Shaun McGirr, EMEA Director of AI Strategy at Dataiku, spoke to Information Age about how he goes about leading a strong AI strategy and empowering customers

With over 20 years’ experience working with data as a practitioner across multiple industries, Shaun McGirr has been consistently looking for innovative ways to drive value from data. McGirr joined Dataiku as an AI evangelist in February 2021, before taking on the role of director of AI strategy for the EMEA region later that year. His focus has been on helping customers maximise value on their paths to Everyday AI, working with data science as both a creative and technical discipline across a diverse range of stakeholders.

In this Q&A, McGirr discusses the keys to AI leadership success, the biggest challenges he has faced along the way, and the biggest AI trends that he sees emerging in the coming years.

Could you talk me through your AI strategy and the team that helps you see this through?

My team has a variety of backgrounds. In my case, I’ve served in data science and data leadership roles, but have also spent time in academia and data consulting, as well as previously being a public servant in New Zealand. We’re all at that career point where we could have continued on the trajectory that we were on, but chose to do something different that pushed us further out of our comfort zone, while using what we already know.

Many other data companies, including some of our competitors, have evangelist teams. But one interesting thing about our team is that unlike those that focus purely in marketing or technical fields, we’re a bit of a blend. We like to be out in the field working with customers, because it allows us to use feedback to get better ideas, and test those ideas. Likewise, many of us technical people don’t stay in technical lines of work — we always look to connect technical knowledge with the commercial side, to bring those two sides of the organisation together. That way, we can collectively build a story and stay on the same page.

What would you say are the most important skills for an AI leader to have in order to succeed?

Empathy; the ability to listen; customer orientation; and the ability to resist the temptation to ‘solutionise’ — focus on business problems first. None of the skills I just listed are specific to AI, but are important in the tech industry generally.

Every technical field goes through the same cycle: there’s initial interest; then a lot of money is spent, for little results upfront. One of the great things about being in AI now, is that big bang that consumed a lot of resources for uncertain returns is now receding in the space. There’s a lot more realism now, that is actually healthy and helpful, than there was between 2010 and 2015. We’re finally at that level of maturity where listening and showing empathy, while avoiding the temptation to solutionise — instead focusing on the business problem to solve — has led to us realising the value of data that marketing and software markets years ago.

The interesting thing here, is some people seem to dislike that because it makes AI seem less special. But in my point of view, the more ordinary, or ‘everyday’ AI becomes, the more impact it will have. Five years ago, my role would have been quite difficult because it would entail a lot of hot air around crazy stuff that we didn’t envision businesses achieving. Now though, it’s much more realistic, and we get to re-use what’s worked before. Data experts need to be able to compromise and go into less techy areas of business for this to work.

What have been the biggest challenges you have faced in your role?

I’ve been here since February 2021. Before this, I led a team of six data scientists and data analysts, as a leader, manager and practitioner. I was a hands-on data person until the last day.

From when I joined Dataiku, one of the most interesting challenges has been learning when to step away and hand the role of selling Dataiku’s services to the then-500, now over 1000 colleagues at the company. I, as well as my whole team, have been end-to-end self starters, so personally adapting to being really specialist has been challenging, as it’s my first time working in a tech-first company.

In terms of interacting with the market, the toughest aspect has been how crowded and confusing the terminology can get. When talking to customers, I tell them it’s ok to be confused, because the words we use to convey what we do are very similar to that of our competitors. That just goes to prove the value of my team — if you can navigate that route through what the customer already has to find the differentiating value we can provide, that is key.

What advice would you give to other tech leaders on how to be successful in their position?

I’ll repeat some advice that I was given years ago: in a world where more and more is automated, the most important thing we can invest in is our own learning around changing what we already know how to do into something else.

There’s been a lot of fear around automation possibly taking up jobs, but wherever you stand on the ‘AI is going to kill us all’ debate, we can agree that humans still have an advantage when it comes to creativity. Finding ways to combine two different elements won’t possibly be achieved by a computer for a long time. The advice I got was ‘once you stop learning, find a new job where you can get that learning rate back up’.

From when I was at school, all the way through university to getting my PhD in 2016, the job I do now didn’t exist. So if someone is starting out today, they need to entertain the idea that no one has set out that job description for a role that will truly fulfil them. You really need a diversity of experiences, in order to be ready for that new position, and you also need to put yourself out there, with the work you’ve done, to get noticed.

We need to look at our careers as these gardens with forking paths — it’s never a narrow highway where the more work you do, the more successful you become. Unless you’re out there, putting your point of view out to the world, you might miss something amazing.

Looking at the next five years or so, what AI trends you see emerging that will help organisations drive value?

The cost of getting started has dropped drastically over the past five to 10 years, due to cloud providers, open source and other available avenues. There’s now that question of making those 17 ways of getting started work alongside the enterprise tech stack, and drive that value. This trend is set to continue, with AI now in everything we do. More of it will be in platforms like Outlook, collaboration suites, and other tools, and it will be easier to take advantage of that. But that also means that more people will be doing it, so gaining the competitive edge will be a matter of finding new ways to optimise and innovate.

There’s also a big wave of regulation coming, so as the impact of AI continues to infiltrate work and society, it will be increasingly difficult to manage. There will be regulatory responses and push back, so to response we’ll need to keep in mind that the more people who are involved in building AI, the more successful we’ll be at deploying the technology where it’s needed.

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