Mara Pometti is a data-savvy strategist who sits at the intersection of Al, data journalism, and design. She has a human-centric approach to data and algorithms, helping organisations adopt Al by using human needs as a lens to frame complex analytical problems.
Tell us a bit about yourself, background and your current role?
My current role is Associate Director at McKinsey & Company, but I define myself as a data-savvy humanist working at the intersection of Al, data journalism, and strategy. My job focuses on helping organisations to develop enterprise-wide Al strategies while building the foundations for AI governance. In my approach, I use human needs as a lens to uncover new opportunities and frame them as AI initiatives that solve real people’s problems and are delivered responsibly.
To transform what I do in a systematic process, since joining McKinsey last year, I have developed a human-centred methodology for AI strategy and governance for C-suite executives. By grounding AI into real user or customer needs, this methodology moves multidisciplinary teams towards a strategic operationalisation of AI by ensuring two main things; firstly, that each AI initiative is strongly aligned with an organisation’s business strategy, secondly, that teams develop the proper safeguards and governance enablers needed to scale AI with trust across an enterprise. In this way, this framework not only outlines the steps that should be taken to drive the AI adoption but it also lays out a plan to do it in a responsible manner, by ensuring compliance with both industry specific and AI regulations while mitigating risks.
How to execute a human-centred approach to AI is the major theme of one of the books that I wrote; Artificial Humans: a humanistic approach to algorithms (in Italian, Umani Artificiali). This is a vital topic, especially now that we are dealing with generative models. In fact, part of my role also relies on mentoring and advocating for human-centred AI to democratise complex AI concepts so that they can relate with everyone, not only with experts in the field. My articles and speaking engagements aim to achieve exactly this goal.
Part of this democratisation effort comes from my background. In fact, by training I am a data journalist. Using data to uncover hidden stories and tell them with beautiful visualisations that could make data more accessible to people has always been my passion. This is what led me to a career in AI.
Did you ever sit down and plan your career?
Yes and no. I had a plan in mind at the start of my career but I certainly didn’t foresee myself ending up shaping the role of AI strategist and working at McKinsey. The plan was to do something different to begin with. I wanted to be a journalist, as I’ve always been passionate about travelling the world and telling stories. And yes, I graduated in journalism – data journalism(!). Yet, the irony is, I’ve actually ended up doing just what I love, but not in the way I expected.
I had an idea of what I wanted to become but I never really planned as I like to leave room in my life for unexpected opportunities. Data Journalism led me to the tech industry when I joined IBM as the only data journalist in a team of data scientists (yes, it was challenging and super exciting at the same time!) and then to McKinsey. While this may seem an odd career path considering my background, it makes total sense to me. I clearly see all the pieces of my journey coming together in a coherent picture. Looking back, I realise that I let my path be driven by my curiosity and passions, which is something I strongly recommend for anyone starting their career – rather than planning stringently in advance, without leaving room for the unforeseen.
Have you faced any career challenges along the way and how did you overcome these?
I have faced many challenges throughout my career. In all honesty, when I started out in the world of data, I found that it was very difficult to bring a completely new perspective in this domain. I’ve always faced a huge challenge in making people envision what I wanted to do with AI, human-centred strategy, and data storytelling. The challenges started when I was a data journalist trying to showcase the realm of possibilities that existed for newsrooms with data and AI —when these were not major topics yet! Sure enough, I kept facing that challenge when I changed industries. I think that bringing something entirely new to the table is a big barrier to overcome in itself, as often people are unable to picture with clarity the same future you see. Doing that requires a good combination of imagination, creativity, and foresight.
I’ve had to work twice as hard to explain my vision to others. I had to devise ways to articulate my thoughts and ideas so that others could envision them clearly by creating opportunities for myself to both bring my ideas to life to support my vision with tangible projects and strengthen my portfolio. However, the reward of this hard work is certainly worth it. Today, although I still face the same challenge from time to time, I feel that I’ve proven that the concept of an AI strategy that I had envisioned for organisations was actually something that they needed. I was just a bit ahead of the curve when working on it.
Putting myself into situations I wasn’t familiar with was also challenging: saying I found myself out of my comfort zone once I started working with data scientists at IBM would be an understatement. I was the only data journalist in the team, which is, still today, something completely far away from the traditional roles you would find in the tech industry. Developing relationships with experts that were totally different from me was a big challenge. I focused on building trust with them, by leveraging my skills in coding to learn the inner-workings of their roles in AI to find a common language to connect. By doing so, we learned from each other and reached that point of intersection between different skills where magic happens.
What has been your biggest career achievement to date?
My biggest career achievement to date has been shaping a new job role for myself as an AI strategist by envisioning the need for this new profession when AI wasn’t even a thing. By doing that, I started paving the way for other professionals that struggle to define their role because their skills span across different domains. I mean, this is how it should be, right? Yet, organisations continue to find it difficult to include such eclectic profiles in mainstream teams, because it’s difficult to shape and embed new ways of working and roles into legacy processes and operations. What usually happens is that either you adapt to the old model or you have to constantly find a strategy to let stakeholders understand the proposition of the new model you are creating and prove that it generates business value. Doing that requires a lot of perseverance and a strong entrepreneurial spirit.
That’s why it’s nice to have predecessors that have already opened the door for you. I wasn’t so lucky and I had to figure out how to design my own way. I hope that the path I’ve taken can serve as proof to other experts that they can build your own career, even without having a foot in the door.
For example, some of my mentees reach out to me because they want to know how I became an “AI strategist”, what that entails and what projects they should work on. I hope that the path I’ve started on can begin a fundamental change in the way organisations shape AI roles.
My biggest rewards have always come from serving a much bigger purpose than accomplishing achievements merely for myself. In pursuing the effort to make this career path legit, I feel that I am helping someone else, with a similar profile to mine and that can relate to my experience, struggle less than I did and achieve their goals faster.
What one thing do you believe has been a major factor in your achieving success?
I’ll caveat this by saying that I’m not sure I would say I have “achieved success.” I personally believe that the path to success is long and something you look back on at the end of your career. Who knows, maybe I’ll never really feel like I achieved it. To me, it depends how you define success. What are the benchmarks you measure success by? Through which context do you frame success? I think of my personal success in the same way I position it to clients when we’re building AI strategies.
Of course, I am very proud of my achievements to date and I can say that success for me comes from having achieved my dreams. With that in mind, a major factor that allowed me to achieve my dreams was curiosity. The relentless quest for learning new things, seeking out a better understanding of the dynamics surrounding me, asking questions to more experienced people around me. Each of those were key to what I’ve achieved so far. Without my natural curiosity, I wouldn’t have won a scholarship to study journalism in the US, I wouldn’t be working in the AI field, and certainly I wouldn’t have published two books, which was one of my biggest dreams. Curiosity helps me to navigate different worlds and see patterns between them. Following those patterns made a huge difference in my career and, as far as I am concerned, is what success today looks like to me: achieving my dreams driven by my curiosity.
What top tips would you give to an individual who is trying to excel in their career in technology?
The most important advice for anyone looking to start their career in tech is don’t be afraid and never think of yourself as a non-technical professional. The word tech often sounds intimidating because it immediately brings to mind “coding and data”. This can be particularly daunting for people without a background in computer science or engineering and they may erroneously assume that the tech industry isn’t for them. Bullshit. I am deliberately using this word with the nuance that Harry Frankfurt, the author of the book “Bullshit” used in academia.
Surely, to work in the majority of tech jobs these days requires knowledge or at least a proficiency in coding. However, I feel this is independent of whether or not a person works in tech. Programming languages are the languages shaping our life and decisions today so we should all be literate in coding. Beyond that though, to excel in the tech industry I believe another factor is crucial – the ability to merge data and coding skills with our intellectual human virtues, meaning, critical thinking, empathy, emotional intelligence, love for the truth, and systemic thinking. This is especially true today, in a world where generative AI is reshaping our cognitive paradigms by instilling doubt into the content we consume, spreading disinformation, and blurring the line between what’s “human-generated” and what isn’t.
More than ever, we need super vigilant people that can work alongside algorithms, with a deep understanding of how they work and how they’re built to ensure we remain in control. The art of solving human problems will still be handled by people. These people who know how to critically analyse problems, interact with and control AI systems are the type of experts that I called “data-savvy humanists” in my book.
In addition, people should also be open to working with those who have different expertise from themselves. Working with experts across various fields will only enrich your knowledge and skills. See yourself as a sponge. Soak up as much of the expertise from those people around you as possible – it’ll undoubtedly help you to flourish.
What barriers for women working in tech are still to be overcome?
Without doubt, the key barrier that must be overcome is having very few female role models. Women are generally intimidated when it comes to learning to code or taking on roles in traditionally male dominated fields like tech. I’ve often heard even accomplished women in executive roles saying “I didn’t consider myself technical enough to take on my role”. This is an erroneous mindset that I believe is rooted into an educational problem. Girls are rarely presented with all the opportunities they have for themselves and the new fields of study and jobs that are arising thanks to the advancements in technology. Thus, they are taught that they can be very good teachers, perhaps good doctors, and definitely good mums, but not, for instance, data scientists.
Challenging this mindset and changing the education of women is key for driving gender equality. Rather than pigeon-holing women and girls into typically female-dominated subjects at school, educators must encourage them to expand their horizons in STEM.
What do you think companies can do to support and progress the careers of women working in technology?
It’s simple really – companies need to hire more women. This means hiring women at all levels of the business, in all the same roles and at the same pay as men. It’s essential to have women in leadership roles and around the tables where decisions are made, in order to make significant strides towards gender equality.
Additionally, companies should encourage more men to coach and mentor women. I work closely with some of the best mentors at McKinsey and the majority of them are men. This holds true also for my past experience. The best mentors I ever had were men. The support and guidance for women in tech should not be solely from women supporting other women, but from men too. Men have to be on the front line driving gender equality by making themselves accountable for ensuring young women have the opportunity to thrive in the world of tech.
In an ideal world, how would you improve gender diversity in tech?
To drive gender equality and improve diversity in tech, it needs to stop being viewed as a tick-box exercise. Too many businesses hire just for the sake of saying they have a diverse team or they meet X quota of female experts. That’s not sustainable. There should be equality because recruitment is structured in such a way that women are hired at all levels because they are valued. Organisations should be pushed to promote women and give them opportunities to speak and be heard, in the same way that men are and always have been.
Another way to improve gender diversity is by launching education and mentorship programmes, for women to be mentored by men.
What resources do you recommend for women working in tech?
Honestly, what will resonate with you depends on the types of content you enjoy. For me, I love podcasts, magazines and newspapers – you can see my journalism background coming to the surface, can’t you?
As a starting point, I highly recommend listening to In Machines We Trust, from MIT Technology Review, The Fork by The New York Times, the Financial Times’ Tech Tonic podcast and Leadership by Harvard Business Review (HBR).
Reading both HBR and MIT Sloan Management Review will also stand you in good stead, as will the CIO Newsletter from the Wall Street Journal. I’d also highly recommend two great books that really shaped my thinking on AI: Rebooting AI by my friend and scientist Gary Marcus, and The Alignment Problem by Brian Christian. If you give these recommendations a shot, let me know what you think.
