Bridging the Gap Between Engineering and Business Strategy with Dr. Elena Alikhachkina

In this episode, we’re joined by Dr. Elena Alikhachkina, Chief Data and AI Officer of TE Connectivity, director, board advisor and author, to discuss why AI requires a product mindset, how learning data fuels continuous AI loops and what boards must do to govern AI responsibly. She explains why technical skills alone are not enough, how trust is changing in the era of digital employees and why 2026 will be a turning point for data and enterprise transformation.

‍

Key Takeaways:

‍

00:00 Introduction.

02:38 AI is reshaping data careers and elevating product and business skills.

06:00 Product leaders go beyond dashboards to uncover the real business problem.

09:43 Data teams must embed in operations and “walk the floor” to drive adoption.

13:33 Data is not oil — it must power continuous learning loops.

16:15 AI pilots fail when they do not close the loop with user feedback and new data.

20:20 Boards face confusion and must translate AI into governance responsibilities.

23:00 Data governance and data readiness must become board-level metrics.

25:00 Leaders will manage digital employees and agents alongside humans.

28:21 2026 will move AI beyond chat interfaces into embedded enterprise systems.

30:50 The AI opportunity is underestimated, but security and compliance may slow scale.

‍

Resources Mentioned:

‍

Re:Coded newsletter

National Association of Corporate Directors website

Johnson and Johnson website

TE Connectivity website

‍

[00:00:00] Elena: I think it would be interesting to see how universities are gonna start changing in 2026 because it's a problem, right? So, I mean, students cannot find a job.

‍

[00:00:10] Elena: Universities cannot provide the proper education. so, you know, people almost have a, to get a double degree. Right. So it's not good enough to be technical. You need to have a business degree. It's not good enough to be business. You have to understand technology, right? 

‍

[00:00:27] 

‍

[00:00:53] Anthony: Welcome back to Data Masters. Today we're joined by a leader who views the evolution of data and ai, not just as a technical shift, but as a professional journey. Dr. Elena Ali Hasina has been a global leader of data and analytics across a number of large global organizations across a wide variety of industries, including TE Connectivity, Roche.

‍

[00:01:23] Anthony: Danone j and j, just to mention a few. But Elena is not just an executive. She's also a longtime friend and a mentor to the industry. Through her career newsletter, recoded career, which I would encourage everyone to go check out on LinkedIn in the newsletter, she challenged us, us to stop thinking of data as the new oil and start thinking about it as a living business process.

‍

[00:01:53] Anthony: She has a PhD in economics and a master's in software engineering, and she's really the perfect guy to help us think about our legacy mindsets and prepare for a new Ag agentic AI future. So Elena, welcome back as one of our first repeat guests on Data Masters. Welcome back to the show.

‍

[00:02:15] Elena: Thank you so much, Anthony. I'm always happy to be back.

‍

[00:02:19] Anthony: So maybe, let's start with the career newsletter 'cause that's something very new since we last talked. And maybe share a little bit about your vision for the newsletter, what you were thinking about. And I mean, there's just so much rich content on there. I would encourage people, but maybe a, a few things that you think have resonated effectively.

‍

[00:02:38] Elena: yes. Thank you for mentioning it So. With development of AI and me having been leading really large organizations sometimes over the thousand of people organizations, I started realizing more and more that people I have been leading for, for ages actually in the, in the biggest danger of ai. So people who work in data analytics and ai, Their job is changing. So we talk about the business job is changing, but, but actually see that the job is changing much quicker for technical people. So AI can write the quote, so like, data scientists, profession is not as hot as it used to be because remember we have been saying it's the success profession, right?

‍

[00:03:24] Elena: So not anymore. AI can also. Get insights much quicker than any analyst can get, right? So I started this, this newsletter to help people who are, have been always a part of my teams. I have been leading them and coaching for, for ages to think differently about next steps, right? And next steps actually coming from looking at those into soft skills, looking into business skills and becoming more, a product owners, basically the product career, right? So we all know in industry of Google, like in a Google world, in Amazon World Meta Award, right? So the product teams, play a critical role. But if you look at traditional enterprises, healthcare industry, consumer goods, they never put like a product skill to be the skill. And now with zi, it's absolutely needed, right? So this is why I started this newsletter to give a people who I, I have been leading my entire career to think about the future.

‍

[00:04:30] Anthony: So the idea here is to not think about the data analyst job as. Primarily generating code, but being that product manager for lack of a better term or product leader. share a little bit about your view of what it means to be a product leader. I mean, when I think about, and you use the analogy of Google or Meta the classic product leadership role is about understanding.

[00:05:00] Anthony: User requirements, translating that into language that an engineer could understand. And I think also in many cases, translating the product functionality that an engineering team builds into language that a prospect or a customer can understand. I think that makes sense to people, but how does it make sense in the context of a data career?

‍

[00:05:20] Elena: So this is absolutely exactly the same, right? Because with ai data is becoming at the center of every application. Right. So, and so. Exactly as you described. The user centricity is the key. So understanding the business cycle, understanding how this data, how ai, how technology are the part of the business cycle, right? so ability to properly ask questions because like, you know, like in, in reality what I'm seeing, like traditional business analyst. not deeply asking questions, right? So traditional business analyst is saying like, oh, you need a dashboard? Okay, great. I'm gonna create a dashboard for you. Right? But you need to go deeply into like what problem you're trying to solve, right? So, I'll give you. Kind of the contrast, like what is happening now and what we, what the product manager is gonna do. Right. So, not long, a long time ago, we have been running the workshop, right? And during the workshop, our business analysts have been, you know, pushing business to identify the problem. So the problem came, we need to decrease the number of suppliers. Right. So, and traditional analyst is gonna say, okay, great. Decrease number of suppliers. I'm gonna go and build the model. And, you know, and you're gonna be able to decrease, right? you're the product product owner, you, you're not gonna stop on this, right? So you are gonna actually ask additional questions. Imagine that we decrease the number. Here is the 10 suppliers you're gonna remove from the list. How it's gonna work, right? So this is where the real, the real question is gonna come because when we ask this question during the workshop, business said, oh, wait a second.

‍

[00:07:00] Elena: No, I cannot remove the suppliers because, because they are critical. Okay? So then it does mean, why we asking to build a model in the first place? Right? So, the product mindset is to go deeper into real problem, not stopping into. Kind of, you know, here is a solution. Okay. How it's gonna work, how you gonna make decision, is it really gonna be adopted?

‍

[00:07:25] Elena: And how it's gonna be adopted and by whom, who is gonna make this type of decisions, right? In reality, it's such a rarer skillset, right? So, and, and this is the person, you know, I'm saying this because I have been leading these teams for ages people usually excited to get their hands dirty and, and start doing the technical work, right? But the skillset which is needed is creating which I call like AI loops, right? So AI Loops is basically a continuation of learning, continuation of business processes. The model gets in the value, right? So these, these are the skills which I see quite of mission and they are missing on both sides, right? So the difference between Amazon. And traditional enterprise, you know, let's say healthcare company, right? Because Amazon builds in products for external customers in traditional enterprises. There are products, data products, I mean, and data products. I'm calling Nudge just data, but it's like AI is also the data product, right? So they build it for internal customers. So, it's really different metrics, relationships, right? So it's the product management skills, but you do have your internal customers. Yep.

‍

[00:08:40] Anthony: one of the techniques that I saw you writing about that I really loved is this idea of walking the floor like being very present in the physical space of. The the customer to use your language. We've experienced this even at, at Tamer with a retail customer of ours. We literally had our product managers go and spend time in the retail location watching how store associates interact with customers, and then understanding why it was so valuable to that retailer to create a 360 view of the customer and understand the household relationships.

‍

[00:09:19] Anthony: That the a store associate needed in order to provide a great experience for that customer as they came into the store. And I think this is a good example of exactly what you're talking about. So, but to bring that back to you for a second it's your view is that a data analyst who, who've used their job as primarily sitting behind a computer writing code.

‍

[00:09:43] Anthony: Who's not gonna be successful in a gen AI world, but somebody who's present in the day-to-day operations more likely to be successful. Is that fair? 

‍

[00:09:52] Elena: yes. So basically you're not gonna be successful by building your technical skills. You are gonna be successful by your ability to connect technologies with the business processes and being able actually to create, which I call again, this like AI loops basically. Right? So in AI Loop, just to clarify it, the. Has a data component, has a union system component, user experience component, right? And knowledge built in, right? So, if there is no such loop, somebody needs to be an architect of this loop, right? So, and I do see this the role as a. As a product manager role. Right. So Anthony, I'm super happy to hear, to hear that you actually send in your teams to do this observations because this is so powerful.

‍

[00:10:37] Elena: This is so powerful, right? So, I mean, I had situations when data scientists have been showing me proudly like a predictive maintenance model at the, at the factory and never, never ever visited the factory, right? So, I mean, not surprised that this model didn't work in real life. I mean, it sounds like funny, but when we come, like, so for example at the like CDIQ conference in Boston, so this gets discussed between everybody, that in reality people far away from business process. People like in data analytics are not fully embedded. so I'm thinking the model and roles, because the product management role is actually embedded directly into, into the business, right? I, I do see quite a big shift is gonna, is, is going to happen, right? So this, these are people gonna be actually sitting on the business side. Maybe only infrastructure is gonna be managed by like CIO, know, CCDO, whatever, right? but you know, the, the real solutions like a data analytics solutions they will be sitting with the business because they are the business.

‍

[00:11:44] Anthony: Right. Actually this is a, a really good segue or point. There's this, I hear this a lot in software companies where the technical leadership in the organization will say need to understand, or, or I, you know, what is the business say, you know, or we need to check in with the business. And I always remind them like.

‍

[00:12:07] Anthony: You are the business, right? You're in the business of writing software. There, there is no them like you are them, they are you. There's no this distinction between the technical people who write code or analyze data and some other entity within that company that is the business. Like, no, you're a, if you're a manufacturing company and there's a factory floor, like.

‍

[00:12:31] Anthony: That's your factory floor, to use your example. Or in, in my example, if our customer is a retail organization and we're providing software to that retail organization, you know, that's the business. The business is delivering the software. So there is no, the business like that and, and this sense of us and them within any organization, I think is quite corrosive.

‍

[00:12:55] Anthony: But a. Extending that for a minute. You also have, I think, what is a really important insight about the role of data in that conversation. The historically we've traded treated data like a resource, something that means to be extracted or mined or refined. The common analogy, which admittedly you have been very vocal against, is this idea of data is oil.

‍

[00:13:19] Anthony: You know, data is the oil of our, of our time. And I will also for the. Record admit to having used that metaphor, but so what's wrong with that metaphor and, and how do you see it differently?

‍

[00:13:33] Elena: Yes, definitely. I remember we had a conversation about this. So be because I mean, it's, it's kind of attractive to say, oh, data is a new oil. Right? And I understand why, why people say this, because they, they basically trying to compare. This to gold or compare to like, oh, this is expensive, we have to take care of, right? So what I, why I don't like this analogy with the oil. It's because we burn in data, right? So, and what is important in AI and in my upcoming book, I actually have a full chapter where I explain about how AI is changing the business model. And so, AI. Is creating like a, like a loaning loops in the business, right? So, data is a number one part of your successful AI loop, right? So basically you, you have to have a new way, new data, which I call the loaning data. Right. Which I think actually the, the tamer solution is a perfect analogy to loan in data you know, you combine in different parts, but you constantly loan, right? So, comparing to the oil, you just, you know, take a copy and burn it. Take a copy and burn, and this is what we have been doing for hs, right? Take a copy, create a dashboard, take a copy, create a, you know, the PowerPoint presentation or like a model, right? So many AI pilots which companies are doing right now are not actually creating the loop, right?

‍

[00:14:55] Elena: Because they, they take the data. Copy, make it really perfect, great copy, put into the model, you know, get the result. But the purpose of AI is to learn, right? So, you know, you need to constantly feed more data, constantly create, you know, this type of loop, right? so the terminology I love to use is loaning data. So this is what we need for the future, the data, which is constantly learned from interactions, from customer interactions, from you know, usage by people from you know, different, different types of data actually coming in more and more into the system.

‍

[00:15:34] Anthony: so your criticism of the O oil analogy is that it's a one way direction. You take, it's your use, your framing, you take the oil, you burn it. And I. Very much agree with that because I think if you look inside many enterprises today, it's like the floor is littered with unused dashboards.

‍

[00:15:52] Anthony: It's a, you know, but maybe the analogy is more like a lithium ion battery, which is you, you charge it up. And then you discharge it, you use it. And in that sense, your AI learning loops are really about sort of charge the battery, discharge, the battery charge the battery, dis bring in insights push those insights out.

‍

[00:16:13] Anthony: Is that fair? Hmm.

‍

[00:16:15] Elena: Yeah, no, a absolutely, it's absolutely fair. So let's give actually example, like, GENI type, right? So, many companies have a lot of docu documents, right? So could be legal documents, could be. Re research documents could be engineering documents, right? So companies started creating repositories which GNAI can mine and get a different type of insights from this type of repositories, right? but there is no loaning data here because what is happening is people work in the old way, right? So now I am, let's say the date, the researcher who is creating maybe like a, like a formula in pharmaceutical company so I can type in this tool, I can find what was done before what other people did, right? but the user experience is kind of stopped because people take this and put back, back into Excel spreadsheet. So they, they do not put, but the knowledge, they, they are learned personally. They are created back to the system, right? So, you know, to close the loop, we need to, to put the, the user experience, the user interactions and the data back to the system, right? So again, the, the pilots are great. They may, might create the productivity, but they are not creating the true acceleration loop. And, and I'm not the first one to talk about you know, the acceleration loops because acceleration loops have been discussed back in like 1970s, right? So it's like how we create the business models when we have a continuous loneliness system, right? So now since we have ai, it's becoming a reality. We, we can create onion enterprises.

‍

[00:17:55] 

‍

[00:18:34] Anthony: very much agree and the pace and quantity. That you can process is dramatically higher. So in your example of the documents, if a manufacturing organization had specifications on hundreds of thousands of variations on a set of products, the idea that a single human being could reasonably read all of that, understand it, digest it.

‍

[00:19:02] Anthony: Produce some sort of insight and output that's possible. But that's a multi-month effort at best. And now that's something that could be reasonably done in a few minutes or hours. And so it's the, it's not the ability to do it, it's the cycle time that's changed.

‍

[00:19:21] Elena: Yes, absolutely. It's like cycle time has changed, right? So we don't have you know, going back to data, we don't have time anymore to create like a rule-based type of systems, right? So we need to create line systems. Yep.

‍

[00:19:35] Anthony: Which I mean, in some sense we have perfect analogies for this, which is people like you, you could hire, you know, tens of thousands of people to read, summarize.

‍

[00:19:49] Elena: Yeah.

‍

[00:19:49] Anthony: Gain insights, et cetera, but that would obviously not be cost effective. So the, the real opportunity is to think about how to change the economics of this, because now you can have the speed and cycle time you want, but at a reasonable cost.

‍

[00:20:06] Elena: Yeah, no, absolutely. It's, it's exactly, it's like traditional business model. You grow by eating more resources. AI business model you grow by accelerating your AI loops.

‍

[00:20:18] Anthony: So.

‍

[00:20:20] Anthony: We've talked a little bit about maybe the junior people who are, you know, using data in their day-to-day world. The middle management who's thinking about how to create these AI loops and improve cycle times. But the common refrain I hear from boards of directors and, and very senior leadership is maybe something closer to fear and uncertainty.

‍

[00:20:44] Anthony: This idea that we know AI is coming, we think it's important, we're not quite sure what to do about it. And the, you know, the, the best in class seems to be just telling everyone AI's here and they should install it or something, and. You know, that's it. And then hoping something good happens, which is hope is not a great strategy.

‍

[00:21:04] Anthony: I guess the good news is you're writing a whole book on this. So share what you're writing. 'cause I think it's totally fascinating. And, and without ruining the surprise maybe share some insights you gained out of writing on the topic.

‍

[00:21:17] Elena: Yeah, so, absolutely. So, in 2025, joined National Association of Corporate Directors and I'm completing my certification as a board director with this association right now. so what I noticed by attending corporate director sessions that. There is such a big confusion in boardrooms.

‍

[00:21:36] Elena: So this national association actually doing quite of good work they do quite of webinars. They invite different parties. Right. But what I found that that it's like so complicated. It's not really translated into responsibilities of the board directors. I took a chance being operating executive.

‍

[00:21:56] Elena: For so many years to translate it right. But I did not do this alone. So I do have actually, co-authors for this book. So one co-author she's a professor, is in nyu. And the second is my former boss actually from Johnson and Johnson. Right. and plus we interviewed a lot of people.

‍

[00:22:14] Elena: We interviewed over 150 people across North America and across Europe. People who are board directors, people also who operate in executive and technology and data. So this is how this book came in place, right? So I think the key aspects in this book maybe like what is basically the knowhow, I would say, what is really different I mean, first of all, not, not so many books for, for the boards right now, 

‍

[00:22:40] Elena: right? 

‍

[00:22:41] Elena: The, the books which are come in, they usually explain the technological side, right? So in my book I'm explaining first of all is how the business is changing, right? So this is where I'm going more into, different business loops and explaining what is changing in terms of the governance because, you know, the purpose of the board is provide the business governance, right? And translating this into traditional responsibilities of the corporate board directors, right? So it's almost like analogy you know, how the board have been doing. Financial financial duties or have been doing, for example, like information security, right? And comparing what needs to be done for ai. So also the second I am putting quite a lot of effort in this book explaining that the governance of AI board is starting from data. So data governance have to become the board responsibility, and I'm explaining why it is. So I'm introducing the score which actually my PRIETARY score and I'm gonna have a digital diagnostic coming with the score. it's a data rating. Like, so boards the same way as they measure right now, information security. at every board meeting they actually talk about like, what is my info security score? And it's quietly available industry. We, we need to start measuring is what is my data readiness score, what risks I have. you know, with environments and the data, which are feed in different systems, right? so, and the sort is related to about, you know, the, the people development part, right? So what type of skills we have to develop in people. so this is how this book is different from, you know, from others because others are mostly focused on, on technological side and explaining technology. So I think we have enough books explaining technology.

‍

[00:24:31] Anthony: and again, it connects nicely to what you were saying before, which is as a, even as a junior practitioner, as a data analyst, it's not about the ability to write code as a middle manager. It's not about the ability to just impose AI strategies but really starting to understand the business, the underlying business process, the AI loops that you can create.

‍

[00:24:53] Anthony: And now you're translating that. Into a language and a set of strategies that a board could understand, which I think is extraordinarily valuable.

‍

[00:25:00] Elena: and there are so many changes because I would, I would say Anton is so there are some, you know, questions in this book. I had so many conversations when we had we have been interviewing people for the book, right? So for example, I'm talking to the sales leader, somebody who is reading, like really big national sales organization, over a hundred people, right? And he's super excited to get the agents working for him to get a Gen ZKI and, and I'm asking the question is like, how are you gonna manage performance? And he was like, what do you mean? I was like, this is your employee, right?

‍

[00:25:33] Elena: So like now you have a hundred people in the future, you're gonna have maybe 10. People, and you're gonna have a hundred digital employees and they're gonna be performing on your behalf. I mean, who is gonna be accountable if something goes wrong? Right? And, and you can see that this person did not ask this question because the assumption was, everything technology is a CIO accountability.

‍

[00:25:57] Anthony: Right. Someone else's problem.

‍

[00:25:59] Elena: is going to be managing agents for me. No, no, no. Sorry. You are gonna be managing agents because these are your digital employees. Right? So basically these are type of new skills everybody, you know, everybody need,

‍

[00:26:14] Elena: right? another example, which I think this, this week in my newsletter, I actually talked about the trust, right?

‍

[00:26:20] Elena: So like what is the number one dysfunction of the team Trust? I mean, everybody's gonna say it because we all read Patrick, and so in your book, right? but is it the same trust? No.

‍

[00:26:32] Anthony: does trust mean? Yeah. In the context of a.

‍

[00:26:34] Elena: is now data is a part of the trust. You know, people don't trust data, don't make decisions, don't take actions, right? So data technology is a part of the trust. So did we ever think about this? No. Never. So even like development of the people is, is now becoming really different.

‍

[00:26:51] Anthony: And people use this language a lot when they're using, for example, coding agents. I don't trust what it did. I have to go back and verify. I need to write test plans and. and use that as a, I mean, really if you think about in software engineering, a test plan is just a way of verifying that changes have not broken downstream functions, et cetera.

‍

[00:27:12] Anthony: So, yeah, no, a hundred percent. So this idea of trust is different. You know, the kind of personal trust you create with people is different, perhaps, although, frankly, people use the same language in the context of ai. They, they, they anthropomorphize it quite a lot. And by casting our eyes forward into the future, I'm gonna put you on the spot as a, noted expert, an author, and you know, someone who's thinking deeply about this stuff.

‍

[00:27:40] Anthony: I'm gonna ask you to put your reputation on the line and make some predictions for 2026. I think it's fair that we could characterize this last year. This is my opinion as a kind of pilot phase of ai. I think a lot of organizations have in your, in your using your framing, they've given people the technology without any real sense of what they're gonna do with it, with the kind of hope that, well, hopefully someone will do something good, you know?

‍

[00:28:07] Anthony: So if, if 2025 was the AI pilot. what does 2026 hold for us as it relates to data analytics, ai corporate governance, et cetera. So make some bold predictions.

‍

[00:28:21] Elena: Yes, absolutely. So I, I would make maybe prediction, but also maybe some recommendation to people how I would approach it, right? So first of all, I hope in 26 we are gonna stop thinking about AI as a, as a chat g PT on my phone. This, this is, this is the majority of people think about ai, right? So, and really start understanding that AI is way, way bigger than having the GPT on your phone. I really hope that 2025 companies are gonna start paying more attention into. they already have embedded in systems like, so SAP, Salesforce, I mean major applications. They already have so much ai go and use it. It doesn't even cost you money. You don't, don't even need to move the data, right? So it's already system and SI sitting and systems and go it.

‍

[00:29:11] Elena: So this could be the first. Step, you know, to maximizing, right? We are gonna see the growth of the agents. I mean this is the reality. So many development companies, I'm in conversations, they have enormous demand for coming for agent KI, right? So, we still have barriers and we are probably gonna continue having barriers in information security. Because you know, it's not well developed enough to get into like, you know, truly AI application and adjacent applications, right? So, I hope we are gonna see some better solutions come in, in information security and compliance space these, these are the areas which actually gonna slow, slow down ai, right? I think it's also gonna become a year of the. Turning point to more attention to data. Right. Because we were, we were hoping that 25 is gonna be this year. It's kind of yes or not right? but I already see kind of, you know, signs on the market that data is becoming like,

‍

[00:30:12] Anthony: Maybe 20, 25 was the year people realized they had a problem,

‍

[00:30:16] Elena: Exactly.

‍

[00:30:17] Anthony: is the year they start putting in place some solutions.

‍

[00:30:19] Elena: I think 20, yeah, 26, 27 people gonna start, you know, thinking about solutions, right? So I also hope that 26 people are gonna realize that given people a technical training is not gonna be good enough. Right. and I can definitely confirm this from my recent and manufacturing like North America like, manufacturing Leadership Council, because I was talking about the digital, digital leadership and entire population of manufacturing leaders.

‍

[00:30:47] Elena: They said yeah, we don't need to teach more tools. We need to

‍

[00:30:50] Anthony: Right.

‍

[00:30:51] Elena: people how to think. Right. So I think this become a reality. Universities, I think it would be interesting to see how universities are gonna start changing in 2026 because it's a problem, right? So, I mean, students cannot find a job.

‍

[00:31:07] Elena: Universities cannot provide the proper education. so, you know, people almost have a, to get a double degree. Right. So it's not good enough to be technical. You need to have a business degree. It's not good enough to be business. You have to understand technology, right? So I think these are type of changes we're gonna start seeing coming.

‍

[00:31:28] Anthony: Well, I think those are very pressing. That'll be very my hope and goal is that we can hop on a similar podcast in six months and check in to see how directionally correct we are. But I think.

‍

[00:31:40] Elena: absolutely let, let's do it. And I just want maybe to finish and say this loudly so people talk about the eye bubble. See the bubble. I actually see that, we underestimate an opportunity and, and I'm saying this being through social and digital kind of, you know, smartphone type of, you know, bubble, right? So, we might have a bubble when we find, a lot of companies, who maybe not have enough experience in understanding real business processes. So in this site may, maybe there is a, a little bit startup bubble when, know, companies get funds, but they not really don't see how they're gonna be embedded into future enterprises, right. In general AI is not bubble it, it's gonna continue you know, continue developing. And it's not just my words. So I was listening JP Morgan presentation. So, they kind of agreed with me that there is no, such as AI bubble.

‍

[00:32:41] Anthony: Yeah, I, I actually read an interesting analysis that sort of showed past. Bubbles, the.com bubble, the, you know, the past technology bubbles and they went back and looked at what people were predicting at the beginning of the growth phase of safer, for example, e-commerce. And in all cases. They underestimated the investment and impact that this supposed bubble had.

‍

[00:33:08] Anthony: Even if, you know, the, there can be these correction moments in with a slightly longer arc of history. The sense was that. In general, people underestimate these things. And so in that context, I think you're probably right, like we've underestimated the degree to which this technology will impact lives and, you know, hopefully for the better and mostly it seems for the better.

‍

[00:33:32] Anthony: But, but it will, they'll underestimate as opposed to overestimate.

‍

[00:33:35] Elena: Yeah, no, a hundred, a hundred percent agree. Again, like, uh, learnings we got from smartphones from when social came in place, right? So we, we definitely underestimated, right? And, yeah. we are gonna see different type of enterprises. We're gonna see different type of companies the same as during the first wave.

‍

[00:33:53] Elena: This is where, you know, Uber was born, like, I mean, completely different. Like, completely different business models came in place, right? Amazon grew so much. I mean, these are all the kind of the first wave of digital transformation, right? So, now. it's, I mean, it's becoming reality. the only side, what, what they're gonna see, kind of gonna slow us down is definitely info security and compliance. So these, these are two sites. I would recommend companies to start pay attention more, because doesn't matter how you're gonna scale, if you're not gonna be able to report back how you model is working is requirement now in the European Union. And that's also, you know, requirement by d different regulators in us. yeah, you're gonna slow you down so, you know, pay attention to info security and compliance.

‍

[00:34:43] Anthony: Excellent. Good advice. Elena, always a pleasure. Thank you for coming back and let us put on the calendar a six month check-in to see how you're. Predictions are doing.

‍

[00:34:52] Elena: Thank you so much, Anthony Always happy to connect. 

‍