Exploring Data-Driven Transformation at Turkish Technology and Turkish Airlines
Serdar Gürbüz, General Manager at Turkish Technology, a subsidiary of Turkish Airlines, takes center stage at the Red Hat Summit 2025 to share invaluable insights into their pioneering journey of digital transformation and data-driven innovation.
In this session, Gürbüz explores Turkish Airlines' shift towards a data-driven mindset using Red Hat and OpenShift technologies. Highlighted by theCUBE hosts Rebecca Knight and Rob Strechay, the discussion focuses on efforts to democratize data access across business units, enabling non-technical users to leverage data for solving complex business challenges. The conversation reveals how this transformation enhances customer experiences and operational efficiencies at Turkish Airlines.
Key takeaways include the measurable impacts of artificial intelligence (AI) and data integration into operations, as demonstrated by the $10 million annual fuel savings achieved through optimized aircraft route assignments. Gürbüz emphasizes that collaboration between technical and business teams is crucial for uncovering effective AI applications. Their success underlines the importance of leadership support and strategic change management in overcoming organizational resistance to new technological paradigms.
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Serdar Gürbüz, Turkish Technology
Exploring Data-Driven Transformation at Turkish Technology and Turkish Airlines
Serdar Gürbüz, General Manager at Turkish Technology, a subsidiary of Turkish Airlines, takes center stage at the Red Hat Summit 2025 to share invaluable insights into their pioneering journey of digital transformation and data-driven innovation.
In this session, Gürbüz explores Turkish Airlines' shift towards a data-driven mindset using Red Hat and OpenShift technologies. Highlighted by theCUBE hosts Rebecca Knight and Rob Strechay, the discussion focuses on efforts to democratize data access across business units, enabling non-technical users to leverage data for solving complex business challenges. The conversation reveals how this transformation enhances customer experiences and operational efficiencies at Turkish Airlines.
Key takeaways include the measurable impacts of artificial intelligence (AI) and data integration into operations, as demonstrated by the $10 million annual fuel savings achieved through optimized aircraft route assignments. Gürbüz emphasizes that collaboration between technical and business teams is crucial for uncovering effective AI applications. Their success underlines the importance of leadership support and strategic change management in overcoming organizational resistance to new technological paradigms.
Serdar Gürbüz, general manager of Turkish Technology, a subsidiary of Turkish Airlines, joins theCUBE’s Rebecca Knight and Rob Strechay during Red Hat Summit 2025 to share lessons from the airline’s data-driven transformation journey. The conversation focuses on democratizing access to analytics and enabling non-technical teams to harness data for complex business challenges.
Gürbüz details how Red Hat and OpenShift technologies support operational improvements and enhance customer experiences. A standout example includes a $10 million annual fuel sa...Read more
exploreKeep Exploring
What method did Turkish Airlines use to generate over $10 million in fuel savings last year?add
What approach did the team take when implementing transformations in a big enterprise like Turkish Airlines or any other airlines?add
What are some AI implications for an airline and how can predictive models and computer vision technology be used in the aviation industry?add
>> Good afternoon, everyone, and welcome back to theCUBE's live coverage of the Red Hat Summit AnsibleFest 2025. I'm your host, Rebecca Knight, alongside Rob Strechay, my co-host and analyst. We're in the home stretch here of day two, and this one's going to be fun because you spent a lot of time on airlines. A lot of your life on airlines.
Rob Strechay
>> I've flown this airline a number of times and had a thoroughly enjoyable experience, which is what you want. Trouble-free.
Rebecca Knight
>> Yes. In a time when airline travel is often abusive, that is high praise.
Rob Strechay
>> This was not abusive at all. This was a pleasure to fly.
Rebecca Knight
>> So, with that, I would like to introduce our next guest. He is Serdar Gürbüz, general manager Turkish Technology, which is a subsidiary of Turkish Airlines. Welcome so much, Serdar.
Serdar Gürbüz
>> Thanks.
Rebecca Knight
>> So, you were on the main stage this morning talking about the problem the Turkish Airlines had. So, I would love you to tell our viewers a little bit about your choice for Red Hat and OpenShift, but what was the problem that you were trying to solve here?
Serdar Gürbüz
>> Actually, it was not like a problem, it was a vision. So, five years ago, so when AI was not a hot topic at that time, so data was a hot topic and we were telling people, we were telling the top management and middle management in the company that we need to do something in this domain, because if we don't do, the digital transformation will be missing. That's why we have started a program, a data-driven transformation program. So, it goes along with the digital transformation program of the company. And we decided this will not something similar to other IT projects because for other projects, you start a project and there is a starting point and technical teams, the IT teams worked on those projects. And then, you finish the project, you celebrate it, the cakes, et cetera. But this is different compared to IT project because the organization had to transform with the data-driven mindset. What do we mean by data-driven mindset? So, each time a decision made within the company, so they need to be based on a data, because if we do our job like that, so the customers are happy, like Bob, or the operations goes smoothly. That's why we believe the data should be reachable by each people, but we cannot open all our data to everyone because some data may be private for top management or some different departments. So, that's why we first democratized data for the business to see and to play with the data, to make analysis and advanced analytics using that data. And in order to do that, the business unit users were not technical people or data scientists of our IT team. That's why we started a training program for the business users, and we take them into our training courses and we teach them how to analyze the data and how to play with the data, like data scientists. And using data, they started to solve their business problems and we get the sponsorship from the top management. So, we train the people within the organization. And the third thing, the technical infrastructure should be ready for them to do things. So, in that space we choose OpenShift and OpenShift AI, so it's scalable, so it's flexible, so the developers both within IT and the business users can work on their workloads independently and they can build, they can upload their data and they can build their own applications with the help of IT teams, of course, and they can solve their problems. So, that's the brief story of our journey.
Rob Strechay
>> So, you had a great example earlier on around your dynamic aircraft route assignment and some of the KPIs and measurables that you were able to see out of that, which I think, to me, a lot of companies still struggle in seeing, really, what is the success of AI? Talk to that a little bit.
Serdar Gürbüz
>> Yeah, of course. So, the key thing actually we learned a lot along this journey, because at first we started with the technical people. We tell the business units, "Okay, these people will solve your problems with the AI tools, with machine learning and et cetera." But we realized that we don't know the business problems. The business teams know their business very well, and in the project project, we see that a team member from the business unit and from the operation teams, she said, "So, we have a problem. So, we are assigning aircrafts to the routes, but we don't use the data." Because there are so many parameters in it, the number of customers within the aircraft, the number of passengers, the type of the aircraft, and Turkish Airlines has a very wide range of complex aircraft types. And we decided to put an optimization model, which learned from our machine learning algorithm for the aircrafts that we own.
And we combined the machine learning model with the optimization model and it generated an output, which the model says, "This aircraft should apply to this route." Because when we do that, we have the fuel savings. And when we added it up, so in a year, last year, for example, more than $10 million saving from the fuel Turkish Airlines have gained. So, this is so measurable. And top management is also... The CEO is a sponsor of the program from the start, that's why he gave all the business units, "Okay, you need to find use cases with finance. Okay, with legals, with sales, marketing." Each department has this goal on their list. And each year they need to work on a data science project or an AI project with us at least one project, and each one of them should have a measurable impact like this. So, if you don't show the value to the business users or to the company, so there is no need to work on that. Of course we have some R&D stuff, but this project like this, like , so it's very powerful for us to show the value of the project. And we didn't stop there because the model has parameters, the flight type, or all of the data coming from the flight and all of the data coming from the passengers. It is the same for other airline as well. That's why we put that model on OpenShift again, and we make it can be deployable onto any other cloud and we make it a data product. Now, we are serving it to other airlines as well. So, any other airline wants to use our model, so they can put their data and we can give the optimization output for them to optimize their fleet, according to the full optimization.
Rebecca Knight
>> So, the way you're describing this, the mandates, the use cases, the showing business value, and even the way you started describing how you started working with Red Hat, that it wasn't a problem you were trying to solve. It was a vision that you had about the kind of airline and the kind of business you wanted to be. A lot of this sounds to me like change management. It was becoming a data-driven organization. You're a technologist, but you obviously had to overcome a lot of, perhaps, stubbornness, maybe, within the organization. How did you work through those issues too to make sure that you were having the outcomes that you wanted to see?
Serdar Gürbüz
>> Yeah, it was not easy. Each transformation in a big enterprise, like Turkish Airlines or any other airlines, have hurdles and we needed to overcome this along this journey. And one of the things that we did best... Actually, once we got the sponsorship from the CEO, we went to each business unit and we listened to them. "So, what are your problems?" We didn't call them with the tool, "Okay, this is AI and this is machine learning, so we are here to make a project with you." This doesn't work. So, we went to them, "Okay, tell us your problems." So, they tell us their problems and we analyze the problems. If we can put a solution with the data that we have. For example, we build a model to predict how much food consumed within a flight. So, it was easy for us because we thought, "Okay, we know how many passengers were in the aircraft and we know how many consumed in the aircraft." So, that was the assumption that we have. "Okay, we can predict each aircraft, how many food will be consumed." But we realized that when you don't consume the food, it's not counting. It goes to the trash. And for us, it's a valuable information because we need to predict the next flight. That's why we built another survey field for our cabin members to fill out how many food didn't consume in that flight? So, we get that input and then we analyze the data, and the next hurdle is putting it into operation. Because in day-to-day operation, they are looking at how many passengers we have in this flight. Okay, let's say 200. Okay, put 200 food in this flight. But now, we are adding something different for them. So, we are predicting, okay, hold on. If there is 200 passengers in this flight, you can put 190, let's say. And it was very hard for them to accept it because it's not the way they do their business for the last 20 or 30 years. So, in order to change the behavior, change the day-to-day work routine. So, it was hard to manage it. But again, communication with the business unit and getting all the requirements of the problem from the business unit is very much important. And if you solve their problems, so they can be the champion of the technology, they can be the champion of the solution.
Rob Strechay
>> So, I think, again, you talked about some of this stuff earlier today. And for those who missed the keynote, you're talking about various different AI, not just generative AI and chatbots, but moving beyond that, more agentic. And you also had some stuff around computer vision and helping manage flight delays and things of that nature. Help people understand how that... Because that, to me, I was very... We all hate delays. I mean...
Serdar Gürbüz
>> Yeah, there are a lot of AI implications for an airline, because aviation industry is very complex, it takes very different disciplines from planning to operation. So, that's why we used predictive models for sales forecasting, or revenue management, or stuff like that. But for the ground operation, we had a solution for our ground operations team. So, they were taking the exact times of the operational activities, events like bridge has connected to the aircraft or the fuel is filled to the aircraft. So, we had cameras at the airport and we think if we get the footage from the cameras and there is computer vision technology here, so if the computer vision detects those events, so without needing anybody to input those events. So, we can predict if that aircraft has a delay or not. And we started to work on stuff, so the camera footage coming from the airport is detected by the model. So, it detects the timings of each operational events. And according to the previous event times, so it predicts or this flight can be delayed or not, and it alerts to the operation chief and then the chief do something or canceling the delay. So, that's one other implication of AI for the operation. And when we think about other types of AI, like generative ai, so it's the theme for the last year we are always talking about, and there are lots of use cases around generative AI. Because when you put it in front of the customer, you can deal with the complaints or you can deal with the feedbacks of the customers. If you put it right next to your call center agents, so they can do their job in a faster and more accurate way. And if you put into your finance departments side, so it can analyze contracts... We have so many contracts and it can detect some valuable information better than the finance employees at the same time.
And lately we are working on code assistants. We have 2,500 engineers working at Turkish Technology, so each of them building different products of aviation. But when we put code assistants right next to them and they work with code assistants and we see there is a lot of potential to increase the productivity and we are building faster in terms of software development. And in quality perspective, the code assistant is helping us to increase the quality of the code that we are currently building. So, now we can put gen AI in every use cases around Turkish Airlines.
Rob Strechay
>> So, final question for you. We're here today. A year from now, when you're back at Red Hat Summit and we're talking again, what do you hope you can say either about where you are with AI, where you are with OpenShift, how the infrastructure has continued to evolve? What do you hope to be able to say next year?
Serdar Gürbüz
>> Yeah, that's a good question. So, currently, we are developing our own applications, mostly on our own on-prem data centers, because we believe data protection is very important and that's why we choose OpenAI. And for other applications of Turkish Airlines, we are also using OpenShift platform. Because when we needed to put them on the cloud, so OpenShift provides us the flexibility to put them on private or public clouds at the same time. So, for the next year, so I want my team to work on so many use cases around generative AI, but using the GPT tools that were served on-premise on our own data centers because we are building this environment. So, we have a GPU farm and OpenShift AI is helping us to manage that GPU farm. And we are building our own GPTs using open-source GPTs, like Llama or DeepSeek, and then many others. And we wanted to be ourselves, because we are a product company at the same time. We are not just the IT power of Turkish Airlines Group, so we are building products for aviation industry. That's why we want our products to be authentic and to work on our on-prem systems, in our private and secure systems in our way. And next year, I expect these environments will grow and we'll add more feature, and then we can use those environments to build our products very faster and securely.
Rebecca Knight
>> Well, we're going to look forward to that conversation in 2026. Serdar, thank you so much for coming on theCUBE.
Serdar Gürbüz
>> Thank you, Rebecca. Thank you.
Rebecca Knight
>> I'm Rebecca Knight for Rob Strechay. Stay tuned for one last segment of today from the Red Hat Summit AnsibleFest 2025. You're watching theCUBE, the leader in enterprise tech news and analysis.
Rebecca Knight
Rob Strechay