1893 – Navigating the Entrepreneurial Space with Sani Abdul-Jabbar

Josh (00:00:04) - Hey there, thoughtful listener. Would you like consistent and predictable sales activity with no spam and no ads? I'll teach you step by step how to do this, particularly if you're an agency owner, consultant, coach, or B2B service provider. What I teach has worked for me for more than 15 years and has helped me create more than $10 million in revenue. Just head to up my influence. Com and watch my free class on how to create endless high ticket sales appointments. You can even chat with me live and I'll see and reply to your messages. Also, don't forget the thoughtful entrepreneur is always looking for guests. Go to up my influence. Com and click on podcast. We'd love to have you. With us right now it's Sani. Abdul Jabbar. Sani, you are the founder and CEO of VezTek USA. Your website is VezTek USA.com. That's VezTek usa.com. Sani, thank you so much for joining us.

Sani (00:01:16) - Thanks for having me. It's a pleasure.

Josh (00:01:18) - Yeah. So give us an overview of the work that you do with VezTek USA and who you serve and the impact you have in the world.

Sani (00:01:24) - What we do at West Tech, it's a IT consulting firm that I founded in 2006. It's an emerging technologies company that provides two services. One is advisory, the other is hands on software development for mid-sized to large scale corporations, as well as government agencies, primarily in the United States. Emerging technology, as you know, the definition of that changes very frequently. It used to change in 2 to 3 years. Now the definition changes about every 14, 15 months. And, as you can imagine, currently it's artificial intelligence, machine learning, blockchain. And so that's all the hype and the real stuff too. There's some really interesting stuff going on in bringing these technologies into legacy systems and legacy corporations without causing any disruption. So despite all the fun stories that you hear in the media, some good, some not so good, the promise of AI and these emerging technologies, it cannot be denied. The magic happens when we don't throw away all the old tech and old means legacy, everything that organizations have been running for decades, how to mindfully and seamlessly bringing these new tech emerging technologies into that old, older infrastructure and evolving that, transforming that over time seamlessly without causing disruption.

Sani (00:02:40) - That's where we shine.

Josh (00:02:42) - Yeah. Well, I think this this addresses a pretty big issue, right? So if I've got a legacy solution and we know we need to move quickly here, there's, you know, if we're not adopting AI into our current processes, if someone suddenly tries to do, let's say an organization just tries to get this done on their own, what might the consequences be? Or where might they potentially miss an opportunity or, you know, really kind of mess it up?

Sani (00:03:17) - So we had a study done last year, late last year of mid to large sized corporations in the US. And the question was there were several questions, all focused on the AI adoption in those companies. Over 78% of the business leaders, they reported that they believe their organizations will be transformed within the next three years by AI, about 80% of them. They reported that they believe within the next ten years, if they do not adopt these emerging technologies, they will not exist. Only 4%. As a matter of fact, less than 4% reported that they are putting in any significant resources into building sustainable AI power tools for their organizations and bringing AI into their organization significantly, with any any significant investment.

Sani (00:04:04) - Most of them were more sort of focused on using AI for today, the benefits of AI today, but not really thinking of it as a competitive advantage. And what I mean by that is, if you and I both have access to any AI tool, pick ChatGPT the most popular one, right? So Josh has access to it and he has access to it, but Josh has no competitive advantage over Sony. Sony has no competitive advantage over Josh because we both paid $20 a month. We both have access. Big deal. However, if we took all of Josh's previous conversations and writings and emails and articles and public speaking up all of that, and we trained a custom GPT model on that, and then we said, now help Josh become, you know, his helper or assistant and help him. Now Josh has a competitive advantage over Sony because Sony has no access to that data. Right? That's what I mean when I say organizations are focused on getting advantages or trying to find advantages of using AI today, but not building it in a sustainable manner for the future, where they would create their own custom tools, use their custom data to train those tools.

Sani (00:05:14) - That's where competitive advantage comes from. so your question was, what's the hurdle? What's the fear? Fear is the hurdle. So the difference between 78 to 80% of the leaders saying that we're not going to exist in ten years if you don't use AI, or our businesses are going to get impacted in three years, but only 4% actually significantly investing in it. And the reason for that, when I ask was fear. Fear of disruption, fear of making a mistake, fear of not knowing, fear of something new. And the solution of all of these fears is pretty simple. You know, talk to someone like me, someone at my organizations and other people in the industry who do this, you know, all day long looking into new tools, new technologies and and finding answers to the question how to. Bring these tech into legacy systems seamlessly. That's where, as I shared, that's where the magic happens. That's the simple solution. It's the fear that's keeping the leaders from committing to to to these these new tech.

Josh (00:06:10) - Oh, sure. Yeah. I mean, we don't obviously we don't want to make a mistake. We don't want to, you know, cannibalize, you know, existing clients or systems or, you know, make mistakes there obviously. And then I think there's also this fear that we might invest a lot of resources over the next handful of months or six months or whatever. And by the time we get there, it's already outdated. Tech like that's a concern like that is a concern, you know, just kind of dealing with the how and when do we jump in and how do we do this knowing that, you know, all this work might be for naught if we get to a point six months from now and like people don't want that. There's way better technology today.

Sani (00:06:52) - Yes. So that's analysis paralysis. The opposite of that is a request that I get quite frequently to frequently, we want to bring AI and we're going to replace all our systems. And can you do it in two months? No, no and no.

Sani (00:07:09) - First of all, you don't want to replace all your systems. You don't want to blindly just, you know, throw a baby out with the bathwater. You know, you don't want to destroy what works. You don't want to cause disruption in your processes that you have built over the years. That's your asset for your organization. You don't want to do all any of that, and we can do it in two months. It's going to take time. First, we have to understand where there's low hanging fruit in your organization, where there are opportunities, which projects are likely to bring the highest ROI for your organization. So we have to study all that. It goes back to the basics. Anyone in any sort of product development understands that. You start with the need. You start with a pain point. You don't start with a solution and then go out looking for the pain point. Right solution. Looking for problem doesn't work. You have to find the problem and then match that with a solution.

Sani (00:07:58) - And in it's a long conversation, but in very short, like how do you find the best projects? Where's the low hanging fruit in organizations? In most organizations? Look, I does not work without data. You need data. And that's the reason organizations and parts of organizations or industries or parts of industries that have the highest amount of reliable data, that's where you'll see the low hanging fruit for AI adoption. Yeah. An example of that is your financial services, your healthcare, industry wise and inside companies, where do you have the cleanest data finance, accounting, sales and then everything else?

Josh (00:08:36) - Yeah, I bet that you've seen some pretty exciting adaptations of AI enabled tools. I, you know, AP, you know, just some of these APIs that we can kind of start plugging into. Anything come to mind that you've, you know, practicing or helped out with over the past six months? You're like, this is cool. Let me tell you what we figured out. And anything immediately come to mind.

Sani (00:09:01) - Is there a question about tools or about use cases? No, no.

Josh (00:09:04) - No use cases, like so say, you know, a client was trying to solve a problem and they were able to adapt, you know, new tech. And then the outcome was, you know, ended up being huge for their customers or for their model.

Sani (00:09:19) - Yeah, absolutely. so we have a situation where the client or organization, they got sued by somebody, they are in the healthcare industry and the data is private. And the question was about their ethics and practices over the use of that data. Some drug trials data, for example, they were accused of messing with the data and misrepresenting it. They were accused of disclosing some of the information that they shouldn't have. And the people who who sued this organization, they knew that the issue is not proving right or wrong. The issue is going through this mounds of data, tons of data, and then looking at everything and looking at, you know, how different, trial data was used or how different pieces of information was reported or disclosed.

Sani (00:10:03) - that could have been a nightmare without AI tools with with machine learning tools, we were able to create, machine learning models that were able to quickly go through this year's worth of very, very large amount of data, set sets, multiple sets, and provide enough information for the lawyers to then take it to the court and say here, proof that we did not misrepresent information, we did not falsify results of drug tests and so on. So that that was a tremendous success story there. And other than that, oh, a foreign country in the Middle East, they are looking to use their, historical data from the last 50 years to plan for the next 50 years. And the challenge in that country is that not all the data is digitized. A lot of that is physical copies paper, not even in English language. So we had to first understand. Well, the first challenge was how to get that data into computer systems. Then to understand making sure that systems can actually read it. It's not just a matter of scanning the document and having PDFs.

Sani (00:11:07) - You need to be able to read it. And then the next issue or challenge was then to look at the trends and patterns, and then using those patterns to offer insights so that you can plan for the next 50 to 100 years. that took some time to do, especially on the first step where we had to get all that information into computer systems and be able to read that foreign language, Arabic language, in this case, but eventually, it happened. And and that was exciting. And there's a long list, of other projects like that.

Josh (00:11:37) - Yeah. you likely saw any kind of keep your ear to the track on what may be coming around the, the corner for us. in terms of new tech, I mean, to me, it feels like we're absolutely living in, you know, Moore's Law, you know, just this accelerating, just excitement of, like, new stuff. And I feel like, you know, I'm learning about stuff when everybody else does, basically when it hits the news.

Josh (00:12:05) - And I'm curious if there's anything that you're keeping your eyes on that maybe isn't necessarily yet in the public consciousness that you're like, yeah, you think this is cool? Wait till, you know, we finally catch up with X, Y, or Z. Are there any emerging technologies that that are really exciting for you, or that just kind of a matter of time before we start to see this?

Sani (00:12:28) - Well, the emerging technologies that I find exciting may not be very exciting for general public. Yeah. Okay. I'm a nerd.

Josh (00:12:35) - Because, you know, of the potential impact that that that causes. No, this is actually a big deal. yeah. So I'm interested in.

Sani (00:12:43) - Your big deal. What we see right now, this ChatGPT so far. Because that's what most people think about when they think AI. Right? They're not thinking anything. anything beyond that. This is all fun and games, if you think about it. It has done a lot. ChatGPT is a powerful tool. Not to say that it's not.

Sani (00:13:00) - It is, no question about it. But the promise of AI and the possibilities of AI and machine learning tools, that's just tremendous. We haven't even scratched the surface yet, so to speak. With it has come some challenges. You have you have heard about digital doubles, and if you use a digital double for nefarious purposes, then we call it deepfake. You have heard stories. Bank heist taking place in Hong Kong. There was a bank heist in February, $5 million. It wasn't a bank heist, sorry, an international corporation. And they paid out $5 million, as a result of a deepfake, powered scam. And there's a lot of other bad stuff going on. So data privacy, that's important. Cybersecurity, finding ways to reliably detect what's AI driven, what's real, what's not, what's authentic, what's not, what's forgery, that kind of stuff. That's very important to me, because if I cannot come to you with confidence and say that, you know the content that I'm presenting to you, Josh, this is authentic, real content.

Sani (00:14:02) - If I can prove that you're not going to trust me, you're not going to play with me. Yeah, right. So that's important. Then the regulation side, that's very important because unless we have rules of engagement, I cannot take this tech to large corporations or governments and say, you know, use this tech because there's no rules of engagement. What's the expectation? EU approved a set of laws, I think about a couple of weeks ago, and that's like the first comprehensive set of laws anywhere on the planet, and it's pretty well done. My concern with the rules is that if regulation is not done mindfully, it can hinder the progress of development. So as long as there's mindful regulation, we need that now. Also think outside outside tech, outside AI. When any technology evolves with that, an ecosystem evolves around it. Right. I was in a meeting with Microsoft a couple of months ago, and they presented a data point, which I found fascinating. They said that in 20 this was 2023.

Sani (00:15:00) - And they said in 2023, 3% of the world electricity production was being used by emerging tech, your blockchain, AI, that kind of stuff. They said by the end of 2024, the estimate was that 8% of the world electricity production will go into running those emerging technologies. That's not sustainable. So now you have to find ways to produce energy cheaply and in abundance. That's amazing. We can right. So with so that happens then your you you mentioned Moore's Law earlier. Tech is evolving. We can throw more and more computer power on it more and more data on it. So now your decision making power is evolving. If you look at five years, ten years ahead, what's happening is that Josh can think of any crazy idea and just say to AI, hey, I have this idea. Test it in 50 different ways, and within moments it'll test it because energy is cheap and tech is G, right? Imagine if we can have abundant energy, cheap energy, and abundant computation power. Where can we go?

Josh (00:16:02) - Yeah, let's get AI working on those ones.

Josh (00:16:04) - I love it and I know it's being done, sonny boy. Our time has gone by so quickly VezTek USA usa.com. Who should be reaching out? Like what might be a good indicator that they're really ready to have a serious conversation with you. based upon some things that they may be experiencing within their organization.

Sani (00:16:24) - any organization that has large amounts of clean data or even semi clean data, they are good candidates for these technologies. Anyone looking to bring emerging tech seamlessly into their legacy systems, who are people who are concerned the issue is not people are convinced already. The leaders already understand that emerging tech is needed, is required, or that other than that, their organizations stand no chance against that competition. The issue is where to start and where to start is talking to us just, you know, give us a call and tell us what do you have in mind, the answers that you need to or the questions that you need to ask yourself are the same traditional questions we ask every time we think about digital transformation, or we think about product development.

Sani (00:17:05) - Do I understand the need? Do I have data? Do I have people subject matter experts in those areas? Right. So you bring these and resources, right. You bring these 3 or 4 things together. That's when transformation happened. That's when products get developed. That's when, you know, you bring in bring new technology as competitive advantage into your organizations.

Josh (00:17:24) - Yeah. Your website. Best tech. USA. Com to our friend that's been listening to our conversation, what would you recommend their next steps be?

Sani (00:17:32) - the easiest way to connect with me is via LinkedIn. Just my name Sani Sani. And, connect with me on LinkedIn. Talk to me. Tell me you know what you're thinking. And if you have if you already have any ideas how these these technologies can be bring into your organization. Good. We can bring advisory as well as hands on development help. And if you need just to explore ideas, what can be done? Again, you know we're happy to help. Happy to talk.

Josh (00:17:59) - Excellent. Sonia Abdul-Jabbar, again founder CEO of VTech USA, found on the web at VTech usa.com. Sandi, thank you so much for joining us.

Sani (00:18:11) - It's been a pleasure.

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