:11:30] hell? Like, you know, who doesn't like data? Hey. Yeah. But, but seriously, it's more about understanding your business objectives, understanding where the business is going, what they are trying to achieve.
If you are able to translate. Your data outcomes into business language. That's the key. And today I see that in the ground, and I've seen it all the time. Big data landscapes, big CDOs fail because they work in silos. [00:12:00] And that's not like a myth. It's, it's it's known fact out there in the market, right? So that's why I quickly learned.
My best bet to build a data landscape or an estate is to work with the business to understand their problems and solving that efficiently through data and incrementally building the data estate by providing those values quickly.
Taking the metaphor of of love a little bit further, eh, and maybe it's [00:12:30] also a pressure theory. I think everybody's interested in love. Yeah. But thi this episode is gonna be called for the love of data, by the way. For the love of data. Yeah. It's written itself. It's written itself. I, I fall out of love when the quality is very poor, you know? And I think data and quality is always like this interesting topic, especially also in the eyes of business.
How do you relate to that? 'cause usually it's the business that also provide input. Yeah. To get the right quality of data.
Yeah, absolutely. It's garbage in, garbage out. Right? That's, that's what it is. [00:13:00] And then, and in love language is the same. You know, if you don't respond to somebody, they're not gonna come and kiss you, I think, right?
That's simple. That right. For me it's, it, I'm keeping it simple, right? Like, so it's, it's, it's really building those basics. This foundation data is the input and output to an organizational tech stack or for any business, uh, insight. It's just making sure that, you know, the, the organization as a whole takes [00:13:30] responsibility of the data they deal with It's from day one. It's from the start of the value chain, right? Making sure that there is literacy, there is, there is education about that, like right from somebody who keys in the client name or the product name. In the first Excel sheet to the, to the CEO, who's looking at the balance sheet at the end of the year, right?
Mm-hmm. It's that full value chain. So unfortunately you are, [00:14:00] you're supposed to allow data at every point in this journey, and you will sometimes fall out of LA then fall in love back. Hey, just, just keep the log going. That's what I will say. Just continuously start building those foundations. You know, there's those good controls, that good data governance, that ownership and, and that's the only way you get good outputs at the end of the tunnel.
One of the things that used to happen in the world of data, and it's probably happened for like the last [00:14:30] 30 years, is sort of data structures and master data structures and all of those sorts of things have, have been the bane of. The, the life of a lot of organizations that have been trying to leverage the sort of value that you are talking about.
And there's been many, uh, millions of dollars spent on programs trying to cleanse data, you know, get, get data architectures in place, get all of that structured approach to it. I dunno whether the analogy here to extend the analogy is like, if your boyfriend's [00:15:00] looking scruffy, how do you get him a new outfit?
I'm trying my best to sort of crowbar the analogy back in 'cause it's so good.
Right. Okay. An architecture, any architecture is perfect. It's a textbook exercise, but also, you know, a good architect can bring the real technology into life. To the architecture work, right? Mm-hmm. So when you start implementing, that's where the gap is.
That's where you will see that the systems does not talk to each other. The data model existing in one [00:15:30] system does not talk to the data model in another system, and you're not getting great outputs at the end of the day, right? So, I know I'm gonna hop on this few more times. This is all about building your basics.
Get your basics done. Damn right. Yeah, and I say that every single time because if you don't know, first of all, start with your right. What do you want as a result of having good data? Hmm. And then work from your right to left as to as for the results you [00:16:00] want to derive. What are the ingredients, which has to go into that mix?
And then, do I have all the ingredients or do I have all the good quality ingredients? It's, it's exactly the same. Like how do you, when you cook, you plan your menu. When you plan your menu, you plan your ingredients. When you plan your ingredients, you source your ingredients. You source good ingredients, you get good food.
Is it easier now with the advent of the sort of technologies that are around at the moment to leverage strong? [00:16:30] Intelligence from a poor data set. And by that I mean the failure of previous cleansing exercises over the course of decades. I'm not talking about in the last five years or so, but over the, over the early decades were all to do with the fact it was just too hard.
Like organizations just sort of crumbled, partly 'cause of the academia of architecture and the way that you're talking about it was just too difficult to get done. Yeah. Or it was too costly. With the advent of things like fabric and, you know, I, I guess aspects of ai, [00:17:00] which we'll come onto. Is it easier now to leverage data, do you think?
Or is it just as hard to get those basics right.
So definitely the automation, AI and other tech, other new technologies ha you know, have, uh, led to faster time to decisions. Yes. But has it led to good decision making, good quality data? I dunno. Right, right. So if you have. At your [00:17:30] source, good data available, then the technology can help you to bridge the gap between the output and the input.
Yes, faster, but also in a way that it can inter, you know, the interpretation gets, you know, different visuals and different taxonomies attached, attached all that, you know, take a fabric, for example. Right? However, at this very basic, at the very start, if you have crappy data, no amount of technology or AI can do anything because AI is also [00:18:00] dependent on data.
Yes. You, it can come up with optionalities of. Making that data much more visual or make making the poor data available to you faster. However, poor data is equal to poor data. There is no way of having poor data converted to good data without having manual interventions and those logical models, and having physicalizing those logical models into play as to how you're turning that into good data. So. You know, [00:18:30] Indy May, maybe to take the other side of Dave's question, the, the other thing we're seeing is that there's more data than ever before and it's growing and it's becoming a lot more diverse in both structured and unstructured data. And we, we even have data being generated a lot more by AI as well.
So are you seeing that that is becoming. Another aspect that might be a challenge or an opportunity around how you keep the love romance going with data with your business.
I definitely see it as an opportunity, but that's my personal view. [00:19:00] The reason I see it as an opportunity is when you have good amount of data sets, make inferring, the output from that becomes easier rather than having less data set and having more gaps in it.
So to get a holistic. Uh, data set from various sources, and then making decisions based on that for the business is the most viable option rather than having insufficient data. So most times [00:19:30] business fail because of insufficient lack of data, right? Mm-hmm. So the more is less concept does not. You know what the here, right?
You need to have more is more. More is good. That's what the business want, and it is good. It is always good to have more data, in my opinion. It's just making sure that you are able to work with the data which you have in hand, and then understand the data. That's most important thing.
So let's talk then about understanding the data and [00:20:00] the culture around becoming a data-driven organization.
So the human side of this now. So it's one thing to create dashboards and and sophisticated readouts on data. It's another to be able to absorb that as a human and then do something with it. So what are your experiences of taking an organization on that journey?
People are the most. Important aspect of any transformation, but not just data transformation And [00:20:30] understanding what they're expecting out of this transformation or the OR data is more important. That's where we bridge the gap between the business and technology. Hmm. Right. So making sure that. There are CRISPR plans and then you are bringing people in the journey earlier on in the journey is very, very important when it comes to data, because data, as I said, is then put on an output to any system which we have in place.
Mm-hmm. Or for any system and in [00:21:00] any organization. So bringing them earlier on in the journey, but also. Taking them along the journey is very, very important and very difficult. I'm not saying it is easy, but it is, but it is very important if you want to be successful with data. Once they start seeing the value they derive from data, I think then it becomes easier.
But the first leg where you'll need to bring them and then keep going on with them, not behind them or about, you know, in [00:21:30] front of them, but going with them is the most important step in this transformation.
So if we imagine then that say, dashboards are like the first interface that some business executives have to data, and they're, they're going on that journey you're describing, which is, how am I Assimilating that data and using it differently to drive my decisions as a, as an executive in the organization. And before I might have just been using it to underpin, now I'm gonna use it as a lead [00:22:00] indicator. So I'm moving to, I'm being, I'm, I'm now using data to help frame how I'm leading the company.
If that's that, that first interface to data, if you like, for want of a better term, how does then the interface shifting to a chat GPT or gen AI like interface, how does that help? 'cause you're still, it's still an interface to data, isn't it? And, and do, do I need to go through that? Learning how to use data through dashboard, like [00:22:30] interfaces first or as a human on a change journey.
Can I just leap straight now into Gen ai? 'cause I can chat to it. Right. So I, I think the world is changing much more with how we deal with data, with, especially with artificial intelligence coming into play. Hmm. And, and it's not just, I think, I think that's what we are all seeing. So if I were. So it's, it's more about learn the [00:23:00] tools, but marry them to critical thinking and ethics.
Mm-hmm. Right? Like, um, regulators don't care how elegant my Power BI dashboard is, or my Python is if, if your models are making biased decisions. Right, right. Like, so for me it's more about in, in, in, in the era of ai, the real superpower isn't. Your dashboards coding faster than your chat [00:23:30] GPT or whatever it is it's, it's asking better questions. It's curiosity compounds.
Mm-hmm. Right. And asking those better questions than chat GPT and making sure that you are able to marry that with the outcomes you want.
Yeah. India. I really like what you said there, just to build on that we're seeing now that there's a blessing and a curse because I think a lot more people are [00:24:00] excited and wanting to embrace more AI because there's so much hype of it in the market.
So I'm sure you see this in your business, many more people are trying to explore. That might help with the cultural change in the adoption. But you mentioned the need to have critical thinking and the way to interact and extract the right truth outta that. How are you trading that balance? Like how, how do you keep the interest, keep the engagement, the cultural shift up, but make sure people are understanding how best to use these tools?
So I think there are a couple of stages where we are going [00:24:30] through on that journey, right? One is to making sure that we understand the business outputs or values and bringing that in the front of the value chain as to how we are doing what we are doing and why we are doing. That, that's the most important thing, the education and then the literacy of what we are trying to achieve through our world.
The storytelling is more important there. [00:25:00] The data office members are not great storytellers historically. That is why I'm, I'm slightly taking it into a different topic. That is why we are getting a curious bunch of, you know, diverse people in our team who could actually marry up that business value and that data technology benefits together and.
Tell the story back to the business as to why we are doing what we are doing and how we are doing it and what we are trying to achieve and how we will [00:25:30] take them to where they wanna with the use of data. So that's what I see it. Yes, it's a blessing and curse with ai. But also AI is not one thing. Hmm.
Right? Like AI is not like one thing. Right? Like there's so many, uh, different. There is generative ai, there's large language models, there's deep learning, there's, there's there's different ways. Mm-hmm. Where artificial intelligence can disrupt the data field or even the organization. So we just need to [00:26:00] make sure that we apply that literacy angle right in the front and then be proactive about what we are trying to achieve.
Could you tell a little bit more about that multidisciplinary team Indy? 'cause do you have data stewards? Do you have data scientists? Uh, how do you make sure that there is a broad team that you have in that storytelling sense as well to get that integrated in your own data team?
Absolutely. This is a fantastic question to be honest, because I think over the [00:26:30] years, if somebody asked me, what have you learned?
I think I have learned to build a bloody good data team, and then I am very, very proud of that, right? You'll need to have those ninja robos who can actually build your API pipelines those data pipelines. Who could. Then you'll need to have those really good business acumen, uh, you know, people who would bring those, those, those large business [00:27:00] benefits, those understanding of those business frameworks, process, and.
Policies and with, with the right mix of those, bo those candidates, you are able to then supercharge and accelerate the data journey, right? Because once we understand business and who can build those data, bring those data together. Terrific combination. Trust me, it doesn't happen overnight, but you'll get one or the other and train the, train them [00:27:30] both in the, in the mix and then they all train each other and they become this ninja robo community.
I like that analogy. And also how you also make that change sustainable through people over time a hundred percent. So one of the things I, um, so there are a few principles I go by as a chief data officer because, but also I don't want to live and die as a chief data officer, and I know that, right. Um, you know, my love for data is great, but I, I know at some point in time I need to [00:28:00] explore.
Other romantic capabilities too in my life. So that's good. Um, yeah. Deviating from the topic. Um, but for me it's one of the principles, like, you know, wherever we go, you should never build an ivory tower. We should make it more process dependent except architecture. Right? Architecture, they love building ivory towers.
And I was talking to Rob, who's our co-host, who fortunately can't join us today, but he was talking about whether it was possible to build an ivory tower on top of an ivory tower. [00:28:30] And I. I think it probably is, probably is Oh yes. You know, this is why you need to build your architecture team, uh, in, in a way like, so today we have a great architecture setup, right?
They don't sit within my team, they sit outside our team, however, they need to work very, very, um, collaboratively with the CDO team, you know, in Intact uk. And this gives me a bigger benefit of showing them. The practical ifficulties and bringing them into the journey of, okay, like sit with us. [00:29:00] Let's do this together.
Right? Like, can we do, so there is, there is a, there will be a blueprint of an architecture diagram with the wishlist of what you want to achieve, where do you want to go? How do you want to do it? Then there are practical difficulties, right? Like as to is this even a model which can work? Is this a technology stack which can work with us?
Right? It's not one size fits all. It's even having those. Two different communities, the doers and the designers talking to each other. That's the most [00:29:30] important thing. I don't know. There is no bias towards one particular architecture, community or the designing community here. Right. It's more about like making, you don't have to be nice.You don't have to be nice about it. Indie, no, I absolutely love architecture, don't get me wrong. I kind of, I, I like. Having things visual. I, I love it to meet you. Yeah. I'm a very visual person, right? Like, I would like to see things visual. A good architecture will tell you, these are the things I want this is how it should work. But then it's about like the [00:30:00] practicality of whether it'll work or not. Yes. That's the second problem. Right.
And importantly, as you mentioned earlier, making that translatable to a business audience. Yeah. That's like, what does it mean to them? Right. Yeah.
End of the day, right. We are not running a charitable institution. Right. And I'm not working on a, I am not going to work without salary for the organization I work for. It's very simple. Today, I had the same conversation with, with the person I interviewed and I said the same thing.
Yeah. Look, you and I are not gonna sit here and say like, okay, I'm satisfied with my job, so I don't want my salary.[00:30:30] Right. I'm gonna work for a salary and a bonus at the end of the year, and for that, I need to make this business more profitable. And how do I do that? Come, let's solve this problem together. I wanna just move us on a little bit, as much as I'm tempted to. To dive into that because I, I agree with your, you're diving into the salary or my other m Well, well, I'm gonna come back to, I'm gonna come back to the latter.
I'm not done with that yet, but, okay. Cool. Um, 'cause I think AI gives you all kinds of potential upsides there, but I'll [00:31:00] come back to that in a bit. What, where I wanted to go though was staying with organizations and staying with AI entering into the workplace. Where your current thinking is on these notions of human AI hybrid organizations, and, and I'll just frame that up a little bit more, which is on one end of the spectrum, you've got this notion of intelligent agents.
That maybe need to interact with humans, uh, that maybe [00:31:30] need some kind of ai, HR representation, or they need to be taxed in a different way. Eg. There's a series of sort of concerns that, that go around how therefore a human AI hybrid organization works and is it different to a pure human organization.
And at the other end of the spectrum, you've got, they are a tool set. It's a human based organization with a, with a very smart tool set. I wonder how you are thinking about that, that framing [00:32:00] might not work for you. Feel free to mess with that, but where's your head at currently on that whole debate?
See, for me, I don't deny that. There is a AI revolution here. It's happening now. Mm-hmm. I am, I won't acknowledge it and I am acknowledging it. I'm not going to be defensive about, no, no, this can't happen. That's, that's foolish. I know that, but there is. There is a, a little voice in me also [00:32:30] saying is this, is this something really right? Hmm. I'll give you an example. This happened to me in real life now. I asked AI for 10 successful chief data officers. It brought down 10 white men. Sounds about right. Bias, right?
How biased can this be? Mm-hmm. Mm-hmm.
Right? And, and this is where my thinking goes with AI as such, I love it when you [00:33:00] can actually trust it and use it for the automation purposes, for the purposes of, um, you know, getting the monotonous task, uh, you know, taken away, reduce all that but I do have my reservations today with it. For, for all the other ethical considerations I have. Hmm. Um, the future belongs to people who can actually bridge that data fluency with human empathy [00:33:30] and ethicality and creativity with this artificial influence. Right. Um, intelligent influence. Um, but I think we, human in the loop is always going to exist as long as there is going to be humankind.
Hmm. India. I think it goes back to what you said earlier, like we have to maintain critical thinking and keep our brain switched on as we see the decisions that are coming out of these, these tools. And to the other thing you said earlier, and I love your example, like there [00:34:00] could be two problems that might be compounding there.
One is the bias of the existing marketplace of cheap data offices today. So it's the actual. Bias of the dataset of CDOs that the AI is just showing. Or it could also be the bias of the ai. And so like there, the, you know, it's automating that, that's as well. And, and so, um, you know, I, I, I think. To your point, like having to ensure that people still stay switched on, stay critical thinking and how they do this is really [00:34:30] important.
My question to you is how do you lead from the front on that in your life? How do you like set that agenda for your people and your teams?
Oh, it, uh, it's super simple for me. I am very open and honest about what I see and what I, what, what I think. First thing is I tell them, um, I, I actually. Think that it's very easy when you are all on the same page.
And I, my team and I are on the same page as I said to you because I've built a fricking brilliant team [00:35:00] around me. Mm-hmm. And, and I am super proud to say that. Now, I'll tell you, we are nominated for. Two brilliant awards, team awards this year In fact. In fact, we are a new team in place in the industry.
This is the first year as a CDO. I'm in the organization the first year as a CDO team there in the organization. Two brilliant award nominations, and we are in the finale for both the awards. Congratulations. One is the British Insurance Award for data analytics and insights. The second one is the Red Carpet [00:35:30] DataIQ Award, where we are fin finalist for the new ways of working. Congratulations, both of my teams fantastic, and and, and I can't be more happier than that. That tells you volume about how we are preparing our current and future generation of data, people in the organization and within my team and beyond. So. It's, it's very simple. I encourage critical thinking. I encourage ethical judgment.
We talk about where we can use AI and [00:36:00] where we can't, and where and where it, and what are the business benefits attached to it, and mm-hmm we go by that. It's not one size fits all. And the team. Are well known, well aligned to the thought process of business first.
So while we're in that area, and maybe to bring this part of the conversation to a, a close for today, we're asking each of the experts that come onto this look into the state of the nation around AI as Sam Altman.
Observation recently where he [00:36:30] raised three concerns about AI concern. One was misuse of it by malicious actors. So the fact that AI or super intelligent AI could be exploited to develop nefarious uses the loss of control around ai, which is AI systems becoming too powerful to be shut down. And then thirdly.
Accidental overreliance where society slash businesses slash individuals become [00:37:00] overreliant by seeding major decisions over time. I wonder how those resonate with you as cautionary notes for what we need to think about in the future, or do you think that that's just maybe overreacting slightly? Hmm. I see and I reframe AI as an amplifier, not arrival. Right. And, and, and that's really, really from my perspective, how I [00:37:30] feel today. And I want to use AI to augment my resonance rather than AI to compete with me in, in a playing ground. Yeah, the emotional imprint is where the resident lives. It's something no AI can truly, truly replicate our own.
Yeah. [00:38:00] Human empathy, you know, um, deep listening, compassion and vulnerability. They resonate powerfully and no amount of artificial intelligence can mimic that. And hence, my, my, my take on that is it's an amplifier not arrival.[00:38:30] Yeah. So I recently came across Japan's umbrella sharing system. Do you know that? Like millions of people borrow umbrellas when it rains. Yes. And drop them off elsewhere. They are, um, those nice ones that are see through that you kind of hold low and you can see through them. They're very cool. Yes. Yeah.
Yes, I do know. Yeah. So you just drop them wherever. It's the same in some cities you have the same with, um, city bikes maybe a bit of the same, uh, system, but it only works [00:39:00] because of a simple truth. People trust that the umbrellas will circulate back. I think that's the entire essence. Uh, and maybe insurance is a bit like that as well.
Uh, it's society's umbrella. We all share risk because we trust the system will protect us when we need it. But then there's the real challenge when AI starts to decide who gets the umbrella and when. Does it make people feel safer or more exposed? So if you look at into the business aspect, indie of what you do in assurance, [00:39:30] what do you see happening there when it comes to AI and Yeah. Making decisions? Well, that, that umbrella has caught my attention. No, it's um. I don't want to generalize the trust factor to insurance. I think financial services as a whole is, is a trust system, a trust based system. Right. You deposit it in a bank. You, you, you're thinking the bank is having your money secured.
You are [00:40:00] paying a premium. You are thinking the insurance is carrying your risk. They're going to balance it off if there is a calamity, which strikes. Right. So the, and it's the same, right? I'm, I'm, I'm stock trading. I am, I'm of the. Uh, opinion that they are, that the stocks are going to be ethical and they are not, the brokers are not going to go into some paid conversations.
Mm-hmm. Or like handling, uh, so the financial services market is, is a trust-based business. Mm-hmm. And here [00:40:30] artificial intelligence can only help to fast track supercharge and accelerate this, but with human interventions. So the but will always remain here and I am not going to take that away in, in a society like this is what you're saying is, which is if you introduce a few robots into the, into the umbrella sharing Right system.
And those robots actually take the umbrellas and [00:41:00] systematically go and move them somewhere else. Hmm. That the system starts to degrade.
Uh, in Yeah, some, something like that. It's just that, uh, I can't think of not trusting a system which has worked for several hundred years. Hmm. Right. I, wonder sometimes what happens if we have to, because you can see that now we don't have any more bra, uh, uh, branch [00:41:30] banking, you know, branch banking is no more. It's, it's all becoming online, and it's all good and great and fantastic, right? We didn't see that 50 years back, 20 years back, even 10 years back. I used to go into a branch. When my, you know, to do some banking today, we've changed all that be because we are sophisticating ourself through technology. We are, we are, we are open to having digital presence.
Right. I'm, I'm happy to have 10, 15 apps in my, in my mobile where I can bank [00:42:00] freely. Yeah. But also there, what, what are you doing? You're trusting the bank's apps. You're trusting the bank systems, again, technology there, right? How different it is gonna be to trust the ai. You have to trust AI into this trust system, right?
But it, it, it again is, I will still come to the, but like, what does that mean for the system itself? Where can you trust, where can you, where can you just unwind and then say, no, this is something that where I [00:42:30] need to have a human intervention in place, such as underwriting or in insurance, or underwriting in mortgages where no amount of artificial intelligence can understand the emotion or, um, some lifestyle changes of a certain person.
How do you add that human intelligence into that system? Because I absolutely love that you're emphasizing that a lot, but how do you do that and how do you see that from your role perspective?
I make it part of my process. Okay. Right. [00:43:00] I, I make it part of my workflow. I make it part of the system we follow it's not one size fits all. That is a use case, which you derive value to, and then you make human being at, at the front and center of this. Not ai. AI is a, AI is an addition. A is good. AI is sophistication. AI is like your Hermes bag, right? I want a Birkin. That's ai, but having a bag. The [00:43:30] system itself, whether insurance or banking or whatever Right It's, or umbrellas that, or umbrellas. Do, do you think AI could play a role in helping to deepen trust in an industry like, like insurance or, or would it always be a supporting actor?
No, absolutely. I think, you know, today, uh, not just. One particular insurer, like most of the insurers are trying to make sure that there are trusted, uh, data, trusted insights derived from [00:44:00] that with the help of intervention of ai, right?
But how far the disruption has gone. Uh, we are still yet, yet to explore. I I work for an organization which exploits AI to the maximized opportunity possible. We are the world's fourth largest AI labs. You know, in for the record. So I am not going to debate whether we are going to use AI more to exploit all those opportunities and develop more trust. [00:44:30] However, there are areas which are still green and then we are exploring. So. I welcome any opportunity to use AI to make it more trustable.
Very good. Look, what a good note to end a great deep dive into some of the wider aspects of data and ai. Thank you so much for spending some time with us again, sharing your insight.
I am so glad that someone with your perspective is engaged in the world of data and AI today. We certainly need it. So [00:45:00] thanks again for your time.
Thank you very much, and thanks for giving me this wonderful opportunity and hoping to be back with some more successful strides so we can talk about the next lover.
I have no doubt about that. Well look, before we let you go though, we end every episode of this podcast by asking our guest what they're excited about doing next. That could be you've got a great restaurant booked at the weekend, or it could be something in your professional life.
So Indy, what are you excited about doing next? I have three awards I'm going into. [00:45:30] As I said to you, I am super excited. One is next week in British Insurance Award, and then I have a Data IQ award to go to. Then I am also nominated in Women in Tech as a fin finalist this year, so wishing myself all the best and taking all the positive energy. So I'm super excited to go into all these three awards and wish every, oh, we wish you all the best too.
Now, big question though, what you're gonna wear, I've already picked my outfits, if not one, there's 10. Yes, quite right. Are a black tie. Are there loungewear? They're [00:46:00] all black tie and I'm super prepared. Very good. Are you gonna theme it with data and AI in any way or not? Yes. Mys, my Chanels and myself, we are having a date in the, in the wards.
If you would like to discuss any of the issues on this week's show and how they might impact you and your business, please get in touch with us at [e-mail]. We're all on LinkedIn and on Substack. We'd love to hear from you, so feel free to connect in DM if you have questions for the show to tackle.
And of course, please rate and subscribe to our podcast. [00:46:30] It really helps us improve the show. A huge thanks to India Indie, as we already mentioned. Also, Craig for being our guest host on this miniseries, our sound and editing wizard, Ben and Louis, our producer, Marcel, and of course to all our listeners.
See you in another reality next [00:47:00] [00:47:30] week.