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00:00:02.560Hello everyone, thank you for tuning in again.
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00:00:12.320I am Kaylee Batesman, the Managing Director, Community and Partnerships at She Can Code, and today we're discussing From Data to Decisions, leading in the age of AI.
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00:00:31.839I'm lucky enough to be joined by the wonderful Yurina Nedbokova, AI use case architect and customer intelligence lead at Ball, and we're going to explore how AI and data are transforming the way businesses make decisions, engage with customers, and shape the future of technology.
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00:00:33.359Eurina, welcome.
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00:00:35.039Thank you so much for joining us today.
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00:00:37.920It's an absolute pleasure to have you on Spilling the Tea.
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00:00:40.320Thank you so much for inviting me.
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00:00:41.039Thank you.
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00:00:42.640Well, we have a lot to unpack.
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00:00:45.600It's a very big topic and very, very timely.
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00:00:52.240So can we kick off with a little bit about you to start to set the scene for our listeners, please?
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00:00:54.719I am Jirna Netbalkova.
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00:00:56.320I live in the Czech Republic.
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00:01:02.479And as mentioned, I worked as an NIAI use case architect and customer intelligence lead at Bull.
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00:01:14.159For those who may not know Bull, it's a French technology company with a long heritage in computing, relaunched earlier this year with a strong focus on sovereign AI.
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00:01:21.920Today, our company is a global leader in high performance computing, artificial intelligence, and quantum technology.
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00:01:28.079And Bull operates in 32 countries and employs over 3,000 professionals.
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00:01:34.400And I work in the enterprise AI part of the company within the AI business cluster.
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00:01:49.760And what we do is help companies adopt AI and turn it into real business value from identifying the right business use cases to build the technology and infrastructure needed to run them efficiently and at scale.
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00:02:00.560And I am one of more than 300 AI experts at Bull, including data scientists, AI data engineers, architects, and other specialists.
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00:02:02.079And we are still growing.
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00:02:11.280So if any of our listeners are thinking about moving into career in AI, there are plenty of opportunities to join us.
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00:02:14.560And what does it mean, AI use case architect?
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00:02:16.719I would like to explain it a bit more.
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00:02:19.360It sounds very in-depth.
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00:02:20.960Yeah.
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00:02:23.360It's a bit new law here.
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00:02:25.199It came due to due to AI.
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00:02:36.639So as an AI use case architect, I help companies understand where AI can create the real business value and then turn that into something practical.
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00:02:52.479I work with business leaders to identify and prioritize the right use cases, redesign business processes, and connect AI technology with areas such as CRM, customer service, risk, and collections.
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00:02:57.919And a big part of my role is bridging business and technology.
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00:03:10.240I translate business needs and operational challenges into AI solutions that improve customer experience, customer engagement, efficiency, and decision making.
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00:03:16.240I am also involved in developing new AI products and bringing them to market.
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00:03:23.439So I see both the business challenges and what it takes to turn an idea into a working solution.
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00:03:29.280And one area where I still work very hands-on with technology is prompting.
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00:03:31.759I'm sure all of you, you know prompting.
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00:03:34.800So what does it mean in my business practice?
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00:03:45.439Writing the instruction that guy AI, how it should behave, what it should focus on, and what kind of outputs it should produce.
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00:03:50.719And this is exactly where domain expertise and experience matter.
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00:03:58.639People often think that prompting is mainly a technical skill, but my experience is different.
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00:04:09.840The strongest prompts are usually written by people who truly understand the business, the customers, and the industry, not by technical specialists alone.
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00:04:25.600So technology provides the engine, but you still need someone who understands the context well enough to ask the right questions, challenge the answers, and recognize when the results is actually useful.
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00:04:30.720And that's why, for me, technology is never the starting point.
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00:04:33.680I always begin with the same questions.
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00:04:35.600Why are we doing this?
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00:04:38.560Who are we solving it for?
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00:04:42.800Or what measurable business value will it create?
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00:04:48.160I've learned to approach technology this way throughout my career.
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00:04:52.079The technology, the industry has changed enormously.
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00:04:55.120But my approach has stayed the same.
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00:04:58.480And your job sounds absolutely fascinating.
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00:05:02.560It is so much I'd love to ask you about.
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00:05:03.439Challenging.
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00:05:06.639Working in AI, yes, incredibly challenging.
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00:05:15.120And something that a lot of our members at the moment are asking us about how do I move into that area, um, training in that area.
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00:05:27.279It's just kind of it's very new, and um people just know that they need to at least understand what's what's happening there, and um you know, let alone if they want to move into a career in that area.
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00:05:34.480So um I wanted to ask you a little bit more about your career and and and how you landed where you are.
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00:05:40.560Um, because you built a career at the intersection of AI, data, and business transformation.
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00:05:47.120What first drew you into technology and how has your career evolved as the industry has changed?
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00:05:52.160My career started in a very different place.
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00:05:58.879My childhood dream was to become a teacher, and that is exactly what I did.
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00:06:02.319I studied maths, geography, and psychology.
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00:06:12.160And in 1999, while I was still completing my university studies, I started teaching maths, geography.
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00:06:16.240And technology came into my life almost by accident.
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00:06:21.759My boyfriend at the time, who is now my husband, was studying IT.
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00:06:25.040And I sometimes joined him for his classes.
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00:06:33.040We also spent quite a lot of our free time learning things like HTML and SQL together.
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00:06:35.759And there is a funny twist to that story.
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00:06:41.600Today, he works in human resources while I built my career around technology.
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00:06:56.160Of course, of course, maths is closely connected with technology and the analytical way of thinking I developed through my math studies has helped me many times throughout my career.
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00:07:00.879After university, my life changed quite quickly.
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00:07:06.399I got married, we moved to Prague, and I completely changed direction professionally.
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00:07:11.839I joined a marketing agency and I started working in project management.
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00:07:16.399But even there, I was already working hands-on with data.
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00:07:25.279At that time, it meant things like SQL, Excel, Macros, and quite a lot of manual data work.
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00:07:35.199Then I had my two children, and after maternity leave, I moved much more deeply into the analytical side of business.
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00:07:47.920I worked in the telecom sector as a data specialist, responsible for analysis, forecasting, and reporting to support customer service, product, and marketing teams.
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00:07:54.879And quite quickly, I became interested in a question that has stayed with me throughout my career.
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00:08:03.040How can we understand customers better and make better business decisions based on data?
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00:08:12.879So over time, I became less interested in the coding itself and more interested in what the results really mean.
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00:08:17.360I started asking different questions.
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00:08:20.560For example, what can we learn from the data?
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00:08:29.519What decisions should we make differently because of it, and how we can use that knowledge to improve the business.
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00:08:34.799And that was an important shift in my career.
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00:08:52.320I moved from doing the analysis myself to defining what analysis the business needed, and eventually to leading the teams responsible for whole CRM, customer intelligence, data science, and customer analytics.
00:08:52.879 -->
00:08:56.480And my roles became broader and more senior.
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00:08:59.120I also wanted to broaden my experience.
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00:09:04.799So around 15 years ago, I moved from telecom into banking.
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00:09:13.279And I worked on customer strategy and engagement, personalization, marketing, how to automation, and advanced analytics.
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00:09:19.279I also work on the international transformation program across several countries.
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00:09:24.320Yes, so huge experience coming from this time.
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00:09:30.320And during my career, I have met many interesting and inspiring people.
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00:09:36.720I have tried to learn from them and take inspiration from their experience.
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00:09:44.480And sometimes I only realized later how important the lessons I had learned from them were.
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00:09:49.759And that has shaped what I do today as a consultant.
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00:09:56.159I think why think has been consistent throughout my career.
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00:10:02.000I have always worked as a bridge between business and technology.
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00:10:17.200Because I started hands-on, I understand the technical side, and because I later moved into customer strategy, leadership, and business transformation, I also understand the business side.
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00:10:23.519So working with AI has therefore been a very natural step for me.
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00:10:44.000And today I bring together my technical background, business experience, and work with customers to help companies understand where AI is absolutely useful, what problem it should solve, and how to turn it into practical business results.
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00:10:50.159I love everything you just said because it resonates with a lot of our community members.
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00:11:00.159I hear that often.
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00:11:03.600And that actually picked up in COVID.
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00:11:18.559So a lot of our members told us that their husbands worked in technology, and when everybody went into lockdown, they told us that they saw what their husbands were doing for a living and thought, I could do that.
00:11:18.720 -->
00:11:24.159I pretty much do that, but I could, you know, retrain and and do what they're doing.
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00:11:41.279And some like yourself, I I did speak to one member who told us she was a teacher and she wanted to transition into tech because uh she needed the flexibility of family, um, and uh she retrained um after she saw what her husband did for a living.
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00:11:53.039So sometimes it is seeing other people's jobs because we do have this, and why this podcast was started, this misconception about what it's like to go into the tech industry.
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00:12:10.720And the I love the fact as well that you said you moved away from the coding side, or that you realised that there was more you know outside of the coding and that you could move into strategy and leadership, and that is something that does worry a lot of people going into technology.
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00:12:27.759You have to take the coding route, there's nothing else that you can do in between, and it sounds like even though you know you started on that side and moved into um something different, you don't have to be technical or a coder to go into technology.
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00:12:30.159There's there's a whole range of roles.
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00:12:35.519I mean, you must have so many roles on your team um that some are techie and some are not techie.
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00:12:39.279Yes, yes, yes, exactly, exactly, as you're saying.
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00:12:49.120Yeah, it's um it's one of those things that on if you unless you work in technology, you you think it's going to be incredibly technical.
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00:12:57.360Um, but people like yourself uh you know spreading that message of I started in this, but actually that isn't my role anymore.
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00:12:59.759Um uh it's always good to hear.
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00:13:03.840Uh a lot of our members do ask us about that um all the time.
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00:13:09.200Um, but obviously AI, it's moving incredibly quickly.
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00:13:18.399Um, so from your perspective, what are businesses getting right when it comes to AI and where are they still missing the opportunity?
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00:13:21.200It's a great question.
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00:13:22.080Hmm.
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00:13:29.840I think businesses are getting one important thing right, they are experimenting.
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00:13:40.799Yes, AI has moved very quickly from something discussed by technical teams to something that people across the whole company can try and use.
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00:13:50.720I see much more curiosity, much more willingness to test new ideas and much more better understanding at senior management level.
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00:13:53.600That this is something they cannot ignore.
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00:14:03.440But where I think many companies are still missing the opportunity is um moving from experiments to real business change.
00:14:03.759 -->
00:14:10.559It's uh relatively easy to give people a new tool or build an interesting pilot.
00:14:10.879 -->
00:14:15.919The harder question is what business problem are we solving?
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00:14:19.360What process should actually change?
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00:14:20.799Who will use it?
00:14:21.039 -->
00:14:24.960And um, how will we know that it creates value?
00:14:25.440 -->
00:14:38.320And from my experience, the biggest result comes when companies stop thinking about AI as an additional tool and start looking at the whole business process.
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00:14:44.720So, for example, don't just ask how can AI summarize this customer call.
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00:14:51.039Ask what should happen differently after we understand that conversation.
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00:14:53.759Can we serve the customer better?
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00:14:56.080Can we make a faster decision?
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00:14:59.279Can we identify a problem earlier?
00:15:00.080 -->
00:15:06.639And then there is the human side, which I think is often underestimated.
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00:15:22.080People can be resistant to change, some may ignore it, some may be afraid of losing their jobs, and others may simply decide not to engage with the change at all.
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00:15:26.080And this is why education is so important.
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00:15:39.679People need to understand that the technology can do what it cannot do, how their own role may change, and how they can use it to make their work better.
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00:15:45.679If people only see AI as a threat, they will naturally resist it.
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00:15:56.240If they understand it, try it and see where it can help them, they are much more likely to become part of the change.
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00:16:13.440So companies need to invest not only in technology but also in people, helping them understand when to use AI, how to question the results, and how their own roles and processes need to change.
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00:16:30.159So for me, the next stage is not about using more AI, it's about using it in the right places, redesigning the work around it, and bringing people with you through the change.
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00:16:32.639Yes, I love that.
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00:16:41.840Um and I think when AI came out, the headlines were always AI is gonna take your job, you're gonna be replaced by AI.
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00:16:42.159Yes.
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00:16:44.639Um, and exactly what you just said there.
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00:16:50.639I heard a comment recently that I thought that that is it, it's not AI is gonna steal your job.
00:16:50.720 -->
00:16:57.440It actually said um AI is not gonna steal your job, but people who know how to use AI will steal your job.
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00:17:10.319And it's that, it's how do you uh go beyond what you think you should ask AI and how do companies invest in people to use it properly and to get the most out of it?
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00:17:16.160Um, because yes, there was those big headlines about, you know, oh, my job is going to disappear.
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00:17:27.759Um, although looking back on some of my roles, uh, when I was a writer and a copywriter, a lot of those roles have kind of disappeared, but people are using them in different ways.
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00:17:41.920So again, it would be somebody that understands AI would have taken over that job if I had still sat there, you know, trying to come up with strap lines for advertisements on my own, um, rather than using AI.
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00:17:49.519So yeah, um, I couldn't agree more uh with investing in in people in that way um and and how they're using it.
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00:17:55.839Yes, um fully agree, yeah, because some roles disappear, as you are saying, but different van claims.
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00:18:10.400And my role is an example of a completely new role, yes, exactly, and a role as well that your younger self probably never would have imagined that that would be your job title one day.
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00:18:21.920Yes, I love that that technology moves so fast that we don't even know what some of those job titles are going to be, and that yours would include artificial intelligence.
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00:18:30.000How cool is that! Um, but you work closely with organizations to turn AI into real-world solutions.
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00:18:38.319Um, and you spoke a little bit there about what it actually takes to um, you know, turn what they're doing into something that's useful.
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00:18:48.640Um, so what does it take to actually move from having lots of data and exciting AI ideas to actually making better business decisions?
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00:19:08.240I have worked with customer data and analytics for most of my career, and I have seen many situations where companies had very good analysis, useful reports, and interesting ideas, but nothing really changed.
00:19:08.880 -->
00:19:14.240And with AI, I think we are seeing a very similar situation today.
00:19:14.880 -->
00:19:17.839The reason is actually quite simple, I think.
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00:19:27.119The insights coming from the data or from AI analysis is not connected to a real decision or action.
00:19:28.240 -->
00:19:31.920So I would I would start with the decision itself.
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00:19:42.240Who makes it, when is it made, what information do they need, and what would a better decision look like?
00:19:42.720 -->
00:19:46.240Then you need a process that actually uses the result.
00:19:46.720 -->
00:19:58.400If AI produces a recommendation, but nobody knows where it fits into the workflow or what should happen next, it has very little value.
00:19:59.200 -->
00:20:06.799And you also need people, again, people who understand the context and know what to do with it.
00:20:07.119 -->
00:20:21.920With AI, you can analyze a huge amount of information very quickly, but people still need to interpret the result, question it when necessary, and decide what action makes sense.
00:20:22.240 -->
00:20:36.000I see this very clearly in areas such as CRM and customer service is not enough to know that a customer may be dissatisfied, interested in another product, or at risk of leaving.
00:20:36.240 -->
00:20:44.160The real value comes from asking, what are we doing to do differently because we know that?
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00:21:02.240For me, moving from data and ideas to better business decisions means connecting three things the right decision, a process that used the results, and people who understand the context and know what to do with it.
00:21:03.119 -->
00:21:14.960And finally, a way to measure whether it really helped, whether we improved customer engagement, saved time, reduced cost, or increased sales.
00:21:16.799 -->
00:21:17.279Yeah.
00:21:17.759 -->
00:21:18.559You're so right.
00:21:18.720 -->
00:21:20.319It's we're still gonna need the people.
00:21:20.559 -->
00:21:33.119There's always um there's always these moments uh throughout the the tech industry timeline where people have that moment where they think their job is gonna disappear because that part actually isn't new.
00:21:33.279 -->
00:21:52.559I I remember must have been about 15, 20 years ago, and um I was writing for a publication where we spoke about data storage, and um somebody said to me, Oh, you know, storage is always going to be around with that, the bottom will never fall out of the storage industry.
00:21:52.640 -->
00:21:55.759That's so I was in a good position as what I was writing about.
00:21:55.920 -->
00:21:59.759But as I as as that changed, the way that we store data.
00:22:00.160 -->
00:22:21.519Data changed so much that I remember there was absolute upheaval from CIOs and CTOs when cloud storage came about and um virtualization, and I remember the CTOs saying, but my servers are not gonna be on site anymore, and I'm not gonna be looking after dusty boxes and cooling them down.
00:22:21.599 -->
00:22:24.720And there was such a it was just such a big thing at the time.
00:22:24.880 -->
00:22:29.680Like if I'm not looking after physical servers, I'm gonna be made redundant.
00:22:29.759 -->
00:22:31.119What is my job gonna be?
00:22:31.279 -->
00:22:44.240And all that happened was they did something else with their time, that was it, and it saved the company time and money and space and um all of those things that came with virtualization and that little change at that time.
00:22:44.720 -->
00:23:04.079Um, but I remember speaking to so many people that were so worried that their jobs would just not exist anymore because they wanted to physically look after things on site, and um it's almost that that's those same headlines of my job's gonna disappear because of AI again.
00:23:04.799 -->
00:23:07.920Like, no, you're gonna do something else, it's fine, you know.
00:23:08.079 -->
00:23:12.160Yeah, you just haven't figured out what it is yet, but it's fine.
00:23:12.559 -->
00:23:14.799It depends what you want to see, yeah.
00:23:15.039 -->
00:23:16.160Yes, exactly.
00:23:16.559 -->
00:23:17.920Each of us can choose.
00:23:18.240 -->
00:23:18.880Exactly.
00:23:19.119 -->
00:23:20.720Yes, yeah.
00:23:21.119 -->
00:23:30.960It's the tech industry is an interesting one of if you're in it a really long time when you see what happens and um how fast we move.
00:23:31.119 -->
00:23:47.519And uh if you move yourself into the right places as well, um, like yourself, if you you know move into AI because that that is happening, um, and you and uh you know you you m shift your career in that area, um, is uh a brilliant place to be.
00:23:47.599 -->
00:23:55.759And why we've got so many community members that are asking us uh how do I get into AI and uh wanting to train in that area?
00:23:56.000 -->
00:24:09.759So um one other big topic around AI as well is um there's a lot of discussion about responsible AI um that keeps coming up and the importance of uh diverse perspectives.
00:24:10.000 -->
00:24:16.559So, why is diversity particularly important when we're designing and developing AI systems?
00:24:18.559 -->
00:24:24.960I think diversity can be understood from many different angles.
00:24:25.440 -->
00:24:35.200I often say that uh I build teams like a puzzle, every person is important and has their own place in the team.
00:24:35.440 -->
00:24:41.200Everyone is different and everyone sees something that others may miss.
00:24:42.000 -->
00:24:47.440I think the same principle applies when we design AI systems.
00:24:47.839 -->
00:25:00.319If everyone in the room has a similar background, similar experience, and things in a similar way, it's much easier to overlook problems or make the same assumptions.
00:25:01.039 -->
00:25:05.759A more diverse team is more likely to ask different questions.
00:25:06.480 -->
00:25:09.039Does this work for all of our customers?
00:25:09.359 -->
00:25:11.519Are we missing a particular group?
00:25:11.839 -->
00:25:16.799Could this recommendation have an intended consequence?
00:25:17.119 -->
00:25:20.000Are we interpreting the data in the right way?
00:25:20.400 -->
00:25:34.400And diversity can mean many things: gender, age, culture, professional background, industry experience, or simply a different way of thinking.
00:25:34.640 -->
00:25:47.039And for me, it's also very important to bring together business people, technical people, and people who understand the customer and the real process.
00:25:47.519 -->
00:25:52.480They all see the problem from a different angle angle.
00:25:52.880 -->
00:26:10.559Diversity is not only about fairness or inclusion, it also leads to better solutions, different perspectives challenge assumptions, improve decisions, and help us build systems that work better in the real world.
00:26:11.039 -->
00:26:22.000And I think this becomes even more important with AI because these systems can influence decisions at scale.
00:26:22.240 -->
00:26:31.279They can scale a good idea very quickly, but they can also scale a bad assumption very quickly.
00:26:31.920 -->
00:26:44.240And finally, I think this need for diversity in AI development is also creating new opportunities, as we have discussed a few minutes ago.
00:26:44.480 -->
00:27:11.440AI teams do not only need programmers or data scientists, they also need people who understand customers, business processes, regulations, communication, ethic, design, change, and industry context that opens the door to many people who may not have seen themselves as technology people before.
00:27:11.759 -->
00:27:18.559And for women in particular, I think this is a very positive development for us.
00:27:19.200 -->
00:27:40.240There are more ways to enter technology now, and many of these new roles value exactly the skills that women may already have from other careers: business understanding, communication, customer knowledge, leadership, and the ability to connect different perspectives.
00:27:41.440 -->
00:27:57.359So diversity is not only something we need to build a better AI system, it's also one of the reasons why AI is creating new career opportunities for a much wider range of people, I think.
00:27:58.079 -->
00:27:59.200Yes, definitely.
00:27:59.359 -->
00:28:30.799And the companies that are open as well to they want more diversity on their teams for those reasons that you just said, and realize that, for instance, women that are transitioning from one career into technology, you're so right, the overlap with the skills that you have in certain industries and coming over to tech is just such a natural transition, but they might not tick some of the technical boxes that are needed or something specific for that company.
00:28:30.880 -->
00:28:37.680So the companies that are open-minded and say, we just love that you have everything else we need.
00:28:37.839 -->
00:28:42.319You seem like a sponge that is going to learn everything that we throw at you.
00:28:42.480 -->
00:28:46.559We will train you up in the things that you might currently be lacking.
00:28:46.799 -->
00:29:00.720Just come on board because the need for diversity on their team, they realize how important that is, like you just said, and they would rather have the diversity and just train that person up on the last few things.
00:29:00.880 -->
00:29:03.920And we wish more people knew that.
00:29:04.160 -->
00:29:18.160When you see the job application and you don't tick all the boxes, please apply anyway, because they might reach out and say, We know you didn't hit all the criteria, but we would still love to talk to you and fill in the gaps.
00:29:18.240 -->
00:29:19.599So yes, absolutely.
00:29:20.000 -->
00:29:22.000Some companies are very open to that.
00:29:23.039 -->
00:29:24.960Yes, exactly.
00:29:25.359 -->
00:29:32.160And things are still going to change, obviously, in the speed that AI is changing at the moment in the industry.
00:29:32.319 -->
00:29:38.000Um, so looking ahead, how do you think AI will change the way that we work and lead?
00:29:38.240 -->
00:29:48.559And something I know our community is going to want to know is what skills do you think women in technology should be developing now to stay ahead of that change?
00:29:50.000 -->
00:29:51.279Very interesting questions.
00:29:51.440 -->
00:29:53.440I'm going to start with the first one.
00:29:54.640 -->
00:30:01.440I think AI will change both the way we work and the way we lead.
00:30:01.680 -->
00:30:08.160On the work side, many routine analytical and administrative tasks will become faster.
00:30:09.039 -->
00:30:18.960People will be able to work with much more information, ask questions directly, generate first drafts and get supports with decisions.
00:30:19.599 -->
00:30:25.359But I don't think that means that human expertise becomes less important.
00:30:26.160 -->
00:30:28.559I think the opposite is true.
00:30:28.880 -->
00:30:43.440If technology can produce an answer in seconds, then the real value is in knowing and experience what to ask, whether the answer makes sense, and what to do with it.
00:30:44.640 -->
00:30:48.480And I think leadership will change in the same way.
00:30:49.200 -->
00:31:11.759A leader will not only manage people and review their AI outputs, a leader will also need to help people develop and use their new skills, understand when to use AI, when not to use it, how to challenge the answer, and how to think for themselves first.
00:31:12.400 -->
00:31:27.119And I think one of the key roles of a leader will be to build relationships relationships, create trust, and bring people together in the age of AI.
00:31:29.119 -->
00:31:36.319And your second question: what skills should uh women in technology be focusing on now?
00:31:37.519 -->
00:31:39.920First, keep learning.
00:31:40.640 -->
00:31:42.880Technology will continue to change.
00:31:43.119 -->
00:31:50.640So the ability to learn and adapt is more important than knowing one particular tool.
00:31:51.440 -->
00:32:06.480Second, build your domain expertise, understanding customers, finance, healthcare, marketing, HR, operations, or another business areas can be a huge advantage.
00:32:06.720 -->
00:32:15.440And with AI, technology is becoming easier to access, but deep business knowledge takes years to build.
00:32:16.240 -->
00:32:29.920And third recommendation: develop critical thinking and learn to ask good questions, don't accept every answer just because it came from a clever system.
00:32:30.799 -->
00:32:36.960And there is a saying I really like lack favors to prepare.
00:32:37.759 -->
00:32:49.599And for and for women who are not in technology yet, I would say don't be afraid to enter the field, as we have spoken together.
00:32:49.839 -->
00:33:00.319Technology is much broader than many people think, and AI is creating even more opportunities for people with different backgrounds.
00:33:00.799 -->
00:33:03.680You do not have to become a programmer.
00:33:04.079 -->
00:33:17.920You may already have exactly what a technology team needs: business knowledge, customer understanding, communication skills, leadership experience, or deep industry experience.
00:33:18.480 -->
00:33:26.160So look at what you already bring, not only at the what you think you are missing.
00:33:27.039 -->
00:33:28.240Just try it.
00:33:29.359 -->
00:33:30.480That's such good advice.
00:33:30.640 -->
00:33:37.920Just try it because I AI as well is one of those areas that people are quite intimidated by.
00:33:38.319 -->
00:33:42.880They might be thinking, I was going to retrain and go into tech and I'd like to give it a try.
00:33:42.960 -->
00:33:54.079But I mean, tech, we don't do a good job anyway, of you know, um, we use too many jargons, uh, to too much jargon and um abbreviations for everything.
00:33:54.240 -->
00:34:04.559And so many people find themselves entering the tech industry and thinking, oh, maybe that wasn't for me, um, because we just complicate everything in technology.
00:34:04.720 -->
00:34:09.119Um, but nine times out of ten, it's not what you think it's going to be.
00:34:09.199 -->
00:34:14.320Um, and we just wish that people would take that leap of faith and come in and give it a try.
00:34:14.480 -->
00:34:18.480Um, I think AI is the new area where people are like, oh, I couldn't do that.
00:34:18.559 -->
00:34:22.079You know, that's too, I would have to be highly technical for that.
00:34:22.239 -->
00:34:29.039And um, at least to have, you know, a STEM um background and a and a strong computer science background.
00:34:29.119 -->
00:34:41.119Um, but we speak to so many of our members who have transitioned from weird and wonderful career routes into technology and somehow everybody lands in the tech industry eventually.
00:34:41.280 -->
00:34:52.719Um, but it's uh sharing the way that they get in is so helpful to other people that might be slightly nervous about um retraining and giving it a go.
00:34:52.880 -->
00:35:01.360So um that is brilliant advice um and lovely advice to end it on because we are already out of time, it's absolutely flown by.
00:35:01.519 -->
00:35:04.719Um thank you so much for taking time out to chat with us.
00:35:04.800 -->
00:35:11.039I know our community are gonna love um hearing all about what you do, uh, very timely at the moment.
00:35:11.199 -->
00:35:14.320Um, and we get so many questions about AI.
00:35:14.480 -->
00:35:18.000So thank you so much for coming on and having a chat with us today, Irina.
00:35:18.159 -->
00:35:19.119It's been a pleasure.
00:35:19.840 -->
00:35:21.440Thank you again for having me.
00:35:21.519 -->
00:35:23.039I really enjoyed the conversation.
00:35:23.280 -->
00:35:23.920Thank you.
00:35:24.239 -->
00:35:24.719Thank you.
00:35:24.880 -->
00:35:30.559And for everybody listening, as always, thank you so much for joining us, and we hope to see you again next time.
