Idan Zalzberg, Chief Technology Officer at Agoda
Enterprise AI adoption has crossed the tipping point. Globally, more than 70 per cent of companies are now using AI in at least one function, while overall AI spending is projected to exceed US$2.5 trillion in 2026. What was experimental just two years ago is now operational.
But results have not kept pace. Many companies are still struggling to turn AI investment into tangible outcomes, exposing a widening gap between capability and execution. The issue is structural. AI is not just another software layer. It changes how software behaves. Traditional systems were deterministic and testable. AI systems are not. They introduce variability, ambiguity, and a new class of risk.
This is forcing companies to rethink leadership, talent, and product design at once. Decisions are increasingly made under uncertainty, engineering roles are shifting toward adaptability, and users now expect outcomes, not just tools. Travel platforms are an early test case for this transition. The category is fragmented, high-intent, and decision-heavy, making it particularly exposed to both the upside and the pressure of AI-driven change.
At Agoda, this shift is already taking shape. The company runs more than 90 generative AI use cases and has backed this with a move to a 26,000 square metre tech hub at One Bangkok, consolidating nearly 4,000 employees. As software becomes less predictable, how teams work together is becoming as critical as the technology itself.
To understand how this plays out in practice, Agoda’s CTO Idan Zalzberg outlines how the company is rethinking engineering, talent, and product in the age of AI.
When AI first hit, there was a lot of noise, fear, and conflicting opinions internally across most companies. How did you cut through that and actually align Agoda around a clear direction?
I think we are all being challenged. When the sea is stormy, the role of the captain becomes much more important. When everything is smooth and predictable, you have to ask yourself, what is my job really? Leadership ultimately comes down to making decisions, and when those decisions are shaped by uncertainty and diverging paths, they matter even more.
For us, developing an “inside-out” AI strategy early on was critical. At the time, people simply did not know what to expect. Should we go into it? Should we use it in programming? Is it good or bad? Is it going to take my job? There were many voices, and a lot of fear.
In that environment, leadership had to step in, make clear decisions, and bring confidence to people. We had to show that we have a strategy, that we have a point of view, and that no one is being left behind. A lot of people were asking what this means for them personally, and we had to give them an answer. This is how we see it. This is our mindset.
It was about making decisions, communicating them clearly, and reinforcing that message consistently to build confidence. I think this was a moment where leadership across companies really had to show up. You can also see examples where that did not happen, where leaders said one thing and then walked it back. That is very jarring, and it breaks trust. Once that confidence is lost, it is very hard to regain.
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Has the rise of AI changed your philosophy on what makes great engineering talent, and if so, how?
What we are seeing with AI is that it introduces a fundamental difficulty. It makes software unpredictable. Traditionally, software worked in a very clear way. You define exactly what you want, you build it, and then you can verify that it behaves exactly as expected. That model is now gone.
With AI applications, you no longer know exactly how the system will behave. You cannot always guarantee that it will meet the requirements in a consistent way. Even if something works once, it does not mean it will work the same way again. That level of unpredictability is new for most engineers.
Data scientists have been used to this way of working, but now this mindset needs to extend across the entire engineering organisation. That is one of the biggest challenges we are dealing with, and we are still learning how to handle it.
This is also why starting internally was so important for us. We wanted to build that experience and help people get comfortable with the idea that software is no longer fully deterministic. AI is now embedded in many parts of the system, and whenever it is involved, you cannot assume a fixed outcome. It is not as simple as saying five plus five will always be ten. Sometimes it will say eleven.
Because of that, building the right evaluation frameworks and ensuring that you are actually improving with every iteration becomes much harder. It is something we are learning together with our teams.
It also changes what we look for in people. We need engineers who are curious, open-minded, and comfortable working in this kind of environment, because this is not something you can approach with a traditional mindset.
As AI agents become more capable, how do you see the role of OTAs evolving beyond search and booking?
I do think AI agents could become the new OTAs. What we are seeing is that customer expectations are evolving very quickly. It is no longer enough to just provide a search and booking tool. People want more autonomy, more assistance. They want something that actively helps them, not just a platform where they do everything themselves.
Today, many people still see OTAs as trustworthy but relatively basic. You can search, you can book, but ultimately you are the one making all the decisions. Of course, there is already a lot of intelligence behind the scenes in how we rank and recommend options, and that has been driven by machine learning for a long time. But expectations have shifted.
Users now want more than recommendations. They want context and reasoning. They want to understand why something is being suggested. They expect the system to connect different parts of their journey. If they have just booked a flight, the hotel recommendation should take into account things like distance from the airport, arrival time, and whether early check-in might be needed. They expect a much more holistic and proactive experience.
And expectations rise very quickly once people see what is possible. I remember when AI-generated images first became popular, and people pointed out that the fingers looked wrong. But if you step back, a computer had just generated a photorealistic image from a simple prompt, something that felt like science fiction just a few years ago. Yet almost immediately, the expectation shifted to perfection.
The same dynamic is happening in our industry. As soon as users see what generative AI can do, that becomes the new baseline. They expect more, and we have to evolve to meet that.
Beyond coding, where are you starting to see AI have the most impact across the organisation?
AI is starting to show up everywhere. We have already talked about programming and development, which are clearly strong use cases. But what we are increasingly seeing now is adoption on the office side as well.
This includes tools like Excel and PowerPoint, and more broadly, work that sits between creative thinking and operational execution. Things like creating documents, reading and summarising information, building presentations, and helping people communicate more effectively. These are areas where AI is starting to have a real impact, and it is evolving quickly.
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On the engineering side, while core coding is already well supported, everything around it is still catching up. For example, reviewing code, debugging incidents in production, and understanding what went wrong are still emerging areas. The ability for AI to reason through issues, analyse problems, and explain what is happening is only just starting to come together.
So while some parts of the stack are already quite mature, many of these surrounding workflows are still in the early stages. That is where we are seeing a lot of new progress right now.
What does the future of travel booking look like if AI can take on a more proactive, end-to-end role for the user?
This is a question we are asking ourselves a lot, and it is more important now than ever. At the same time, it is not entirely new for us, so it does not come as a shock. In fact, it is quite exciting because generative AI is finally enabling a vision we have had for several years.
The easiest way to think about that vision is to look at what travel agents do and why people still go to them. Planning a trip end-to-end is hard and often stressful. There are many decisions to make, and every time you hit that “book” button, there is hesitation. Are the dates correct? Does everything line up? Is this the right hotel, or should I choose another one?
What we want to do is remove that stress while still keeping the user in control. Imagine having a personal travel agent who works only for you, understands your full history, your preferences, what you like and what you do not like. Instead of you doing all the work, they prepare the trip for you and guide you through it.
They might suggest options and ask, how does this look, or would you prefer something different. You can respond, adjust, and refine. Maybe this time you want a more relaxed, beach-focused trip. The system adapts instantly and reshapes the plan around that.
The goal is to create an experience where you still feel in control, but without the stress, and with a high level of trust and confidence that you are getting exactly what you want. That is where we want to go, and we believe we can get there.
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Image credit: Agoda
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