Last year, Daria Davydova earned her master's from the HSE Graduate School of Business and launched a new line of business at Alfa-Bank. Earlier this year, she won a prize at the TECH & AI Awards for the best AI solution for business process optimisation. In an interview with Success Builder, Daria talks about her background in consulting, 'going to the gemba' at the Revda Non-Ferrous Metals Processing Works, the benefits of having tea with sweets, and the real potential of AI.
— Daria, is the TECH & AI Awards a prestigious award in the digital world?
— A small spoiler: this is not the only award we have received for the solution we developed; therefore, we can say with confidence that our work has been recognised by the community at the highest level. And what we are currently developing will be even more significant.
— Can you explain the essence of your solution to someone who is not familiar with the field?
— Since this particular solution is just one of many large-scale solutions that affect not only Alfa-Bank’s operations but the banking industry as a whole, it is probably worth explaining first what I do. I work in the Alfa-Bank department responsible for all internal operational processes—that is, the processes that take place after direct interaction with a client. There are almost two thousand such processes, and my team’s task is to automate them using artificial intelligence.
Speaking very briefly about the solution in question, we expanded the functionality of our existing RPA (robotic process automation–Ed.) platform by integrating AI capabilities, enabling it to create RPA agents.
— As far as I know, this is the second stage of your career, while you started out in a completely different field.
— I’m a rare case of someone who started out in the field they trained for and gradually acquired new expertise. My first degree is in applied mathematics, and for a long time I was looking for ways to put it to use. I contacted various industrial companies and attended career fairs where representatives of oil and gas companies were present, since I was studying at the Russian State University of Oil and Gas. I kept asking them, 'Do you need mathematicians?' And they would answer, 'We need pipe-rolling operators,' or 'We need geologists,' or 'We need drilling engineers.' Eventually, I started looking for opportunities in other industries. After graduating, I joined Beeline, a telecommunications company, where I worked in the targeted communications department. My job involved building customer churn forecasting models, response models for specific communications, and similar solutions. You could say that I entered the field of Data Science when it was still in its infancy. Historically, at least in Russia, the field began to develop primarily in the banking and telecommunications industries.
— So you have been developing alongside the field itself?
— Yes. At first, I worked simply as a data scientist. Then I joined a consulting company, where I was promoted to lead the Data Science implementation practice for banks, telecom, and retailers. We had major clients in all these sectors, and I gained valuable experience in each of them. But at some point, I realised that I wanted to be closer to the business, understand its objectives better, and have a direct impact on them. In consulting, I had reached a point where I could no longer immerse myself in every project. As I took on more responsibility for managing people and overseeing broader portfolio objectives, I became increasingly disconnected from the actual projects. This prompted me to focus on a specific business, but after a while, that was no longer enough either. I realised that I needed to move into industry.
— Going into industry seems like quite an unorthodox decision. What prompted it?
— Maybe it’s because I’m from Berezniki, the second-largest city in the Perm Region, the chemical capital of the Urals and a major industrial centre. There are many mines and factories producing mineral fertilisers and other products. Since childhood, I’ve always heard that real men work in a factory. And although my father is a Candidate of Sciences in Physics and Mathematics and never worked at a machine, he was involved in improving production processes. So, in a way, he was part of that world too. So I started looking at job opportunities in the industry where I could build on my previous experience. But after speaking with several companies, I realised that they all preferred candidates with prior experience in their particular industry. And I didn’t have any at the time.
— It’s a classic problem: everyone wants employees with experience, but how can a newcomer gain that experience? Did you find an answer?
— One evening, it occurred to me that if I didn’t have any industry experience, I could learn from people who did. In other words, I needed to get some training. I always feel less confident in areas I don’t know much about, and one way to build that confidence is to learn as much as possible about a new field. At the HSE Graduate School of Business, I found a Master’s programme in Production Systems and Operational Excellence. I had never heard of production systems before, but I thought that first, the programme included the word 'production,' which was closely related to what I was looking for, and second, it focused on management, an area in which I already had professional experience. But what really caught my attention was the promise of site visits to manufacturing facilities. I wanted that kind of hands-on immersion, so I decided to give it a try.
— Did you find what you were looking for at HSE University?
— I don’t think I fully understood what we would be taught when I enrolled. But in the end, I got much more than I could have expected. This master’s programme completely changed my perspective. I call it a mini-MBA because many of the subjects are the same, and even some of the instructors are the same people who teach the MBA programme. I did my term paper in collaboration with the Ural Mining and Metallurgical Company (UGMK), which is based in Yekaterinburg. I’m grateful to the members of the Academic Council who helped me secure the placement. I took several periods of leave from my main job and used the time to travel to Revda, a city near Yekaterinburg, where I worked on an A3 consulting project at the Revda Non-Ferrous Metals Processing Works. My task there was closely aligned with my previous experience: to improve the production planning system. But instead of simply gathering requirements and then moving on to develop a planning model, we observed the processes firsthand and spoke with employees to understand the root causes of why the existing planning system was not working effectively. As a result, I designed an algorithm, but I also developed a programme of organisational changes needed to make the solution work in practice. Incidentally, I’m still in touch with the people who are working on it today.
— Was there any resistance, like, 'Here comes a smart girl from Moscow trying to teach us how to do our jobs, but she’s never seen the actual production machinery'?
— Naturally. When I arrived and started asking why their planning system wasn’t working, their first reaction was, 'Who are you anyway, young lady?' I was a visiting student from Moscow, a young woman in a facility where almost all the employees were men. But I managed to win them over and change their attitude. I think the key is to treat everyone with respect and understanding. I have great respect for production workers because I believe they are among the most genuine people, and perhaps they could sense that. I would visit their workplace, bring some sweets, and invite them to have tea. I would ask how they were doing and listen to what they had to say. At some point, they warmed to me and started opening up. It turned out that before I came along, no one had asked them how things were going; they had only been asked, 'Why isn’t this working? Why isn’t that working?' Once we established a rapport, the whole process changed. I think that was one of the reasons we were successful. I wrote my master’s thesis at a plant in Sergiev Posad, and that was another incredibly valuable experience.
— But in the end, you still chose to work for a bank.
— I didn’t want to work directly in my new field as an operations improvement manager. I wanted to combine both my previous experience and the expertise I had gained during my master’s programme. And that was an even greater challenge. I explored various opportunities and spoke with several companies, including the manufacturing companies where I had done my term paper and master’s thesis. But they didn’t have positions where I could combine process improvement with AI implementation. I found such a position at Alfa-Bank, and I’m very happy with it. While working in a bank may seem as far removed from industrial production as possible, that’s not really the case: in the operations department, many processes are organised on a conveyor-belt principle, much like in manufacturing. The departments themselves are even called factories. And here, too, I have colleagues with whom it’s a pleasure to sit down for tea and sweets.
— What are some of the concerns bank employees have? Are they worried that AI might replace them?
— When I first joined the operations department, I heard some negative comments, such as, 'Now they’re going to take our processes apart, introduce AI everywhere, and make us redundant.' But the reality is that the parts of the processes that can be automated have already been automated, while the tasks that are still performed manually carry too much risk to be delegated to AI. Our solutions—and that's how we position them—are designed to make employees’ work easier, not replace them. To address these concerns, we promote AI adoption within our department and train employees to use it effectively. Just today, we held an AI prompting hackathon for our internal staff, and everyone enjoyed it.
— How long ago did you join Alfa-Bank?
— Last year, right after completing my master’s. Alfa-Bank didn’t have this line of business before, so I built it from scratch. Now I have a large team developing and implementing all these large-scale projects. We plan to launch four more this year, so we have big plans ahead.
— How do you determine which banking processes need improvement most? Is it better to optimise what already works or rebuild the least efficient parts of the banking machinery?
— Naturally, we start with projects that are relatively easy to implement but can have a significant impact—the so-called 'low-hanging fruit,' a concept we learned at the HSE Graduate School of Business. But when we are planning a large-scale transformation—and I would describe what we are doing in the operations department as transformation—we don’t just treat individual symptoms but develop a strategy. We begin by 'going to the gemba,' an established principle of Japanese lean management, to see the actual process firsthand. All my visits to manufacturing facilities during my studies were also examples of 'going to the gemba.' Then, based on what we observe, we develop a strategy that guides us in selecting the right projects.
— Are all banks interested in AI?
— Not just banks. AI is currently riding a wave of hype. In reality, artificial intelligence is a broad field. It includes classical machine learning, which has been developing for more than 20 years, as well as newer technologies such as LLMs (large language models) and VLMs (vision-language models). And since the beginning of last year, we’ve also seen a surge in the implementation of AI agents. These are the latest trends, the cutting edge of the field. Other banks are investing in them too, as they don’t want to miss this wave.
— How big, in your opinion, is the hype around AI?
— There’s definitely a lot of hype. Just recently, I attended a university open day with my brother, and AI was the buzzword of the day—it was mentioned as something being studied across different departments. At the same time, many professionals in the job market highlight AI implementation experience on their CVs, even without a good understanding of what AI is or how it works. The same kind of misunderstanding is common among both managers and employees. For example, after I joined the bank, we started collecting employees’ ideas as to where AI could be applied, and people came to us with all sorts of proposals, assuming that 'AI can figure it out.' For some reason, many people now see AI as a universal solution to every problem. But that’s not the case. Over time, as the hype subsides, AI will become just another tool that virtually all employees are expected to use—much like Excel, for example.
— What advice would you give to future colleagues who are still studying?
— If you want to work on AI implementation, learn the nuts and bolts. First, you need a solid foundation in mathematics. Second, you must understand the potential benefits of a particular model. Mathematicians often focus entirely on algorithms: ‘I’m going to implement this algorithm, achieve a certain level of performance on a specific metric, and everything will be fine.’ But they may overlook how exactly the model will be integrated into business processes. This is something you need to think about from the very beginning, at the model design stage. There are many potential pitfalls, so it’s better to anticipate them in advance.
— You’re a very enthusiastic person. What is most important to you in your work?
— It’s important to me to know that our solutions benefit both the business and the wider community. If I had to define a measure of success, it would probably be the value we create.