Women in AI: Pursuing Project Careers in Tech

STEVE HENDERSHOT

The artificial intelligence revolution, and the projects and jobs it’s creating, means there’s a golden opportunity to carve into gender gaps in AI and project management. But similar to AI algorithms, it takes some learning and training—organizations must recruit women in AI project leadership roles, and plenty of interested professionals will need to upskill to make the transition. So what’s the most effective path to get there? Stay tuned.

In today’s fast-paced and complex business landscape, project professionals lead the way, delivering value while tackling critical challenges and embracing innovative ways of working. On Projectified®, we bring you insights from the project management community to help you thrive in this evolving world of work through real-world stories and strategies, inspiring you to advance your career and make a positive impact.

This is Projectified. I’m Steve Hendershot.

Male project leaders outnumber their female counterparts 3 to 1, according to PMI data, and the emergence of a growing field like AI creates a prime opportunity to cut into that imbalance. But it’s going to take some work: Only 30% of AI talent was female as of 2022, according to the World Economic Forum’s Global Gender Gap Report 2023.

There are plenty of success stories, which means that if organizations and project leaders can learn to replicate them, then there’s a path forward. So today we’re talking to a couple of women who are working in and are passionate about AI, and they’re both looking to outline ways for others to follow in their footsteps.

Before we go to those conversations, we want to mention the free resources PMI offers to help you with AI upskilling. Head to PMI.org/AI, and you can join the PMI Digital Community to connect with fellow project managers, and also read the latest on how AI is transforming project management. And be sure to check out “Generative AI Overview for Project Managers.” This free, hour-long course not only dives into AI tools that project managers can use to boost efficiency but also shows how to apply them like a pro. It’s all at PMI.org/AI.

Now, let’s go to our first guest in Amsterdam: Lucy Todorovska, head of technology for HR, legal and corporate affairs at Heineken. She spoke with Projectified’s Hannah LaBelle about the gender gap she’s seen in her career, how diverse teams can help create unbiased and ethical products, and her experience leading generative AI projects.

MUSICAL TRANSITION

HANNAH LABELLE

Lucy, let’s kick off our discussion with your experience as a woman in tech. Thinking about your time in the tech and AI space, what does the gender gap look like when it comes to leading AI projects? Do you see many women taking charge of these initiatives?

LUCY TODOROVSKA

I was 19 years old when I started working for Coca-Cola Hellenic, and I started as a management trainee. I started working in technology, and from my experience, I see that approximately the percentages are 25/75, so 25% are female, and that’s not really ideal. The gender gap is starting even in schools and in universities, where we see that in engineering and technology subjects, you almost don’t have any females.

I had an opportunity recently to join one event in a university, which is based in the Netherlands. Students were able to pitch their ideas in the area of entrepreneurship and engineering. Can you guess how many girls or females were present there, out of 98 participants? Can you just guess?

HANNAH LABELLE

I’m hoping, like, at least maybe half?

LUCY TODOROVSKA

I was super surprised to see that there were zero females.

HANNAH LABELLE

What?

LUCY TODOROVSKA

And I was like, “Oh, my God, how is that possible?” So I thought, “Well, we have a lot of work to do in order to inspire young females to start exploring those kinds of subjects.” So I really believe all of us, we need to play a role in it, otherwise the situation is not going to get better.

HANNAH LABELLE

How would bridging a gender gap in AI teams, and really thinking about this specifically in AI projects, benefit not only the teams themselves but also the projects that these teams are working on?

LUCY TODOROVSKA

Especially in AI, what is very important is to focus on transparency, explainability, how to make the AI solutions ethical, how to make sure that they are unbiased. The more different perspectives you have, then the probability that you have a better solution is higher. This is something generally valid for diverse teams; they are able to build much more user-centric products because they have a better understanding of the user needs because they just have more representation of the users in their team. When you have different types of people in the team, not just with different gender but also with different experience and different skills, then you can have much more constructive debate. And you can avoid the pitfalls of narrow thinking because the whole group is having the same idea or the same kind of thinking.

HANNAH LABELLE

Let’s talk about some of the recent AI projects you’ve worked on. What are you looking to bring to these projects as a leader? Are there certain skills that you’re leaning into, trying to boost diversity and inclusion, things like that?

LUCY TODOROVSKA

The main projects are related to generative AI. I wanted to make sure that we have certain experiments going on and just learn out of it. So I was working on a project using generative AI in combination with our internal database for a Q&A for HR. So basically, if you’re an employee of Heineken, you can ask different questions. The experiment we were working on was to use generative AI to answer those questions.

There are a lot of things that are unknown, especially in the domain of risks and risk management. What is really helping me is to coach people to ask questions and to make sure that we are not missing anything. Or at least if we’re missing something, it’s not something huge. I’m trying to make sure that we have people with different skills and different perspectives.

HANNAH LABELLE

How do you think project professionals, or kind of the tech industry at large, can encourage more women to be pursuing careers in AI?

LUCY TODOROVSKA

I think this is the million-dollar question. I think we should start building communities, and also to share what is possible, what kind of career paths are available for young people to understand that it’s not necessary to be a software developer to have a career which is close to AI. But also to understand how you make a solution secure or how you translate the business demand into the technology solution, or how you engineer the solution or how you architect the solution.

It’s just to expose them to what are the different options, and then to get a better understanding of what are the skills which are needed and how those skills are connected to their own skills. And the other thing which I’m passionate about is to try to provide them with more role models, to provide them with more mentors who can help them to avoid mistakes that other people were making.

HANNAH LABELLE

Let’s talk about mentorship. You’ve had personal mentors, like your mother, as well as professional mentors. What are some of the biggest lessons you’ve learned from them?

LUCY TODOROVSKA

So especially for women, I think it’s really important is to be bold and to know what is your worth, to follow your dreams, to follow your passion. And then surround yourself with successful women that you can learn from, that they can also support you in your journey. Because mostly the mistakes that women are making are similar or the same, so you can just have shortcuts and learn from the mistakes of others. And, of course, choose mentors and sponsors who can actually help you to progress in your career and support you through the way.

I just joined the Women in AI [group] in the Netherlands, and I believe that this is my way to support the community. So I’m joining already for a second year their mentorship program, and I’m mentoring young female talents, which is also something very useful for me to understand how young people are thinking. And actually it’s a two-way journey because I’m learning a lot from them, and they’re learning a lot from me. So for me, it’s really important that I share my experience and what happened actually in my career so the other young females can just avoid the same mistakes and they can learn from my own experience. And, of course, we have a different way of mentoring and coaching people. So it’s really something unique based on what exactly they need, and I’m trying to follow their lead.

HANNAH LABELLE

As we’re coming to a close, what’s your top piece of advice to women who are looking to lead AI projects?

LUCY TODOROVSKA

My main piece of advice is to find a way to be authentic and to be yourself, because when you’re yourself, this is like the best version. Everyone else is taken, so just be yourself. And if you’re yourself, and you don’t feel well about it, maybe the environment you are in is not the right one, and that should give you a sign that you need to change the environment around you, but don’t change yourself because of the environment.

When I started my career, I was in [a] male-dominated environment, but I was also really young. So the combination of those two really made my life difficult because at [a] certain point, things were not working out and I thought that it was because I’m young, but I didn’t really give enough thought about the fact that I’m actually a female and I am young. I had a lot of challenges in my professional life because of this.

Evolving and learning and progressing through my career, I learned a lot and I observed a lot. In the past two, three years, I was very interested in diversity, equity and inclusion, and I started really to understand why I was experiencing difficulties—mostly because I am female and not because I was young. When you start to educate yourself and when you start to talk with other women who have the same challenges, then you learn the reason and how you can react to it. I think in the past couple of years I evolved a lot, and this is exactly what I learned—that I have to be myself, no matter what, because this is my best version. And if there is something in my environment that is driving me to change myself, that means I’m not in the right environment. I had [to take] different steps to change that, when I see that this is the case. And I am, right now, in an environment where I’m happy, and I can contribute, and I believe that I’m bringing value to the company. Let’s support women. Let’s support each other and make sure that we have more women in AI, more women in tech, to make the world better.

MUSICAL TRANSITION

STEVE HENDERSHOT

As organizations prioritize AI, professionals need to familiarize themselves with the tech and how it impacts their businesses and projects. Some are even taking it one step further and building technical expertise in the field. Nammi Sriharan, a senior project manager of innovation and IT digital delivery at the public transportation agency Metrolinx in Toronto, decided to go for it. She took formal courses to learn all about data science and creating AI models, transforming her career in the process. Nammi is also the Toronto city lead for Women in AI Canada, a nonprofit organization that’s working to increase inclusivity in the field. We talked about why she’s passionate about AI upskilling and getting more women in the field—in both technical and nontechnical roles.

MUSICAL TRANSITION

STEVE HENDERSHOT

Thanks for joining us today, Nammi. Let’s start with your AI journey. You’ve been working with business intelligence and analytics for a while, but you decided to go all-in on AI during the pandemic. Can you tell me why and how?

NAMMI SRIHARAN

It was the evolving landscape of the industry and my personal interest that fueled my desire to transition into AI. It’s a move that I contemplated even before the pandemic, but unfortunately, the demands of my job had previously hindered the transition. The pandemic, however, provided the opportunity of the downtime for me to make this long-awaited shift.

I enrolled in a comprehensive one-year program focusing on big data analytics, advanced data science and predictive analytics. Throughout the program, I had the opportunity to engage on a hands-on project to apply theoretical concepts, gaining practical experience in Python, which is the coding language, and constructing AI models. I did some courses on people analytics and supply chain analytics as well. On top of it, one important thing is actively participating in networking and community engagement. That became a pivotal part of my journey. I joined various online AI communities, attended virtual conferences and took part in webinars. This proactive networking led me to connect to seasoned professionals offering valuable insight and mentorship opportunities.

STEVE HENDERSHOT

As you took these courses and connected with people, how did your notion evolve of what AI was and how project teams might engage with it?

NAMMI SRIHARAN

From a project perspective, I wanted to make sure when I lead a project, when I talk to people who do the technical aspects, I wanted to be able to understand what they do. I needed to know [how] to talk about it. So, for me, learning was the way that I’m able to connect the business and the technical folks. Because when you look at the industry, the senior management have the vision of how the company needs to transition into AI. And the technical folks have the knowledge to make it happen, but sometimes what misses is translating that business need into the technical need. For me, my goal was, from a project perspective, to be that bridge that understands the business need as well as the technical need.

If you don’t want to learn the technical [side] and still lead the AI project team, that’s doable because you have expertise. As a project manager, your job is to manage the project. Within your project team you have subject matter experts on certain areas. So as long as you have your team set up properly and you have their trust and you have the relationship with that technical focus, then I think anybody as a project manager still can manage AI projects without having the technical background.

STEVE HENDERSHOT

So have you noticed a gender gap? And if so, is that different from your previous project experience?

NAMMI SRIHARAN

Yes, of course. Underrepresentation is a big concern in AI. Personally, I come from [the] aerospace industry, so for me, I have seen this underrepresentation throughout my career. When I switched to AI, into tech, it wasn’t a huge change for me.

STEVE HENDERSHOT

So what are the benefits of addressing this gender gap? What kind of problems or biases pop up when AI project teams don’t have gender diversity?

NAMMI SRIHARAN

Addressing the gap requires some focused efforts to encourage women to pursue careers in AI. The importance of why we need this gender gap bridged is [that] diversity drives innovation. When you address the gender gap, it brings diverse perspectives in AI teams or project teams per se, fostering innovation and creative problem-solving. Guarding against bias and enhancing problem-solving is crucial because AI is as good as the person and the data that was used to build it. [An] AI system can create bias if it didn’t get developed and tested with a diverse perspective.

Bias in algorithms can lead to discriminatory outcomes. There’s so many examples out there, such as facial recognition systems. One thing I like to highlight is this is not just about gender. It’s about diversity and equality in terms of color and culture. It brings diverse viewpoints shaped by the unique cultural, social and professional backgrounds. Diversity again fosters innovation in problem-solving, leading to more comprehensive and robust solutions.

STEVE HENDERSHOT

I want to come back to the potential for algorithmic bias, and the impact if it occurs. Why is diversity especially important in this area as we enter this new AI era?

NAMMI SRIHARAN

Like I said, it’s as good as the person and the data that is used to build it. When we look at the historical data and how the world has been in the past, we all know there has been a lot of discrimination and diversity issues. For example, if you’re using [an] AI model to hire or select candidates to interview, you want to make sure the model that you use to build that AI system is bias-free. Because otherwise what’s going to happen is, when the data goes through that model, you’re going to reject a lot of these good candidates based on a data which potentially had some bias from a historical perspective. Historically, it could be male dominated in that particular field, or it could be a certain cultural or certain color people that dominated that particular work. If you use that data to train, then you’re going to repeat that. That is why it’s important that we select our AI project team with a diverse mindset—so they have the knowledge to challenge this data. They have the background to cleanse this data properly before they can build the system that is going to decide your future.

STEVE HENDERSHOT

I know you’re also working to help more women work in the AI space, and you’re part of an organization that held a career fair specifically to hire women for AI roles. Tell me about that.

NAMMI SRIHARAN

I’ve been part of this community called Women in AI for almost three years. I took on the role to lead the Toronto City Chapter in January 2023, so just about a year ago. The first-ever event I hosted was the Women in AI Career Fair in June of 2023, which I plan again to repeat this year as well. It was a rather big-scale event for Women in AI, attracting over 22 major organizations showcasing shared interests in fostering diversity within their workforce. We had over 1,100 registrations from job seekers for that event.

Our goal was to provide a platform to bridge the gap between the talented professionals and leading companies in the AI tech industry. And for us, it was the opportunity to give people the face time to connect and chat directly with hiring managers and recruiters. Providing that platform was important. Because there’s always a perception—AI careers are only for those advanced technical background people. But the reality is, AI encompasses various roles including nontechnical ones such as project management, ethics and policy. So women can contribute to AI from various professional backgrounds. We see a lot of career pivoters into the field of AI. The career fair was an opportunity for them to see: You don’t have to be technical. And I think, also, it’s the networking and exposure of potential employers helps them to see what the industry holds and what benefits they can gain from it.

STEVE HENDERSHOT

That’s great. So how can project professionals or the project management profession at large encourage more women to pursue project careers in AI?

NAMMI SRIHARAN

Encouraging women to pursue STEM education is critical. Role models and mentorship, exposure to successful women in AI, can be inspiring. One of our goals in Women in AI is we conduct different initiatives featuring prominent females in AI to serve as valuable role models. We have [a] mentorship program. We have panel networking discussions, those kinds of activities where people can listen in and get inspired by a story that may relate to their style as well. Creating inclusive workplaces where diverse perspectives are valued can attract and retain female talent in the industry.

STEVE HENDERSHOT

What’s your advice to project leaders to help them encourage women to pursue AI project leadership?

NAMMI SRIHARAN

As a female project leader, my advice is if you see talent, if you recognize somebody’s potential, be a sponsor to that person. Take that person under your wings. Mentor them, bring them on board. I have done that in the past. Recognize the talents around you, because some people might not feel they’re ready for it. But as a leader or a project manager, you know the potential of the people who are supporting in your team, and help them realize their capabilities.

STEVE HENDERSHOT

How about what you would say to women seeking careers in AI?

NAMMI SRIHARAN

Be you. Be authentic. Our journeys are so unique, and there’s no need to copy or compare to others. My personal belief is: Embrace the continuous learning and networking. That’s something I do for me, and I will advise anybody to do that as well. AI is a very dynamic field, and staying informed about the latest developments is crucial because when you wake up tomorrow and AI has gone 100 times bigger. Engaging in [a] professional network both within and outside of [the] AI community, that provides valuable insights, support and opportunity for collaborations.

STEVE HENDERSHOT

Speaking of AI getting bigger and changing day by day, let’s look ahead—what’s on the horizon for AI? Please be as precise as possible, we’d like you to get this exactly right.

NAMMI SRIHARAN

I wish I could be right and get the lottery numbers correctly. AI is here to stay, and it’s evolving by day. I am excited to see deeper advancement in areas such as ethical implementations and healthcare applications. AI has potential to contribute significantly to the global challenges, especially if you look at the climate change solutions, for example. In terms of risk management and project evolution, I think I foresee a more intricate landscape. The growing complexity of AI systems will likely lead [to] increased emphasis on explainability, ensuring that the systems are not only sophisticated but also transparent.

So with that said, security measures will [be]come more crucial, and [we will] see enhancement as AI becomes more integrated into the various domains. It will highlight the need to [have] robust cybersecurity because now you are going to have to safeguard your sensitive data. The other piece of the puzzle is the regulatory framework. More of a cross-disciplinary collaboration is expected to become even more critical. So policymakers, specialists, they all have to work together to make sure that AI is becoming more responsible as we grow day by day.

STEVE HENDERSHOT

Thanks so much, Nammi. This has been great.

NAMMI SRIHARAN

Thank you. Thank you for the opportunity.

STEVE HENDERSHOT

And one last time for our listeners. You can visit PMI.org/AI to check out PMI’s free AI resources.

Thanks for listening to Projectified. If you like what you heard, you can listen to more episodes on your preferred podcast platform or visit PMI.org/podcast. And please subscribe to the show and leave a rating or review—it’s always great to hear from you. Hope you can join us next episode!