Scaling Artificial Intelligence in Malaysia (featuring: Maybank, Tenaga Nasional Berhad, CIMB and National Artificial Intelligence Office)

In this episode, we feature a panel from the AIBP Conference in Kuala Lumpur with Datuk Ir. Megat Jalaluddin Megat Hassan, President & CEO, Tenaga Nasional Berhad (TNB); Dr. Siew Chan Cheong, Chief Strategy Officer, Maybank; Pedro Uria-Recio, Chief Data and AI Officer, CIMB; and Shamsul Majid, CEO, National AI Office (NAIO). Together, they share their perspectives on how Malaysia can scale artificial intelligence responsibly while remaining globally competitive.

TNB, Malaysia’s largest electricity utility, has been embedding AI into energy operations to improve efficiency and reduce emissions. Maybank, one of ASEAN’s leading financial services groups, highlights how data fundamentals, not just access to technology  are the real differentiator, and why AI will become a basic requirement rather than a competitive edge within five years. CIMB, another major regional bank, outlines a progression of AI maturity from productivity tools to copilots, autonomous agents, and eventually AI teams. From the policy side, the National AI Office discusses Malaysia’s framework for responsible AI and the importance of ethics and standards.

The discussion captures both the opportunities and challenges facing enterprises as they prepare for the next S-curve of digital transformation, balancing innovation, compliance, and workforce readiness.

Transcript

AIBP: 0:00

In this episode, we bring you a panel discussion from the 49th ASEAN innovation business platform conference in Kuala Lumpur with leaders from tanaga, Maybank, CIMB and Malaysia's national artificial intelligence office. You'll hear how Tenaga Nasional is embedding AI into energy operations moving beyond automation to improve efficiency and sustainability. Maybank shares why data, not just technology, is the true competitive advantage, and why AI will become table stakes within five years. CIMB outlines the stages of AI maturity, from basic productivity tools to copilots, autonomous agents and even AI teams. And from the National AI office, we discuss Malaysia's push for Responsible AI and new standards. Please enjoy this episode from AIBP. The AIBP ASEAN B to B growth podcast is a series of fireside chats with business leaders in Southeast Asia focused on growth in the region. Topics discussed, and include business strategy, sales and marketing, enterprise technology and innovation.

Sue Yuin - AIBP: 1:16

Okay, good morning, everyone. When I was practicing or preparing for this session yesterday, where I was kind of caught using AI to prepare for it. So my team member actually told me, maybe they should replace me with an AI avatar. But I trust the panel here, the gentleman here, would help me do my job. Okay, and I also hope that the audience can help me a little bit by keeping the questions coming in over. Slido, I'll make sure to include that in the questions. Help me a little bit to stay human, staying useful, and hopefully help me with my next pay negotiation. Okay. Can yah?, okay. Not funny. Nobody joking. Still fail to AI, okay, while we start off a little bit, maybe I can get each of the panelists here today to introduce yourself a little bit your organization, and also maybe one thing that you see AI making a very big impact in your daily life and organization, I will I did a bit of stalking in the tenure of like each panelist. So I thought I thought I'll start off with Datuk Megat, my God first. Who's the longest in your company? 16 years in tanaga? Right? Correct. Also, we know that, oh, my god, the longest among this panel.

Datuk Megat - Tenaga: 2:34

So yeah, please. Assalamu alaikum. Very good morning. First and foremost. Thank you very much once again, for giving the Tenaga Nasional the opportunity to share some of our focus area with respect to digital and AI today, Yeah, I think, as I So personally, I've been in Tenaga Nasional for many, many years. And the history is that I got a scholarship from Lembaga, electric negara, some of you may not be able to recognize that, to do a further studies, and then I joined tirager national, and has seen the, I would say, the journey of of the industry across the whole value change of energy for Malaysia as well as as the world, And as we understand today, the journey that we are embarking today is a journey from digitalization into artificial intelligence. So for Tenaga Nasional National, we started the journey of digital and digitalization quite number of years, and we have introduced all this concept and also implementation with respect to digital up to the point of the best of the digital tools, in terms of rpa, robotic process automation, we believe that is the so called the height of the digital world, where we are able to deploy modules of RPA within the operations and business of Tenaga national. So today we have now another, what we believe another S curve, the opportunity for businesses to actually adopt the next level of automation and digital which is artificial intelligence, meaning that we the automation journey may lack those intelligence, but today we are embedding this into part and parcel of our business operation. So that is, I think, make us the Tenaga Nasional excited with respect to the program that we have today, and happy to share many of those that we have comes after this. Thank you.

Sue Yuin - AIBP: 5:23

Thank you, Datuk Megat, we will deep dive into some of the initiatives that you mentioned earlier on, I think Dr Dr. Siew, earlier on, you nodded your head a little bit about Datuk Megat, sharing from his previous curve to this curve, maybe a little bit about yourself, and also maybanks initiative.

Dr. Siew Chan Cheong - Maybank: 5:39

Thank you. Good morning everyone. It's my pleasure to be able to be here to share the Maybank experience. So my name is Chan Cheong. I oversee the strategy and transformation of Maybank, and within my portfolio, I also have a team that basically looking at data analytics AI and also venture we invest in digital fintechs to enhance our capabilities, similar to Tenaga Nasional journey, Maybank started our so called AI, or advanced data analytics journey, well before the covid era, and when we first started, obviously, we look at as much as possible to to introduce new use cases or offerings to our customers. I mean, glad to see that just now, our SME team won an award in terms of how we integrate reconciliations with the banking software to help our customer to reduce the effort by 80% but we also have, in a while, I think, right after the covid, we introduced things like instant approval of mortgages or SME loans, so that customers can actually get their loans approved and then get the money in the account within five minutes. So these are the things that we have done now, going forward, we think AI will only give us the competitive advantage for maybe another three to five years after that, I think it will become a table stake. So with that in mind, the senior management is thinking about how we can further accelerate so that we get as much benefits as possible in the next three years, so that by the time, in five years, we expect that, you know, most people, or at least, you will not get any more advantage. So we have been very focused. And the things that we're focusing on now is, rather than only looking at use cases, we are actually looking at the fundamentals. For example, data, how we create a scalable, efficient data infrastructure, not only for the data that we have within Maybank, but how we can connect to external data, so that, you know, we can maximize the benefits of available technology, because, to us, is technology, especially talking about a lot of software, hardware, LLMS. This will become commodity. You know, it's a matter of, obviously, investment, but many companies will be able to access similar technologies, but the real things that really differentiate this will be data. So therefore, going forward, we're going to spend more time on fixing our basics, which is the data, and then partner with the industry players to scale up our data analytics and AI capabilities in preparation that three to five years from now, it will not become a competitive advantage.

Sue Yuin - AIBP: 8:44

So data and analytics and AI are the key focus for Maybank. For yourself and I think Pedro, well, your title here says Chief Data and AI officer. So exactly what you're working on, maybe a quick introduction to the team. Yeah,

Pedro - CIMB: 9:06

Hello, yes. So Well, I'm very, very happy to be here with you. In general, I try not to talk much about the bank, and I prefer to talk more about data and AI in general, and how companies can take advantage of of data and AI in different industries, right? And when I think about artificial intelligence, I think there are different stages that somehow every company has to follow, maybe in different orders, depending whether you are regulated, whether you are more customer focused or customer facing, or you are more operationally facing. I think there is one phase that every company has to go through and that. Faces personal productivity through AI is deploying tools that are generic, that allow every employee to be more effective, and thinking about drafting emails, making minutes, and thinking about deep research and thinking about having meetings that are recorded, and you can ask questions about the meeting. Was I mentioned in this meeting? I'm thinking about these kind of things. I'm thinking about making videos, maybe for training of your workforce, you can make a video with an AI actor, general productivity tools, right? That's the first phase, and it's very important, because what is key here is to expand the use of these tools to as much of the workforce as possible, to change the mindset of people. What is going to kill a transformation is that people on the ground don't believe in it. People on the ground are opposed, right? That's the first stage. Second Stage in general, is once people are used to start taking advantage of these productivity tools. You can start having companions, co pilots, assistance, AI assistants for specific types of jobs, right? These are agents, but they are not autonomous. These are operated by a person, right? And here we can think of in banking. We can think about about tools for relationship managers, where you can see the leads that you have to propose to each customer. You can add questions about the products. You can create investment proposals, maybe for some customers. You can have companions for customer service agents that are helping them answer their questions. You can have co pilots for that are tailored for fraud investigators in banking and in other industries. You will have for for some very specific jobs. You can have a specific assistance that are used by the employee. Once you are clear with this, then you go to the next stage, which is having autonomous agents. These are agents that don't require an employee to operate them. They're autonomous. They are running on their own. Of course, somebody has to supervise them, right? Of course, somebody has to supervise them, but things such as applications of many loans or many products can be done autonomously. Marketing is something that can be done completely autonomous. Most marketing campaigns in almost any industry could be very highly automated. So that's the stage where you have autonomous agents. Then you go to the next stage, and we, I don't think anybody's ready for this, and that's when you start having teams of agents. So instead of having one agent that is doing the marketing campaigns, you have teams of agents. And these agents are calling each other. We we don't have products yet for that, but they are going to come very fast when you are onboarding an AI employee and you will have a teams call with the employee, telling him, well, this is what I am expecting of you, telling him, now you need to get your credentials to assess these databases. Once you have that come back, then he calls you back, and then what do I do today? And then you are talking to that person in the same way that you will be talking to a human right? Maybe sending messages on teams, maybe sending emails in the same way, maybe calling that AI that is going to come very soon, and I don't think anybody's ready for that. And some point of these stages, companies will have to decide, when do I go direct to the customer with AI? And that's a very important decision. You want to be ready. You don't want to make mistakes in front of a customer, right? That's why most companies are starting with employee facing cases, and then later they go to client facing cases. If you are regulated, probably you will do that a little bit later. If you are less regulated, you will probably do that a little bit earlier. It depends on the industry, but that's a key consideration, right? So I think AI is going to completely change the way it works. I don't think anybody is ready. And I think in particular, Malaysia needs to get more ready, and that's why initiatives like this, innovation awards and everything related to innovation is very important, because I think we have to get one step up in terms of innovation.

Sue Yuin - AIBP: 14:39

Thank you, Pedro. I think you've also more or less covered a few different questions that I have for you in your introduction. But that's good. We'll come back on, maybe your experience, versus what you see overseas later, versus Malaysia in a bit. And last but not least, I think Sam Nanyo, you've been with NAIO for the past seven months. Now, seven months. How? Has it been like, Oh,

Sam Majid - NAIO: 15:03

thank you sue. And first of all, congratulations to AIBP for having this event. Once again, I can see that the chairs are all taken out. And also congrats for putting a a woman with three other guys or four other guys, because otherwise there's no diversity. So AI is about diversity. I just want to start there, because we see that the when the narrative of artificial intelligence comes from only one source, then we are in big trouble. So we need to get the narrative of AI coming from many sources, many types of knowledge base, domains and industries. And we saw this actually starting all the way back since 1956 AI started back then 1956 but at that time, like I said, it came from only one source scientific domain. You need a PhD in Computer Science and Mathematics and a mainframe to run a AI. So that's the single source at that time. But these days, that same capability in your hands, in your phone, in your car. So devices are getting more and more powerful. So Malaysia saw that we cannot be left behind. Otherwise we'll Malaysia will be left die standing, I have to say, because the other neighbors and also the world, are like what Pedro was saying. They are moving super fast. You will if you Google up the amount of activities in AI, investments happening all over the world, plus the usual suspects, Elon Musk, Donald Trump, and many others putting effort and conversation into AI. You know, this is a hot space. So saying that being a hot space Malaysia needs a bit of framework to go forward. Otherwise, it's like, you know, I go here and you go there, and there's gaps everywhere. So next month, in August, we have Malaysia will have the ASEAN AI Summit. Please block your calendar, because at that time, Malaysia will have a few things that we've been asking for, like, do we have a game plan? Do we have responsible AI guidelines? Do we have the right people and players? Do we have international attention and many, many more. So a lot of questions will be answered at this event next month, but saying that it is also not just having answers, but also to be able to lead everybody else down this AI journey, meaning your uncle, Ernest. Pachi. Aunties, everybody need to know that AI is not going away. As a matter of fact, if you look at some scenarios, aging people, your mom and dad, for example, living alone back in the kampung, you know, they have no one to talk to, but we have tech, GPT or some AI form that we talk to. So there's this journey where there's a lot of evolution that may happen. AI for aging population, AI for healthcare, AI for transportation, and AI in schools. So everybody needs to be given some sort of a guidance guideline, like a book on how do we all go forward? If you are an engineer, if you are a student, you are SME, you are unemployed. How do you use AI to make yourself better? All this will be answered pretty soon, because there is a call to action for all of us, especially the country, per se, not just enterprises, not just government, but individuals and companies. So that's what national AI office was set up for long answer to your question, but it's set up also not to just touch and go, meaning you just disappear after that. It's also set up because there is this whole moving landscape of, how do we make AI safe with we are hearing or seeing so many news these days on not only AI being used by good people, but AI being used by bad people. And the incentive for those that are doing bad things are a lot bigger than incentive for people that doing good. So apart from having an AI Act, which I will not talk about, because that's more in the, you know, law and regulation and and I think, apart from that, we all need more guidance. That's what NAIO is doing. For example, we need more standards. We need more ethical ethical documents. We need more guidelines and framework and things like, how do we share data the philosophy of for example, in some situation, we bring AI into our organization. Our data cannot go anywhere. In some situation, we bring our data to the AI. The perfect example of bringing our data to AI is what all of you are doing every hour. You contribute your data to social media, and you're doing it and so. You're putting your data into AI, which, this does not even sit in Malaysia, and guess what's been monetized you. You don't want that same situation in very sensitive ecosystem like healthcare, defense. You don't want defense data to go to AI. You want the other way. You want AI to come to where the where the data is. So all this thinking, guidance, navigation, that's what national AI office does.

Sue Yuin - AIBP: 20:30

Thank you. I think I see a lot of questions coming in, right? And I guess setting the tone of, like, today's discussion is all about, like, how can we scale AI and maintain the global competitiveness? And I think, like Sam, you mentioned a lot about what NAIO is working towards, and next month the event itself, but maybe before next month, we'll start having a little bit of difficult questions to address that's coming in from the audience itself, right? Because I think one thing that stood out to me when we did the poll early on was about responsible AI. And there are a couple of questions that were mainly asking about, while we have, while we look at like aI infrastructure, how can we make sure that, what are your thoughts, maybe around ethical, AI, responsible, AI, and also, what kind of standards, or can government, or should government, be intervening? I think this is mainly for Sam. I was

Sam Majid - NAIO: 21:24

gonna give a chance to speak first, there you go. Later, okay, yeah, responsible AI is a big conversation because the short answer is some jurisdiction, European Union, for example, they just slap automatically an AI act. That means you, you just got to know what the act is. And if you are in the game of building AI systems, you do anything wrong, you get caught, you go to jail. Malaysia. We are not going there yet. We have to build up the foundational component about making sure that we we raise education level. We tell people what is responsible? Ai, what is ethical? AI, and we also built in the other guidance that should come with it, things like data sharing Act, which is already placed, Data Protection Act, cyber security act under CSM, and many more other peripheral ecosystem that needs to also come up along as we build the other ecosystems. And as you may imagine, this is a this is a journey, because the technology players, the big tech guys, they are super far ahead. And if Malaysia doesn't have a cohesive action, we'll be super far behind. So therefore, there's all these other considerations that not only you as an enterprise, need to know what to do when it comes to deploying AI, but do you have internal controls? Do you have internal guidance, like when it comes to policy, sorry, procurement, when you buy an AI system, does procurement know how to validate whether the AI system is sovereign or not. It's not just about where your data center is. It's about what about the model that you use, what about the data that you contribute, and other things. So document needs to have some sort of answer, risk management, some sort of so some things you don't need the government to to do it now you can do it first, so that you can build that safeguard, responsible AI within your enterprises. Standards is one of them, where it can be a collective of standards that we develop for the for the industry, or it can be something internal to your organization. But don't stop there, because it's not just about the AI in your organization is also about AI for the public. Once the AI is being used by the public, be transparent about it. You don't want the public to question, why did the AI decide this for me, be open about it. So hence the seven elements of national AI guidelines and ethics that mostly came out two years ago. And there'll be a more and more of this guidance document that the government will give out, that you should download and study them and implement in your AI systems. The seven elements are things like transparency, pursuit of human happiness, human in the in the loop, and many more, which we can get it from the mostly website.

Sue Yuin - AIBP: 24:19

Thank you, Sam, and I think what Sam mentioned echo across like the industries that we cover, because all these are heavy, critical, heavy sector and also very compliant sectors. And maybe I would get the perspective from like Dr Sue first right, because I think a couple of questions is also around, how is the banks? Where do you see banks AI models being compliant, and how are you ensuring that it compliant with like the AI ethics for most of the data and the AI use case that you're building?

Dr. Siew Chan Cheong - Maybank: 24:50

Thank you. So maybe I will just start from even beyond banking, not only banking, because I'm not sure about Petro generally, banking are very concerned. Conservative. We find it very difficult to innovate because of the restrictions we have, but we are trying to change that. I think the difference between the previous technological innovation or industrialization, right compared to what we are facing here is think previously is more about productivity improvements, right? It's more about how we can have low input but have higher output, whether it's fewer people, we can have more information and so on. But I think this time around is slightly different. I think this time around is not only about productivity or efficiency improvement, it's also influencing how we think. So, I think when we come to responsible AI, I think it's not about deep fake only, right? I think we, especially some of you might have seen in social media, there are CEOs of a bank saying that, you know, you should invest in here. Obviously, those are not the actually from the actual CEOs. But I think even in the day to day social medias, we are being influenced by AI. So AI or technology today is influencing the psychology, the society, how people vote, how children when they're exposed to certain social media, how they behave, and obviously, in some countries, they're trying to restrict children right to get exposed to new technology, or limit the exposure to technology. I think that is broader than just productivity improvements, and broader than just how we make sure that in banks, we use AI to serve our customer responsibly. So the responsible, the responsibility, should not be only thinking about regulator, government, frameworks, processes, I think as maybe as parents, for example, right? How we guide our children. So I think that's as a society. I think we need to think about that while AI give us a lot of benefits, but I think how we use it is important now back in maybang. So we actually have, to me, sometimes it's too too stringent way of testing models or industrializing our AI tools, because we have to go through a very stringent process, and sometimes we gear towards the fact that, you know, if we are not sure, let's not do it, even though there's a benefits, because we are regulated by bank, negara. And, you know, we also have presence in other countries, so Singapore, mass, Indonesia or JK, so, so we have a specialized team developing AI. We also have another team actually looking at checking AI, which is developing the framework to make sure that the model are tested. There are no biases, right? If you're not sure, then maybe we don't do it now. So there's some sort of a check and balance so it's beyond the security, but also biases, privacies and so on. But having said that, I think we need to move along. We need to move faster. So rather than only thinking about the negative side, I think, as a bank, we think that that's not an option, because, at the end of the day, a bank, in fact, we are a data company, so we're not producing any products that's in chairs, laptops and so on. We are essentially a data company that offering our product is essentially data which is financial product. So so this is a journey that we have to go through, and we will work with the industrial partner. But one thing for sure is we have to use machine to check machine. We cannot rely on people to check machine. So the process that we put in place is actually most of them probably not relevant anymore, because they're relying on people to check the products. I think so that's the direction that we have to head to. We have to develop the AI model to check another AI model.

Sue Yuin - AIBP: 28:58

I like what you mentioned about how do you get machine to check machine and AI to track AI having Pedro, you do have some experience in this segment in and you mentioned a little bit in your book, maybe your thoughts from a or for CIB perspective, right? Because a couple of questions that come here is very much around data. They How does CIMB balance data accessibility with security and compliance?

Pedro - CIMB: 29:27

Was, how do banks remain compliant, right? And again, I'm going to talk about banking in general. And as you have said, banks in this country and in any country in the world, are supervised by the respective central bank. In this case, bank, negara Malaysia banks have very large teams for compliance. All of them are. Um, there is always a head of ethics. There is always a very robust process for deploying it systems, including AI, right? So it systems have to be checked before they are deployed. There is a risk assessment when you are procuring a system, an AI system from a vendor. There is a risk assessment, there are data privacy teams. So there is cybersecurity teams, so banks are extremely prepared for compliance, for data security, for cybersecurity is extremely unlikely that data bank is not going to be compliant. That is more likely to happen in other industries that are not regulated, just just because of the nature of it, right? So industries that are more internet native, newer industries, I don't know, e commerce, retail, all these industries that are not regulated are much more likely to have problems in terms of unethical use of AI than data bank. This is true for Malaysia, and this is true for any, I would say, any country.

Sue Yuin - AIBP: 31:15

Thank you, Pedro, and I think maybe a little bit more difficult question, since that thought, Magat, you're the most senior here, because some of the questions here are very much about organization, right? And as a CEO of the Tenaga Nasional, you have to manage this a bit here and there. So I mean number one question, how do people request for budgets to run certain pilots for AI? What do you see? What are the things that you track when it comes to new budgets requests or new case use case requests.

Datuk Megat - Tenaga: 31:47

Thank you very much. I think maybe I probably go one step before that. I think for for tonight, we mentioned about ethics and scalability. I think those are the two objectives that we want to achieve. And from the organization point of view, we are very clear that scaling AI is the business imperative, but at same time, we understand that scale with ethics. So to be able for us to do that within the organization, we provide the policy guidance on how this scaling with ethics come into the picture, so we put up a framework with respect to the purpose of getting the AI itself. So we define four specific purpose for AI application within the organization to ensure that we are very much ethical in the objective that you want to achieve. So it is about revenue growth. It is about cost reduction. It is about objective of customer experience, business efficiency. It is about minimizing or reducing the risk for the organization. So that is the first step that we have taken, respect to implementing the AI within Tenaga National. And after that, we look across the circle the elements of intelligence that's available, whether it is the kind of it is respect to the technique of video or imaging, respect to technique of forecasting, whether the technique of trying to do the expert system that we have so within that technique, we look across the value chain of the Tenaga Nasional which come from generation, transmission, distribution, as well as the customer fronting. So for example, we look at how AI can provide us the best outcome with respect to the coal blending that will minimize the carbon emission for a power plant by the same time achieve the best generation efficiency. So this kind of, I would say, purpose, make it very, very ethical. And as you can see, the objective is beyond the company, we are talking about climate resilience, for example, with respect to carbon emission. So this cut across with respect to the organization, and we also looking at the asset performance, for example, how best we predict easier they are the one doing anything. How best we predict the life of the cable that supply to the customers, so that we recognize the interruptions, or we focus and do something before breakdown happen. So this is for a customer experience by some time, reliability of the system. So these are the fundamental element that. We put in place with respect to where to play and also how to win. So on top of this policy guidance, we also look at this so called AI, human interactions. So we understand that the perspective of AI, we can have models that relate very much to human iteration. And for a power company like us, we believe that this is very much required. So we look at the four model of application, FAI whether it is man in the loop, meaning that human is required to put input man on the loop. Sorry, I must say, this is just a it's not a gender based it's a human in the loop, human on the loop, human over the loop, but human out of loop. So this is also a very critical element where ethics come into the picture. So we feel that the human interaction is still required, so most of our application now focus on human in the loop application so that we combine the intelligence of the artificial plus plus the human. So these are the framework that that we are we are looking at so with respect to the performance of measurement, which is what your question is, we also identified and create the AI power index, as we call it. And there are three components that we measure today. First is actually the business value resolution, what the value it gives respect to the AI, whether it's customer experience, whether it is the revenue growth. So that is one component. The second is the utilization of AI within the organization. So here we look at the employees participant. How many employees now can actually write the code respect to the AI applications, and I think the that one of the utilization, we look employee participation, and the fourth is actually a building capability. So here we are talking about the employees being able to have access to capability building, and how many employees that have gone into the, for example, basic awareness of AI up to being able to do, do the do recording. So here tonight, connection, I think we are probably a bit lucky. I consider myself lucky because we have a corporate Academy. So we are using the corporate Academy where we actually give the awareness from users to builders with respect to AI. So this is where the opportunity of our employees to be part and parcel of it. So these are the three metrics that become the the guidance of the performance or adoption of AI, business value, participation and also capability. So on the capability, I also would like to share that the Tenaga Nasional have a university unit, and probably many of us is aware, and recently we launched a degree program called Bachelor of Science in artificial intelligence. So that is started in June 2025, so last month fresh from the oven. So I encourage and would like to do some pitching here for the university, in sense that any of the talent of Malaysian Welcome to actually take a course in in in AI, so that the ecosystem for the country will be better in the future. Thank you

Sue Yuin - AIBP: 38:48

that I think should be music to your ears, right? Sam, because I think we are looking to build a sustainable talent pipeline, especially when it comes to AI, in the interest of time, right? I have just one last question to each of our panelists, right? I think maybe looking at what's the most like question here, What if, let's say, you have like to look back at the journey, and if you have like, more time, more resources, what do you think AI, what would you invest in on AI on and what would you encourage people to look at AI differently?

Sam Majid - NAIO: 39:29

Yeah, if you have the ability to go back time Malaysia, I mean, I have to, I have to speak on behalf of Malaysia, because that's what national AI office has been tasked for. I would say there are two parts. Number one is we, we and all of you should actually spend more resources into research and development. Research and Development Grant or funding. As you know, it's not cheap. It's sometimes, some people say money down to. Brain, but it is necessary, because you can see, when it comes to the topic of AI, the companies that invest most in research and development are the ones that's driving the conversation. Now, I was in a meeting in Bangkok recently, and the smaller countries, much smaller than Malaysia, they all are trying to countries are coming together to to stand in front of big technology companies. That's what's happening now. Big tech is controlling AI conversation. Smaller countries have got lesser ability because they don't have the ownership of IPs. They don't have any other legal, not say, legal standing, but they don't have much bargaining power. So more and more countries are coming together because they need to have that collective authority to stand against what AI may be of negative coming from the big technology companies, and you see the amount of investment, for example, the latest one is open AI. This came out with a new enemy, and they've been hiring so many people, and they're putting a lot of money into where AI is going to go, and this is in the deep science space. So if I want to turn back time, research and development into deep science, number two is actually putting a lot of AI philosophy and framework into our school education now is actually talking to the ministry of education, higher education and many more, and most of them are coming up with AI aware syllabus and framework ecosystem coming up soon. But if we were to turn back time, it should be done earlier, so that then our kids are not just using AI as just for joy, but our kids are actually using AI very well aware of the disadvantages and the advantages AI, I have to remind everybody, it's just a tool, and if one is not sure of your core calling, that means you don't know why you became a doctor, why you became an engineer, for example, then a tool can replace you. But if you are very sure of why you join University unit and to take up the Bachelor of Science, you're very sure why you're doing it, then the second action after that is you want to use the best tools to support why you are doing it. Therefore you will use AI. So to me, that calling of you need to know your inner self. What is your superpower? You can be a teacher. You can be a chef. You can be a technical technologist. And therefore, once you know and you polish that up, you know that your next aim is to find the best tools. AI is the best tools. The third one, if I were to say, is the more we use AI, unfortunately, the more energy we all will use. And that goes back to TNB, by energy consumption of AI engines, a lot more hungrier. And to be honest, we don't have an answer yet on how do we make AI more energy efficient? It will just go up and up and up, because once you use an AI search, you don't use normal search because AI is so convenient. But is there an answer behind the scene, in the server room, in the data center that actually reduces energy No, so we are all out there in the quest of trying to figure out how much natural resources of energy that we need to really have, because in a meeting like this, we are telling everybody to start to use AI.

Sue Yuin - AIBP: 43:35

Thank you. So we need to use more AI, but we need, I think Datuk Megat is using AI to try to optimize the energy as well. So it's okay, let's see Pedro. Any last words about Yeah,

Pedro - CIMB: 43:54

I don't think I'm now. Hello, yes, well, I think what Sam is saying is totally true, right? I used to work in Malay in Thailand for three years. Before that, I was in Malaysia, and after that, I came back to Malaysia, and Thailand is extremely innovative in digital technologies. Ai, most of the banks in Thailand have been for long the cloud, for example. And people are very digital. They are very, very innovative. The IT spend per GDP in Thailand is quite high, actually. I saw some statistics from from some of these consulting companies, where Malaysia has a lower it spend per GDP than than Thailand. So if there is something that I would change, if we could go back in time, 50 years, 60. Year, 70 years, is, how could we have made the country more innovative? Because I think that's that's really the key, right? How can we invest more in detail, invest more in AI, find new ways of doing what we are doing in ways that are more efficient taking leadership in certain industries. I mean, in palm oil, we are, we are leader. We are leader in certain industries, right? How can we do more of that? How can we have people that are hungrily looking for opportunities or doing things better, right? And I think that in some cases, some countries in Southeast Asia are getting very much ahead. If you look at Vietnam, if you look at Thailand, we were talking about Thailand, even if you look at Indonesia, right, in Indonesia, they have invested so much in technology. They have gone so much ahead in certain things right, like with GoJek and the commerce companies order, right? I think that is what I would want to change, but it will require to go back not a few years, but a little bit longer, to create this spirit of innovation in the newer generation.

Sue Yuin - AIBP: 46:19

Thank you. Pedro. Dr Siew?

Dr. Siew Chan Cheong - Maybank: 46:25

So if you were to turn back in time, what would we do differently? I think today, for for the AI development, I think the problem is not in ideation. In fact, we find that there are too many ideas that you can access quite easily. Because if you go to search in Google AI use cases, you probably get a few 1000s quite easily. So I think ideation is not an issue. I think the issue is industrialization. So how we industrialize the idea so that it can come up to a real, tangible benefits for the organizations? So the phase that we have gone through in Maybank was there's a lot of excitement, a lot of hype in AI and because the ideas come out so easily, and I also have team members that, over the weekend, they come up with a POC, and we have many, many POCs ideas. But when it comes to, okay, how we productionize it, how we industrialize it? That is not over the weekend. You can do that will take months to do it, because you need to have the basic foundation to set up. So I think if we were to do it again, I think we really need to have the discipline. So it's less about investment or infra. I think it's a discipline first to say that, right? Can we maybe just pick one business line to make sure that we accelerate the industrialization? As an example, let's say SME. Can we have a SME business to be the leading edge in terms of powering by AI and then show what really good look like in the organization? Obviously, there will be a lot of noise. Say, why you don't do it, why you don't do it this industry, this product is service. Why you don't, you know, help me on my demand and so on. But, but for the benefit of the organization, maybe we need to show what could look like in in a very clear vertical first, then we built the foundation using that vertical. And then once we build that achievements and shows that how it can benefits the customers, how it can benefits the organization, and we know how to do it, then we can replicate very quickly. So, so that's one thing. I think discipline, focus. I think that's one the other thing is, um, personally, I think we could have adopted cloud based technology much faster, uh, especially as a bank, I think bank has been very careful. So if you really want to scale up AI, I think it's inevitable that you need to access to technologies that are enabled in the cloud environment, especially when it comes to data analytics. So there'll be the two things, discipline, right? Pick a focus area that we make it leading edge, and then the second one is probably be faster in terms of adopting cloud enabled technology.

Sue Yuin - AIBP: 49:11

Thank you, Dr Siew. And last but not least,

Datuk Megat - Tenaga: 49:13

thank you very much. I think in the last session, I think what Tenaga national would like to actually provide and play a role is is actually beyond the company itself. So what we would like to see happening is that on the national agenda. I think Pedro mentioned a lot about Malaysian I think on the national agenda, probably that the number of things that we can actually promote, I think number one is actually to probably the country has a clear AI objective, a common objective. So is it we want to focus on energy transition for. Example, is it about food process? Food production is about climate resilience. So I think if we have one or two, three good objective for the country as part of the AI objective, then it will actually propel the industry. Second is we know that the bedrock of artificial intelligence is data, so we like to see a framework that curates a data sharing among the companies like like us here, a government's government agency as well as tech players. I think this is key, if the data sharing will be make convenient to all the industry players. The third is, of course, we are looking at the country, how best we promote, AI, with respect to the incentive that the players can have, apart from the so called, the benefit that each is each company can get, there could be some kind of a good incentive for us to play within the within the ecosystem. So what I'm trying to say is that Tenaga would like to promote the ecosystem play of AI and for the country. And I believe this is going to be the best way forward. So everybody has a common objective, has a common data and benefit from the incentive that it may as part of the ecosystem. So lastly, I think Shamsul mentioned about energy and AI. So we are thinking about a tagline, energy for AI. AI for energy, maybe that that be the deadline in the future, because we understand today, the AI element of data center is actually providing the positive growth of electricity industry in this country. So thank you.

Sue Yuin - AIBP: 52:01

Thank you very much. And I like how the talk about Datuk Megat ended off the panel quite aptly. We are all here to kind of collaborate and build up the ecosystem from a technology perspective, innovation perspective, and thank you everyone for your attention. Pass it back to you.

AIBP: 52:18

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