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Welcome to Lead with AI, a podcast in which we speak with business leaders bringing AI to their work, teams, and organizations.

In today's episode, we speak to Helen Lee Kupp and Nichole Sterling, Co-Founders of Women Defining AI, about how they're revolutionizing AI adoption for women and shaping the future of work.

Helen is a former Strategy & Analytics leader at Slack and co-author of the WSJ Bestseller "How The Future Works," bringing a wealth of experience in guiding companies through exponential growth and reimagining work for the AI era.

Nichole is a visionary entrepreneur and expert in adaptive intelligence who has a proven track record of building resilient, AI-powered companies in legacy industries, making her a driving force in creating workplaces of the future.

Here are five key insights you'll gain from our conversation:

1. How AI is redefining what it means to be "technical" in the workplace
2. The importance of creating a culture of experimentation with AI in organizations
3. Why diverse voices are crucial in shaping AI's development and implementation
4. Practical strategies for demystifying AI and making it accessible to everyone
5. How AI can make work better, instead of replacing people. 

Key Insights from Helen Lee Kupp and Nichole Sterling

Here are the actionable key takeaways from the conversation:

1. Redefining "technical" skills

‍Nichole emphasized how "AI is really starting to blur the lines of what it means to be technical and what it means to not be technical." This shift opens up opportunities for more diverse participation in tech.

Encourage your team to explore AI tools regardless of their technical background, and consider implementing AI literacy programs that cater to all skill levels.

This will all help to ‘demystify’ AI and defeat imposter syndrome, enabling everyone, including women and non-binaries, to use AI to its fullest.

2. Creating a culture of experimentation

‍Both Helen and Nichole stressed the importance of fostering an environment where people feel safe to experiment with AI.

Helen suggested thinking about AI as Lego bricks, providing the tools and encouraging fun, creative exploration.

Create dedicated time and spaces for your team to experiment with AI, ask questions, and celebrate learning outcomes rather than just successes.

Even just having the conversation about AI can make a big difference here.

3. Diversity in AI development

‍Research shows that only a third of women use AI, compared to half of men. Experts believe that women’s jobs are more at risk of being replaced by AI.

Helen emphasized that they want more women to define space, to have a voice in it, and to have a hand in shaping technology.

This applies to your organization, too. Actively seek diverse perspectives in your AI initiatives.

Consider partnering with organizations like Women Defining AI or creating internal diversity-focused AI working groups.

4. Practical strategies for demystifying AI

‍Nichole shared an example of using AI to simplify grocery shopping for a specialized diet, demonstrating how AI can solve everyday problems.

Encourage your team to share practical, relatable examples of AI use in both professional and personal contexts.

As Helen said, help people have the two crucial aha moments: at home, and at work.

This can help make AI more approachable and less intimidating.

Helen also urged all of us to “let go,” and be vulnerable to our teams if we don’t know something about AI. This is completely expected and natural, and will motivate everyone to get more involved.

5. AI is Here to Make Work Better, Not Replace People

‍Helen urges all leaders to find opportunities to improve jobs rather than looking for ways to replace people.

Echoing what BCG X’s Matt Kropp said in the first episode of this season, AI can remove toil and deliver joy.

Having this conversation out loud will remove some of the fear that people have of AI, especially when paired with the two aha moments. 

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Transcript:

Daan van Rossum: Before we dive into the background, what are some things that you would give as a practical tip for company leaders and for team leaders when they're trying to get people onto AI? What are some things that they should do?

Nichole Sterling: Sure. I would say first that you need to start having conversations about AI. It's more prevalent that companies are not talking about it, and they're not providing their employees with a pathway to start experimenting and to start experimenting responsibly with it.

And a lot of employees are out there asking, what can I do with it? How can we use this to improve our day-to-day productivity? Our day-to-day work flows, and organizations are actually scared to jump in and set those guidelines. So I would say across this board, it's more important that more organizations are having those conversations instead of shirking from them.

It's oftentimes folks that are in the day-to-day, like on the front lines, who have these insights and how AI could be used, but they just feel caught. They don't know what to do. They want to experiment. They are experimenting. We know they are, but they're not getting guidance from their organization on how to move forward.

Daan van Rossum: Why do you think that is? What's the barrier for companies to dive into it, to actually let people experiment, and to get forward? Because I think we all can agree that AI is going to be a big part of how we work and how economies and companies will run in the future. 

What are some of the barriers that you're hearing about as you're talking to maybe people in the community or other business leaders?

Helen Lee Kupp: You probably saw this in some of the past conversations related to the future of work and flexibility, but there is a core piece to this. That is about leaders letting go of control, right? Rather than having the answer up front. This is exactly how we think about future AI technology. Here are the tools that we implement, and this is exactly how you should use them.

That's the traditional way of thinking about software. But now, similar to flexible work, it really is about being willing to say, I don't know what I don't know yet. And I am completely open to finding out and exploring together. So really, it is about building a culture of experimentation and taking that sort of big idea and making it possible.

When I talk to people in the community and those who have gone through the course, there are two things that come up.

One is within their organizations. They're just like, Oh, I don't know how to share the experiments that I'm running. If I'm doing something, if I'm learning about what's working or what's not working, or if I have reflections, where in the world do I share that outside of my team?

The second is, where do I ask questions? If I have big questions about how I might use some of the data that I have or what tools I can use to really start to do different types of AI use cases, who do I ask? Where do I send that question? And so there's a lot of this process around setting up a culture of experimentation that is actually more important for leaders to define than having the exact answer. This is the thing that you want to do.

I'll add one additional thing from the community and the course that we do. People who've taken your course have really loved ours as well. We created it mostly because we wanted to get past all of the articles and conversations about just the tech for the tech's sake and get to the different ways that this can be valuable for me. What are the use cases? And get people started on that before diving into it. Let's talk about context windows. Let's talk about these sorts of technical features. What people really want to know is: How do I get started? What is the thing that I can do with it? And then, how do I build from there?

Daan van Rossum: So it seems like, at least obviously, people are self-selecting into a community or a course like yours. So, therefore, people are very eager. But it seems that there are quite a few people out there who want to get started. And we've seen this in the Microsoft data. We've seen this in recent Asana and anthropopic data.

There are way more employees that are actually using AI than companies that have a well-informed strategy or even an adoption plan, and everything you just mentioned in terms of, okay, it's about giving people room to experiment. It's about having a place to go if you have questions, but also about sharing your successes so that others can learn from them.

I think the Microsoft data showed that like 52% of people are using it in secret because they're worried that if they show that they're using AI, they're going to get fired or people think that they're lazy or worse, they're going to get even more work. So those are some of the best practices that companies could pretty much universally implement because they don't really cost that much.

Is there anything else that you would add to that in terms of what companies should do right now? It's almost like what you're hearing in your community—what people are lacking. The inferences are probably what the company should do. What are some other things that companies could do to, again, get into this world of AI?

Nichole Sterling: Yeah. One of the things that I think makes us successful in what we do is that we have this asynchronous remote support community that is in the background and that's helping folks, just like Helen had said. Where do I post? Where do I have a question? Where can I feel supported in my journey?

Because ultimately, one of the reasons we started women defining AI was because we saw a gap in usage. Some research last year showed that only a third of women were using AI, compared to over half of men. And so to have that supportive community where folks can ask the questions, say, I don't know, like, where could I find this information?

But that's hard to do sometimes in companies. Where can I post the question so I don't feel stupid when I've got all my peers and folks looking at it and my boss also having access to it and everything? So it's a little bit more difficult to replicate and to do it genuinely in the workplace.

This is something that makes our community so special: it does have that supportive background for operating.

Daan van Rossum: Probably the peer-to-peer part is just as important as ever; this is not a top-down thing. It's not like you say, here's the mandate, here's the policy, all the analogies to hybrid work and flexible work.

It's not about having something big come from the top down. It's really letting people experiment. And the peer part is really important because not even one person in the company, not the most talented, either the CIO or CHRO, would be able to share every tool you should be using or everything you should be doing with those tools.

So the peer part is probably really important. So you mentioned some really great data there and a little bit about the mission. So maybe we can take that step back now and hear about how you founded this community? Where did it start? What's the journey been like?

Helen Lee Kupp: This time last year, ChatGPT had already launched and Gen AI was starting to go pretty mainstream, and it was around summertime last year that we both saw Jeda, where the AI adoption rates between men and women were widening. And this is compounded by the fact that, for most AI experts, when they look at the jobs that are going to be replaced, a larger portion of them would be jobs where women had a larger percentage of the workforce.

So this was just a thing that was really important for both of us, as we were doing our own work around our personal mission and leveling the playing field for women at work. And we started this as an experiment, quite honestly.

I don't know how many people know this about women defining AI, but when Nicole and I first got started, it was just recognizing that, wow, this was a big problem that this gender gap existed. Honestly, in technology, this gender gap has always existed. If you think about what percentage of STEM fields and roles are occupied by women.

So it's not like we set out with this big idea that we were going to fix all of the gender gaps in technology. We just wanted to do better in AI. And we knew that we were going to explore and get hands-on with AI. It's how Nicole and I learn. We're very hands-on. We are very willing to run experiments and fail. And so the thought was, if we're going to do this anyway and we're going to learn, we might as well share what we're learning with a group of people who are willing to come along on the journey with us.

And when we did this, honestly, we thought it would be this small group of women, maybe a study group of five people, throwing up a sign-up sheet. And to our surprise, by the third day, we had 50 people who had signed up. By the end of month one, it was 100. We continued to grow so rapidly at some point that I think I turned cold. I was like, we got to pause because we need to figure out if we are doing something here in earnest.

But it was clear that there was a lot of energy from women in our network to find a place where they felt like they could ask dumb questions. They could try something and fail. And that would be okay. This concept of a study group was really where it started. But the community came about when we realized that there was so much more energy in wanting to learn, share, and collaborate together.

Daan van Rossum: Pausing to see if Nicole wants to build something on that. 

Nichole Sterling: No, she nailed it in terms of saying, we're just like, what are we doing? Should we do something? I think we should do something. Let's do this thing.

Helen Lee Kupp: I know. We were like, what is this thing that we're doing? The whole reason I think this community has been really successful is that we are walking the walk. It is the absolute ethos of what we do in the community that determines how we run the community.

We're always just willing to say, you know what? I don't know if this is a thing that we want to build in the community, but let's experiment and try to see if it's useful. And if it is, then let's figure out what the next step is beyond that.

So the course actually came about because last year, when we started the community, we had these organic study groups that formed, and Nicole actually led one of our first study groups within the community to build a custom AI chat bot.

This was before; it was much easier with custom GPT. We were still doing this in code. We got a group of 7 or 8 women together who had never coded before. And she said, you know what? We have a bunch of smart women. Maybe? Probably? Figure it out.

Nichole Sterling: Probably.

Helen Lee Kupp: Probably. To be fair, at the time, I was like, I don't know. This is an experiment. And I think maybe we could get 80% of the way, but Nicole, if you want me to find like an engineering TA or something like that to help us cross the last mile, I will do that.

And Nicole was like, Helen, just give me the weekend. Let me figure it out. Let me see what I can do with my friendly AI. Co-pilot and teacher. And then, I kid you not, on Monday morning, she DMs me in Slack, and she says, I got it. I'm like, what do you mean you've got it? No, I got it. I got it to work. And I'm like, no, really? And then we jumped into the Lunch and Learn study group, and we got the rest of the women in the study group to build their own custom AI chatbot in an hour.

And it was this “aha” moment where we're like this. This is a way to feel like we can together tackle something that is hard, ambiguous, and new for all of us. And then we built the course from that insight, where we're like, okay, how do we do more of this? But, as we scale, we need more of an on-ramp for women in the community to get started.

So we can't just throw them into building an AI chatbot in code from day one. But what's that on-ramp journey to get them closer and closer to doing that in these little cohorts of groups within the community so they can learn, share, and inspire each other? That is the genesis of that experiment, and it's been really well received.

Nichole Sterling: When we did that cohort of the build your own chatbot, the women in the group started to say, wow, this really demystified. It really just pulled back. And I was able to peek into this black box that seems so scary. And nobody knows what's going on in there. And that really started to resonate with us—this idea of just demystifying, and as we've talked about getting hands-on, it doesn't feel so scary.

And it's Oh. Okay, I get what's going on now, and that is super important because what we ultimately want women to do is go through these courses, go through these small projects, and then apply it to their real world. We can't be in every single industry; we need the women to learn from us and then go and find ways to apply it in their world because we need more women looking at that.

Daan van Rossum: It's so beautiful; it's so perfect, and I like that it actually started in a community through a study group. And then you formalized it more in a course, which then leads people to a saver-like landing ground when they get into the overall community where people have already built chat bots.

In everything that you say and even in how you're approaching the topic, you make AI seem a lot friendlier than I think a lot of people are thinking about it themselves. I'm wondering when you're thinking about organizations and you're thinking about how they're introducing AI, and obviously the people in your course are also people who will be introducing it into organizations and who will take other members of their team or of their company along on that journey.

Maybe those people will not look at AI as something very safe and friendly and a very helpful companion to do all of our work better and to reduce the toil of our work, as BCG would say. How are you looking at that balance? Companies and team leaders eventually want to get people along on the AI journey.

They see all the benefits of AI, but there may be some fear, some doubt, and some uncertainty. Should I use it? Is it going to replace me? What will the impact of AI be on my job? How do you think companies should balance that? 

Helen Lee Kupp: I want all leaders to move away from thinking about AI as replacing work and more towards how AI can make work better. I think if we can use that framing and that language when we talk about these tools, we will remove that sort of overwhelming factor or the fear factor. And actually, incentivize more people to experiment and be creative with how they're using these tools.

The thing that we see constantly is that I like to talk about two “aha” moments within the learning journey for people within AI. You have to have both a personal “aha” moment that's a little bit more fun and useful in your personal life and a work “aha” moment where you see how this can really augment or accelerate some of the things that you're doing at work.

But you only get that dual “aha” moment when you're not afraid to get started and you are in this more open-minded exploratory mode. And in this phase of Gen AI, we forget that we're so early in the cycle of AI adoption. Gen AI is new, and I know this because our core paradigm for how we use AI tools is still the chat interface that was introduced by ChatGPT.

And what that tells me is that what we're looking at is that we were talking about Legos earlier, right? It's like having someone dump a bunch of Lego bricks for you on the floor, and they're like, Do something with it. And you're like, you look at it and you get a sense that this could be really powerful; I can build a lot of things with it.

But the average person sees a bunch of Lego bricks, and they're like, maybe I don't know exactly what to build from them. And you want to get them started with little things before they start to see that big structures can get built, right? Before they can start to inspire, what are the Lego kits that you can start to frame up for more people?

And so we're still in the Lego brick world right now; we're not in the Have One, actually. Lego Kit World. We're big Lego fans.

Daan van Rossum: Metaphorically. It's a house on fire, but yeah, 

Helen Lee Kupp: Yeah. That's the one that I want in my office. But even for leaders, as they think about upskilling and training, don't think about it as this giant program that you have to build from scratch or have to implement a six-month strategy. (See our recommendations for the best generative AI courses and AI change management.)

You're just thinking about, even in our course, what are some of those easy ways, easy use cases that you can start to train people up on, that you can share, and you will be surprised that for every single use case that we have taught in our course, there are always 5 to 10 different variations that the students will take. And do something just slightly different, and you're like, Whoa, I haven't thought about it that way.

And it is more about inspiration and creativity, and those play aspects in this phase. That's more important than anything else. And yeah, how do you get AI to make your work better? Maybe more fun than just being so focused on how I can replace these types of work. How do I make it replace jobs? How do I think about peer productivity?

Daan van Rossum: Yeah, the true line of everyone I've spoken to in every major case study I've seen is that it starts with, how can you make the quality of a job better? How can you increase the enjoyment of your work? Because you're offloading all the crap that you didn't want to do to start with.

Helen, I'm just wondering: Is that different for women than for men? Because obviously we're talking in the context of women defining AI, are there differences in terms of how people perceive it? I knew early on that there was definitely a difference in adoption, for example. So as companies are thinking about this, should they think about separate initiatives for women, or should they just approach the overall way that they introduce AI differently? What are some things to think about? 

Nichole Sterling: Yeah, there are definitely things to think about. Across the board, you look at different studies and whatnot, and women tend to be a little bit more hesitant to just jump in the AI. From a safety perspective, there's all the things that come with traditional technology and this imposter syndrome—I'm not technical enough, and Helen and I just hate that term.

Because AI is really starting to blur the lines between what it means to be technical and what it means to not be technical, if I can spin up within an hour and get 7 other women to build a chatbot from scratch and use ChatGPT as my co-pilot, I can run code and make it work. It's like, well, wait a second now.

This is something that Helen and I are both really passionate about; it's increasing the number of potential builders, software builders, company builders, and builders in general. That's what AI is really allowing. And there's also the fact that I think some of my favorite use cases that come out of our community aren't always work-related ones.

Women are still predominantly spending more time outside of work, child rearing, and housework-related things. Some of my favorite use cases are coming out of that arena and helping women to just gain more from their day.

I'll give you an example. So we've always known the use case of, okay, here's my shopping list, and tell me the meal that I can make out of it or whatnot. But somebody in our community recently posted, and she said, Listen, my daughter has a whole new eating plan that she has to abide by; it's really restricted. It's so overwhelming. Cooking and, just in general, gathering all the things is so overwhelming. So she uploaded her list. And then she asked ChatGPT, Where can I find this in the aisles of Safeway?

And the ChatGPT spit out the list for her with all the aisles in Safeway that she could go down. And so instead of spending hours, not just identifying the list and then going to the store, it took her 30 minutes, she said, at the store. And it was like, that's the creativity that we love to see.

And that's helping, especially women. Again, more child rearing, more housework, outside of work—it's getting them time back. And that's really important.

Daan van Rossum: Amazing. What a fantastic example! Obviously, from a company perspective, it makes sense, but why are we being so rigorous about AI for work, AI for home, and AI for play?

If it can just make our lives better and easier, then it will benefit all realms, because if I'm less stressed at home, obviously I'll show up differently at work as well. And so it's just like a fantastic example. I love that example.

Then you are not just focusing on people individually; later, in teams, you are embracing AI. I've also seen you guys post about people leading AI itself. Who are the researchers in AI teams? Who are the people training the models? Obviously, all of that also has a diversity impact and a gender impact. Maybe share a little bit about that side of things as well.

Nichole Sterling: So there's a couple of things that come to mind with that in terms of... We usually say we're too proud. We're trying to demystify AI for a lot of women and non-binary individuals, but we're also helping to define it. Who's having the conversation? Who's starting the conversation around AI?

As Helen had mentioned, it was predominantly men when we first started defining AI. And we wanted to get more women's voices out there. Not only are we advocates on the road, going to conferences and trying to spread the word, trying to get more women visible in this field, but we are ourselves also builders. I have an AI company that I'm building.

It's important for me to lead in that way to help show other women what's possible. Is it harder to get funding? Absolutely, 100%. Just because you're a woman, we still only see between 2% and 3% of VCs actually funding women-led businesses and women-led startups.

It's crazy. And yet, where there's a will, there's a way. And the defining part for me is that both Helen and I continue to build; we continue to show folks it's possible, and there's always the ups and downs. Again, someone posted, and we all have these feelings, like, Is this the right thing I should be doing? Should I be starting my own startup? And that's again where the support of the community comes in. It's yes; get out there to that startup. We're here to support you. We're here for the ups and downs because we need more women visible in the space. It's both a demystify and a define.

Helen Lee Kupp: We want more women defining the space, to have a voice and a hand in shaping the technology because fundamentally anything that we build into software has structure, a defined way of working that we bake into features.

It's like how a grocery store organizes its aisles. You're like defining how people move through the store. Software is very similar. Anything that we put into and build into—the things that we use every day for work—will naturally define some of the structures for work.

And so we want women to actually participate and really define that space, but it takes getting them started today and getting them started yesterday to get there and build that intuition. And that, to me, is really important about making it friendly and accessible. Because, like I said, we're at the very beginning stages of this Gen AI journey, even though it feels like everyone's behind, we are in a stage where we're still asking a lot of questions about the models and the tech.

If I asked you, when was the last time you were scrolling through Netflix and you asked yourself? Well, I wonder how the algorithm works. And is it showing me the right things? Is it biased? How should I think about the data behind my Netflix recommendation? You would probably look at me and think that I'm absolutely insane. Nobody asked that.

That's because algorithms have been baked behind features and black boxes since the beginning of using software. And we will get there with Gen AI, right? We will get there when it's no longer the new shiny thing that everyone's very curious about. And we will get to a point where we forget to ask the questions.

So now is actually the really important and great time for women to get started, to build their intuitions at a level where we understand the real questions that we should be asking about the data biases and how to structure a prompt so that I can mitigate some of that.

There was a great case study from Textio where they were like, we're building out more AI systems to help read through and rejigger job descriptions. We also need to do our own testing of how bias might show up in the technology, which is great. You want these questions and these discussions to happen at this level when we're all building these features and figuring out where AI should live.

I will tell you by and large, like the women that we talk to, the women in this community, they care so deeply about it because when we're using these tools, it's very obvious to us when we're like using a custom GPT to generate cartoon images of ourselves. Why does it have to look a certain way? Like smaller boobs, please. Smaller. Can we just, yes, even smaller, or was there someone in the community who was like, I just want a normal, average-looking body for pictures of a family and a female?

And she was like, I cannot find the right words to prompt, like an average-looking person. And so we feel it deeply because it's just a lived experience. And so we need more people building that intuition now so that they can build that into the products and the ways in which they structure the algorithms, etc.

Daan van Rossum: Like you said, this is something that, when this whole field started, there should have been way more women involved already. But the only other thing that you can do now is make sure that at least some people start today. And you guys are really kickstarting that movement, which I think is super important, both on the defining and demystifying sides.

To close out, what is one thing that I can do personally, and what can any leader in any organization do today to further your mission? Where can we help?

Nichole Sterling: Yeah, we're always trying to spread the word to get access to more women. So send them our way, women and non-binary individuals. And like we talked about at the very beginning, just starting to set a culture of experimentation. It still surprises me, and this just caught me off guard. I tell this story frequently. Several months ago, I was talking to a woman, and she said that whenever I use AI, I feel like I'm cheating. And my heart just dropped.

I just wait. You're cheating. What do you mean by that? And it's because, as women, we've had to always work 10 times harder to feel that we were at the same level as everyone else. And so this idea that we now have somebody to support or that we have something to support and help us.

It almost seems like we're not serving, so I'd also listen out for things like that. I was surprised when I posted that on LinkedIn. How many women chimed in and said, I also feel that. And so, to be able to normalize this and say, It's now a superpower," and this is something that the top CEOs have always had support from some sort of assistant.

We get to democratize access to an assistant, and now women can have access to that. And so, listen out for things like that. Don't assume that everyone is at the same starting line and sees AI the same way. There are still things that we're trying to overcome as women, non-binary individuals, to feel like maybe we're even still deserving of them.

Helen Lee Kupp: I want all leaders to think more expansively about AI skills. On their teams. It's clear that AI skills are in demand, but I'll tell you that the most important thing that I look out for within our community is that I tell new members all the time. I'm like, I value every level of expertise, whether you are starting from day zero all the way to being great at ML engineering and can build your own algorithms.

And that's because the person who is brand new to all things AI will ask questions that make me think about how else to make the AI work, the use cases, and the things that we're doing more accessible to other people who are in the early phases of their journey. No question's a bad question, effectively.

I want people to bring their perspectives, their experiences, and their concerns about AI at every level of the journey so that we can continue to drive AI adoption and usefulness in the right ways. And so it actually matters that leaders are thinking beyond themselves. If I want to hire a person who knows exactly how a GPT works, for example, that's great.

I also want someone who can think about the use cases. I want the person who thinks about how you get people started and then everything else in between. So really getting leaders to think expansively, I think, will help not just include those who are not traditionally in tech and engineering but also include those voices and give them space to show their expertise, share their perspectives, and together shape where AI goes within an organization.

Daan van Rossum: It couldn't end on a better note. Thanks so much, both, for being on today.