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SheCanCode's Spilling The T
SheCanCode's Spilling The T
The 11%: How we get more women in tech leadership roles
Heather Dawe joins us to discuss her journey as the UK Head of AI & Innovation at UST and Chief Data Scientist, reflecting on over 25 years of experience shaping AI innovation in government, public sector, and industry.
Heather's insights are timely as the UK government explores AI's potential in revolutionizing public services, exemplified by recent developments like the Anthropic deal. As a recognized global AI expert, Heather has influenced international dialogues on AI through prominent media platforms such as the BBC, The Guardian, Sky News, and the Financial Times. Her discussions range from AI's impact on the job market to ethical considerations in technology.
Join us as Heather shares her perspective on navigating the tech industry as a female leader, highlighting the challenges and opportunities she has encountered along the way. Her advocacy for diversity and ethical leadership underscores the importance of increasing women's representation in executive roles within technology, aiming to inspire future generations of leaders in AI and beyond.
SheCanCode is a collaborative community of women in tech working together to tackle the tech gender gap.
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Hello everyone. Thank you for tuning in again. I am Kayleigh Batesman, the Managing Director Community and Partnerships at she Can Code, and today we're discussing the 11% how we Get More Women in Leadership Tech Roles. I've got the incredible Heather Dorr, who is here to discuss her journey as the Chief Data Scientist and Head of Responsible AI at UST, reflecting on over 25 years of experience shaping AI innovation in government, public sector and industry. Welcome, heather. Thank you so much for taking time out to come and have a chat with us today.
Speaker 2:Hi there, Kayleigh, it's great to be here. Thank you very much for asking me along.
Speaker 1:Thank you. Well, we'd love to kick off with a bit of background about you, if that's okay. A little bit to set the scene for our community sure.
Speaker 2:So so, like you say, I'm chief data scientist and head of responsible ai at ust and um. In my role, I work with large uk and european companies, mainly globally global too to really to help them um to do the things well, to help them to achieve the things they want to achieve with data science and AI. I've got a quite as you kind of alluded to earlier. I've got quite a varied career, varied background in the sense of I've worked in my career so far, around 25 years. I've worked. I started out in industry. I've worked in government for the civil service and also for a 10-year period in the NHS largely the central NHS Run my own startup for five years and then came to UST, where I've been, as I say, my role is to assist large companies to benefit and gain value from AI.
Speaker 1:Amazing. I love that you run a startup for five years, because that's quite a very different environment. That's something we talk about a lot in our community that people that do that. It's not for the faint-hearted.
Speaker 2:No, it's not, and it was a great experience, something I wouldn't say no to going back to in the future. But what's that's helped me to do? I mean again, sort of through my career, the ways in which you work and how you work. To have experienced quite a wide array of them is quite good because it gives you the you know, you can kind of understand where the people are coming from. Um, you know, we were with startups now and I've got a greater appreciation of their ways of working because I've run a startup, for example, but likewise with government and industry, you know there's a huge amount of similarities but there's also differences and you know and have that appreciation and, like you know, working with academia which we do a reasonable, you know a good amount in UST and I've done through my career the different different sectors, different sort of, have different ways of working, and that familiarity is really helpful, I think.
Speaker 1:Yes, definitely, and and having that variety. We get asked a lot in our community about. People worry about moving around, almost think it's a disadvantage from it looks like you're hopping around, but actually so many of our members have actually said something similar to what you just said. And all the different things that you bring from different industries and all of those skills and then running a startup, for instance, and then it's almost like the stars align and you find yourself in that role that is perfect for all of the skills that you've learned along the way yeah, I mean I'd agree with that.
Speaker 2:I mean there's there's something about staying in a role for a little while to sort of show you can, what you can do and achieve. I'm not well, yeah, I mean, you know, I there have been times in my career I've moved around a reasonable amount and there's also been times I've I've stayed around in a role and I, you know I am, you know I've got friends, I've got colleagues who've been in their roles for 20 years plus and or in or in, certainly in the same company or the same organisation. And I don't think there's, you know there's merit to all of these things. I think. But, like you say, there's merit to all of these things, I think. But like you say, I mean I agree, the experiences I've gained through moving across different, you know, across government, like let's talk about the NHS, and then and then into, you know, through a startup, into industry, has been really helpful.
Speaker 2:I think you know the ways in which, today, my teams, you know, day, my teams, you know we use learning that methodologies that I've done in health, for example, in the financial services, uh, you know it's, you can bring different methods, different places and and they prove very effective and and I'm that, that's just an example I've done that a lot through my career yeah, yeah, and, as you said, um, as you said, you've had quite an impressive 25-year career shaping innovation in AI and data.
Speaker 1:What drew you first to the field, though? What kept you inspired along the way, and what drew you to the field in the first place?
Speaker 2:That's a good question. I mean, I did a math degree. I mean I, I was always, yeah, I, I did a math degree at university, um, and I've, you know, always been fascinated by maths and the ways in which you can certainly um probability theory, statistics, the ways in which you can use um mathematical theory to predict what's going to happen next, and really that's the basis for all AI, pretty much. So when I left university, I actually started off as a software. I mean, data science didn't exist back then. I'm quite an old data scientist and you know. So I started off as a software developer, and that was working in telecoms, working in retail, with large data coding and, you know, innovating with apps and services in different ways in those sectors. And at the time I realized that if I could apply, you know, machine learning, statistical modeling to data at scale, you could do some really cool things. And this was, you know, more than 20 years ago and through my career, I've been driven to do that more.
Speaker 2:I mean, I do very, whilst academia has had its attractions to me through my career and obviously academia, you know, universities work with business a lot. We work with business a lot. We work with business a lot. You know it's the real world problems that that you know applying these methodologies in the real world have. It has its challenges because you know the real world, in a good way, is messy. It's not, you know it doesn't necessarily conform perfectly to a logical flow of things and and that has its own challenges. But at the same time, the thing is you can achieve when you can apply these methodologies in in in. You know, like I say, real world system, you know, in the nhs, for example, in in in big business, can have a big impact and, um, you know, I suppose I've been driven to do that through. It's a fascinating area and and it's it's not lost any of its, any of its fascination for me through my career.
Speaker 1:It's interesting because you obviously were interested in STEM when you were younger. Did you make that connection, though, with the real world problems that you can actually go in and make such an impact as you do, or what did you think of that at school? You just thought, you know, I love maths, I'll just follow maths.
Speaker 2:Well, I don't think I really thought about it at school, but it was when I really started working. You start to look, you know, I mean I recognise now I pretty much have always been an innovator. I didn't really recognise that in myself when I was younger. I just thought, well, everyone thinks in these ways to sort of build, you know, sort of design and build solutions and stuff. And I wasn't even thinking about solutions.
Speaker 2:It was more like, actually I could see how you could use analytics or machine learning, or if I could predict what's going to happen next, I could do something with that useful, you know, and and that's that's what's been my drive. So it wasn't really a sort of active observation there. This is stem, this is, this is. It was more like, well, I mean, I do very much, I do very much enjoy maths. I mean, I'm also quite an artistic thinker, so I'm, I'm yeah, it's. But maths as a subject is fascinating and apply, you know, using um, using, like I say, using probability, probability theory to help improve things has been my driver really. And, you know, on a scale which generally means building out information systems to support that.
Speaker 1:I love the fact that you said artistic thinker, because I've heard that before from data scientists and people don't quite realise that as well, that you've got data and being creative, creative together and then it's almost like data scientists are like more people should know that.
Speaker 2:No, I I'd agree. I mean date, I mean I, data science is actually a very creative subject, you know, sort of profession, if you know, if you like it to solve. There's often lots of different. Well, you know you have a problem, you have a business problem, you have a challenge, yeah, what? In whatever capacity? You know you need to understand it and explore it. And then you do need to you, I mean, you know you need to have understanding of the theory. Well, and appreciation of how you can apply machine learning, you know, probably, to actually solve the problem. But at the same time, it takes a level of creativity to do so. And and you know that, I mean I do think you know the creative, the creative part is what a data scientist can really bring to a problem and and really, you know, come up with some really good solutions.
Speaker 1:So so, yeah, creativity is an important part of my role, my job um, and I wanted to pick your brains a little bit about tech leadership today, because it's something that a lot of companies, um struggle with, about, you know, not just ladies coming into tech, but how to to retain them as well, move them through to leadership roles, because only 11 of executive tech roles are held by women. Um, so, from your experience, what are the biggest barriers that women face in reaching leadership positions in ai and also technology as a whole?
Speaker 2:I mean, I think I get asked this question a lot and and I think there's a number of different things that contribute, you know, contribute to it, um, and it certainly does begin at school, um, and the way in which maths and STEM subjects are still very much seen, as you know, for the wrong reasons, I think you won't be surprised me saying that I think you know, is a boy's subject in school and and so it kind of starts there, um, and it goes through the education system and and obviously you know, and, and obviously huge you know, lots of efforts being made to change that and we need to continue to do so. Um, the role of role models is really important and I've realized that more is. I've got two daughters and I think I realise that probably more when I look to the world through, or try and look to the world through, their eyes than my own at times. So role models are important and when we get into the workplace, there's a whole number of facets here. I mean, I work for UST, we're an employer that push forward, you know, women in their roles and I think actually most employers do, or at least that's what they endeavour to do, but obviously that's not permeating through to, as you say, leadership roles in technology. I mean, part of that is because, um, you know, only 25 percent, or roughly 25 percent, of the tech workforce are women, but 25 is still actually a lot more than 11. So, you know, we, if, if that's yeah, we should be at least at 25, if we can reflect the general um, but but that in itself, you know, it's still my women are still in a minority in the workplace, you know.
Speaker 2:But I think it's also important to recognize that it's like things like imposter syndrome. Women are much more likely to experience imposter syndrome than men and you know, what can we do to combat things like that? Because that's that's I mean. You know, I've experienced imposter syndrome for a long time through my career, so personally it's affected me. And it's things like imposter syndrome that we can look to help people. It's not only women who, but the majority, to overcome, and you know, and things like I mean, making it straightforward for people. And it's not, again, it's not only women who take career breaks when they either have children or have caring, other caring responsibilities, but making it as easy as possible for people to come back into their career and then develop further in their managerial and leadership careers are really important and steps are being taken, but you know, that's it's certainly some of the things that slow down the change that we're talking about.
Speaker 1:Yes, definitely you're right. It is something that we get asked a lot from companies about um, for some reason, ladies get to sort of mid-career and then they drop out for lots of different reasons family, for instance, was one that you mentioned. But how to make sure that they come back or have that role model there to inspire them to come back? And it's interesting you said you have daughters because they're going to have that role model from home, they're going to have that influence from home. Did you have that? Or did you just think I enjoy maths? Or was it a teacher or somebody that kind of? Yeah?
Speaker 2:I mean I, I think I always enjoyed math. I mean you know my, yeah, my parent, my dad, was an engineer and my mum's a teacher, so I had parent both. You know, they both had careers, um, but I don't necessarily, yeah, I think I just really, I mean I, I enjoyed a lot of subjects, um, and I suppose I had to make a decision and I chose maths, and but then, you know, yeah, so yeah, I don't, I don't know. Yeah, I suppose I just followed my nose and decided I wanted to pursue that.
Speaker 1:Yeah, and then that's the thing, though, even if you know ladies are, they follow that, and then they go into the world of tech and somewhere along the line, they also decide perhaps this isn't for me, so, like you said, that imposter syndrome can kick in and you know, maybe their work environment.
Speaker 2:Well, in my first job I was a software developer, so I live in yorkshire. It was in yorkshire and I was pretty much in a close to whole male environment and, um, whilst I wasn't made to feel unwelcome in a stretch of the imagination, people were very friendly. You know, it's still very, I mean, I think I just I'm, yeah, my sort of I. Then really I just saw it as this is the thing, that this is how it is, and I didn't really think about it that much. It was just, I just saw it as this is the thing, this is how it is, and I didn't really think about it that much. It was just I just carried on.
Speaker 2:And but you know, when I look back now it's like, oh, that, yeah, you know that would, because you know I'd be in an office of, I'd be the one woman in an office of 15 odd men, you know. And now that's like, hmm, you know, but but at the same time I just got on with it and I didn't. I don't really, yeah, I didn't really feel it made a difference until really I had children.
Speaker 1:And then you start to notice, um, uh, different things, things, things change a bit when I had kids, I think yeah, definitely, and you're right, there are different things along your career that happen as well, and then people sort of think, oh, maybe this isn't the company for me, or perhaps I need to move to a different environment. Or you're right, sometimes that just happens as as your career happens, and then you want to move around to different companies, um, that might have more sort of support networks um, completely, I mean, support networks within your employees are really important, I think, and and it's and, and you know, and that, and the culture and the working environment.
Speaker 2:Um, I've probably moved twice because of because of those things. Most of the time I've moved because I've wanted to, to explore something new or do something different.
Speaker 1:Yeah, and so you've worked across government, public sector and industry, and you have a varied career, as we touched upon how do these sectors differ in their approach to AI innovation and what lessons can they learn from each other?
Speaker 2:That's a good question, because I think I mean it was an interesting one. You know, I spent, like I said, I spent a long time working in the NHS and I was building data science teams and we were building some really novel systems and ways of working with data 12, 15 years ago that actually I didn't realize at the time that, um, the nhs data, certainly in the central nhs, was actually very mature and the things you could do with that data you know, in a secure, safe environment, to be clear. But the things you could do with that data, um, I went out to industry imagining it would, it would be similar, but actually the relative maturity of industry at the time was, was, was actually behind the nhs. So so there are things you may and so on my assumption that industry would be miles ahead of the nhs and that wasn't actually the case. So sometimes you make these. I mean the nhs over the past decade or so has probably suffered somewhat through. You know, the levels of investment have gone into back office systems, so that may well have changed. But I suppose what I'm trying to say is there's always an assumption that industry will be ahead and it isn't actually necessarily the case. So that's one observation In terms of the ways in which AI gets developed.
Speaker 2:I mean, in the public this is very generalized, but in the public sector, you're seeking to do something for the, for the to to make something more cost effective.
Speaker 2:No, you're, you're seeking to. In industry, you're typically seeking to make something more efficient or whatever you know, looking to get the edge so you can make money. In. In the public sector, you're actually typically looking to to either save money or or improve the quality of care or the quality of education or whatever. So I mean, I don't know how much. I don't think that actually has an impact on the way you develop ai, um, and so, as such, I don't necessarily think there's a distinction between public and private sector. I think there's pockets of ways of working within both the public and commercial industry that it would be very useful for both to learn from. And you know, I suppose what I'm trying to say is you know you get innovation at a real fast pace in private industry. You get that in the NHS and other places too, in pockets, and actually the learning, the sort of cross-fertilisation and knowledge can be beneficial both ways.
Speaker 1:Yeah, I think almost a shared theme across all industries at the moment and sectors is the worry of AI and that fear of how people are using it, the data that is included in it, and I think that's getting better, where now people kind of understand a little bit more about how it should be used and who's using it and why. But I think definitely at first that conversation was more around nerves and the headlines were quite, you know, kind of it's going to take over our jobs, and so that's kind of an overarching theme of most.
Speaker 2:I think that those nerves are true across sector. I think that those nerves are true across sector. So you know the sort of yeah, and I mean AI is changing our industries and will continue to do so for a while yet, I think, and I completely understand the nervousness.
Speaker 1:Yeah, yeah, and the different sectors and industries can definitely learn from each other in that sense. The UK government their recent engagement with companies like Anthropic suggests a growing focus on AI in public services. How do you see AI reshaping the public sector in the next few years?
Speaker 2:I think. I mean I think much in the same way that the moment I think it's across the piece that businesses, organisations are seeking to understand how they can use AI in positive ways to bring improvements to their business or whatever the aims of the organisation are in an area like the public sector. Because I mean, we all want better public services but we don't want to pay more taxes to pay for them, and the point I'm saying that's a glib statement, but I'm not saying that's wholly true, but the point you know it's to we're not. You know the public sector has the opportunity to save money using AI that it can then push into either frontline care, frontline education, better council services, et cetera, et cetera. So I think that's probably the first area of focus for the government to seek to get more out of the taxes we pay for frontline services, whatever they are, by using AI to bring efficiencies, um, and I think there's a lot of opportunity to do that.
Speaker 2:I mean, of course, you know things using ai to improve the quality of care, the quality of, and in that I mean to you know, um. I was talking recently with a, with a clinician who's working to um to bring to use ai more in in his pathology work it's things like that and that requires a lot of clinical knowledge and awareness and testing and exploration and, importantly, scientific research that shows that using AI in the ways that they're approaching it is better than current clinical care or the clinical process. That is really important work and it's going to take quite a while and it will come to fruition in time. The opportunities to get efficiencies in the public sector are right there and I think the government are quite aware of that.
Speaker 1:Yes, definitely. I think those fears at first were kind of how is it going to be used? And, like you said, it's also building up that evidence that it is making things more efficient and over time that will show that more people will have faith in how it's used and why it's used. And whereas those concerns at the moment are still around, you know you still need human intervention to check.
Speaker 2:Yeah, I mean, you know the thing is I, I use ai on a day-to-day basis as part of my job.
Speaker 2:I use it to, I use it to code and explore stuff, and you know, and myself, I think, my goodness, you know, it's going to put me out of a job as a data scientist and and I don't think it is, I think it's going to change my job and or change the way I do things at least, and actually will speed up some of the things I do and enable me to you so I'm speaking myself as a data scientist, not just for me, if you see what I mean but it will enable data scientists to use their creativity, as we were talking about earlier, to do some, you know, really progressive well, different progressives, new stuff and relevant stuff for their whoever they're doing the work for, um, and yeah, and I and I think that will enable me and my colleagues to do more.
Speaker 2:So I think it's very much. The thing is it is going to change, it's in the process of changing how we work, and that's as much an opportunity as it is a threat. In fact, it's probably more of an opportunity than a threat. And so to you know I'm not saying just blindly go and you know, crack on and use AI to your hearts and tanks, as you've alluded to earlier, that you know there's security, things are ethical, etc.
Speaker 1:You know issues that you need to think about when you do it, but but to explore it and and use it, I think is an important part of most of our jobs now yes, yeah, and and freeing up that time to to do other things and to be more efficient, that that list as well, that you want to get to, that you can never quite get to because you have too much else to do, or and and sometimes there's always going to be those people that um don't react very well to change. I remember years ago I used to cover uh data centers and I remember when that started to become virtual, a lot of CIOs and CTOs were like I need to look after physical service, this is my job, this is what I want to do, and there was this sort of pushback against. But if I don't look after physical service all day, what do I do?
Speaker 2:and actually it just frees up your time to do other things, and that there's always going to be that pushback of people thinking I don't, I don't like change, I don't want time to do other things but and it's also important for us to you know, for industry and employers to recognize the, the, you know the skills, the very human skills that we bring to our roles and the relevance of that, I mean I I think that will come out more in time. So you know the importance of those and and because it, because a lot of the time I think you know it's going to be how we work with AI, not how AI does our job for us.
Speaker 1:So so yeah, yes, that's a good point. Um, you're a vocal advocate for ethical AI. In your view, what are the most critical ethical challenges we must address as AI becomes more embedded in our lives?
Speaker 2:Yeah, I mean, there's quite a lot I could say here.
Speaker 2:So you know what's the most important thing I think I suppose I'd say while we're having this conversation in the first place, you know we're here talking about how we can.
Speaker 2:You know the 11%. But if we look to AI and recognize AI is trained on data and data carries a lot of bias and actually so AI inherently reflects the data and the culture it's trained on. So if we train it on western data that's dominated by particular points of view, that's what we're going to get and what we're going to see in the ai and you know what's the dominant sort of force in in western society today. Recognizing that and and seeing that actually the importance of diverse data, diverse points of view in both the people and the data the people that build the AI and the data it gets trained on will ensure that we have more culturally rich and diverse AI in the future. And I you know we're having this conversation today because of the dominance Well, the 11 percent question that's happening in ai today. So I think that is actually one of my biggest concerns yes, you're absolutely right.
Speaker 1:the the data that it is trained on um is a concern that, where we're at currently um to to think that ai is being trained on the data that we have currently, which some companies have been working so hard on to change, it can be concerning as to how we will move forward with ethical AI.
Speaker 2:And, like I say, also have ensuring diversity in the people who develop it, because as much as you get data bias in the machine learning models that drive AI, you also get developer bias. So you know, it's those kind of things. I mean, yeah, the importance of it. It's the right thing to do to achieve diversity in the technology workforce and it's also required to ensure AI is diverse.
Speaker 1:Yes, yes, developer bias. I haven't heard that one before. I wanted to ask you quick about young women coming through the ranks. So for young women and also underrepresented groups looking to build a leadership career in tech, what advice would you give them and what changes would you give them and what changes would you like to see from companies to support them better? Anything you wish, someone has told you. Now, looking back, I think.
Speaker 2:I mean I personally it would have. I think, is it it? Role models are important. Like I say, I mean it's not, they're not the only. You know, I mean I'm a role model, whether or not, but that's not the only. It's not the only thing is I actually see it as a responsibility to stand up and talk about these things, because I know that visibility is really important and that's so. That's one of the things. It would have been helpful for me, but and at the same time I suppose it would have been that it's it's taken me some time to build up the confidence.
Speaker 2:The thing is, I thought, you know I'm an innovator, so I've thought differently about things through my career and now I see that as a strength. I didn't because and it wasn't always encouraged or facilitated in me, and the thing is, you know, for for women coming through, unless until we get equality in the technology workforce, you're actually you know there's going to be different points of view that you have. That might not necessarily be the, the, the general view, and that's not just a woman thing. That's probably diverse. You know diversity as well.
Speaker 2:So so what am I saying that? I suppose I'm saying it's it's important for companies to to just champion. You know, recognize that the differences people have and actually that the strengths that you get from a diverse workforce and to encourage and facilitate it, and that leadership role as well. You know a leader needs to be able to empathize with people working with them and to understand their strengths and weaknesses and how they can help them to develop themselves in the ways they want to.
Speaker 2:And you know, fundamentally we're there to lead, to make sure we get work delivered. But you know the ways you can do that by helping your, you know the people working with to to grow in the ways they want to, in ways that meet what you need to deliver. But at the same time, you know the trick to pull is it's recognizing that more and helping people to develop in the ways they want to, like I say holistically, recognizing that a diverse workforce brings better outcomes, and that's been measured. I haven't got the statistics on the sort of tip of my tongue, but it's been proven that. You know, greater diversity in the workforce leads to better business outcomes. So you know, recognising that and seeking to grow on that.
Speaker 1:Yes, yeah, I love everything you just said, especially with being an innovator and wanting to try new things. Sometimes, when you land in a company where you're allowed to be like that, it's almost a culture of. I worked in an American company that was very sort of Kayleigh if you're having an idea, test it. Test it and prove that it works. Don't cost us any money, but test it and see what happens, whereas some companies you're not allowed to do that and you do find yourself thinking, well, even if I come up with something different, I'm only going to get shot down and you actually go into yourself and you stop coming up with it.
Speaker 2:Yeah, and I think that, kayleigh, I think you just hit it on the nail on the head with me. That's what I've done through my career. Sometimes I've been shot down and sometimes I've been championed, and you know UST is a company that champions innovations. I'm grateful for that. And it's the other organisations I've worked through for my career.
Speaker 2:I mean, I could tell, you know, there have been moments in my career where an individual has made a huge difference and said, yeah, heather knows what she's doing. You know, let her crack on and grow. And you know, and I've delivered and really shown the promise of things. I suppose it's as you you said, you know it's. You have organizations you champion that you have have ones that don't that sort and and it's that's not the only aspect of you know, we're talking about innovation here, but, but you know, rather necessarily than diversity, although it's that recognition of people's different people's skills and um and and the ways of thinking you know, and that, and that's what I mean about empathy, to sort of, do you understand that that that people may have different ideas, you will think a bit differently and actually that's a strength, you know, let's, let's use that and do some really cool things together.
Speaker 1:So especially, something hasn't been working as well and somebody comes up and says you know what, why don't we think about this differently and try something? If you're shut down at that point and you can see that perhaps change is needed or they need to at least embrace a slightly different way of thinking, and you're still shut down, then you kind of realize, actually that's not for me and I need to find a different environment there. I love what you said about role models as well. I completely agree with that, because actually that's not for me and I need to find a different environment there. I love what you said about role models as well. I completely agree with that, because I think as well, role models at different stages throughout people's careers are also important, not just the ones at the top, because I've sat at events before and you hear some inspiring talks from like a big executive and you're looking at her and you're thinking she looks fantastic, she's got a fantastic job. I can't relate.
Speaker 2:How do I get where I am now to? So that, as you're saying, the sort of pathway people through along that pathway is really important. I completely agree with that, so, so yeah.
Speaker 1:Yeah, there's someone just a couple of steps ahead of you and they're and they might even not even realize that they're being a role model, um, but if they're just visible, that helps, because then it's like it helps you with. Well, how am I going to get to that where I'm speaking at an event and I'm an executive and I travel the world and also that's not something that everybody wants to do as well. I find those people were put up there as role models, but actually some people just think that sounds like hell to me. I don't want to be that person. I would. You know, I'm looking at somebody else and they don't realize that I'm looking to them as a role model and and the expertise that they have in their field and they might not necessarily be somebody that's very out there, um, and you know, out there giving public speaking and all of that you know people might be thinking that is not for me and that's just it, isn't it?
Speaker 2:it's that recognition of the, the, the, yeah, the, the sort of the, the richness of the career paths we can have, the. You know the differences in those and and the visibility of of different people, you know. And if it's we're have, you know the differences in those and the visibility of different people, you know, and if we're talking about increasing women in the workforce and different women in those different places, to show people younger in their career, of the places they can get to, I mean, yeah, role models are a part of that for sure, definitely.
Speaker 1:Heather, we're already out of time. I could keep picking your brains on this for a lot longer, but we uh are already out of time. I'm afraid it has flown by. So thank you so much for coming on and having a chat with us on spilling the tea today. It's been a pleasure, thank you very much, kenny, thank you thank you and, to everybody listening, as always, thank you for joining us and we hope to see you again next time.
