The Reality of AI Today: Beyond the Hype

With Carlos Pineda — Head of Data Analytics & Insight, Diageo North America

Where is AI actually creating value today and where are organisations still getting distracted by the hype?

In this episode, Ben Parker speaks with Carlos Pineda about the reality of implementing AI inside large organisations and what separates experimentation from meaningful business transformation.

Carlos explores why successful AI starts with understanding the business problem, having the right data foundations and embedding AI into real processes rather than simply adopting the latest technology.

The conversation also covers leadership, stakeholder engagement, experimentation and why organisations need to think about AI transformation end-to-end if they want to create lasting value.

You’ll hear practical insights on:

• Where AI and GenAI are creating genuine business value today 
• Why AI initiatives need to start with the business problem 
• The importance of strong data foundations 
• Why business understanding matters alongside technical expertise 
• What prevents organisations from successfully integrating AI 
• How experimentation can lead to scalable transformation 
• How leaders should evaluate the cost and potential value of AI 

Chapters

00:00 The reality of AI today
01:53 Meet Carlos Pineda
02:51 Carlos's career and leadership journey
08:02 Where AI is actually creating value
09:58 Why business understanding matters
13:13 The challenges of implementing AI
30:38 Understanding the cost and value of AI
33:39 Final thoughts

Thank you for listening! 

Transcript

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carlos 0:00

If you see in general, AI has started to live in our day-to-day life, a lot more than what we see in the corporate world. And the corporate world right now is playing a catch up with our personal life. If you talk to anyone, my mom, my sister. Any friend they talk to, any AI, generative AI engine like chat, GPT or Gemini wherever they use, they chat almost daily, two, three times, four times, 10 times a day. I can ask ai what are the ingredients that I have? It will gimme an amazing recipe if I, even for the, for my medical, yeah, I can ask question about, that you cannot ask only to the doctor because it's hard to get to them. You ask question to ai and it gives you, a directionally good answer. The way compar is, when Excel was created, I don't know how many years ago, nobody's now, it's, it's in the day-to-day. Live of everyone and you use Excel in and out as a productivity tool. Nobody's tracking what the value of Excel is. It's just taken for granted.

ben 1:15

Welcome to Data Analytics Chat, the show where the world's leading data and AR professionals shared decisions, lessons and breakthroughs that shaped their careers. Today I'm joined by Carlos Pineda, head of data analytics at Diageo leading manufacturing company. In today's conversation, we'll explore Carlos's career. Here is journey, the leadership lessons learned, and most importantly, we'll discuss the reality of AI today where it delivers value and where it doesn't. Carlos, great to have you.

carlos 1:48

Yeah, great to be here. Thank you, Ben, for the invite.

ben 1:52

Not a problem. So Carlos, I guess before we dive in, could you share the listeners a quick introduction to who you are and the work you're leading today?

carlos 2:02

Yeah, sure. I'm Carlos Luis Pineda. I'm originally from Venezuela, but have been living in the us, particularly in New York for the last seven years. I'm the head of data analytics and insight for the Agile North America specifically, but also have a role in the global markets to ensure we have. A consistent strategy and consistent delivery of our data analytics and insights strategy across the globe. My, my role is really sits in the intersection between the business strategy, the business process, the business value, and the technology and how we deliver data and analytics not only for the sake of delivering technology, but also on how do we transform the business to deliver value.

ben 2:47

Brilliant. So a lot has changed in, even in the last couple of years. So I guess then, like looking back in your career was, what was the decision that most accelerated your career?

carlos 2:59

Yeah I have been, I've been working for the last 25 years already, but if you ask me what has been the decision that has made the biggest difference in accelerating my career I will mention two. One, it has been like most of the role I have taken didn't exist before. So I have been shaping my career by creating the opportunities that really will deliver my growth and creating those roles for me rather than waiting for a role to exist and go after it. The second one is, has been, I have been, I have had the opportunity to live in. Five different countries and trans transfer from Latin America where I originally started my career to Asia Pacific, then eh, Eastern Europe, Latin America, back and now North America. So it has been, that multicultural experience have also been a key factor. Probably a third one that I would like to mention, Ben has been around shaping my career and doing different. Things across the digital transformation have done SAP implementation in the back office supply finance, but also have done head of technology in a specific market, in a specific region, and in the last seven to 10 years more focused on the data analytics space. So shaping my career holistically has been also a key for accelerating my career growth.

ben 4:29

So you've worked in many different sort of, obviously intake, but I guess different areas, which is probably, it helps you become more well-rounded, would you say?

carlos 4:39

Definitely

ben 4:40

And has there been anything that's held you back? Do you feel, looking back now,

carlos 4:44

say that again.

ben 4:45

has there been anything in your career where you've held yourself back or are you quite risk adverse? I.

carlos 4:52

I would say y like ma, many times we believe our career is all about we delivering and being the per the smartest person in the room and delivering the final outcome of the work that we are doing. Probably one of the thing has been on creating a stronger relationship in all this while you are delivering the outcome. I would say that's something that I have learned across the years on how important it's to build the strong relationships at the same time that you are delivering outcomes.

ben 5:24

Okay, cool. Interesting. Then was every leader's got a everyone's got a battle story. What's been the toughest leadership challenge, where data didn't give you an answer where you've had to use, I.

carlos 5:36

Yeah, in general for me it's the importance of the business acumen and how do you drive transformation from the business processes and from. The business understanding on how it works. Sometime we jump into data or in technology in general, just for the sake of delivering data and delivering technology. Versus really understanding, what are we wanting to achieve as a business, right? As a, as what is gonna make the biggest difference for the people driving the decision making? And that's not a data question, right? That's a more a human factor question. And and real understanding of the business where we are operating.

ben 6:18

Okay. And in what belief about leadership did you have early in your career which you later realized was wrong?

carlos 6:27

I, it is probably what I mentioned before is it is all about delivering outcomes and delivering results and. Sometimes like creating a strong relationship, connecting deeply with the business where you are operating is very important. Or e equally, or even more important. And I think that really makes a difference when you really understand your business, when you have a strong relationship with our stakeholders and you are able to deliver a transformation. Like an end-to-end transformation, not only like data analytics or technology in general, digital transformation, but really impacting your processes your people, the culture that is where you are making a difference.

ben 7:09

Yeah, so it's a lot of just, I guess the stakeholder engagement element there then is the key for you.

carlos 7:17

There is, it's a stakeholder engagement, but also that business acumen that's really understanding of how the business operates. What is the outcome that you want to achieve and really what is the key question that you are trying to answer versus just, trying to jump to solve the problem directly.

ben 7:36

Yeah, and I think these, and now with the way data's going is getting even more domain focused in my opinion. If you, obviously a lot of the tools there, technology there, can do the heavy lifting. I think if you can know the business acumen side of that whether it's the industry you're gonna stand out in this field now 'cause you'll be able to make better decisions and help the team members, but a lot better.

carlos 8:00

Yeah I think that's a spot on Ben. We are gonna see more and more that, there is a lot of eager to go after ai and how do we implement AI in our businesses. If you see in general, AI has started to live in our day-to-day life, a lot more than what we see in the corporate world. And the corporate world right now is playing a catch up with our personal life. If you talk to anyone, my mom, my sister. Any friend they talk to, any AI, generative AI engine like chat, GPT or Gemini wherever they use, they chat almost daily, two, three times, four times, 10 times a day. While in the corporate work, we are still figuring out what is the best way to leverage the power of.

ben 8:48

Yep. Yes. A lot of business are still, it's a lot to learn, isn't there? There's a lot to, it's a lot of impact, especially when businesses wanna become more data driven. This it's completely transforming a business, isn't it? Basically.

carlos 9:03

It is. And that's where this piece of the human element and the clarity on the business outcome that you're trying to achieve, start becoming critical. You need to be laser focused on what you want to achieve because if not, it becomes too big. And then you drown and try to, like a good example is this. AI data ready term immunology that people are using a lot these days. If you try to make it AI ready generic, generically, it's gonna be very hard to do it right. You need, ready for anything is very hard to achieve right now. Now if you want to have it ready for a specific use case, eh, for a specific technology, then that's more late, more focused kind of work, and then you can deliver. Significant more value.

ben 9:50

So then if you could give one piece of advice, career advice to a data professional who wants to lead, what would it be?

carlos 9:59

I think understand your business, understand where the business outcome, understand the processes, understand the human element of that process, where that you want to transform and then partner with the market. It's not a. It is not a technology or data work. It's really like a business transformation, what you want to drive to really deliver the value.

ben 10:21

And also you mentioned you've worked in multiple countries. How did you go, because obviously every country's got a different way, different or different culture. How did you go about learning the business from that their that point of view?

carlos 10:33

Things, talking to people and it connects with what I was mentioning on relationships, right? If you are working in a commercial team, for example, going out with a sales rep. To understand what is their day to day, where are the pain points? It's very important if you are working with the finance team, sitting with them and understanding what they want to achieve, how their day-to-day looks it's also very important. Same for marketing, understanding where your priority brands, how do you learn about the consumer? How do you segment your market? So those conversation, those relationship is what is gonna what is gonna make the difference. So you really can address, that human element of the problem solving.

ben 11:13

Yeah, no, that's good. And then I think it's, when you hear like business leaders where they spend time in a certain division to understand the problems and 'cause that's going to, that's gonna get so much value. You see it from their point of view, obviously, when you're in leadership. Responsibility is different to being like in the field, isn't it? So you need to understand the pains they're going through and then you can be able to, you've got more clarification of what to do and how to make impacts.

carlos 11:40

Correct. Yeah. That's key. At this moment, especially with all these, I think hunger or we are having a lot of strong sponsorship from senior executive to drive ai, but people are looking at it like, as easy as. AI is in our day-to-day, right? As you chat with Chad, GPT or as you talk to Gemini. But when the, you are in the business, reality is completely different. And then you need to guide them to understand the complexities, the risk, and then you know how to make it happen in a, yeah, in an optimal way.

ben 12:17

Yeah, no, definitely. It's, yeah, no, I think it's fascinating to see that now business and technology are getting closer together, aren't they? Like before we were further up field now, obviously, I think 'cause now everything's like blending in together now. I think you'd need to be knowing like, what is finance doing? Finance needs to know a bit more about tech. It's knowledge is getting more embedded across the company, isn't it?

carlos 12:42

It is. And it, it is amazing how the youngest professionals are more and more tech savvy. So technology like, pure technology work start being, more accessible for everyone. So it is that where we as data analytics experts or professionals, we need to have to bring that bridge between. Yeah. What is the business and the technology and we having a strong knowledge of the technology, then we need to create that strong knowledge of the business as well.

ben 13:12

Yeah. Okay. Just move into the data topic so we can look at the sort of reality of AI today and where the value is so obvious. There's a lot of talk with generative ai and obviously it's. Yeah. Where is generative AI improving outcomes today, and where do you see organizations forcing it, where I guess traditional AI already works better?

carlos 13:35

I think that's for me is one of the critical. Items man is like under really being able to, eh, understand where the right use cases are traditional ai. When is a generative ai and when is a agent? Ai, right? In many organization or many people that you talk to, they see it like as a progressive thing. Like what? Traditional AI is the lowest level. Then you do gen ai and then the highest level is a Gentech AI today. And probably it's gonna evolve and soon we're gonna be hearing about a new terminology. But I don't think that they are simply different ways to approach the problem. There are many problems that are still the right way to to address them. Is is agent sorry. Traditional ai. For example, forecasting, these kind of things are, very straightforward with a traditional ai, AI is driving a lot of value on, chatbots or where you have interaction with your consumers or interaction with customer service. That's where we are seeing. Of a lot of generative ai, very good examples. And then driving productivity in general. Nowadays before you wanted to summarize a meeting or to prepare a very important document, you probably will take many hours trying to, trying to create that document. Nowadays with generative ai, your day-to-day life is a lot simpler. You are more productive, eh, agent AI, again, is like very repetitive tasks, automating workflows, this kind of a space. So finding the right use case for the right solution for the right use case is starting to be like a an art and a science at the moment because that's really represent a big difference.

ben 15:17

Yeah, so I've been here like a lot more. Impact has been on around like getting more value from like the customer service type activities at the minute. I've been hearing from a lot of firms. Would you say that's same with

carlos 15:29

Yeah definitely. That's where the value is being captured today. I would say the dream of generative AI is what everyone talk about, chatting with the data. People want to chat with their company data and ask them question what is my pro, what is the product that has more opportunity? How can I drive growth when I, for a specific category, this kind of question that will be great to ask to, to generative ai and get the answers. But that's where the biggest challenge is to make it in a safe. Compliance way without,

ben 16:01

yeah.

carlos 16:01

Taking significant amount of risk.

ben 16:03

So there been many like AI use cases that like sound really impressive in boardrooms, but then quietly failed to deliver value.

carlos 16:12

Yeah. I can tell you a couple, in the, in this terms of. And chatting with the data. We have experienced having a strong data foundation in the commercial space, but then when you connect any technology that we have tried to chat with the data, you start seeing the challenges of how can you connect all the data that you have available and really have an, a very good experience from the. From the user perspective that they can ask any kind of question and get accurate answers. We have done some pilots and proof of concept that really haven't, hasn't, they haven't worked because of we can call it data readiness but it, but in reality is that the amount of data and connection of data that you need to have in place to be able to satisfy a user that in. They're in their day-to-day life. They're using chat, DP and any other tool like, frequently, very hard at the moment.

ben 17:08

Yeah, no, definitely. It's a lot, especially now obviously, companies had a lot of data anyway, but now obviously we've, I guess the last couple years here, then boom, it's, companies got so much more data, haven't they?

carlos 17:19

Yep. And the interconnectivity of the data across functions, right? Like you want to, you have the full spectrum, you want your finance data to connect with your commercial data, with your supply data so you can make more holistic decision making, right? So it traditionally we have been doing very good job on by function, creating the right set of foundation, but then. When you start connecting the data across different function and having real cross-functional view of your data, or even a consumer-centric view of the data that started being the challenge on being able to answer the question in an end-to-end manner.

ben 17:57

Yeah, no, I think this is where the bit, now it's obvious there's so many tools out there. This is where, like we mentioned before, like the business domain and I think more like the strateg, your strategy, how you can integrate all these. The data into your, like your ai, that's where you're gonna get the value. But obviously it's a challenge, isn't it, for businesses and this, it's where it's not the thinking. The heavy thinking now is the brains. You need to get that aligned and then that's where you're gonna add so much value. One, if you get it right.

carlos 18:27

Yeah. And one of the key for me there, Ben, is how do you have a clear vision? Like how does the, how do you know if you building a house or you have the full view of how your house will look like, but then you start building it by pieces in a consistent manner, right? You cannot build a house from, nobody will live here for 10 years because we are building the foundation. So something like that, you need to start, delivering pieces by pieces, but they all need to integrate at the end. And I think that's the challenge for us is like to really have that strong vision that is gonna be evolving because technology and solutions are evolving very rapidly. But having that clear vision and then doing it in a te way. Which short deliverables in a way that, our sponsors and the business are seeing value consistently. Ev every three months you're delivering something that really make a difference. And then you expand it and expand it. And then eventually we're gonna have the full house and the whole neighborhood, eh, available for a lot of people to live. But it will take time and we need to recognize that it will take time, consistency. But also continuous delivery for the stake. Don't get, tired of waiting.

ben 19:40

Yeah, no, definitely. Obviously. It's key. I think obviously going back to your house is obviously, most, obviously it's like companies have already got the house built, but then they've gotta rebuild it and put AI into it. So it is obviously it's unraveling it, isn't it, and then building it again, but it's gonna be a challenge. It's not just like easy that evolving the business into AI now, isn't it?

carlos 20:02

Yeah and again, what I mentioned at the beginning, Ben, is it's not only a technology challenge, it is not only a data analytics challenges. We need to consider it as a. As a human transformation challenge and we need to consider all the elements that are around it. So you are gonna implement a new use case. You need to review what is your new process, how the new process looks like how the, that what they call user centric process, right? Like data centric process, like how the data is in the center of the decision making and the process that you're running. But then also you need to evolve. You know what we used to call data literacy was more about. Training people about data, but now it's more like around data culture, how the data first culture is embedded in the people that are working with data. So it is a really like a deep transformation that includes what, change management practices, technology practices, data analytics practices. So we need to consider that as a business transformation and not as a technology implementation of or a data analytics piece of work.

ben 21:08

Yeah, this is obviously, you've got strong background in transformations and obviously like even compared to an SAP one, it's, this is completely, it's across the whole business is transformation, like you said, and it's. Like you said, you gotta deal with obviously the change of tools. You've got the people, you've got the culture, the new way of working, isn't it? Everything's getting changed and it's, yeah it's a slow, it's just gonna, obviously you need to do it efficiently and effectively and it's gonna be a slow burner because obviously people will take time to adapt as well.

carlos 21:41

Yeah it's completely different when you implement SAP. People don't have any alternative to do their work, right? They need to do, use SAP. It is not oh I can't use it or I cannot use it. If you want to create a sales order, you need to go to SAP and create it. If you want to close a book, a financial book, you need to go to SAP. So it is really not, you don't need to convince people why is it better to use SAP or not. It's part of the day to day. There are elements that you review the processes and you drive transformation, but it's enough. Is you don't need to impact their culture. Now when we are talking about data analytics, it's it's really a cultural shift, right? Like not in Bend, where we're seeing that there is a new revolution that is AI revolution. People, people, the way people learn, the people, the way people access data, the way, people operate in the day to day is changing dramatically with all these yeah, with all this new technology.

ben 22:37

Yeah, definitely. Okay. So when leaders say we need to do something with ai, what's usually missing from the conversation? And I guess what would be the first question they should be asking instead?

carlos 22:49

I think the first one is. reality what is that? What is my priority to solve? What are the key, how do we split it in the small in a very targeted use case that you can drive value immediately. We cannot do AI for the, as I mentioned before, for the sake of ai, but we are gonna create data, AI data ready? Just general ready to whatever you need that is gonna be a challenge that you're gonna get into and then you will never get out. I think that's, for me is the critical part to have clarity of where is the biggest value, the, that you can deliver value in the short term and demonstrate so you can gain momentum to continue evolving.

ben 23:31

And of course, just jumping in, like, why? Why do you think so many people get this wrong? Because I've, I, the amount of people I've spoken to, and they have clients, just, they want to implement gen ai and then like they, they'll say, what's the use case? And they just want it. Do you think it's just everyone's been sucked into the tool or, because a lot of people just. Like you say you need a business outcome. This is expensive to implement if you want to get the right return. So what, where do you think many people go wrong with this? Is it just they've been sucked in or?

carlos 24:03

I think there, there are several factors. I think that the hype of AI has made people to jump into it without really knowing what it is. I can tell you three years ago you attend to data analytics conference and everyone was, showcasing pre pro proof of concepts and how everyone is trying generative ai. Three years after you're attending this same conference and you're hearing the people acknowledging it is not that easy. It takes time. It's expensive. So we, you need to be targeted. That's one thing. I think the hype has made many people to jump, to pioneer on this. And that's what it takes, right? The second factor that, that I would call out goes around let me think on how to position it. In a way. Think it is the fact that in our day-to-day, so simple, if you feel like anyone, and I mentioned that before, everyone is using. Using AI in their day to day life. To have a, if I want to cook tonight, I can ask ai what are the ingredients that I have? It will gimme an amazing recipe if I, even for the, for my medical, yeah, I can ask question about, that you cannot ask only to the doctor because it's hard to get to them. You ask question to ai and it gives you, a directionally good answer. So it feels simple in our day-to-day life. So why not? Why shouldn't it be as simple as it is in our business, right? In our corporate world? I think those two factors are making life harder for everyone.

ben 25:34

Yeah I think in, obviously now it's been around a couple of years, I think people expect that like impact straight away. Now, I don't know.

carlos 25:41

Yeah, because it's easy in our day-to-day life. It's very easy. Nobody saw how many years I know, probably was behind chat, DPT to be ready. Even if you ask me now, how many since when that has been in development, I don't know, but I just know that it's very easy to use.

ben 25:58

And then so what, what should the, like the first question, like a leader come to you to ask them? Is it. How can we solve it or how, what would be the sort of ideal first question from you or, and also for people that are obviously looking to implement ai, what should be the sort of starting process for this?

carlos 26:14

In my particular case, we have many years of having a clear strategy and our stakeholders are very well educated about the importance of having the strong foundation to do any ai right. I think that has been a critical piece of our work to deliver, to deliver that consistency. Like having AI with without the right foundation is not gonna deliver value. But at the same time, it's also going to be mentioning all, all over is you cannot do foundation ready for any, for everything. So having that laid out to say, okay, what is the use case that we want to go after? Then what is the foundation that we need to deliver that use case? And what are the processes impacted? Who are the people who are the user persona? Again, having that complete view before starting any work for me is the critical. Is critical for success.

ben 27:05

Okay. Yeah, no, I completely agree. So what AI workflows are materially,

carlos 27:11

sorry to interrupt you but yeah, I think having the conversation is what is important. What is a critical factor with our stakeholder, our executives, is if you take the time to explain and to, for them to understand that it takes, end to be done to really deliver value of ai. They will understand. Everyone is the problem is when we say just yes and jump into the work. Yeah, that is a recipe for failure.

ben 27:35

Yeah, no, I think the knowledge transfer is obviously key, isn't it? There? 'cause you've got business, obviously then they'll understand the business problems. But obviously as a techie you need to, where your storytelling, the capabilities come in and able to articulate the tech terms in a business focus makes it a lot easier.

carlos 27:54

Definitely that's important because talking about, m models and architecture and all that will not make it, will not make it happen, so we need to find the skills on, on, as you mentioned, you storytelling, connecting the dots between business outcome and technology, and using, day-to-day terminology for us to explain the work need that needs to be done.

ben 28:16

Yeah. If you're speaking too techy, you're gonna lose them. So what go back to the next question then. What AI workflows materially faster or more scalable today because of generative ai? And do you have an example?

carlos 28:29

For particular in ai in my world, in my, I. I will tell you like the day-to-day kind of tasks, like emailing and creating documents and like summarizing meetings. I think that's where we are seeing significant amount of value. There are a lot of use cases in that and people are using it. The way compar is, when Excel was created, I don't know how many years ago, nobody's now, it's, it's in the day-to-day. Live of everyone and you use Excel in and out as a productivity tool. Nobody's tracking what the value of Excel is. It's just taken for granted. I think that's where we are gonna get with the generative AI tools that, like copilot charge, GPT or Gemini, or any other available there.

ben 29:13

Yep. No.

carlos 29:14

Still, what we are all looking is what are that big use case that will make a difference. And so far being transparent, there isn't, there is a clear one that I can call you out. That I can call out as a huge success.

ben 29:28

Yeah, no, a lot of people do say it's more like the operational side of things is where it's obviously it's making businesses' life a lot easier. I mean it's, and it's also, I think it's also to, like you said, it's, people expect fast things, but obviously this is still relatively new, isn't it? So we're still, it's still like a baby, still learning. Businesses have gotta adapt change and obviously have a strategy in place to make that impact, like you said, be that what, where do they wanna go forward? Think, and then obviously then you work back, don't you? And then you gotta make the changes gradually.

carlos 30:00

Correct, correct. And I think Ben. Experimenting is important. That, that's where you're gonna unleash, unleash the value eventually is the more you experiment, the more you learn, the more that you know you, you can adapt the solutioning. And I'm sure we are gonna get there where we're gonna see a lot of generative AI use case bringing value. But it takes, for people to experiment on what we were discussing before. Day-to-day users are more and more tech savvy and we are gonna see a lot more innovation coming in, in those spaces. And yeah, we're gonna see it happen very sooner rather than later.

ben 30:36

Yep. No, definitely. So what's the real cost, financially or organizationally of deploying AI that isn't embedded into the day-to-day decision making?

carlos 30:47

I think the, it is still to be seen, Ben when we implement these pilots and these proof of concept you quickly see cost scaling, very fast. So determining the real cause of implementing AI in our organizations is not clear yet. In many case you discover as you do the use case or the pilot. What I can tell you is that the cause of not doing is really like you're gonna be staying behind and then you're gonna probably do competitive advantage in your business. So being there, being open to experiment, to kill pilots when they don't work very quickly and to start a new one is gonna be critical.

ben 31:28

Yeah, I mean if, yeah, to get up and running and to make headway, if you're not doing it, you just gonna fall behind. 'cause like I said, it impact will be made. It's just, again, it's adapt, adapting and transform transforming your business just takes time and it's a learning process.

carlos 31:47

Yeah. And what is critical is to, to have that process that where you capture what are those proof of concept that are delivering value. Which one do you want to scale to a pilot, to a full scale? Which one do you want to kill early? Because if not, you end up, having got many called proof of concept graveyard of a lot of proof of concept is still running there. People like nobody using it. So having that governance on what goes ahead, what needs to be killed, what need to scale is gonna be very important to keep this thing agile and delivering value.

ben 32:20

And that's also where the leaders, that's where you make your money. You make these important decisions.

carlos 32:26

Exactly

ben 32:27

So then if a CEO asked you for just three questions to evaluate, like any AI initiative, what would they be in which one is most often ignored?

carlos 32:39

What is the business value, I think is the key one that I will call. Like how we're gonna track success of this, right? Sometimes we don't define that at the beginning, and then, we end up in a trap of of feelings over facts. That is very one of the key items. The second one is what I have been mentioned several times is the end-to-end transformation. Where are the processes? Where are the, who are the people that are gonna be impacted and what need to change? Probably the third one is about governance. Is our data well governed for us to be able to make that use case a success or not? Because that, that, could be a key for a success or not.

ben 33:19

And is there quite like questions that executives rarely ask about ai? About ai, but should.

carlos 33:25

Can you repeat that question, Ben? Sorry.

ben 33:27

It there like specific questions where executives rarely ask about ai, but like Absolutely should.

carlos 33:33

Nothing come to my head at the moment on that space.

ben 33:36

Okay. Not a problem. Okay, Carlos, thank you for joining us. It's a great conversation today. Really appreciate your time.

carlos 33:44

No, thank you, Ben, for having me and great to have this conversation with you.

ben 33:48

Brilliant. Cool. And thanks for everyone for listening today. And I guess we'll see you in the next episode.