Top Google Cloud and McKinsey Expert: "Stop Wasting Money on AI - Do This Instead"

Most companies aren’t failing at AI because of the technology.

They’re failing because of leadership, incentives, and execution.

And it’s costing them millions.

In this episode, a former CIO, Stanford AI PhD, and global AI transformation leader breaks down why most AI initiatives never deliver meaningful ROI — and what business leaders must do differently.

If you’re a CEO, board member, Chief Data Officer, or senior executive navigating AI investments, this conversation will challenge how you think about AI strategy, org design, hiring, and transformation.

This isn’t about hype.

It’s about execution.

You’ll learn what actually drives AI ROI, why endless pilots happen, how AI leadership must evolve, and the structural mistakes that keep companies stuck.

• The 3 reasons most AI initiatives fail
• Why hiring engineers won’t fix your AI problem
• The “AI Translator” role every organization is missing
• Why AI leaders must report directly to the CEO
• The execution gap blocking AI ROI worldwide
• How to structure your first 90 days as a Chief AI Officer
• Why endless AI pilots are often intentional
• The dangerous myth about humans in the age of AI

00:00 – Why AI ROI keeps failing
03:18 – From Wall Street CIO to AI transformation leader
07:01 – The 3 real reasons AI initiatives fail
10:37 – Where AI leadership must sit in the org chart
15:55 – Are we in an AI bubble?
17:51 – The execution gap nobody talks about
19:18 – Why some companies move fast — and others stall
23:31 – Real use cases: Where banks see ROI first
31:52 – The first 90 days as a Chief AI Officer
34:45 – Why AI leaders fail (even with great resumes)
38:02 – Rapid fire: CDO vs Chief AI Officer
39:27 – The most underrated truth about AI

Dr. Maxim Afanasyev is the Financial Services Industry Head for Asia-Pacific and Japan at Google Cloud, where he's leading AI strategy for one of the fastest-growing industries across some of the world's most dynamic markets.

But what makes Maxim's perspective truly unique is the journey that got him here. He holds a PhD from Stanford in Operations and AI, spent years as a derivatives trader at Royal Bank of Scotland, became Chief Investment Officer managing over a billion dollars in AI and tech investments, then pivoted to become one of McKinsey's first "Analytics Translators" - a role McKinsey predicted would need millions of professionals worldwide.

From there, he led the digital transformation of an $11 billion corporate banking business at Standard Chartered across 50+ markets, before joining Google Cloud where his work has contributed to FSI revenue growing at 2x the market average for three consecutive years.

He's behind some of the most impressive AI deployments in financial services - including HSBC's Anti-Money Laundering AI system that processes over a billion transactions per month and cut false positives by 60% while increasing true fraud detection by up to 4x.

Please like and subscribe to our channel to get notified as soon as new episodes and content are released.

The AI Visionary Podcast is a platform where AI leaders share bold, unique and tangible insights about pathways to realizing meaningful ROI from AI, pitfalls to avoid and their success stories.

These conversations go beyond the hype to uncover what it takes for organizations to be successful in their AI transformation journeys from a leadership, talent, strategy and operating model perspective. 

Co-hosted by Joel Azariah and Milind, new episodes release every month via You Tube, Spotify, Apple Podcasts and various other Podcasts Channels

Links

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Joel Azariah LinkedIn Page
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Transcript

SPEAKER_00 0:00

Hello everyone. Welcome to the first episode of the AI Visionary Podcast. My name is Joel Azraya. I help chief data and AI officers to implement their data and AI strategy by helping them to recruit the best talent based on deep market insights and relationships. I also host platforms like this one, this podcast, where I bring together different AI leaders from different industries to share their perspectives, their ideas, and knowledge for the benefit of the wider AI community. Our aim of setting up this podcast is to help share knowledge around how AI leaders have built pathways to generating meaningful ROI from AI, the challenges they faced, along with their success stories. I'd like to hand over to my co-host Milind, who will make an introduction about herself and then we'll start with the podcast.

SPEAKER_04 1:04

Thanks, Joel. Hi everyone, I am Milind. I have about two decades of experience spanning industries. I started in business consulting, where I worked with governments on policy and regulatory matters, with private sector companies on strategy matters. Post that I've been uh core team member of a successful startup. I've turned around a loss making uh business uh in a completely different sector. And nowadays, uh yeah, I'm working in the field of AI. I have three master's degrees, uh, obviously one in business and one in AI. And with this podcast, we try to bring together all of the experience and expertise that we have, Joel and I, as well as our guests. Thank you.

SPEAKER_00 1:54

Excellent. So let's start with our very first guest. We were actually really lucky that this guest has agreed to be on our podcast uh with some amazing experience and insights to share. So I'd like to welcome Dr. Maxim Afanasiev to our podcast. Um, Maxim is the financial services industry leader for Asia Pacific and Japan at Google, where he's leading the strategy for one of the fastest growing industries across some of the world's most dynamic markets. But that's just where Maxim is right now. His journey is truly fascinating. He holds a PhD from Stanford in operations and AI. Uh, and he's also spent time as a derivatives trader with Royal Bank of Scotland and then moving on as a chief investment officer, managing over a billion dollars in assets. Uh, he then pivoted to become uh one of McKinsey's first AI translators globally. Uh, and you know, this is one of the critical roles that we have even today. And from there, you know, he's led multi-billion dollar AI transformations across Standard Chart and various other companies. So, Maxim, great to have you here. Welcome to our podcast.

SPEAKER_03 3:01

Thank you. Uh, thank you for having me here, Javelin Malin. Excellent insights.

SPEAKER_00 3:07

Thank you. So, Maxim, we just touched on it briefly. You know, you have an amazing career journey. You're very successful CIO managing multi-billion dollar investments, um, a role that you know most people stay in decades for. What made you move made that transition from investing in AI to helping companies building and deploying AI systems yourself?

SPEAKER_03 3:30

I'm actually a mathematician and uh AI researcher by training and mindset. When I was uh completing my PhD at Stanford, uh Lehman browsers collapsed. And investment opportunity at that time was too fantastic to miss. So I joined the asset management. The fund I was managing was uh long with leverage uh technology stocks uh and since 2009 and the returns exceeded 100% per annum on average. However, I was observing one thing being in the industry is that uh my peers, other investment managers, they were receiving uh signals about industry trends, uh ideas from two sources. One, primarily, is reading saleside reports, the second one is speaking to analysts. Views across all of saleside reports and analysts looked the same to me. So I was looking uh to get uh signals uh which uh first hand, and uh other investment managers are missing. So I had to be creative. AI Spring began about a decade ago, and uh organizations across different industries were looking uh how to apply AI to solve their business problems, business trends, industry trends which are happening to. And this is where my applied AI background and experience became handy to get those signals. So I started uh weekend job uh of uh consulting uh uh private, non-public companies uh on AI use cases and deploying those AI use cases. Three years later, uh it uh became quite obvious that AI is going to transform business model across pretty much every industry. And this is when uh McKinsey reached out and uh asked me to join the firm to build its AI transformation capabilities. So I moved then from uh investments uh to my original roots, which is applied AI.

SPEAKER_00 5:40

If I just expand on that a bit, Maxim, like what did you learn from investing in these AI companies that influences how you help companies now on their AI transformation journey?

SPEAKER_03 5:52

Ability to sense signals whether this uh organization is uh genuinely applying AI to innovate and transform. Many organizations are making up AI journeys, or there are some organizations which uh charmed by illusion of a quick wins from downloading some of AI products, profile of the person, organizational position of that person, and uh mandate of that person who is appointed to lead AI transformation, just these three signals are enough for me to tell you whether this organization is going to be successful in its AI journey, whether it will benefit from AI. Fantastic.

SPEAKER_04 6:47

Perfect. Thanks, Maxim, for sharing that. This is a perfect segue to uh another question I have in my mind. Since you have seen industries from so many different perspectives, and uh a lot of conversation I hear about uh AI is not working, where is the ROI? I've spent so much, we've been doing this much, yeah. I hired two data scientists and still no ROI. So, what in your opinion would be top three reasons why organizations are unable uh or why AI initiatives fail in uh in organizations or unable to get their ROI.

SPEAKER_03 7:29

First reason uh is a profile of uh the person assigned to lead the transformation. Second reason is where this person has been placed within the organization, and third reason is what was the main data and resources provided to that person. Let me uh melind for a second to deep dive in each of those three reasons. The first reason profile is number one and by importance, in my opinion. Many uh organizations uh appoint people with wrong skill set to lead the AI transformations. For instance, uh some organizations appoint engineers. Yes, but technological innovations is a minor piece of applied AI story. Right profile of the person, applied AI expert to lead AI transformation is the person who has uh innate ability and years of experience across a broad technological domain, not just only AI models, but also understanding what the other systems exist in organization and how they all interact in industry overall. The person with a deep business domain expertise, who have spent uh years on the ground, let's say in banking, uh structuring uh deals with the clients or bringing new banking products to retail bankers, preferably across different divisions. And number three, very critical one, is transformational experience. The person who is able to figure out how to motivate people, how to change the workflows processes within organization accordingly. To in McKinsey days, uh, we called such people AI translators, and uh we urged organizations to nurture internally that talent. These are the people who are able to execute and deliver AI transformation. In uh Google, for example, uh, we expect uh senior product managers uh to have broad technical understanding, uh, business domain knowledge of their product uh areas, as well as transformational or entrepreneurial experience and combining all these accounts and knowledges together. So, this is number one uh profile of the person who is leading uh AI transformation. Reason number two is where that person is placed within organization. This applied AI expert must report to chief executive officer and be part of the board, given how big transformational impact is coming from the AI on the business models. Very few organizations have it. And number three is mandate of that person. That person should have uh right resources available to him or her to deliver the transformation. It's too common across organizations which want to fake the AI journeys is to hire AI head with uh zero resources. So uh these are literally three reasons why uh so many companies end up in a continuous cycle and of endless uh pilots uh or endless uh for decade cleaning uh data. Because they need to have the right execution, like expertise, applied AI experts on McKinsey terms, AI translators to lead this transformation, to be positioned in the right place in the organization and have resources to deliver this. In my personal opinion, we have uh maybe globally about 200 uh individuals who have uh profile qualifying as a mature applied AI expert. And this is number is very small. And we as a society I must scale and grow applied AI expertise and awareness in order to obtain at scale around all those trillions of the dollars which are invested in the AI today. True. Yeah. Those numbers are getting astronomical. Exactly. I understand it's a drop in the ocean, but I was uh motivated by this. I actually started publishing some of the learnings uh uh on uh LinkedIn of what uh myself and my colleagues have been doing in applied AI transformation. So hopefully to pass some of the transformation because something should be has to be done on around that. Yes, such things as partnering maybe uh between the big tech companies and uh public enterprises on how to build some applied AI blueprints uh so that the people can use them uh to take the fundamental AI models and literally deliver the value, the applied AI experts uh to share their learnings, their knowledge, how to teach these three things together, broad technical understanding, business domain knowledge, and transformational expertise to deliver ROI on AI, employed AI.

SPEAKER_00 13:29

Excellent. I think Maxim, you touched on some points that I see and resonate with me on a daily basis, seeing my clients advising them as a talent advisor for you know senior talent within data and AI, a lot of companies making the same mistakes that you just pointed out. And one of the roles that you mentioned very clearly, the chief data officer role, which is undergoing massive change, you know, both from a technological and people point of view. You work with CDOs across you know 50 plus markets. Um, how do you see this role evolving in the next three to five years, both from a technology and a people perspective?

SPEAKER_03 14:05

Yeah, that's a fantastic question. Thanks for uh bringing it, uh Juan. And this touches uh not only CD CDO, it's also head of AI, uh head of strategy, etc. etc. In my opinion, in five or ten years, org structures will look very different. The roles will look very different. It's going to be applied expert or AI translator, number two person in organizations, reporting uh to CO, sitting on the board, and this entire zoo of uh CDO, head of AI, uh chief operating officer, chief technology officer, head of strategy, head of uh some transformations will be part of that person's team. And uh that person, by combining technological understanding, business domain knowledge, transformational knowledge, depending on the specific organization, depending on the specific industry, level of the maturity, the vision is going to be shaping what is the exact responsibility of each of those people who have those different titles today.

SPEAKER_04 15:31

Thanks, Maxim, for sharing that. Um even though you seem to have uh uh yeah uh touched upon how orgs will change, yes. But nowadays, yeah, uh there is a huge narrative that uh there is way too much about high uh hype about AI, Gen AI. We are now in some bubble, and the bubble is going to explode at any point of time. And we have always been talking about these transformations, and people said the same thing about when RPA came, and yeah. So, could you share with us and our viewers uh what is your view on where we are in this hype cycle? If it is a hype cycle, what is the vision uh that uh you have uh or your opinion about Genii?

SPEAKER_03 16:18

If I take uh my investment manager's uh experience uh uh head, crash or the bubble uh happens uh when um investors believe big in some idea, but then idea doesn't deliver value. When it comes to AI, for AI to deliver value, we need three things. The first one is technology. Fundamental AI models are five, ten years ahead of uh industry readiness. So we are good. Okay. So the tick box excellent. That's good to know right somewhere to be successful, we need uh the business and leadership and regulators buy-in. These days it's unprecedented for AI. So the second tick box is here, but we are missing the third tick box execution. We are missing uh for at scale of applied AI experts, the people who can bring technology, domain expertise, transformation, and deliver to execute on this all that gold which uh the scientists have been able to create with the models, which we have been entrusted with all those uh budgets and buy-ins from the leaders, uh regulators, etc. So we need uh we uh need it's definitely a big expectation about AI, and we need to tick this third box as a society. To do so, we need scale applied AI expertise, awareness to nurture that applied AI uh capability to place it in the right spot into the organizations and provide with the right resources to drive the transformation. And this is what is determines whether we are in the bubble or not in the bubble.

SPEAKER_00 18:45

Understood. So that last point you spoke about execution, right? There are some companies, Maxim, who are implementing AI models, for example, in in under six months. Um, and some other folks will be like, oh, this is this is impossible. You know, they may take years in in other organizations. What's enabling the speed and what's causing the difference as well? There's a big dichotomy between some organizations being able to do it really well and some organizations really struggling.

SPEAKER_03 19:15

Leadership of those organizations. Some chief executive officers and the boards, they genuinely want to differentiate first with AI. They invest into finding, hiring right applied AI expertise, applied AI leaders in their organizations and providing them with the right setup, mandate to deliver the AI transformation. This is the companies which are going to win in their industries. On the other hand, there are companies where leaders might be just waiting for their retirement package. For those leaders, applied AI experts is a threat for their job security. Very often in such organizations, uh they look into AI applied AI very narrowly. Is it just purely uh technology? And they are imitating, because you can you cannot avoid AI these days, they are imitating AI journeys by knowingly often jumping into not two, three years, five, six years of pilots, or a decade of uh cleaning the data. So to answer your question, Joel, it's a leadership and it's incentives.

SPEAKER_04 20:54

Maxim, you have been uh yeah, you have highlighted how important um business skills are, yeah, uh even for the person who's driving it, yeah, even more than coding, considering, and this is surprising considering your background uh or your career starts from there. So, uh, what would you advise uh when these leaders are in place and they now have to build their teams which will drive the organization? What are the kind of skill sets they should be looking at? Uh how much uh emphasis on coding, AI ML skills, transformation skills, not just for the leadership, but for the team. And how should the team then uh yeah uh be placed across uh in the organization?

SPEAKER_03 21:40

Start with getting uh right applied AI leader on the board. Right. The one who has broad technological understanding, business domain expertise, and transformational uh expertise. And uh Mature in each of those domain knowledges. This is the person then will be who is rightly equipped to build how the AI strategy and AI transformation will look like. What uh skills are missing in the organization today? And it'll be able to put together all those Lego blocks because they are unique for every organization.

SPEAKER_00 22:28

That makes complete sense. And it kind of ties into the previous question about why organizations do well versus organizations that struggle. And I see this on a daily basis organizations saying they can't find people, but it's also the type of organization they are that leads to the kind of candidate they attract.

unknown 22:47

Yeah.

SPEAKER_00 22:48

Because if they already in a certain way, they're not going to be able to change dramatically to attract the best talent. And that's also their, they're not able to see that because they already consider themselves doing really well in the area that they are. So it takes them a while to understand. I think they try many times and fail. And then hopefully they're humble enough to realize that actually it's their problem, not the not the person they hired or the person they're attracting.

SPEAKER_03 23:14

Agree. Exactly. Yeah.

SPEAKER_00 23:16

So I think with that also, you know, Milan touched upon Jenia. We have a lot of conversation going around agendic AI. And you, I'm sure you advise a lot of your clients on agendic AI, you know, systems that can plan, decide, and act, not just respond to prompts. Um, for a lot of folks in banking, financial services, it seems like a world away. Is it possible for you to share an example, you know, at a high level that you've seen work for your clients and that you see this technology transforming banking and financial services for consumers like us?

SPEAKER_03 23:52

If I can paraphrase your question, uh what are the most of the banks, what use cases are most of the banks uh find the easiest uh to start with their journeys generically financial crime prevention. Financial crime prevention is a cost game against criminals, and it's a perfect uh use case for Gen DKI. If I take my McKinsey head, uh experience uh head, we try to structure things uh in a so-called missy way. Banging use cases I can split across uh three uh pillars. First one, use cases which help to grow revenue custom experience. Second pillar is uh use cases which help with operational efficiency. And the third group of the use cases is uh risk reg compliance, financial crime compliance, prevention, etc. If we look into each of those three groups, we can find that uh it is the last one: financial crime prevention and reg compliance, where the number of the problems on average which you have to overcome in, and it is not technological problems, it's not only business domain problems, it's a lot more transformational problems, okay, which are the easiest to start with overcoming and solving. This is an average estimate, and definitely across the organizations it does change, but very often it is the third bucket, financial crime prevention, where the organizations uh find fruitful to start their AI journey simply because people adopt such use cases easier. Because everyone wants to fight crime, but not everyone is happy to get a product which is then expected to replace uh that person.

SPEAKER_00 26:20

I think that's the biggest challenge is companies also need to look at how AI is going to impact their workforce and prepare their workforce for that. Because people will resist as much as they can, even if it's a great technology, because it might impact their job.

SPEAKER_03 26:36

Exactly.

SPEAKER_00 26:37

So we're, I think I was speaking to one of my clients about you know creating roles that focus on helping the culture of the organization adapt better to make it more receptive to the technology that AI transformation is going to bring to their roles and then also make it more productive for their workforce.

SPEAKER_04 26:55

Spot on. Yes. Considering we were we are talking about fighting crime, yeah, and uh compliance-related issues. And you have excellent uh personal experience in this field. So today, if you had the freedom to implement uh, let's say an uh AI regulation, especially let's say for financial services, what would your dream regulation be?

SPEAKER_03 27:20

I would uh focus on the regulation, which in my opinion uh would uh help uh the industry overall the most. Once such regulation is uh mandatory models uh reporting, uh which is uh requires organizations to share with the regulator what are the AI models powering its uh this organization's uh core systems. Why is it important? Because this uh then helps to identify concentration risks and so-called uh herding risks, when many of organizations are relying on the same underlying foundational model. Luckily enough, I'm not the only one who is thinking about that. EU EI Act, uh, which is uh coming into power shortly, has a lot of regulations, and this is one of those regulations.

SPEAKER_04 28:30

That's a very interesting point you bring up. Yeah, at the moment, there are not so many foundation models. Yeah, we if we look at uh one year back, there were quite a few companies developing financial uh foundation models. But today there seems to be a few where consolidation seems to be happening. We have one from Google, OpenAI, a few Chinese, yeah, uh Cloud, but there are not so many around. So even with uh regulation, any thoughts on how uh we could avoid such a uh such a scenario?

SPEAKER_03 29:05

AI is not only Gen AI. Gen AI is uh a very hot topic today, right? But it is not a solution for everything. This uh regulation would make uh industry hopefully start thinking in this dimension and motivate uh the financial institutions to think when you need to apply LLM models, where you need to apply traditional AI, and uh that not all LLM models and not all the use cases need to have LLM uh models. So as uh we get the visibility, at least at the regulatory level, on which organizations where apply LLM models, it will also motivate organizations to think two times whether they need to apply traditional AI or they need to apply LLM in that specific uh domain. And what are the risk that the organization is uh going to incur from those uh specific LLM models? The regulators will be able to think, to see what are the risks uh maybe happening uh there as well. Thanks, Max. But it's purely for the transparency, and then it will structure the motivations accordingly, this regulation.

SPEAKER_04 30:37

Oh uh thanks, Maxim, because uh I completely uh agree with you on this one, yeah, that uh the existence of such a regulation itself will uh will uh uh yeah reduce the systemic risk because by making the organizations uh aware that they must uh yeah take care of this. Exactly. Yeah. Thanks, Maxim.

SPEAKER_00 31:00

Great. So just moving on, coming back, Maxim, to you know, the leadership in AI that you see in banking and financial services, right? A lot of the success that you can attribute to a chief AI or chief data officers, how they start and set up their role in an organization. And I'm sure a lot of folks reach out to you for for advice. So, what advice would you give to somebody who's starting out a chief AI or chief data officer role in a bank? And, you know, for their first 90 days, that we would deem success would be a success, because in the first 90 days, oftentimes, you know, they have to do a whole lot of things to prove okay, I'm actually the person that you hired and I'm gonna be a success for your company.

SPEAKER_03 31:47

I will uh answer your question uh in two components. The first one if you as a person who came as an head of AI came to the field and this comes from organization or whatever, yeah, that you need to prove something within the first 90 days, yes, you're in the wrong place. Second, uh you still need to do something within the first 90 days and beyond. My advice is uh to act as applied AI product manager who is solving for AI adoption and the business value, not for the number of the use cases to be delivered as soon as possible. Now to three things which uh on average I would expect such a person to focus during the first 90 days. Number one, due diligence across uh technology, business situation and transformation readiness within the organization. That's number one, due diligence. Number two, solve for aligning uh within organization what should be AI strategy and the roadmap and how the organization will look like in three, five, seven years as an AI kicks in. That's number two. Define and align. And number three is uh get the right partners internally or externally. So these are the three things which I would uh expect this new applied AI expert to focus on for the first 90 days within organization. Due diligence across each of these three domains, defining and aligning strategy and roadmap, yeah, and getting the right partners.

SPEAKER_00 34:11

Just to expand on that, and those are amazing points, Maxim. I completely agree. A lot of times, you know, folks take they come with the right skills and experience on their resume. They interview really well, but it doesn't work out for different reasons. What are some of the reasons you've seen? Okay, person looks great on paper, but then it doesn't work out for the organization. And of course, there's responsibility lies on both sides, it can't just be on the individual. But just from your experience, do you want to share a bit on this?

SPEAKER_03 34:43

Either the organization didn't do the right level of due diligence on the person, or the person didn't do the right level of due diligence on motivation of the organization. True. Getting due diligence done right, especially two-way, yes, two due diligences, not easier. And this is where the people like yourself, Joel, become so important. True. So to answer your question, the reason why it didn't might not work is that because the person was rushing for the title, for the promises, didn't wasn't provided a good opportunity to do due diligence, or organization uh uh rushed to hire that person because of the hype. Uh the person uh maybe looked great on resume, created his or her resume with Gen Eye, yes, uh and uh was a wrong profile, or was wrongly positioned within an organization, or wasn't provided resources within an organization. And uh this where your GL role becomes so fundamentally critical as a linking block between uh the applied AI experts and the organizations, especially today because of all the GNEI uh into your right, uh you can create fantastic profiles. There is a recently a research paper was uh released by one of uh Dartmouth uh uh professors. She was uh analyzing um from one of the freelance um uh websites, uh applications uh which the people were submitting for the jobs. And uh in the past, as put uh so-called Stiglist theory, the per the people who had right skills, they were able to invest more to prepare the better responses, and it was like a very noisy but still good signal for the people to identify who is the right uh freelancer to pick up. But she found that over the past two, three years this stopped working because whether you have skills, whether you don't have skills, Gen EI can create you fantastic resumes, fantastic uh replies for those uh uh and it becomes more harder to do the diligence. And we as a society uh have to navigate this somehow. One way of navigate is because of the people like yourself.

SPEAKER_00 37:24

That's where my job is uh under a lot of pressure now because I have to really determine who's an expert and who's actually just talking for the sake of talking. And then maybe like this podcast is a way of doing due diligence and ask people a lot of questions and validate if they can actually do their job or not. Fantastic. Great points, Maxim. Completely agree. So that kind of brings us to the end of the first section of our podcast. The next one is actually a surprise for you, Maxim, because you're not prepared for it. So we want to make sure that this is more interactive and authentic, it's called the rapid fire section. The questions are pretty simple, so no need to worry so much. It's not curated by Gen AI, it's only by us. So we we'll we'll start, we'll alternate between Melin and I. So it's just choose one or the other answer. It's pretty direct. So, first question chat GPT or Cloud? It's rapid fire. You know, deliberately left out Gemini was even questions.

SPEAKER_03 38:27

Can't include Gemini. Uh you mean which one is better or which either either, whatever you think at the current level of uh usage and attention from the people, we see more people using ChatGPT than Cloud.

SPEAKER_00 38:47

Okay. All right.

SPEAKER_04 38:48

Well, in fact, next question. Chief Data Officer or Chief AI officer? None. Applied AI expert. Excellent.

SPEAKER_03 38:58

Centralized or decentralized AI teams? Centralized on the applied AI expert. And for that person then to decide how it will operate. Most overrated AI trend right now? AI is just a technology. That's the most overrated uh trend today. AI story is not just technology.

SPEAKER_00 39:20

AI translator, permanent career path or temporary bridge? Permanent. Well, that brings us to the end.

SPEAKER_04 39:28

May I add one more?

SPEAKER_03 39:30

Most underrated AI trend? That's a very good question. Uh most underrated uh AI because of the AI situation today is that we as a society wrongly started thinking that they emphasize the value of uh human abilities and skill sets and experience in the era of AI. If I it's a very good question, and uh if I can uh take a minute to little elaborate uh on this, that's important. Oh somehow, over the past two decades, we as a society has got uh into an assumption that humans are fungible and just components of a machine. That's wrong. Because of such wrong assumptions, some organizations start thinking that uh because uh of slow, let's say, for instance, uh there is a slowdown in uh sustainability, uh they can take an EHG expert, ask that person to take two, three courses on uh Coursera, and that person to drive uh AI transformation. This is wrong. Applied AI expertise, it's an innate born-in ability to combine broad technological understanding, business domain expertise, and transformation running capability or entrepreneurial capability in some cases. And it takes years of training and experience to get each of these three skills to the level that this person can be called a mature applied AI expert. And this is what is missing in our society today, and this understanding. The most underrated thing today is that we as a society started thinking wrongly that in the era of AI, human are fungible components of a machine. This is wrong. Brilliant insight. Thank you.

SPEAKER_00 42:18

Great way to end the podcast. Uh completely agree. I mean, without the human element, we're we're missing many, many things that we could build in terms of AI. And I think the other way around, thinking that the human element is just fungible, actually diminishes the prospect and possibility of AI.

SPEAKER_04 42:36

Exactly. Spot on it's been a pleasure, Maxim, talking to you.

SPEAKER_00 42:42

Thank you for keeping it. Fantastic. Maxim, great to have you. Thanks for your insights. And to our viewers, thanks for joining us for this podcast. This is our first episode, so please like and subscribe us on YouTube and Spotify and other mediums where we'll be available. And please share your feedback with us and look forward to joining again on our next episode. Thank you.