Webit 2026: From AI hype to enterprise ROI: turning AI strategy into business impact

This talk focuses on the journey from AI hype to tangible business results, emphasizing the importance of defining a clear AI strategy that aligns with business objectives. The speaker, Peter Baltaджиев, discusses the necessity of transitioning from AI experiments to genuine transformation initiatives that yield measurable impacts. He highlights the gaps in understanding among executives regarding the practical implementation of AI, stressing the need for organizations to create a hybrid model that integrates human efforts with AI technology. Key components for successful AI scaling include executive alignment, quality data governance, and effective change management. Baltaджиев points out that companies which embrace AI as a core part of their operations will distinguish themselves as AI-native leaders in the future economy.

Ladies and gentlemen, there are people who visit Webit and there are people who have grown together with Webit over the years. Our next moderator is a dear friend of the community and someone we are always delighted to welcome back to this stage. Through her journalism, curiosity, and ability to ask the questions that matter, she has helped countless audiences better understand the people and ideas shaping our future.

It is once again our honor and pleasure to welcome Marina Tsvekova from Nova TV. Joining Marina today is Peter Baltaджиев, executive and CS board chair at Oracle for a conversation about one of the biggest challenges facing every organization. How do we turn AI strategy into measurable business impact? How do we move from experimentation to execution? And how do we create real value from one of the biggest

technology shifts of our generations? Warm welcome to Marina Tsvekova and Peter Baltaджиев. >> Good afternoon, everyone, and welcome to our fireside chat from AI hype to enterprise return of investment, turning AI strategy into business impact. We had already the opportunity with Peter to discuss a bit about the topic. It's a broad topic, a very interesting one, and he has a real insight into it. So, I'm really

eager uh the to hear what you are about to to tell to our audience. Let me introduce you, Peter Baltaджиев, senior executive at Oracle and serial investor who has been leading AI transformation initiatives across the organization. Welcome, Peter. I'm very glad that you're with us. >> Thank you very much, Elena, and I'm glad to be back. Uh last year we talked about economy, and today we'll talk

about the AI and what AI does to us today. >> Yes, it will be a very interesting topic, and I'm really looking forward to hearing your insights. But, tell me first, why Why you think there is such a big gap between AI enthusiasm and real >> Allow me first to start with a couple of things. Well, a few things. AI is not a technology problem anymore. And

AI also is a business solution, if I may say that. So, AI should be a business operational system. Why this is important? Because we need to move away from being an AI experiment when we look at an organizational setup move towards creating an AI framework that runs the business together with humans. Because it's not about AI and then let's delegate all to it. It needs to be

a bit of a hybrid, but let's move on and discuss a little further these details. I just wanted to point out those few elements. >> Do you have a an easy answer to the question, what is the difference between an AI experiment and an AI transformation initiative? >> An AI experiment is what a lot of businesses are doing at the moment. And why they default to it?

Because they look at it as a technology solution. And what they also do is trying to prove a concept. So, it's more of a POC as opposed to something that drives a business result. What that means otherwise is we would need to look at what the business outcome is and reverse engineer our processes including AI to achieve these outcomes as opposed to run a POC and prove

the existence and value of AI because that's the the the biggest differentiator between companies that and companies that transform. >> Do you think that executives really understand the difference at this point in in time? >> Difficult question to answer because um it all depends on people. And then we come again to the topic. It is people that drive and AI that executes. So, some leaders, some executives,

and some and some industries are much more advanced and much more accelerated compared to others. For a number of reasons, there's regulation, there's data, there's um the data sanity, there's the way executives see um the way business should be should be transformed. And we we reached to the five components on five necessary components on what is important for businesses to become AI native. And if we start

with that executive alignment, so from an enterprise perspective, when we have um multiple executives running different parts of the business, they need to align on what the business objective is and what's the business outcome is. So, that's number one important. Number two, the existence of good quality data. So, when data is desperate in different sources, it's not integrated, then AI doesn't have the the the basis for

it to start generating good results. But then we come down to the governance cuz data is there, AI does its its job. But at the same time, we need to govern this in a proper way. Why? Because we need to respond to the regulator based on the jurisdiction we operate in. But B, because we want to create safeguards in our own business operation not to let AI

run wild and do results that we don't anticipate. So, it's all predictive. But then we move to the other element, which is humans. So, onboarding that, making people excited and making people really bought into the idea of using AI is crucial. And then we reach to the point of change management and that's the fifth component for AI-ready to success companies. And the And And this is The

fifth component is actually something that a lot of organizations underestimate. And then they underestimate it for a number of reasons. They say, "Okay, well, here's the technology. We know where we're going. Off you go." But then But then we need We need to a a pure deployment of AI doesn't mean adoption. And that triggers then the conversation around what's the return on our investment. >> This is

actually my next question because we are speaking about executives and I guess that when you evaluate AI investment, what executives should think of or think of are what would be the return. How do you think and where do you think they should expect returns after investing in AI? >> Yeah. So, if you look at return on investment from an economic perspective with AI in the picture, there's

three components. It's productivity, efficiency gains, and to achieve excellence. And then it's And the third component is growth and innovation. Most companies are operating in the in the on the basis of the first. So, productivity gains. Reduce time to run my marketing campaign. Uh um more efficient logistics when it comes down to transporting my my goods and services to the end distributor. analyzing big volumes of data

and returning um the the outcomes and the results. So, these all goods, but then we need to ensure that we move to the next level, which is operational which is the excellence, which is the efficiency gains. So, we've done the leg work, but then what's the outcome of it? And then once we move towards that, we tick that box in, and then we move towards innovation. How

do we improve our product? Think about um new products in our um in our in our portfolio. How do we attract more consumers? How do we demographics within our offering? So, all these things are more related to where do we want to go next? So, if you past, present, future. So, number one is present, so past {slash} present. And then future towards excellence. >> You spoke about

enthusiasm and about excitement while working with AI, but what are really the characteristics of companies that are successfully scaling AI? Because I guess uh this excitement is not present everywhere. >> Yeah. So, company that scales is a company that invests in a hybrid model. Um they would invest in technology, and uh the technology would be AI-powered, but they would also invest into their into their personnel, into

their into their people. Because people would need um to work hand-in-hand with the technology. And if you look at AI, um let's just hypothetically consider it as a our best assistant. Um so, if we previously had to analyze a lot of data through Excel, run some modeling, and uh achieve some great results, and then propose them, and then those results need to be uh then onboarded um

through our systems, and then obviously we need to uh monitor those ones, and then achieve efficiency gains that way. Um today, what we can do is tell AI not how do I achieve 20% excellence into our uh better efficiency into my uh operational workload, but change the way I do the same thing as if AI didn't exist and now I plug AI So, almost like a reverse

engineering of of the whole process. And then that becomes the the the basis of this hybrid model where AI does the legwork, we as humans decide, and then we close the loop and cycle that way. And this is the way forward, really. Because we don't need to be afraid of AI. AI is not something that will substitute us. It's not the Terminator one or two kind of

a scenario here. It's more how do we It's a new tool set. And if you look at it as such, and if you plug this tool set into the business outcome as opposed as opposed to just use it because we have to, cuz our company tells us, then that that's how we'll achieve the the desired results. >> Okay, so then let's look closely the execution. When AI

moves from demo to deployment, what are the lessons that actually companies learn? >> I can name three lessons cuz we have 5 minutes, so >> Go ahead. >> So, it's um create a scope um that will allow you to scale fast uh if we start doing this across everything we do, then it's highly likely that we'll get we'll have too many balls to juggle. And then we'll

we won't get to where we want. Adopting the hybrid model. And the third element to it is invest from an executive perspective, invest not only in technology, but also into people and change management because AI wouldn't or any technology for that matter wouldn't have any value um beyond proving that it works, proving the concept, and proving that uh um there's a productivity gains there unless people bought

into it. So, adoption doesn't necessarily mean uh well, actually, I'll re- reverse this. Deployment doesn't necessarily So, invest in human. >> Okay. But, if we look ahead in 3 or 5 years' time, uh what do you think will be the difference between the leaders and the followers in the AI economy or it's too early to look ahead? >> No, it's not too early. So, um if you

ask me also of what um there's companies that will not use AI. There will be companies that won't use AI. Uh will that mean that they'll cease to exist? Probably not. But, companies that will be successful and will be the best in their field will be certainly companies that are actually using AI. And um these will be companies that are more AI that they would move towards

being AI Companies that would need to think again I mentioned this earlier. They would need to think of what's the business outcome rather than how do I deploy this? And how to incorporate the hybrid model of human and AI. And also, continuous learning. Not only about learning and teaching the models, but also people. Onboarding people, educating them because this is a very rapidly accelerated process that is

actually happening now. And then, we need to be up our game to be successful. So, not all AI No all companies in the future will be AI companies, but certainly the ones that are successful will be AI native companies. >> But tell me, I guess that everyone here in this room knows what AI is or uses it on a daily basis, but still for those among us

who are still precautious when it comes to AI, what is the one piece of advice that you would give them uh to become AI leaders and not AI followers? >> Well, that would depend on what function they execute. So, from an executive perspective, um just be aligned um and think what Think Think as AI Think of AI as an rather than as an isolated silo of activities.

If I If If I am somebody that is uh running an operational workloads, I'll think of AI as my personal assistant that does the legwork for me um and then I I I do the strategy element. an HR person, I'll think of AI in terms of how I can more successfully onboard my personnel and get them really excited about using it. If I am a salesperson, I

would think of the worst, the best, and and in the middle scenarios of my sales strategy so that I figure out, okay, well, how do I structure this so that I'm successful? there's no one answer fits all, but it's What is the golden rule? What is it that I want to achieve? And then work back backwards or reverse engineer it from from from the tools that that

I need. >> Which is AI. >> I have quite a lot of questions, but because we have five if 44 seconds let which is a long time in uh TV setting. So, I would like to take one question from the audience. Is there someone who would like to ask Peter about the GIF something? Be brave. Come on. Yes, there is a question over there. Go ahead. >>

Hi. What measures are you taking for the technical debt in your company? Do you have some answer on that? >> Yeah, so um I will tell you not from a perspective of the company I represent, but from a perspective of the companies that I deal with, which are companies that are in the financial sector. So, what they do is Do we have 2 seconds? >> What they

usually do is what they're to to gravitate away from is having a very fragmented ecosystem with multiple multiple providers and then trying to consolidate that into into one pool. Not necessarily through one product, but into one pool through integration. And probably we need to I'm conscious of time. I probably we can grab some time to talk about this further if you'd like. Um so I can tell

you a little bit more what companies in the financial sector suddenly do. >> Okay. Thank you very much, Peter. The good news here is that actually this is not our last meeting because we are meeting again in this very same stage at 4:30. So, in case there are questions left, you can keep them for later. Thank you very much for this insightful information. It was a pleasure.

>> Thanks for having me. >> Thank you.