KDIPA Spotlight

Bloomberg

15 hours ago

Transcript

00:00I'd love to begin just by asking you about that partnership and how that transformation has

00:04happened. Thank you. Very pleased to be here. Good morning, all. Formula One has been a great

00:10partnership. I think the CEO says that prior to COVID, every race involved taking three Boeing

00:22full of equipment, and he called it a traveling circus, and we've been able to transform that

00:27completely. Now, you know, amazing how the teams work. There are 24 races around the world

00:33globally, bringing all the signals from the cameras, from all the sound. We bring it all

00:41to London, and then it gets produced here and then taken to millions of viewers around the

00:46world, all in literally a blink of an eye. And that's testament to not just the technology

00:54that we put in place to make that happen, but also very agile team working. And this

01:01is the experience that we transport to our larger enterprise customers like banks and manufacturers,

01:08and we sort of cheekily tell them, if we can do for F1, then we can do that for everybody

01:13else. So I think that's a partnership that we're really, really proud of.

01:18And as companies now sort of bed into this world of AI, how do they move from the hype to actually

01:28practical integration across organizations from your vantage point?

01:34I think the biggest, as of now, the biggest drawback that we see is people are looking at

01:40AI as a tool, trying to see what use cases that we can work with. But really, in our opinion,

01:52AI needs to be embedded. It needs to be absorbed into the DNA of an organization, and needs to

01:58be thought more holistically. We ourselves, we have sort of developed a framework, and we

02:04are asking our customers to probably do the same. What this framework does is we are evaluating

02:11ourselves on one axis on the capabilities, where we look at strategy, talent, culture,

02:18because those are very crucial element of how we think about AI. And on the y-axis, we are looking

02:25at what outcomes we are delivering for that. Is it in terms of cost savings, in terms of improved

02:31experiences for employees and customers, in terms of actual revenue that brings in? So, you know,

02:36real measurable ones on both axes. And we have said, you know, you are in the early stage of

02:42experimentation. You are, you know, and the topmost end is real achievement that we do. And we have

02:49administered that in different parts of our organization, and looking at our HR teams and

02:53how they have, where they fit. So different parts of the organization are at different maturity

02:58levels. But that gives us a starting point on how we improve. So I think my firm view is you need

03:04to look at AI. It's the new operating system. It's the new DNA that we need to evolve. We are also

03:10doing, you know, for example, in the HR, you know, what does, how does a future job description look

03:17for an employee, right? So what an employee does today and what the person needs to do tomorrow for the

03:22same job would be very different with the help of AI. So we are actually writing down what the

03:28North Star job description is going to look like. So that's one element. The employees also, and we've

03:34implemented a full agentic AI integrated to our learning systems. So when somebody goes into that,

03:40they would know where they are today. And in order to get to the North Star job description, what are the

03:46skills that they need to acquire? And this agent would keep nudging the person to say, you need to

03:51acquire the skills. These are the courses available for you. So you need to do that. So these are some,

03:57something I think, you know, you need to move the entire organization along on the full axis of

04:03strategy, talent, and culture, and constantly measure what outcomes you're going to deliver.

04:09That's what is going to help people to move from the hype to reality.

04:13Yeah, because as you say, you need to embed it across the entire organization. And it seems that

04:18maybe some organizations are struggling to move from that pilot phase into really embedding it across

04:25the organization and then being able to scale it. Is that what you're seeing? What do you think

04:29companies need to do to ensure that they are embracing it for scale?

04:34Absolutely. I think we, one of the things that we advocate is more platform thinking,

04:40as opposed to thinking that in silos. And what I mean by platform is there are three essential

04:48elements in platform. So we ourselves have just launched an agentic AI with voice AI embedded in

04:56that. So what I mean by platform is, first and foremost is integration. So the data is all fragmented

05:04in many places. So how do you unify data and get it to a place where it's accessible by the agentic AI?

05:12You don't have to bring all the data to one place. That's another challenge with all the regulations in

05:17different parts of the world. You can't, but you still need to be able to access all the data. And

05:22that's what is a seed for the intelligence. So that's the first thing. The second thing that we're doing

05:27about when we think about platform is it's an intelligent orchestration, right? Because the

05:33models bring the intelligence and there are different models. And we talked about Mestrel,

05:38we talked about other, so different models that brings the intelligence. But what is missing is

05:43more intelligent orchestration across these. And that is where, you know, the context gets lost.

05:49So in our voice AI, for example, when the voice AI calls a customer, it knows the context of the

05:56customer. But this customer, for example, you know, if it is what we're implementing for a large

06:02automotive OEM, is the customer has been looking at cars and this person has been looking at more

06:11adventure oriented. They want to travel long distance and it could be another person who's looking

06:16more at safety features. So when a voice AI calls that customer for giving a nudge, it would know

06:22the context of this customer and fine tune the conversations according to that. So you can get to

06:28the first goal of booking the test drive, because that's the first goal. So that is something that

06:35the whole world is moving towards now context engineering. And that context gets lost. Even humans

06:40lose that context. Whereas with AI, you can remember the context, you can bring that all together. So that is

06:45the second layer that I talk about, which is how do you bring more intelligent orchestration once you

06:50have unified the data. And the third is about governance, right? The guardrails that you require

06:55so the trust can be there. The guardrails to make sure that the models are not drifting and, you know,

07:02you can make it more self-learning as well as wherever is required, the human interventions that is

07:08required to make sure the models don't drift. And once you start drifting, then you lose the trust in the

07:13model, right? So these are the three important elements of a platform, and we've been able to

07:18bring that together. So my, you know, my formula for doing that at scale, besides the first thing

07:23that I talked about, the maturity, is about platform thinking, which brings the integration,

07:29intelligent orchestration, and appropriate guardrails for that. And not the, as part of the governance,

07:37also I would put cost as an important element, because people don't think about the cost. People

07:42know that, you know, when they move to cloud, you know, I say it's Hotel California, you go in and

07:47you can check in, but you can't check out. The cost is very high, the egress costs are very high,

07:52the costs are, with AI, especially when people implement at scale, they're going to be doing so

07:57much of inferencing, the cost of inferencing is going to really skyrocket. And people have to think

08:03about it very consciously when they, when they, you know, choose the platforms, when they choose

08:08the models, and think about how they have to scale. So I would put, you know, these three as critical

08:14factors, if companies have to move from pilot to the scale stage. And we have to wrap, which is

08:23really unfortunate, because we could talk all day, which we can come and continue to do afterwards. But

08:27just real quick, before we do, I just want to understand from you, do you think organizations

08:32are ready? Very quickly, do you think they are ready? And what they need to do to make sure they

08:36are prepared across their infrastructure, across cloud compliance? Honestly, I don't think people

08:42are ready for scale. I think the, the infrastructure is, is very fragile, you know, across the networks,

08:50and when you talk about networks, the LAN, the WAN, the cloud, the data is fragmented. And all these

08:56fragmentation, you know, means that the, you know, AI is just going to add more stress to this fragile

09:04infrastructure. So one of our customers that I was talking to in, in London, they said they've

09:09transformed the applications, they're doing AI, but network has been the last bastion of change. And

09:14somehow network is seen to be the, the last bastion to touch. We believe that people have to start

09:21thinking about it, prepare the grounds of the entire digital infrastructure, especially if you're a

09:25multinational company, how do you think about your network, you know, where your data resides,

09:30how are you going to integrate all of this, and make it really, and that's why we came up with this

09:34concept of digital fabric, a digital fabric that truly helps companies to uncomplicate things,

09:40and then you can innovate from that.

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