I’m excited to share this conversation with Khrystyna Sosiak. Khrystyna is a product manager at TomTom. Before that, she was a lead AI coach at Intel and a senior data scientist at mBank. During this conversation, Khrystyna shared her approach to navigating the complex landscape of AI projects, which includes investing in research, strategically placing bets, fostering stakeholder support, and embracing transparency. 

Links

Khrystyna On LinkedIn

Transcript

[00:00:13 ] Himakara Pieris: I'm, Himakara Pieris. You're listening to smart products. A show where we recognize, celebrate and learn from industry leaders who are solving real-world problems. Using AI.

[00:00:25 ] Himakara Pieris: Khrystyna welcome to smart products.

[00:00:27 ] Khrystyna Sosiak: Thank you. I'm super excited to be here. Thank you for having me. 

[00:00:30 ] Himakara Pieris: To start things off, could you tell us a bit about your background, um, what kind of environments that you've worked in, and also what kind of AI projects that, that you've been part of?

[00:00:39 ] Khrystyna Sosiak: Yes. So, uh, currently I'm a product manager at TomTom. I'm working on the external developer experience and uh, and analytics and billing topics. And in past I was working on the machine learning operations platforms and, uh, in my previous experience was a data scientist. So I was actually working with, [00:01:00 ] uh, with machine learning and with artificial intelligence before I moved into product.

[00:01:05 ] Himakara Pieris: What would be a good example of an AI project that you worked on? 

[00:01:10 ] Khrystyna Sosiak: Probably one of the most, Exciting and interesting, , products that we've been working on that was very powerful is, , understanding the customer's behavior and, and the patterns.

[00:01:22 ] Khrystyna Sosiak: And then based on that ing uh, the right products. So I was working in banks, so we would analyze. All the data that we can find about our customers, right, of course, with two G D P R and making sure that we only use the right data, but, and then making sure that all the communication that goes to the customers is the right communication about the right products and in the right way.

[00:01:46 ] Khrystyna Sosiak: So really understanding the customer needs and, uh, the stage of the customer life and saying that's, that's what the customer need at this point, and that's how we. Understand that and how we can communicate and [00:02:00 ] sell it to the customers. So it's not about only making money, but it's understanding how we can actually.

[00:02:06 ] Khrystyna Sosiak: Go through this journey of life with the customer and supporting them. So, and understanding that by the data that they're generating and by the insights that we can find in this data. And sometimes, you know, and like data that you have like that generated by your transactions and by your history, like, It's a really specific data that show a lot about the person that probably some people even don't know about themselves.

[00:02:33 ] Khrystyna Sosiak: And the real goal is how we can use it for the benefit of the customer and not to harm the customer, right? And, um, we really change the way that we approach them. Uh, we approached the, the marketing communication with the customers, what was very interesting and transform transformational to see how very old fashioned organization would really move in direction into the [00:03:00 ] AI and making sure that all the decisions and the marketing strategies are powered by ai.

[00:03:06 ] Khrystyna Sosiak: So yeah, that was very interesting. It took us a long time. We made a lot of mistakes on the way, but it was a super interesting learning experience. 

[00:03:17 ] Himakara Pieris: If I take a step back, so we're talking about mbank a consumer banking operation and reaching out the customers at the right time is something very important to, to become that part of the customer's daily life or, or their journey.

[00:03:32 ] Himakara Pieris: How was that done before and what point. Did the bank decide to explore AI as a possible, , solution to, possible tool to improve the, communications with the customers? 

[00:03:46 ] Khrystyna Sosiak: I think the turning point was understanding that where the, you know, not only trends, but like the industry goals, right? And really AI powers the financial industry and the financial industry thing [00:04:00 ] in general.

[00:04:00 ] Khrystyna Sosiak: It's been very innovative in, uh, Trying to adopt the new technology and trying to make sure that the customers get the best experience before it was all triggered by the events. So you can imagine, I mean, it's still used widely, right? And when we talk about recommendation systems and like how the communication is done, right?

[00:04:20 ] Khrystyna Sosiak: You open the webpage, you open the app, and you, you scroll through some pages, you know about the credit card, for example, and then, Next day you would receive the email saying, Hey, here's the discount. Or in today, someone would call and say, Hey, we saw that you are interested in a credit card. Do you want to order the credit card?

[00:04:41 ] Khrystyna Sosiak: We have this discount for you. And usually it was triggered by one event, right? Or the, the sequence of events. But it's also very event triggering, right? So you only can. You only can base your recommendations on what customer actually does on the webpage. You don't really go into details [00:05:00 ] of like, okay, what are the factors about the customers that can affect that and what is actually the things that they need?

[00:05:07 ] Khrystyna Sosiak: It's, um, so yeah, it was something that was used. For years and, uh, it worked. You know, there was some success rates there, so I cannot say it didn't work, but we know that moving forward expectations of the customers are higher because when we live in the era of ai, when you have, you know, Netflix and Facebook with the recommendation title, your.

[00:05:30 ] Khrystyna Sosiak: You know, reactions and like what you see, what you like, what you don't like. Really we need to be there as well. And just saying you clicked on something and that's why we think it's could be interesting for you. It's not good enough anymore. 

[00:05:45 ] Himakara Pieris: Sounds, like the previous, , approach for doing this is purely driven by specific events.

[00:05:51 ] Himakara Pieris: You have a rule-based system. If you click on this page, then you must be interested in this product. Let's unleash all the marketing communication , to sell that product [00:06:00 ] towards you. Whereas now, , the idea is we can possibly make this better by using ai. , To make sure that we are making more personalized and more relevant recommendations to the customer.

[00:06:10 ] Himakara Pieris: And by doing that, you improve the customer's experience and you would also improve the sort of the clickthroughs or, or, or signups for that product that you're, that you're positioning for the customer. , so when you start there, so it sounds like it started more with a. With an experimental approach.

[00:06:26 ] Himakara Pieris: Is that right where you're saying, okay, we have this way, we are doing things now we have all these new tools that are coming to the market, coming to the world. Let's pick them up and see whether we can move the needle, , with these tools rather than the, the method that we are doing now, which is our baseline.

[00:06:42 ] Himakara Pieris: Is that a fair assessment? 

[00:06:44 ] Khrystyna Sosiak: It's for assessment and to be honest, it's for assessment not only about this project and not only about this experience, about almost all of the experiences that I had with the big companies or even small companies trying to get into the AI and trying, [00:07:00 ] you know, if it's. Not like the, the companies that actually build it, right?

[00:07:03 ] Khrystyna Sosiak: That they're trying to adopt it. It's really about, we have some data, we see the trends, we see that our competitors are using it, so how can we benefit from it? And I can see very often, like also talking to my colleagues and to my friends that there's very. There's a lot of companies that would hire like, uh, machine learning or, uh, engineer or data scientists say, that's the data we have.

[00:07:26 ] Khrystyna Sosiak: We have no idea what we can do with it. You know, try to figure something out. And I think sometimes there is some wrong expectations about Right. What we can do and what we cannot do. So yeah, it's all started like that, right? We have the data. Here's the set of the business. Problems that we have, and then let's iterate.

[00:07:46 ] Khrystyna Sosiak: Let's see what gonna work, what not gonna work. And a lot of things fails before something starts working. Right. And I think that's a learning experience that once you, you cannot, like, you cannot get there. [00:08:00 ] If you, they make mistakes and learn on a way, because then your experience and your success is much more meaningful because you actually understand what you've done and how you've done it and why you made those informed decisions about some steps of the machine learning process that we have.

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