The conversation with Lisa Weaver-Lambert unpacks the multifaceted role of women in the rapidly evolving AI landscape. With a rich business transformation and strategy career, Lisa emphasises the necessity for non-technical leaders to gain a robust understanding of AI technologies. Her book, The AI Value Playbook, culminates her extensive experience and aims to empower executives with actionable insights to integrate AI into their organisations. Through her narrative, she illustrates how AI can transform industries, provided that leaders have the right knowledge and tools.

Takeaways:

• Women are underrepresented in AI and tech, often facing challenges in fundraising for startups.
• Diversity in teams is crucial, with women bringing valuable perspectives to AI development.
• Practical engagement with AI technologies can empower women to embrace leadership roles in tech.
• The rapid pace of AI development requires continuous learning and adaptation from all professionals.
• Domain expertise is critical for applying AI effectively across various industries and sectors.
• Women should leverage mentorship and community to build networks and advance their careers.

Find Lisa on LinkedIn.

Lisa’s book: The AI Value Playbook: How to make AI work in the real world

Links referenced in this episode:

• www.microsoft.com
• www.accenture.com
• www.berkeley.edu
• www.escpeurope.eu
• www.oxford.ai
• www.kaggle.com
• www.openai.com
• www.deeplearning.com
• www.spotify.com

We don't see that creeping up on us sometimes, that difference between the announcement and then sort of wide impact. And I think all of us have a real role in society as to understanding how AI is going to impact our particular industries and domains. And I do think that women have a really important role to play.

Hello and welcome to Women WithAI.

My guest today has extensive experience in strategy and business transformation and has held executive leadership positions at reputable brands across multiple industries. She's also an author and has recently released The AI value Playbook, how to make AI work in the real world.

So I'm really looking forward to hearing all about that.

But before we do, let me tell you a little bit more about her Lisa Weaver Lambert has been leveraging data to solve commercial challenges for over two decades through her roles at Microsoft, Accenture and in the private equity sector. Her work spans multiple industries and regions, including Europe, the US, Asia Pacific and Africa.

In addition to her extensive experience, she's also held executive line management positions and served on various boards. And she co-hosted the private equity technology podcast. No pressure here then.

Lisa began her career working on acquisition, retention and fraud detection programs in the financial services industry.

Since then, she's worked with some of the world's best-known brands, leading them towards success and earning her recognition as a leading woman in technology.

Recognising the transformative potential of AIH, Lisa's mission is to equip non-technical leaders with the knowledge and confidence to leverage AI for their organisations and with degrees from not only the University of Lancaster but also the top-ranked ESCP business school in Paris, plus further qualifications through executive programs in business process designed for strategic management at the MIT Sloan School of Management and Data Science at the University of California's Berkeley school for information. I think she's very well placed to do that.

Lisa also recently founded the Oxford AI Studio, an initiative aimed at bridging industry with researchers and students to tackle real-world business challenges and drive innovation. Lisa Weaver Lambert, welcome to Women WithAI.

Well, thank you, Jo, thanks so much for inviting me.

Oh, it's fantastic. I mean, tell me, how do you have time to do all that and write a book?

That wasn't even going to be my first question, but I'm just so impressed with everything that you're doing. Well, maybe you start by, yeah, telling us about your journey, how you went.

From not all at once, I think.

I mean, I enjoy patterns, languages and statistics and those themes have been quite common through the progression of my career and when I started writing the book, I had a very clear idea about what I wanted to achieve from the book, how it was going to be organized. And that followed a pattern of working as well that I'm very used to.

I'm very used to collaborating globally with different people, getting their insights and experiences, and bringing everything together into playbooks that executives can use. That's how I approached it.

Fantastic. So perhaps, yeah, you can tell us or tell everyone about your journey, how you went from fraud detection to becoming interested in AI.

I guess, as you said, it's the patterns and that kind of stuff. But, yeah, how did that lead you to where you are today?

In my first roles early in my career, when I was working within financial services on databases, I was working predominantly in what we call data management.

And it was a great discipline to learn because it gave me the experience of working with data quality, which obviously hasn't gone away, the requirement for that. And I also moved to the US early in my career, and that had a big impact on me.

And I was lucky enough to experience the value of large datasets and what you could do with them and how you'd use analytics to solve real world problems.

Wow, fantastic.

And so writing a book, was that sort of always something that you knew you'd do, or was it kind of you gathered all that knowledge and just thought you had to put it down and help other people?

Well, it was definitely an idea that emerged during the COVID area, and there were really two motives behind at which, which sort of converged. So, firstly, at that time I was working in private equity, and that was an industry where I had limited experience at that time.

And I realized that when I put thinking down on paper that I had a breakthrough in terms of or cut through, people understood the concepts, the methods and the challenges a lot quicker. And then secondly, I was also working closely with CEO, who was grappling how to integrate AI into his.

It was quite a complex integration and growth strategy for this company. And he started to get in touch with other business leaders that were leveraging AI.

And I started to ask myself, how could I scale this type of understanding, take it into other geographies, and how could I codify these practices and learnings that could be applied to other organizations?

And essentially, I really wanted to equip non technical leaders with the knowledge and confidence to leverage AI in their organizations and do this through in depth and sort of wide ranging conversations with practitioners from CEO's to leading CFO's and data scientists in established as well as new companies.

Because it's all about, it's about the data, isn't it as well, making sure that people know what they're doing, what they're using. I mean, what insights did you get? I mean, what did you learn while you were doing research for the book?

So I learned a lot of commonalities actually, as well as differences. So as I said, I sort deliberately out a diverse group of contributors for the book, considering factors like geography, role, gender, ethnicity.

And I noticed a trend that women in data and AI are more likely to work for established companies rather than leading high growth companies. And I mean, those that are founded sort of six, seven years ago that are cloud native.

This set of companies has integrated AI and adopted LLMs into the architecture and business models and has a strong commitment. However, look, there are exceptions, like for example, Natalie Gavo, a female tech entrepreneur that is featured in this book.

But the trend, I think could be partially contributed to the fact that, you know, women do face a lot of challenges and fundraising for technology startups. Sorry, Jo, women focused startups count for only 40%, 14% of unicorn startups.

But in general, while women are still represented in the humanities, they remain considerably underrepresented in maths degrees, 43% according to MIT, and that's a trend that is fairly consistent. And only 25% of women makeup data engineering and software engineering.

And I think, however, that these stats shouldn't disparage women from going into AI. But I do think that these are indicators as to why we don't see as many women in AI as we could have the potential to have them there.

Yeah, I think. Do you think that's just because historically girls aren't pushed to do it or just not encouraged, or just.

I don't know, because I get, you know, I've spoken to sort of, you know, I'm speaking to lots more women sort of about these kind of things.

And a couple of women that I spoke to that haven't actually been on the podcast yet said that they'd gone to all girls schools and actually that was how they then got into the STEM subjects because, you know, there was no pressure, there was no sort of feeling that you couldn't do it or that you shouldn't do it. You know, it was kind of, and they really know, valued that and, you know, almost didn't want to say it.

And that also that very feminine trait of almost being embarrassed of saying that, well, you know, it was probably because of this. And I think sometimes those stats about, oh, well, there aren't enough women kind of, it puts you off sometimes as well, doesn't it?

Like, so how do you so.

But I do, you know, I mean next week I'm on a panel with two guys and I was looking up their backgrounds and one has a mechanical engineering background and the other one, I think he did natural sciences at Cambridge. So, you know, there aren't like, I think, yes, we do need women in more women doing mathematics and software engineering.

But it's important to note that, and this also came out really strong in my research, that the way forward is multidisciplinary teams and they are critical for success. And I did some research around, well, what are the qualities of AI leaders today?

And of course, technical expertise is a key component here, but also business acumen. And there are plenty of women doing MBAs or in strategic roles.

Women who are in academic research and also sort of visionary and strategic thinkers came out as well. And the ability to work across different areas such as efforts, psychology, economics.

So I think that sometimes, yes, it's important to keep pushing women towards the disciplines, but I think there are already plenty of opportunities for women to go after. And technical expertise is one facet of leadership. But there are also other facets as well.

Yeah, definitely. I mean, going back to what you. Sorry, what were you going to say?

I was going to say that, you know, I really like, you know, the phrase that I've often heard from Billie Jean King, which is if you see it, you can be it. And I think now there are enough women role models for women to start seeing it. And, you know, data and I is really a team sport.

So you just have to find a way of getting on the team. And, you know, I hope that the interviews with the ladies in the book that I've just written will inspire others.

And the other point I just want to add is that domain expertise is critical.

So if you've built up a career in education, law, medicine, the importance of domain expertise can't be underestimated because when you're applying these models into these domains, you need to know the domain and the impact the model is actually having on the domain as well.

Yeah, you're so right. It's like finding what the problem is.

And if you've got that experience, then you can find it and you don't necessarily be the one that's actually writing the code or doing the sort of, the technical bit, is it? You need, as you say, you need women leaders.

I mean, I'm just going to go back to what you said about how women are more likely to be working in established companies.

Do you think that's because of risk or do you think it's just because, as you say, because women aren't being given the funding to sort of start their own companies? It's like, how can we?

That's probably a combination of different factors. You know, I think that it's probably, yes, there's an element of not getting the funding and there's an element of risk in there as well.

And the other roles that women still play predominantly in their lives and sort of managing everything. So I think it's probably multifaceted, but I do think that this is a time when women entrepreneurs can really shine.

Yeah. And I think we need to push ourselves forward more.

And I think that's the thing, isn't it, supporting each other as, I mean, from your perspective, can you see how women can become sort of more strategic in opening doors for themselves?

That's a good question. I think it's a combination of being more hands on and strategic by more hands on.

I think women need to have that discipline of bringing AI in the room to actually engage with the AI tools and platforms, directly experiment with these large language models and explore their capabilities, being very practical. Make a list of things you want to do with AI and try that.

And if it fails, save it and try it again in six months because the whole area is just moving at such pace. And really embrace a sort of trial and error approach, document their success and go deeper into the understanding of AI's strengths and limitations.

And from all the leaders that I interviewed, some of them had very strong technical backgrounds and then others, you know, I'm thinking of contract pod AI, which is a legal, it's a legal based AI company and the founder comes from a legal background. So I was really interested to understand how he had carved out his journey. And I think it's so important.

I mean, the other leaders who are from software backgrounds, I mean, they're the started to rethink how they run their companies as well as the sort of product and service interfaces with their customers as well. And through this experimentation, you find that the models are surprisingly good at some things and then very poor in others.

And I just think be very practical and hands on and have AI plan your week and add calendar updates while you hit the gym. You know, look for low level productivity gains where AI can be applied.

And if you're feeling up to it, then there are, you know, sources like Kaggle. That's a great place to find datasets.

And just to get back to your question about how do women be more strategic, I think that today there is such an abundance of online courses, tutorials, you know, all the leading tech companies as well have courses that they do and universities, etcetera.

I think that women need to be thinking ahead, so understanding the impact on their industry, understanding what job roles are actually emerging in AI and how AI will impact their industries in the future. For example, going back to the legal profession, I was talking to a lady who's working for a really large telco.

She's got into data governance and she wants to increase her career in AI. And there are an enormous amount of new jobs emerging that require a legal background. I mean, just to mention some, you know, a few.

There's this compliance, there are policy advisors, legal counsel, legal technologies and legal engineers, and really start staying informed of these new jobs.

And it's, you know, the quickest way of doing that is just setting up, you know, Google alerts in your field and then you can also join communities and engage with others who are actually in your field. So, you know, the legal profession is coming up again. But I spoke to a lady who's now formed a community around AI law just for women.

And I think this can open up lots of connections and I think, look, private WhatsApp groups can also be super powerful. I think keeping updated is super important.

So reading AI related news or research papers, you know, the AI community is still hanging out on, on X, but there's also a lot of information on LinkedIn as well.

And you know, follow the announcements from Google or Microsoft or anthropic or OpenAI and you'll start to get a sense of what is coming into the public domain from these companies. I think mentors as well, they can guide you, whether it's, you know, a quick informal connection that can be hugely powerful or something ongoing.

And I think women need to understand the type of company that they want to work for. So is it a large tech company or is it a new tech company or is it in financial services?

And I think really in the tech companies or in financial services, you're more likely to find an abundance of these roles at the moment. But I think it's just important to look into different role types and understand how your skill sets can transfer across.

And I think that's really, really important.

And I noticed this week, for example, there was an announcement between Vodafone and Google for a ten year strategic expansion of their existing partnership to bring AI powered services to Vodafone's customers in Europe. And there must be opportunities in there.

So just keeping an idea on where investment is going, that'll give you a trigger as to where you can go and who you should be contacting and who you should be aligning yourself to.

That's fantastic advice. And that actually makes me feel inspired and makes me kind of excited about it.

Because you're right, because it's having people that are experienced in one sector or area what your passion is, but then using AI to make it easier or make it quicker or to help you or to give you time to do other things. And you're right, you don't have to be the AI expert, you just have to know what you're talking about and how AI can then help you and help that.

And I think that's, that's really great advice. And as you say, like reading. And I mean, I've signed up to so many newsletters since looking into AI, and some of them are just far too technical.

But you're right, others are about partnerships or about fun things.

And I know that I need to embrace AI a bit more, you know, so I haven't, I've sort of dabbled with the note taking ones, you know, that kind of like do your notes while you're talking to someone. Yeah. But then I found that I was like, oh, brilliant. I don't need to pay any attention because it will take all the notes.

And then afterwards I was like, oh, I wish I'd made my own notes as well. So it's just getting used to it and how you use it, isn't it? But I know. What are your favorite AI tools that you use?

I mean, do you use AI to plan your calendar for the week, like you said?

I do, but I just. Coming back to note taking, I'm a huge fan of Otter AI, I take it. You know, it's like a lipstick for me.

It doesn't, you know, it comes with me everywhere and it just gets better, better as new releases come out. And it's a huge time saver for me.

And sometimes you can get engaged with a conversation and you lose some of the details, maybe that you haven't heard because you're focusing on a different part of the conversation. And just even having Otter AI attend a meeting that you don't have to tackle the time to attend is a huge time saver for women.

So I think that any of these types of tools are worth experimenting with. And I just want to come back to your point about fun, and I think that is a key point.

These LLMs are quite ludic at the moment in terms of what they do and how they present back. And Openaiden has had quite a blast with their announcements and having a flirty AI talk back to a young engineer, et cetera.

And it is fun, and it's really, it's like having a crazy, dedicated team of interns that, you know, do really strange things at times, but can also save you an awful lot of time in other areas. Once you've known, once you start to know, well, how I, how can I use that? How can I work with that?

Yeah, totally. And it's.

Yeah, I think it's just encouraging other women to use as well, you know, I mean, like you said earlier, it's important to have representation from lots of different people, from everybody, really, you know, because, as you say, we might all be there going, oh, yeah, great. They've got a flirty female AI talking to the engineers. What about me? What about the female engineer?

You know, we need a flirty voice to come back to it.

So I think it's important, isn't it, that we have, you know, there's women up there as well as men that are pushing AI and kind of leading it and feeding back to it. That's the other thing that I've learned as well, just using chat GPT, you know, the more you tell it. Well, no, that isn't quite right.

And that's when people, they have a tone of voice one, don't they? And you can sort of tailor it. So it's actually coming back to you. So I think you're right. It's just using it and playing with it.

And I think that's why allyship is so important. Isn't it just women supporting women, especially in tech, because it can seem a bit scary and daunting.

Yes.

And I think that, look, what came up over lunch with some women working in technology yesterday was that, you know, and this, you know, one of the ladies, she's got a formidable background, but then, you know, sometimes when we're asked to sort of step out of our comfort zone and what we perceive that we are trained for, we back away from that very, very quickly and we underestimate, you know, the suitability or the adaptability of our skill sets and our capacity to learn and grow. I think that's super important. And we all do it.

Yeah, I think it's the classic, isn't it? It's almost like you sit back and just expect to be rewarded. Not expect as in like, oh, yes, obviously I'm fabulous, but it's more.

Surely someone will recognize what I'm doing. Isn't it obvious? I'm doing all these amazing things. I've got this skill set that's totally transferable. I could do all that.

Surely it's obvious, but it's not.

And that's why I think men, or maybe people that embrace the sort of masculine side a bit more are able to sort of push themselves forward and say, well, actually. Or even just take a chance. And as you say, take a risk and just do it. Because really, this is exciting.

This is the time when we need to get involved and everything's changing and no one really knows what it's doing. People that are writing it don't know what it's doing. So let's.

That's right.

Just take a chance.

I think you're making a really valid point because there is a lot of learning that is going on right now, and it's better to be part of that learning.

And the example that I was just sharing, I mean, this lady, she was extremely qualified, actually, for the role, and she was actually asked to go and do another role, but then she thought herself, well, I don't actually come from that background, so maybe I can't do that wrong.

So I think sometimes we screen ourselves out of opportunities, and I think that's where just to come back to your point on allyship is important because other people can support you in seeing. Well, actually, no, you can do that. And I think that that can come from either men or women.

I think just look for people who are interested in your progression, and I think they generally exhibit this type of behavior with their teams. I don't think that gender matters too much. And it might be something continuous.

It might be just a one off conversation, but be ready to listen and learn and take feedback. And I just think of the only bandwoman in my book, Elizabeth Agey, who grew her career at PwC.

She now works at Capgemini, and she did her interview with me while nursing a baby. And she's got a real sense of self and purpose and has made her own path.

And, you know, she really understands the importance of building relationships and asking for help. So always thinking ahead and listening and planning and not getting stuck in just doing what is good enough for today.

Yeah, I think that's great advice. It's just getting rid of those limitations because AI hasn't got any limitations, so why should we?

We just need to put ourselves out there and embrace it and recognize all the fabulous things that we can do.

I think that's fantastic because you should be able to be nursing your baby and working and, you know, having a life and holding down a job and writing a book, probably, and doing, you know, amazing things. I think that's. Yeah, you've really inspired me. I mean, where do you go? Do you, do you have sort of recommendations for.

Well, obviously your book, the AI value playbook. I mean, who. Yeah. Did you, have you said the audience was for sort of, you know, executive leaders, but can anyone pick it up and read it?

Yes, anyone can pick it. Pick it up and read it.

In fact, I, look, I give you very different examples, but one of the people that, some of the people that endorse the book are highly technical and they found that sometimes the explanations were actually very useful for them to leverage with less technical people.

So translating some of the technical side, but in a way that people can really just understand and get to grips with and also people from investment backgrounds and people from board roles as well. So anybody can really, you know, because it's written in a very modular way as well.

And that was very deliberate on my behalf because I like to dip in and out of things and pick something up, come back to it.

And so you can go into case studies, come out of case studies, go into an interview and they're all contained within chapters so you can get through the content at your own pace.

I love that. I think that's so clever. And that's what people need. You don't always have time to look into all the background.

It's like, just get to the point, tell me what I need to know because that's what I've come here for. That's what I need to learn. So look into the future. What excites you or concerns you about the evolution of AI?

So I try to avoid futuristic thinking because I think there's a lot of noise about it and there's more value to be had in understanding what's here. Right now we've got models. The latest GPT was in May and that brought sort of text, voice vision. Vision is still very early.

Claude 3.55 came out in from anthropic in June this year. So, you know, I just.

To try and avoid the rumors and stay close to the releases, I would say what makes me, what makes me nervous is the speed of AI development. And I think every company leader I've spoken to have seen this and I think that the latest book from Yuval Noah makes a strong case for this.

And I think we need time to adapt our businesses and our society to incorporating this technology. So I think there's always a time lag that is between the technology development and then full scale adoption.

And we don't see that creeping up on us sometimes, that difference between the announcement and then sort of wide impact. And I think all of us have a real role in society as to understanding how AI is going to impact our particular industries and domains.

And I do think that women have a really important role to play with in this.

I always liked the comment from the chair of an investment firm that I worked with, and he always said that he liked to work with female investors because they made more sensible choices. So I do think that there's something in that in terms of getting that diversity of thinking in the room.

But I would stay very close to actually what is in the market now, what was released just a few months ago and what is it capable of doing and how can I see the potential for that even if I'm not in the technology division of a company? I think it's really important for everybody to understand as much as they, they can.

And I think, you know, I've come across, you know, people who say, look, yes, but not now or etcetera, but I think, you know, when then, you know, when the Internet came around, when was it that you started to get interested about it? Because it's going to have a formidable impact in the, the social structures that we have today.

Yeah, I mean it's affecting everything, isn't it? Like how we learn how, I was saying, how you take notes, how you listen, how you manage your day and yeah, this is the time to get involved.

You can't ignore it.

It's happening, our health, our education. Absolutely.

It's one of my brothers, he works at a university in Canada and they've had to implement a procedures around understanding, you know, when people have used AI to answer questions.

Yeah, because there is a lot, isn't it, that it's like you should say that it's powered by AI, that you've used AI to sort of do this or not, but then at some point it probably will just become the normal, you know, it's like you don't go, oh, well, I googled it, or oh, I used the encyclopedia Britannica to check. I didn't just make this fact up.

There is some research source validation will continue to be super, super important, which is why a lot of people are building their own models over the top of these large language models.

And I'm thinking about one of the companies in the book that worked in the healthcare industry and helped do all of the training for sonograms and it's a specific niche and they had to learn from all of the hundreds of books, texts, videos, etcetera.

And so they had to really build a very bespoke model to be able to allow, you know, the new people, you know, not familiar with the equipment to be able to ask questions on their, their phones. So precision, there is really, really important and precision in the sort of base models that we're using today.

They're less, it's not there because they only get retrained periodically and it is impossible, absolutely impossible with the vast amount of data they have to be accurate. That's not possible.

Yeah, I think this is where. Yeah, the more sort of reading that we do around this and sort of learning about it.

So that's spotting those opportunities to get involved and do that. So apart from your book, are there any other sort of further resources or reading that you definitely recommend?

Yeah, I do. I do really recommend, you know, checking out the research from companies like Google, OpenAI, DeepMind.

You know, they published their, their findings in innovation. I recommend finding people who you can follow. I mean, for example, I follow Nicholas Carlini.

He's a research scientist at Google DeepMind and he works in the intersection of machine learning and security. So I like to sort of learn out of my, out of my comfort zone. Yeah, I think there's a lot of.

It's about just, you know, there are a lot of, obviously there's your podcast, there are other podcasts depending on what you're really interested in knowing more about.

But I think some of the, you know, even if you go to the websites, the major tech companies, they have on their customer success stories, you can see how they're applying, obviously their marketing pieces, but they're valuable because you can see how AI is actually being applied into businesses and you can extrapolate from there into your own career and your industry.

Fantastic. That's really good advice. I saw as well on Spotify the other day that now it's got a suggestions and recommendations for other things.

So I think, yeah, anyway, you're searching. Just search for AI and learn as much as you can. Well, brilliant. Well, finally, where can people find you, Lisa, and stay updated with all your work?

I'm predominantly on LinkedIn at the moment. I'm about to start a blog, but I'll announce that on LinkedIn. So LinkedIn is the best.

Cool. I'll put all the links to you and to the things we've talked about in the show notes. But Lisa Weaver Lambert, thank you so much for coming on.

Women WithAI. Jo, thanks for having me.