Leveraging AI

32 | A Roadmap to AI Business Impact - Defining and executing successful AI projects that drive business results, with Khalifa Manaa CEO of Shapr

October 03, 2023 Isar Meitis and Khalifa Manaa Season 1 Episode 32
Leveraging AI
32 | A Roadmap to AI Business Impact - Defining and executing successful AI projects that drive business results, with Khalifa Manaa CEO of Shapr
Show Notes Transcript

Could AI transform your business practically overnight? Learn how one company helped a bank slash client onboarding from 120 days to instant in this episode.

In this episode, Isar Meitis interviews serial entrepreneur Khalifa Manaa, CEO of AI transformation company Shaper, about how to successfully implement high-impact AI projects.

Khalifa Manaa, Founder of Shapr.xyz, shares a step-by-step framework for identifying the right AI opportunities, ideating solutions, and executing pilots quickly. He provides a real example of how they helped a bank reduce corporate client onboarding from 120 days to instant account access by leveraging large language models.

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Isar Meitis:

Hello and welcome to Leveraging AI. This is Isar Meitis, your host, and in today's show, we are going to explore one of the most critical questions that business leaders are asking themselves today, which is how to pick the right. AI project and how to successfully execute on them. And we're going to do this with the help of Khalifa Mana, who is the CEO of Shaper, a company that helps businesses from different industries in different sizes, select and implement AI solutions to improve their businesses and improve business results. At the end of the episode, I'm going to share news from this week. There are a lot of really important news this week from various aspects of the AI world. This week's show is brought to you by Multiply AI. Multiply is spelled M U L T P L A I. So just like multiply, but instead of Y in the end, AI in the end. And if you go to multiply. ai, you will be able to find any kind of education you want about AI for your business from this podcast through courses and in company consulting and training and so on. So go and check it out. M U L T I P L A I. AI forward slash services. If you want to see everything that they have available. But now let's dive straight into how to pick the right AI projects and how to successfully implement them to achieve better business results. Hello and welcome to Leveraging ai. This is Isar Metis, your host, and I've got a really exciting show for you today. Everyone now, literally every business leader out there right now, or at least everybody that hasn't been hiding a rock for the last eight months is trying to run AI project in their business right now. Why? Because everybody feels it's gonna give them a benefit and an edge over the competition if you're gonna do it faster than you can get an edge before your competition does the same stuff, and it's true. The problem is very few people actually know what the hell that means to run an AI project for their business, and even less people know how to actually do it right. Our guest today, Khalifa Mana, is one of those people that actually knows how to run this project. Why? Because that's what he has been doing in his company Shaper. He's helping businesses define and launch successful AI projects and products and. It's not my surprise that he is doing it because Khalifa is a serial entrepreneur himself. His main expertise is product launches, like tech product launches. So he for years has been in the business of defining projects correctly, launching them successfully, and applying different processes and systems and mindset in order to make that successful. All he's doing today is he built a business that does that in launching AI projects, and he calls it Running Impact first AI projects. So that's what we're going to talk about today. We're going to talk about how to define launch and run successful impact first AI projects, which as I mentioned, is probably one of the most talked about boardroom and c-suite, topics in the world today. And hence, I'm really excited and humbled to have Khalifa as a guest to the show. Khalifa, welcome to leveraging ai.

Khalifa Manaa:

Awesome. Thank you. super happy to be here. I love the pod. I listen to, almost every episode. I've skipped a few. I'll go back and listen to them. and like you said, it's a very interesting topic because after all this hype around chat g pt, everybody's thinking about, okay, how do we add this AI to our business? And like you said, there's plenty of ways of doing this. Some of them lead to, successful end results, some don't. So that's what we're going to be talking about today.

Isar Meitis:

I love that I, and you and I had several conversation already and I know you have a very well-defined process and well-defined strategy, but let's really define first what is an impact First AI project.

Khalifa Manaa:

Yeah. Okay. let's start with that. This is very, something that is close to my heart. When we started Shaper, we actually had, we defined four principles.'cause I tend to believe that, for you to build the right kind of company, the company where you want to be and you can be proud of, you have to define what that means. What are the core values? The first of those four impacts is impact first, what this means is that it, we say no to projects where we simply don't see an end result that will create an impact. And what I mean with impact is basically, significantly improved customer experience or growth in revenue or cost savings In most cases. We want it to be a number. And so when we, I'll walk you through the process in a bit, but one key part of this process is after doing ideation, what's possible? We turn this into these idea cards and we set a number of potential unlocked value to all of them. This could be something like, a process becomes significantly faster, or it could be that we aim to increase revenues by 20%. It could be that we aim to win market share in a specific segment or something like this. But when impact first, by the way, it can be also some, a positive impact for human beings or the earth. I'm very happy we're, exploring something with W F, which could be,'cause I'm a kind of a sucker for nature, so I'm very happy about that. So in that case, the impact is slightly different. But even there, we set the A number to it. So something that we can measure and something that, ideally will help the company to end up where they want to be in five to 10 years. So it is a meaningful metric that we can measure. we set the goal before and then we can see how much impact were we actually

Isar Meitis:

able to create. Awesome. So basically it's just good strategy and good project management and instead of saying, oh, there's this cool AI tech, let's find a way to implement it. You do it the other way around. You're saying, I assume the first question you ask is, okay. Where, going back to what you just said, where do you want the company to be in three to five or 10 years? Yeah. what can get you there? What bottlenecks exist today? And then you look for the right AI implementation to go there. Is this roughly the process? Yeah. Yeah. It's,

Khalifa Manaa:

and, since, last November, a lot of, a lot of companies are curious into how do we attach this AI somewhere? And it's usually they've seen someone else do something with Chachi vt. Now let's do this, let's do the same thing, which in some cases could actually make sense. but in most cases, maybe not. So our process starts exactly like you said. We start always from top down. So we want to start with, typically our process is five days. it's roughly based on, in my previous life I used to do design sprints, which is a Google Ventures five day innovation format. Amazing by the way, as a side note, this is a bit off topic, but we're looking into doing AI design sprints in product cases where we have a challenge in mind that we want to solve. Basically doing ideation with our G P T or G P T four, testing ideas with AI and so on. Just making it significantly faster and less biased.'cause usually people we fall in love with ideas. Okay. But, so our five day process usually always start with, we gather around the core team for this sprint, and then it's led by the leadership of the company. And it varies. In some cases, this could be the c e o, sometimes it's the transformational officer, sometimes it's the digital officer. But someone from high a top kinda communicates to us first clearly, where do we want to take this company? What do we want to, in an ideal world, what would happen in three years or five years? And this kind of gives us an idea of where do we want to go? And then we start to ask questions of, what's preventing from that happening? and we tend to do it, the, on the first day of this process, we have this session. So we have the core team, we have the leadership. It's very important to have kind of the. Leadership and ideally a champion who can then drive things forward, and have them all in the same room, in, in the beginning. So I

Isar Meitis:

wanna pause you for one second. Who else is on the team? I agree with you a hundred percent. You always want like a C-suite buy-in because then there's somebody who can put his stamp of approval on everything that was said, and represent the vision of the company. But who else sits in that room?

Khalifa Manaa:

Yeah. usually we want to have someone from product, ideally someone looking at the offering level. not a product manager, someone from customer service, sales, it, marketing operations. Basically the key segments that we want, we might look into. Okay, there's some variation.'cause in some cases, companies have already that this is the area where we want to look into, where we want to find challenges or growth. Then, of course the team could be smaller and slightly different. But in an ideal case, what we really want to do is look at it holistically. Let's just look where do we want to take this company? and then look at how could we make that happen. So these are usually the people we want to have in the team. The activities that we usually do are in this first session. They're design thinking tools. they're interviews with other people, making notes of how might we, and this all just is fuel for ideation later on. this usually takes half a day and then another half a day to do documentation. to give you an, an idea of what this could mean, in a banking customer. this ended up being that they want to kind transform their. In a way, their brand image to be, they've been, they've had a very traditional image. They want to freshen it up. they were planning to do a kind of a rebranding, like a facelift for their brand, and they want to be seen as more digital, ready. and then they had, in their wholesaler business banking, they wanted to increase their, market share there. So this was an outcome of where would they want to be in five years? They want to be seen as a digital modern bank, and they would want to increase their, market share in

Isar Meitis:

that. So this is awesome because this leads to a lot of follow-up questions because this doesn't sound like an AI project, right? This sounds like a traditional business strategy decisions of this is the decision we want to take the company. How do you turn that into. An AI process. what are the follow-up questions a company needs to ask itself?'cause this is a perfect example because it doesn't sound like an AI kind of thing. And yet I know you've done this project, so let's continue from here. What are the follow-up questions you need to ask yourself as a company in order to define A, whether this is an AI project, and B, if it is what the project looks like?

Khalifa Manaa:

Yeah. that's by the way, a really critical point. A big chunk of this process is not ai. Of course. we, and I haven't found a way to get to a good outcome by skipping this, like without doing this part. it's just, The other roads seem to lead to a solution first thing, and then we fall in love with the solution and the technology. We forget about why we're doing this and usually no impact. So a key part of making sure that the impact happens is going through this route. Absolutely. Have decided. I,

Isar Meitis:

by the way, I agree with you 100%. I do the same thing. So I, in addition to the podcast and the courses and everything, I do consulting for businesses about how to implement ai, and it's the same exact thing. The beginning is traditional business consulting is let me understand the business, let me understand where you're struggling. Let me understand your bottleneck. Let me understand what you're trying to go, let me understand what resources you have, what resources you're missing, what are the gaps? And then so I agree with you 100%. And I think. That's probably the biggest problem companies have today when they come to implement AI is exactly this, where they want to jump into the tech side because it's exciting and it's cool. And like you said, they see other people doing it and yes, you can solve small problem very quickly, meaning there's ways to harvest low hanging fruits. Yeah. But if you want to change the trajectory of a business like strategically and leverage AI to do that, it's, I would say 75% traditional business strategy and then using the other 25% to apply AI on how to get there and how to close the gaps. But this is exactly where we are right now. In the process you're describing, what do I need to ask myself in order to actually go the next step? Yeah.

Khalifa Manaa:

Yeah. Awesome. So in our process, the next step is usually, looking from inside out. So the third step is bottom up. where we then go into practicality, what needs to happen. But inside out means that we're looking at, now that we've defined what we want in the big picture to happen, now we go into looking from the customer's point of view, how does it look like today? this can mean sometimes we just do, testing, whatever is, interacting with this company. Sometimes we do customer interviews. In some cases we might even do shadowing. So basically go see how their customers are interacting with this business. some companies do this in-house and they do this, periodically. and then we can leverage the knowledge they already have. in this, banking case, for example, what we discovered very, really quickly, was basically the pain points that, enterprise customers when they want to open a bank account. firstly it takes really long time up to a hundred, 120 days. So three, four months. Oh wow. From the moment that I want to open a bank account till you actually get it. And then when we spoke with a few customers who recently got their accounts open and joined, they all had these stories of how initially they felt like they gave a lot of information and documentation, and then two weeks later they were asked about the same stuff again. Then few weeks later, again, then they had no visibility. What's going on? Then finally, three or four months later, they get an email that, Hey, now I have a bank account. So the experience is really lacking compared to what the expectations are. Because we asked that tool, what do you expect a bank account opening be? And it is exactly like it is in consumer world. a digital front end. I give information. Two minutes later I have access to a bank account, then I can start doing my business. So that's the expectation. The reality was very far from it. At this point. We knew very clearly that okay, for them to be able to, or one clear way how they can win market share is to solve this bottleneck. But then of course, we had to figure out, why is this happening? What, why is this taking, 120 days?

Isar Meitis:

Awesome. So let me summarize so far. Step one is gather the right people in the room, which is C-Suite people plus key decision people or key operations people from the different departments that are gonna be involved. Define step number two. Define. Where do you wanna go and what are the gaps that you're trying to close or the direction that you want to take? Step number three is now go and look what's the perception or what's the reality check of the situation of that company by asking clients. So now you have the idea that the company has about itself. Now you also have the reality check coming from the real world of, okay, this is what we see with regards to those topics that the company's talking about.

Khalifa Manaa:

what's the next step? So then we look at bottom up. Now we've identified okay, what's the customer experience? Not great. We know where we want to go, and now we start looking into, what's, for us to win that market share and for us to get to wherever we want to go, certain things need to happen. What's preventing that? and this is where we ask. Questions from kind of the people in operations in this case, what's happening in those 120 days? What's going on? Walk us through the process very clearly, so that we can identify root causes. we tend to map challenges. We tend to look at, for example, one exercise we often do, is what would the 13 star customer experience look like? I think you're familiar and may many listeners, this is a old Airbnb story, which I really love and it actually works really well in, in real life. I'll try to very shortly summarize it. It starts by what would be a five star experience, like one to five stars. What's a five star experience? We map it out and then we think, okay, what's the three star experience? What's the one star experience? Then we know what's really sucks and what they think is good. Then we're like, what would a seven star customer experience look like? In Airbnb's case, it could be that, okay, the seven stars that when you enter the apartment, they come with the champagne bottle, they have a personalized sign, then what would the 10 star experience, what would the 13 star experience? Then it gets into, to the territory of, Elon Musk send a private jet to pick you up. You end up in Mars and then you have a party there and so on. But this kind of gives us, an inspirational what could be possible if everything was possible. And that kind of is again, fuel for where do we want to get to,

Isar Meitis:

we map challenges. That's, I wanna touch on that because I love that point, and I think it's critical in this point of the conversation. One of the amazing things AI enable us to do today is to really think outside the box. And what I mean by that is we are. Humans, and especially humans within a structured environment like a business, we think of things through existing lenses that we were trained to think through for decades. Yeah. Why? Because for decades we were trained to look for process efficiencies. How do I do this process that is the given better, faster, more effective? The reality today that AI enables us in some cases to skip the process and get the outcome. And get the outcome. I'll give a simple stupid example. Let's say you wanna do an s e O project. You need to go and research clients' websites and see which keywords they're ranking for. And then you need to do the analysis of how hard it is to rank for these keywords. And then you need to figure out topics you wanna write about. And then you need to define the categories. And then you need to define the articles, and then you need to actually write the articles. And then you do need to put them on the right places on the website, and you need to do the back linking and need. Now you can put this all into a system and it will do all of that. And you get 160 articles for the specific keywords of your competition with the right ones that you can actually compete for, et cetera, et cetera. And it's holy crap. Like something that used to take a year can now take you five days. Now, yes, you need to have the knowledge on how to do that, but if you have the knowledge, you can get the outcome and not improve. Gradually different steps of the process. And there's multiple examples like that, meaning you have to think without any limitations, especially when it comes to knowledge and software. Meaning, yes, if you have supply chain management, there's no way I can land at 10 time x. More resources on your factory tomorrow, cannot do that. But when it comes to knowledge, marketing decisions, optimizations, software, you can do things, not 20% better, but 10 x. And so you have to stop thinking in the frameworks you had before. Think of the 15 star, let's go to fucking Mars solution because you probably can do it. It's not easy that we're trained to do something

Khalifa Manaa:

else. Yes. And that's the kind of part of the challenge is, like you said, it's so mind blowing that we don't think it's possible. and that's part of the, what we need to do is pray those barriers and using, and we use design thinking methods just to force people to think differently. we force them out of the box. yeah, because like you said, a lot of things these days, that we're just science fiction can be

Isar Meitis:

reality. so let's go back to our process. Now you are trying to figure out what's the shooting for the Stars solution? What's next? Yeah.

Khalifa Manaa:

Just to conclude that point. In this, example case, again, the local bank here, what we figured out after going into it, the root cause was that we have fairly strict regulation on know your customer process, especially for wholesale banking, has to do with, money laundering. And this, we banks want to be safe. so to do this, they need to go through a lot of non-standard documentation related to the company set up. Who are the owners they need to figure out, they have a list of, 50 to 60 things that they need to fight, for example, who are the owners who own more than 7% equity of this company? Who has the power of attorney? all these kind of things. And because these documents are, they're not standards, it's just I. Explain the problem that they have not been able to solve, and because it's because of these constraints, they've stopped thinking about it. it is reality. It is what it is in their kind of case. What they have done, is basic o c R to read documents, but still, there's someone either actually doing this, going through page by page and finding the items, writing it down, or then scrolling through, A P D F trying to find stuff way to make that process faster, hire more people to do this. But that's been it. So that, that was the root cause. usually what happens after this, we start two tracks. One, we do a discovery on data and technology. so basically we need to understand what's the tech ar architecture, what data sources we have, how structured is the data. and that's it's own track with its own people. Usually, it, any data analysts they have, group up with a few of our data engineers to just go through the details and create a big picture view of what are we dealing with. and then simultaneously what happens is with the, core sprint team is ideation. Now that we have this problem, how could we solve this? again, here we use, design thinking tools to, force people to think creatively. We usually start with kind of what's possible, what are the capabilities of AI these days? On a high level, just we understand what's possible and we aim in a process where we start with a kind of like a white hat where there's no restrictions. and when we discuss, we have a rule of, you can't say yes, but. You can say yes, and so you can build on top of things.'cause it's really, like you said, we don't believe that these things are possible. So that's why we want them to really, dream and think big. and then we put on a plaque hat. We look at, okay, can we actually do these things or not? And this process, in my experience, the most important thing is to have a diverse group. So you want, you want techies, so you want to have a machine learning engineer there. You want to have data scientist there. You want to have someone from their it, but you also want to have someone from, customer support who has no technical background. And you want someone from marketing, you want very diverse group of people to come up with potential ideas. How could we solve this? in, in this, case, the solution was actually, fairly straightforward. What we need to do is read through these documents and to identify the right parts of information. From there, what we ended up doing is fine tuning a large language model. We used an open source model, so it, back then, open AI didn't allow fine tuning yet. They didn't have their enterprise plans. so we ended up using a LAMA tool fine tuned with training material. Of course, what the bank has a lot, to be able to find, these 50, 60 piece of information of each companies. in most cases with the documents we ask, we're actually able to perform the K Y C instantly. Of course, then we have. flags. we have risk scores when we need more information. and as we did the first implementation, we left that process manual for now, but now we're looking into using, LLMs to communicate with the customers and automate that part as well. so that's in this case after this, after. So

Isar Meitis:

I wanna touch on that on the last two things you said, because I think they're brilliant and I think they're critical. One is, what's the practical aspect of shooting for the stars and the practical aspect? You said have two steps. Steps number one is really defining what shooting for the stars is from a outcome perspective. okay, we, we wanna do the process faster, that's fine. What does that mean? Okay. It means. Asking for specific documents. And again, I'm now talking about this case, but this could be generalized for anything else. But in this case, it's okay. We need to ask for specific documents. We need to put them in some kind of a database. We need the somebody to read all these documents, phishing for specific pieces of information, following a checklist to verify that we have all of those. And if not, raise specific flags. Okay? That's what it means from a practical perspective. Can we do this with ai? And in this case, the answer is absolutely yes. And I think you touched on another point, and especially in a bank there, and I think even today when OpenAI, even now with their enterprise level that they're saying they're not gonna train on your data. I dunno. but for a bank or a, anybody that has more strict guidelines on data management, whether it's healthcare, banking, financial institutions, legal, et cetera, Defense, you probably wanna run your own sandbox on-premise server that you know where all the data is and you don't have to put it on third party, servers. And so that's another practical aspect of the solution, but there is a practical implementation that allows you to build that shooting for the Star solution. so these are the steps and then you're saying, okay, now you can do the follow up. Okay. So you can go beyond, you are at the 13 stars kind of thing. Okay. Can we go to 14? And the answer is, yeah, we do a step two of the project. We'll figure it out and we keep on going. amazing. You we're about to continue from there in another example. I think, yeah,

Khalifa Manaa:

no, I was about to say that. usually this idea session depends on how kind of wide we're looking at this case.'cause sometimes the challenge is not clear. and or it could be that it, it's a broad challenge. We might end up with 15 potential ideas, how to bring this company forward. So it's actually not that common that we end up with a clear way of solving a problem that can actually create significant impact. So often it's a collection of these. so

Isar Meitis:

I wanna ask you a few follow up questions before we dive into a few more examples, because I think it's critical for business decision makers to understand this.'cause one is how do you then decide, because usually you're gonna have more than one solution. You may even have more than one problem in the beginning, right? You're like, in the very first step you define, you may have more than one problem. You want us, you need to solve in order to grow the business. So how do you define two? How do you evaluate resources that are required? Because sometimes you're saying, yeah, we, this solution can do it. It's gonna be.$5 million and it's gonna take a year and a half, and that's too much money and too long. So maybe it's not the right solution for us. So let's dive for a second to that. Step of here is we finish with ideation to how do we turn this into an actual project, with resources and making stuff happen?

Khalifa Manaa:

Yeah. Awesome. so the first step we do with that, we, I have this template. We use these idea cards basically on every card. We don't yet go into crazy amount of detail, but we estimate complexity in terms of, technology, could be some operational stuff as well. And then we look at, what's the potential outcome of this? And this is where we add that number. So using benchmarks from other implementations, using forecasting, we try to aim with. As precise as possible of how much value can be unlocked with this. Then we do on a high level, we look at, okay, what are the dependencies? what needs to happen? Could we have some legal trouble if we do this? Are there other compliance stuff that we need to, for example, like you said, enterprise, especially finance, healthcare, something like this. Privacy is very important. By the way, on, on a side note, I, my vision is most enterprises will end up having tents or even hundreds of small, very specific, fine tuned LLMs in the end. Agreed. that's another topic. but yeah, it's a process of standardizing each of these ideas, complexity, estimated costs on a fairly high level, and then what's the value that we can unlock based on this? We prioritize that. We create a backlog, and then the top three ideas, We go into a little bit deeper and then we can go back. We usually end this week with the session where we go through everything we did during the week and then we end up with, these are the three things that will create most impact with the least effort. This is what it means in terms of cost, timelines, and these are the implications what we expect to happen if we do this project.

Isar Meitis:

Phenomenal. I wanna ask you another question that's in the top of my head, and I'm sure for other people listening as well. So let's take this particular project, right? So we're looking at, because I think on a very high level, a lot of people will end up with something like that, meaning we have a big problem that can be solved by using proprietary data that we have in the business that we need to train a model on. So if I generalize what you said, that's the generalization of that. What's the general effort from a time perspective and from a budget perspective of doing what I just said? Meaning is this, I assume it's not thousands of dollars, but is it lower? Tens of thousands higher, tens of thousands low, hundreds higher, hundreds, millions. Like where does that fall from? A general idea? And of course every project is gonna be different, but yeah, very high level. Where does that fall on a ballpark?

Khalifa Manaa:

our goal is usually four to six weeks to do a P o c purpose of this. Of course, for all parties to test it, we can actually do it.'cause as we're dealing with bleeding edge and with LLMs, fine tuning is always a, we have a fairly certain or fairly high confidence level, but we still, we always p o c, a project like this after p o c to get into production around four to six months. Yeah, around that cost would be, we're talking about two,$300,000 maybe. Okay. Something like this.

Isar Meitis:

And the p o C is probably 10% of that ish. yeah. Roughly. Yeah. okay. I think that's gives people a ballpark. So you're talking about lower tens of thousands to get an idea if your shooting for the star thing is gonna work and then you're gonna spend 10 x that, which makes sense over the next 46 months to get a working functional thing. I wanna put things in perspective for a second. We're talking about the shooting for the star solution that can transform the business. In this particular case, probably a multimillion dollar banking operation, spending only four to six months spending only X number of hundreds of thousands of dollars. So going back to our conversation, it's a no-brainer.

Khalifa Manaa:

it, by the way, of course, that's part of the reason why we use numbers when we estimate the outcomes. It is basically to make that very simple. We do this thing, we can increase your, revenues by 20% annually. Company knows what that means in terms of dollars compared to the investment. It becomes very often a no-brainer. And why it becomes a no-brainer is simply because, like you stated, this is so transformational. We can take such a significant leap in customer experience in, in, like you said, marketing optimization. It's insane. we have a gaze with just doing dynamic pricing on e-commerce. We're able to increase G M V by 20%, profits, 5%. and these are, big. Big numbers of course, compared to the cost.

Isar Meitis:

amazing. I Khalifa, I think this is a perfect note to end. Like your summary now was you can do stuff that was impossible before and if you stop thinking that stuff is impossible and you start exploring these kind of things through the lens that Khalifa just shared, meaning here's a process you can go through, which is more of a strategic consulting review kind of process, but then the outcome is not limited by traditional mindsets, technologies, and processes. You can go and I'll use the same word you used, transformational change in a relatively short amount of time. Khalifa, this was amazing, really well thought after. Really valuable for companies. If people wanna follow you, work with you, find you, talk to you, learn more about you, what's the best way to do that?

Khalifa Manaa:

you can easily find me from LinkedIn. I'll share on the show notes also my, my email. Feel free to contact me anytime. I'm very much into, I love this stuff, so I'm very open to having conversations even when, when you don't know whether you want to do something or not. Happy to, have a chat about it, brainstorm and think about what could that third team star experience mean in, in your case.

Isar Meitis:

Amazing. Khalifa, thank you so much. Fascinating conversation. I appreciate your time and I appreciate you. Awesome. Thanks

Khalifa Manaa:

for having me. So much fun. All the best.

Isar Meitis:

What a fantastic conversation with Khalifa. He obviously has a lot of experience in doing this in real life and hence has a lot of great insights. The biggest take on this and you heard me say this before AI projects Especially large scale AI projects are like any other business projects. First and foremost, there has to be a business goal that you're trying to achieve and then a strategy that will take you there and then KPIs to monitor how are you getting there and only then finding the right AI solution for that. And now let's dive to a lot of exciting news from this week. There were so many really important news that it was very hard for me to pick just the ones I want to share As always in the show notes, there's going to be links to even more news than what I'm sharing now, but let's dive into just the stuff that we have to share because it was really important And we'll start with Meta. Meta made several different, really large announcements this week. They launched what they call Meta AI, which is advanced conversational AI assistant that runs on all their chat platforms, WhatsApp, Messenger, Instagram, and even their virtual reality and augmented reality devices. So imagine something like chat GPT, including all its new capabilities of the ability to create images, et cetera, that is built straight into all of Meta's platforms. They also announced AI studio platform that will allow businesses to build chatbots into their messaging apps. It's obviously a huge step forward by meta that so far mostly played in the open space world and allowing people to use their models for other stuff. This is, I think their first shot that they're firing in the bigger wars of generative AI domination. They also announced something that they call characters, which is their partnership with several different celebrities and running a virtual version of these celebrities that you can now chat with on the different meta platforms. This allows access to millions of people who would like to chat with their favorite people, but we're not able to do it so far. This is obviously not the real person, but over time, I assume it's going to get better and better. I'm going to be very close to actually chatting with the real person. Meta also announced a partnership with Ray Ban and they're jointly launching smart glasses that will have the Ray Ban quality of sunglasses together with Meta's AI and visual capabilities. So it will be able to record stories straight from your cameras. It will be able to listen to you and respond to you using their AI platform. This is a very interesting, Move forward into wearable things that will connect back to social media, whether I like it or not, I'm not a hundred percent sure at this point, I think my kids are going to love it. I feel that this is another big intrusion into my personal space and privacy, but I definitely see this as an interesting step forward. These are not some bulky, weird glasses there's different versions of them. They all look classy and stylish and yet integrate all these smart capabilities into them. So from a technology perspective, this is definitely a very interesting step forward. Another interesting piece of news that we're behind the scenes by Meta was Lama 2 Long AI, which is an advanced, more capable version of Lama 2, which is already highly sought after and widely used open source model for almost anybody wants to use open source these days and based on the paper that they've released, it already outperforms GPT 3. 5 Turbo and Claude 2 on multiple benchmark tests. so while we just said that Meta started playing in the field of actually leveraging the platforms, they're continuing to play in the open source world as well, releasing better and better models that anybody can use to develop more stuff. This is aligned with the strategy that they've been supporting for a very long time. And from meta, let's move to another giant in the field, which is Amazon. So two big pieces of news from Amazon this week. One is that Amazon is going to be investing 4 billion in Anthropic, the company behind Claude-2. This is a very interesting partnership between on one hand Anthropic that has one of the most advanced. Models out there that just recently added a paid version to its platform, together with Amazon that can provide them access to basically the world and that can give them access to a huge amount of customers that are running on Amazon AWS, as well as The ability to use their computer and power that they have in AWS. So a very interesting partnership between two very big giants that I think in the big scheme of things will provide more access to more advanced models to more people, which is a good thing. the only bad thing is it's still staying within the hands of the same few. Players that will be able to afford to play the big game. Another piece of news that's coming from Amazon is that their Bedrock platform that they've announced and released to a small group of beta users under AWS, Bedrock is the platform that allows companies to build apps and agents using generative AI models through different APIs within the AWS ecosystem, they just. Released it to the public within this platform, people had access to models from AI 21 and Stability AI. And now they're adding Lama 2 from Meta as well. So supermarket of AI models that is available under one umbrella connected to a lot of existing solutions and capabilities under the AWS umbrella. Again, not a big surprise, but I think a very smart move by Amazon by allowing people who use AWS to use basically their models of choice, either pick one and run with it across everything you need or pick the right model for the right product. Job, which gives flexibility to the people who are going to develop a solutions within the AWS world, moving to the third giant on the list today, or maybe the fourth. If we include Anthropic in that list is open AI. The company behind Chachi PT made a huge release this week, which includes multiple new capabilities under ChatGPT It now, as they said, can See, listen, and speak beyond the capabilities that it had before. They've added the capability to chat with it via voice, which existed before through whisper. Now it's built into the platform. It can now speak back through text to speech capabilities. So you can have a vocal conversation with ChatGPT. It can create images within the platform itself while chatting with it back and forth through the DAL E3 integration into ChatGPT that we mentioned last week as well. And another really cool and interesting Feature is that it can understand images as part of the inputs. That's a huge step forward that provides so many amazing use cases. because now ChatGPT can understand graphs and diagrams and flowcharts and images and user manuals, basically any visual thing that you're going to load to it, you can ask questions about it and integrate it into the inputs to your conversation. like I said, the number of use cases is endless, so a huge step forward by ChachiPT that is continuing to push the envelope now that there's more and more competition from other players. The last two pieces of news I want to share are more practical and more of use cases of AI. So one of them is Zapier just launched what they call Canvas. Canvas uses AI to help companies generate workflows and provide templates based on specific user needs. It allows you to automate a lot more than Zapier could automate before with the usage of AI and do this across every process you have either personally or in your organization. This is a very interesting and yet expected step forward where AI is going to be integrated into every process that we have in the business While filling out the places where I shines and allowing people to take the steps the people can do better. And the last piece of news is probably less relevant to most of us, but I think it's important to discuss, which is that Microsoft researchers introduced AutoGen, which is an artificial intelligence framework that simplifies the orchestration and optimization and automation of large language model workflows. And to simplify this to all of us to simple terms, it's using AI to make AI implementation even easier. Easier, so this is the next step of everything, right? We're going to see AI writing code for new AI. We're going to see AI creating new hardware for AI. And we're going to see AI orchestrating and helping other AI platforms run more efficiently. And I believe we're going to see more and more of that. A lot of big things are coming in Q-4 of this year that are going to be very significant to businesses and how AI is going to integrate with them. Until next week, go and explore AI, play with it as much as you can. Learn new things, share them on social media, share them with me on LinkedIn and until next time, have an incredible week.