PushYourAdvantage

Harnessing AI for African Innovation with Bramuel Mwalo

• Push Venture Capital • Season 2 • Episode 15

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What if Africa could leapfrog more developed economies by embracing AI? In this episode, we sit down with Bramuel Mwalo, a visionary tech entrepreneur, to discuss the transformative power of artificial intelligence on the continent. Discover Bramuel’s favourite AI tools, including ChatGPT, and how they're revolutionising his work in research, product design, and communication. We explore the unique opportunity Africa has to adopt AI technologies rapidly and the potential for AI to boost productivity and cut costs across various sectors globally.

Bramuel passionately shares insights on addressing critical challenges, particularly in healthcare, by utilising AI to improve access to medical care and streamline processes for community workers. We examine AI's broader applications in agriculture, transportation, and technology, highlighting the ability to enhance data management and reduce manpower needs. Listen as we discuss the significance of effective regulation and the need for African governments to establish frameworks that promote growth while ensuring data privacy.

We take a closer look at innovative AI applications during the COVID-19 pandemic in Kenya and the broader implications for healthcare. Learn about Syni AI, Bramuel’s AI product design studio, which focuses on building AI capacity and developing applications for data analytics. This episode emphasises the necessity for African enterprises to proactively engage with AI technologies to remain competitive on the global stage. Don't miss out on this comprehensive exploration of AI's potential to revolutionise Africa's future!

For more information on Push visit www.push.africa

Speaker 1:

Welcome to the Push Ventures podcast, push your Advantage, and thanks a lot for tuning in. This is an installment of our Push your Partner series of interviews. In our interview series, we're discussing various topics with different contributors to the African tech ecosystem, and today our guest is Ramul Mwalo. He's a tech entrepreneur focused on creating and scaling B2B tech ventures and a firm believer in Africa shaping its own future. He's very passionate about AI and data science applications, and he started a company called CineAI, an AI product design studio that researches use cases of value to Africa's enterprises. Pramod has a degree in business administration and finance. Together, we'll be speaking about artificial intelligence what else could we be talking about? Right and Cine AI? Of course, what we'll be talking about will be some general thoughts on AI AI for Africa and Cine AI and what you're doing there. So, braml, are you ready to be pushed?

Speaker 2:

Yes, let's do it.

Speaker 1:

Wonderful. Let's start with some general thoughts on AI and why AI? Because obviously everyone's talking about it. Every day you see something in the news about it. So if you're thinking about AI, in the current landscape of AI tools in particular maybe also the big ones or maybe small ones that you've seen which one would you say is your favorite tool, and why? For personal or business use?

Speaker 2:

All right. So right now I think that this it's a cliche, but the tool I use mostly is ChargePT. And then I think there are different tools like, for example, my line of work. We do a lot of research, product design, documentation, communication. So I think the tools that, so I think my set of tools is Chelsea PT, then you talk about Jam, mixed panel, and then, of course, right now in the different design tools there are AI modules built within them. So I think for me, tools I use mostly are things that reduce my interaction with people. Like I don't have to call someone to do that for me if I can just do it in my computer with an AI tool. Yes, Okay.

Speaker 1:

So when you're looking at kind of what you're already doing or what you can imagine AI to do for us in the next five years, what do you feel is the biggest promise of AI technology that we will accomplish within the next five years?

Speaker 2:

I think AI means different things to different people, like, for example let me put it in the context of Africa. I have this theory that Africa stands to benefit the most from AI because our economies are not overlaid with development or industry or legacy issues. Most economies did not have a powerful tool like AI when they were developing, so it means their cost of development was significantly high. Their cost of development was significantly high. Part of my thesis is AI can reduce and we've seen that in part of the research we are doing for data analytics. Just by putting AI in your direct output process, it can reduce your cost by 30 to 40 percent. That means then, if Africa consciously takes AI seriously and puts in the framework and the enterprises begin to adopt it faster, it is faster for us to adopt AI than anybody else in the world. It means then we have a competitive advantage to grow faster at a lesser cost than other economies in the world. So that's the advantage that AI brings for Africa economies in the world. So that's the advantage that AI brings for Africa. But for the rest of the world I think it's just the different use cases, because in healthcare we are seeing exponential speed that is coming to that. In terms of agriculture, there are emerging use cases in terms of being used. I haven't seen one that is to scale. There are basically commercial level. I haven't seen one that is to scale. They are basically commercial level, high, high, high, high, high volume production level. Ai that is being used. Climate action yes, we are also working on some things in climate action which are pretty cool. So I think AI has the capacity to just accelerate productivity In everything you look at AI.

Speaker 2:

There's nothing cool about AI. It's just that it just makes production work easier, whether you're in creative video production, video editing video. What AI? Music production, ai come to work, being able to write a piece of document in five minutes. That will take you a week. Ai. So AI's contribution to the world is just accelerates production and reduces the cost of production. So, technically, what you need to do in a week contribution to the world is just accelerates production and reduces the cost of production. So, technically, what you need to do in a week, you can do it in two days, and all that so it doesn't matter which sector like a doctor, if it happens in a hospital, other than having 10 doctors, if we are talking about, 10 years from now, an AI module can do some of those advisory things and all that. So I think AI is the best innovation towards productivity and that's my perspective of what AI would give to the world.

Speaker 1:

I think that's a good perspective.

Speaker 1:

I agree with it quite a bit because I feel that what it does, it gives also people confidence, because you can use AI to evaluate or get another perspective on your own production yes, exactly, and then you can enhance your productivity by doing that, because you can do that quicker, you can get quick feedback, you know.

Speaker 1:

And another thing I really liked of what you said was that how africa actually has the opportunity because of kind of the opportunity to leapfrog. That's something we also talked about in a different podcast, where we felt that one of the advantages, because, when you compare it with a lot of other countries, you said that they have their legacy systems, they have their legacy databases. What they actually have to do oftentimes is just get all that data, prepare it specifically for an AI in order to make it feasible to use an AI, and for us, where we don't have the data, it's actually you could say it's a bad thing. Yes, but at the same time, you can build for the data, so you can build your databases accordingly, so that you can build for AI, so you can use it from the very beginning and have that conscious choice.

Speaker 2:

Yes, yes, and if you look at it from that perspective and you talk of legacy, you see, legacy is not just even systems, legacy is culture, legacy is HR. Let me give a very weird example. So before colonization, we had industries, we had cottage industry, we had our industries as Africans. Right, we never slept hungry. We had trade happening and there was no money. There was battle trade, but things worked.

Speaker 2:

Then there was an introduction of a particular style of living and after the independence, what we knew as our way of life became substandard to what had already been sold. And then there was this culture of work and stuff. So people had to go to school Doesn't mean there was no education, what we call formal education. There was education when somebody was being born. If they were born from a family of warriors, they were educated to be warriors. If they were born from a family of farmers, they were educated to be farmers. For whatever role you played in that community, there was some type of education. Not the type that came from the West, you know, to Africa. There was some type of education of some type of sort, though we know nothing where we had to start, a formal type of education where you have to go to school, class one, class two, class three, for you to become productive in the emerging economy. Then that came. You had an emergence of clerical officers, people who are the kingmakers, like if your file, if you don't talk to a clerk, well, your file disappears, you're in trouble.

Speaker 2:

Then came the age of computers. Then the age of computers. It simply means that, guys, so what will we do with all the clerks? Either they learn how to use a computer or they get fired. Then it came an emergence of a new world, which is computers. Now came the age of automation, where then not just computers do key in things, but inter-relay of data from one person to another and all that interoperability. Then came the age of ERP systems, systems where half the things have to be done within a computer. For you to get a job in any like like, even before you think of a job, you need to know how to use a computer.

Speaker 2:

So I don't think ai and I think people have made a lot of noise, but if you really think in terms of evolution of industry, it's a natural evolution. It's the same way like we were, like assuming we were the ones who are doing cottage industry, and we need to evolve, to start learning. So AI provides this chance. Where, for Africa when I say context and all that, it's not just even technologically wise, where we have legacy systems or legacy issues it's we haven't made a lot of investment in technology yet, because if you're talking about the aggregate investment in technology US against GDP and the aggregate investment of technology in Africa against GDP, we can't lose sleep because of the investments we've already made.

Speaker 2:

So, technically, we can start Culture-wise.

Speaker 2:

We don't have this mad obsession of a particular principle or approach in tech or you know community that can't change its mind about it. Culture-wise, we are very open and even a big part of our population is young. So, technically, if you say today we are training our young people to become AI users and experts, we are the ones with the largest population of young people that can be educated. So, in terms of navigating towards the AI future, africa is a better market than any other market in the world and we don't have the problem of people fighting for their jobs Exactly my point being replaced. Of course, there are not even jobs. No jobs, no jobs. Yes, technically, if we train our people to use AI, then technically, we increase productivity of our people. Yes, so technically for us, I think Africa, speaking from an African perspective, africa stands a chance to benefit most from AI than any other continent, absolutely because we can just leapfrog ahead and we can build everyone and make sure everyone knows how to use these tools in order to be more productive.

Speaker 2:

And the fun part of it is AI is what equalizes the market for everyone, because I don't need to know what an American knows. Ai knows it for me and I can apply it in my problem. I don't need to know what a Chinese knows, ai will apply it for me. So for Africans, for those of us who haven't thought beyond job security, ai levels the playground for everybody, because it takes the best practices of the world, puts it in a computing engine and then it tells you what do you need from me? How you answer that question is up to you.

Speaker 1:

Same with the language processing and so on. You already have the tools where you can. Now, if you travel to another country, you can just put it in your smartphone, you speak into it, it translates. If you have that life in the very moment, you know that will ease border. You know exactly kind of traits because people can easily talk despite having different languages. So, okay, let me ask you one last questions on the general thoughts on ai. What part of that rapid progress that we've seen, particularly in the last one and a half to two? So okay, let me ask you one last question on the general thoughts on AI. What?

Speaker 2:

part of that rapid progress that we've seen, particularly in the last one and a half to two years, has astonished you most, I think, the more I'm impressed at how simple they have made a tool like Chargemiddich and I think for me, in terms of I see the productivity every day and I think most of us see that productivity every day. There are a lot of cool clips in the internet of seeing an ai crawling uh, an ai coded hardware crawling under the door, going into your, picking something, then, like you know, those weird applications of ai. The other one I've seen is in terms of, you know, being able to just take a photo of your face and you have all these stats on your blood pressure, sugar levels, all that stuff. There's an app that does that with almost 90% accuracy, which is pretty much advanced, because you're talking about the application of facial recognition into a very, you know, intricate part of life.

Speaker 1:

And here again, within Africa, we have the advantage where, you know, there are a lot of people who don't have access to doctors. Exactly my point. We can just make regulation to allow for this to happen. We know that there is an error rate right, but having a, you know, 90% accuracy is better than having no doctor, no doctor at all. Yes, yeah.

Speaker 2:

So some of these things just make it easier for and going back to our point of you know we don't have to go through that whole complexity of you know building this heavy hospital equipment. Just by that simple thing, the community worker has more advantage, has become a more resourceful person, you know. So I think those are some of the cool things, because I spend a lot of time in health care. Most of the work we started with Sini, we spent quite a lot of time in health care and building efficiencies there. I've seen quite a lot of you know, AI applications that tend to make it easier for the patient to access care, and that, for me, is more important than even moving it from more than healthcare business to more of the patient-centric care processes. So those have been my cool, cool applications.

Speaker 1:

Let's stick with that, because the next topic, anyways, is AI for Africa. So you said you spend a lot of time in healthcare, but even if not healthcare so this is open for you to answer but where would you really like to see AI solve a particular African challenge or problem that we're facing here?

Speaker 2:

I think Africa has a productivity problem, and productivity in the sense it takes us longer and it's more expensive and time consuming to do anything on the continent, especially from a project. Wise Number one is getting the right people takes you a while to you know, and then accessing the right data takes you a while. Data takes you a while. For example, any project that basically either requires an investment or a developmental investment. A big part of the cost of the project will go into data, collecting data and putting that data together and since making that data before you start doing anything. Now, if this data is made available day one, most of our projects actually become cheaper, just like that. So, from a productivity point of view and part of the things that we are seeing is, if you are able to provide the highly skilled, knowledgeable decision makers, policy makers, with the right data set, all factors considered like there's no political interest, there's nothing, like we are all doing this for the benefit of the people, then technically the quality of ideas, the quality of solutions, will increase. So I think we have many problems and I know you know probably a good answer would be a micro answer in terms of oh, put it in food industry, put it in manufacturing, putting it in transport. All this, if you look at it, if you talk about food, you're talking about productivity. How do you make the farmer more productive? How do you make your transport less expensive, safe and all that? It's productivity, productivity of everything we do.

Speaker 2:

Come back to tech, the technology industry. There's a time I was interviewing us for a CTO role in my organization and I asked one of the AI interviewers how do you accelerate your development using AI? And they said you don't need many engineers. You get AI to build for you basic stuff.

Speaker 2:

So, technically, nowadays, in terms of even our cost of production, in terms of software a big part, a big chunk of the amount of time you spend in developing software there are things AI can actually develop for you and you can put in the QA procedures that need to be put in place to govern it. So, even if it is not good and bad and I think the arguments will come like oh yeah, it's not good or bad and that stuff, the day, every medicine that you consume has been subjected through the journey of it has side effects, but then the regulator is to decide whether this medicine is good for you or not good for you. So, at the end of the day, I think the solution for AI, the biggest use case in AI, is how can AI be applied in the different sectors to increase productivity, and if our people can be empowered to use AI to improve productivity, I think that will enhance them all.

Speaker 1:

I think you've already touched on some parts that I wanted to speak about in the sense of you know, like you're saying, the opportunity for people to leapfrog ahead by baking AI into whatever you do from the beginning, but you also said something about regulation. So now, when you're thinking about those two points, what is your take on the current conversations around AI, my point always being the European Union is great at talking about things they don't understand and regulating them, and when. When you're now thinking about regulation, what do you feel would be for us the right path for growth, given that there's a lot of regulatory bills, there's complexity in the environment and what would you kind of advise let's say, the current governments in Africa how to tackle it, in particular with regards to AI, because there's a lot of aspects about it data privacy, data availability, all these things. What is your take on regulation and what we need to do?

Speaker 2:

Yep, I'll give an example. When the COVID vaccine came to Kenya and finally it made its way to Africa, there was an issue of prioritization of where this vaccine could go to combat the spread and deal, you know, because it was not sufficient. Right now that problem doesn't, but at that spot, moment, that problem existed. Idea of using the cell site towers to simply track the COVID vaccine penetration per population area, for example. A cell site is that tower that you connect to your cell phone so it puts in a population density, so, without knowing who these phones belong, to anonymize the phones. And then, every time you get vaccinated by the COVID vaccine, you are able to. You get a text. You know the text tells the cell phone provider where exactly is the, what you might call it, where exactly is this person? Yes, but then if you take the COVID spread data and lay it on top of this cell phone data that you have in terms of you mark your cell phones red for those that have not been vaccinated and you mark the cell phones green for those who have been vaccinated, Take the COVID spread data With almost accuracy, you will know your high risk areas and your low risk areas and you'll even know when to move the vaccine efficiently and where to promote more people to get in there. Very fantastic product worked. There was an AI model there that basically makes the process faster in terms of learning and even forecasting and projecting with almost accuracy in terms of spread Fantastic technology. But this technology is not technology that you plan to use five years down the line. It's a spot, a piece of innovation that helps resolve a spot problem. Now, okay, who's the person who can use this technology? It's Ministry of Health, but you walk to Ministry of Health, they do not have the procurement structure or the partnership structure of that type of technology. So, with all the problems that are going on, this technology never saw the light of day. Because right now it is irrelevant, because the COVID proposal is not there Now.

Speaker 2:

I think right now and we are always very quick to be either anti-government, anti-regulation and stuff the government is run by a set of policy, a set of rules and a set of standards that have been put, so you can't think you have to comply with certain things. So I think right now is a time for leaders, decision makers, policy makers and stuff to educate themselves as fast as possible, and I think it is a mutual benefit thing, where I forget the eu. I think for us, we have the benefit because we don't have a lot of noise, we don't know it, we've not been doing it, we are not attached to a particular direction, there is no investment benefit or, you know, influence or prejudice to it, so it's a pretty clean slate. So it allows us to be objective about it. So I think I would basically just encourage the people involved in either governing or building the right policy or, you know, the right incubation environment for AI. I think there's an opportunity for them to educate themselves on how they can enable it and govern it, because I think for me, I'm a big believer, like there's a program we've launched through Cineai. The program is simply we are calling it Lead AI Africa. We build technology, ai technology in terms of application of AI technology and then we get enterprises to work with this technology.

Speaker 2:

60% of our business development process is educating the decision makers on what AI is and how to govern it and how to use it.

Speaker 2:

So we came up with this program, lead AI, which technically is to help these executives, either in government, in corporate, and their shareholders and the policymakers, to understand the context of AI that we are dealing with in our country and in the different sectors.

Speaker 2:

I feel like once we educate and I think that's always the first right now, I think our age of you know, shaping of AI is education. We need to educate ourselves from a leadership position, from a you know consumer position, from an entrepreneur position, not just to jump into the bandwagon because it's cool, with a lot of marketing that the big tech is putting in it. Of course, you will not think there's anything else in this life other than AI, because there's a lot of marketing being put around AI, a lot of PR being put around AI. As much as AI is good, there are gray areas that need to be dealt with and I think the only way we can be objective about this process is is educating ourselves of the technology, of the scenarios, of the potentials, of the risks, of the interventions and sometimes interventions. We may not know them for sure, but learning allows you to evolve with the journey. So I think that would be my perspective.

Speaker 1:

I think you made a great point about the gray areas because in the end you know as much as we're now seeing like how great it would be for healthcare, for example one of the big drawbacks is what we call that AI black box, right? Because yeah, sure, the AI will give you an output after you prompt it, but you don't know how it came up with it. And when it comes to healthcare, you want to have at least an understanding of the reasoning that was kind of put in place in order to for example, prescribe some medicine, because you're giving people medicine.

Speaker 1:

It's not just like saying, hey, here's something you had nothing before, but at the same time you want to have accountability built in. That's one of the drawbacks. Right now, right.

Speaker 2:

Yep. So if you think about a journey like that, so, for example, if you say AI in healthcare and you say no, you cannot apply AI in healthcare at all. Why? Because of data security and personal data and all that stuff. So you don't know what's happening in that black box. But then if you go to the detail, if you talk about healthcare, you're talking about disease control. Disease control has nothing to do with personal data. You're just simply understanding the trends, the disease pattern, and forecasting or predicting behavior, which, technically, is not out of this world.

Speaker 2:

Ai is basic level Clinical operations. Clinical operations in the sense of just understanding the economies of management, of provision of care and then, once you understand these economies, you're able to provide better care. Then you evolve into, you know, diagnosis. Diagnosis now where it becomes oh, I have to share a lot of data about myself that it's probably. We are not ready for that, but you see, we've already applied AI in two very important areas before we went to diagnosis. And then you go to in diagnosis. There are like three buckets where AI can be applied In terms of lab diagnosis, x-ray imaging diagnosis, whatever, and as we go on and on and stuff. Okay, fair enough, we figured that out. You go into now application of robotics in terms of surgery and care and all that stuff.

Speaker 2:

So, before we go to the extremities of some of these innovations, I think there are simpler and that's why I say like in Africa, it's a clean slate. Like, for example, I don't need to hire 10,000 data scientists or engineers to crunch data for me, I just need to collect data. That's the only investment I make and the AI basically becomes my intelligence module, which, technically, is not people's data, it's operational data. How many people went to hospital today? How many had malaria? How many did consume? That data provide for me context, recommendations, ideas, highlights, whatever on the spot. So those are some of the in terms of the hierarchy of applications. That is a good way of understanding on how to manage the direction towards which AI is taking.

Speaker 1:

So let's talk a bit about Cine AI. How would you explain what Cine AI is and what do you mean with AI? In the case of Cine AI, you've already hinted at that right now. Basically, right, you're not really building your. If I understood correctly, you're not building your own framework or LLM, right? So explain to us. What is it you're truly doing, which you've mentioned to some extent with taking the data and then utilizing existing ai already, and how do you make it simple to use for some of the, let's say, leaders?

Speaker 1:

you into that we need to educate themselves because they want information. What is it that you do? How would you, how would you describe how you're utilizing AI in order to create value?

Speaker 2:

Okay, so Cineai is an AI product design studio. So what we do is we do it in three ways. One is capacity building for executives. These are the decision makers who either are the ones responsible for investing and making decisions in their organizations to invest in technology, or policymakers these are people who write the rules that govern an operating environment yes, so we educate them on AI and we cover the basics of what AI is. We expose them to the different technologies in the world and the risks for all of these things and we provide, we expose them to governance framework that come with it, and then we also expose them to innovation frameworks that they could adopt within the organizations to promote safe adoption of artificial intelligence without amassing risk that comes with use of AI. That's one.

Speaker 2:

Number two, we do assessment and advisory. So, for example, we come to an organization. We basically assess your technology infrastructure and tell you are you AI ready and if you want to use AI, what are some of the technical and untechnical investments you need to make to basically become an enterprise that uses AI as its operating environment? The consulting aspect of networking, making sure that companies are ready to use existing AIs yes. And then the third one is we have developed our own AI application for data analytics and you're building that on. Yes, no, that's our own, we're building our own code, so we're using different LLMs. So it depends. We are not engineering our own LLMs, because they're already existing, either open source or direct. So, depending on the problem you're solving, which ones are you using Mostly? The one that we've used most is the LAMI, the Facebook one, the open source, the open source one, the open source one, which is really good. There are some scenarios, especially customer engagement and general knowledge insights. We use the ChartGPT open source, so not ChartGPT APIs which work, and probably three more.

Speaker 2:

What we are currently researching is is there a low-cost approach? Because the battle for AI is not the innovation of LLMs, but the LLMs are just a small part of artificial intelligence, because it is what is mostly used for generative AI, and then there are other models that can do different things in it. But now on the topic of LLMs, the big question is not invention of an LLM model. The big question is the architecture of application. It is not sustainable for me to use LGBT, because technically you're going to pay thousands of dollars every month because if all of us decide to chat with you. You can't afford it like it's it's way expensive. So the big question right now, especially at enterprise level, is what architectures, what uh frameworks should I adapt to basically make some of the already existing llms? It's a cheaper conversation to have than you building your own uh models, which is expensive and we don't have the compute power on the continent to basically match what's being developed out there, okay, so let me phrase my last question the right way.

Speaker 1:

Now, when you're thinking about African enterprises, what would, from your perspective, the main goal to create better adoption on that enterprise level, and how tangible do you believe it is? And if you now, if they were to do it in that way that you idealize, what does the leapfrogging of African enterprises look like from your perspective?

Speaker 2:

Yeah, one weakness we have as a continent is we wait to see what happens. Then we do it Now. The problem with that is, by the time you wait and see, you are not on the table because the rules have already been set. I think it is important for Africa. I know we don't know as much. I know there hasn't been a lot of investment in it, either by the government or by private sector. I think it is time for us to creatively and I'm using that word intentionally, creatively figure out and do what we need to do with air and the reason. If you notice, I'm not telling you that there's a specific direction of how Africans should approach. No, I think it's more of us making up our mind that we are going to do this, but we're going to learn how to do it. But we are doing something. We are not waiting to see what Microsoft comes up. We are not waiting to see what Google comes up with the use cases that we have, for example.

Speaker 2:

Right now, the climate change is something we are feeling, although it's a thing that became popular the last two years in Africa. It's not a thing we say it's a problem of the West, but right now it's becoming very common. Farmers can't really know when to plant, and then most of our farmers are subsistence, smallholder farmers. Farmers are subsistence, no smallholder farmers. If I'm going to put my 100,000 shillings every month to buy seeds and plant, I need to at least know when the rain is coming. That, right now, is a big problem, even just providing every farmer some type of a rain notification system where they get a text telling them for your geographical area and stuff. But yeah, I can make that possible. Of course. You just need to work with astrology, come up with all that. You know those are very basic use cases. And then you're using simple and it does not require the advanced charge GPT AI type of work. That's a supervised learning model that can be built. They're modeling on stuff.

Speaker 2:

So you see, there are very simple use cases that we can learn from, at least when the conversation is at a point where we are getting advanced in this conversation we have some bit of learning of how these things work for us in terms of you know, even just hospital as much as sometimes we. For example, in the US you can't just wake up and go to the hospital, it's this whole rigid process. Where in Africa we don't have that problem. And if there are no medicines in hospital in, let's say, level four, level three, you can at least go to a hospital and you'll be told your problem and then you go look for medicine. It's 40% less of a problem. And then if you apply AI in that scenario, then it simply means there are certain things in our context we can solve with very basic AI applications that don't require us to make those massive investments that basically either the US is making, the UK is making, china is making. There are just some basic things we can learn to do.

Speaker 2:

All I'm saying is let's not wait and see, let's start where we can with what we have. I think that is the best advantage Africa can have. On top of that is education, education, education. Educate your leaders, educate your young population, educate your older population. Let people be inspired towards the future of AI, not to fear it because it's coming anyway. So, in essence, those two things. If we intentionally decide from where we are, we may not have the US level of AI advancement, because you're talking about stuff that are happening even in national security and defense, that deep, really sophisticated stuff, sophisticated stuff. But for us you don't need that stuff, just need to start where we are and educate our generation to simply and I think how you said that, because there was also this amazon storefront vacation, remember.

Speaker 1:

But they all said ai is happening here, but what it was was actually in somebody in india sitting here checking them out. Okay, I'm going to do a summary of talking points. My very best to make sure that I summarize what we just spoke about. First, we mentioned that there's a potential to deep foot ahead utilizing AI, and AI can create new jobs focusing on AI from the beginning. So, rather than thinking of it as a threat, we think about what can it do to help us, and data availability and the ability to access information easily and then execute and improve our outcomes is something that AI can truly do for everyone on the continent, at least everyone with access to AI.

Speaker 2:

Yes, and Eduardo, that point is if you're a CEO right now who is thinking? We did this study four out of five CEOs in Africa. We interviewed about 250 CEOs from about five countries. Four out of five CEOs on the continent know and want to use AI. All of them have a sense of what it can do, but they don't know how to scale it within the organization. So if you look at it that way, then if we can build that capacity and look at AI as an inspiration, not as a risk, then we stand a chance.

Speaker 1:

So AI for me is more of an inspiration, which brings us to the next point of my summary of talking points, which is that leaders should educate themselves.

Speaker 1:

Yes, so they're better to weigh the benefit versus the drawback as well.

Speaker 1:

That counts for companies as much as the government, because sometimes the benefit simply outweighs the drawback.

Speaker 1:

When it comes to data privacy, for example, and the architecture of the application, we have to look at how do we create value utilizing existing elements. We have to look at the cost versus the benefits, and what we have to tackle is the lacking productivity on the continental level. Yes, so we have to creatively figure out how we use this technology for our needs in order to solve for our requirements. And one thing I drew out from the conversation was also that we have to check whether we truly need a fully fledged AI or simply some smart data analysis and the tools which can then automate, based upon the information that we can gather from that, exactly In order to to do something good for us on a continent. So we're basically, like you said, starting where we are with what we have educate, educate, educate. We inspire people and we build solutions which benefit us, and they don't need to be the next huge ai they just need to be built for us and solve for our problems.

Speaker 1:

Perfect, I'm getting out to add from your perspective, pamela.

Speaker 2:

No, let's keep talking about it. It's a broad conversation and I think my goal and my passion is I am open to share a lot of the research we are having at Cineai. We are open to have sessions where we can walk through some of the journeys, like our current clients. What we are helping them do is to build the capacity in the organization to understand, use or design their roadmaps of UI AI use cases. That's simply where we are focused on, simply where we are focused on um. We've seen that once we do that, selling becomes it. Uh, because unless you do that, the company will run out, and that's done.

Speaker 1:

So, then, thank you very much, and to all the listeners, thank you very much for listening in. You can find our presence on youtube, instagram and facebook as push venture capital, and on x or twitter, depending. Which you prefer is hashtag. Push your advantage and we'll be back soon with another podcast on the african technology ecosystem, which is available on spotify, apple music and africpods and, of course, on youtube. Thank you, thank you, thank you.

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