After obtaining a doctorate in mathematics from Paris 6 University, I joined Finaref, partly by accident, but this was a real stroke of luck: they instilled a very strong project-based culture and a very strong demanding nature in me as a manager, irrespective of the fact that I’m a woman. And yet this was in 1999!

When Finaref was incorporated into the CA Group, this offered me new perspectives and I got involved in exciting projects around “data”, culminating in the creation of the CA Group’s DataLab, the aim of which was to create impetus around Big Data and AI in the entities. When this was finished, I decided to join LCL, which, in my opinion, is the best industrial laboratory for AI development within the Group: it’s one of the retail banking networks, but it is centralised, which gives it a critical mass for my role.

I report to the Deputy General Manager for Operations (known as the operations director at LCL) and I am a member of the company’s Circle of Executives: this enables me to steep myself in the company’s strategic challenges and to propose AI projects that are useful for my company.

My very atypical profile (often seen as very technical) has sometimes prevailed over the fact that I am female and sometimes “aggravated” behaviour around this fact, resulting in some individuals (male and female) using their power to sideline me. This will continue to happen and is reinforced in our Group by a criterion other than gender: traditionally, our Group supports individuals with “generalist” characteristics towards executive positions but struggles to support individuals regarded as “experts” in the same way, though many executives now recognise that these experts should also be irrigating the broad managerial strata. This is probably a point that will evolve significantly in future and it is important for our Group.

How would you describe the position of Artificial Intelligence Manager?

This role is so new that it has does not yet have a standard definition in the marketplace. At LCL, it entails first creating and then managing a team whose mission is to build AI use cases for all of LCL’s business lines. The team thus comprises AI experts, i.e. individuals who know how to build algorithms that apply to all kinds of LCL data, thus enabling services to be put in place to support customers in their relations with LCL and staff in carrying out their duties or as employees. Once a use case is operational, we assign its maintenance to a role (which sometimes has to be drawn from within the organisation).

This role is neither female nor male, but is very people-oriented in terms of its cross-disciplinary nature. However, you can see that large corporates are struggling to attract women into this role, by contrast with small entities such as start-ups: perhaps women are looking for a better quality of working life, rather than the certainty of career development?

What are the peak periods of your job during the year?

As we build AI for LCL, we don’t have any seasonality, the pace varies according to the projects we carry out and is clearly influenced by enthusiasm, which always results in our internal customers asking us to work faster and harder. In response to this enthusiasm, our executives take reasoned decisions on the allocation of resources: it all balances out.

We do not yet have any real benefit of hindsight with regard to AI industrialisation projects, so we sometimes have to navigate “by sight”. Because we constantly need to solve complex problems associated with AI, we cannot give way to enthusiasm and have to establish a healthy work-life balance, even if this means asking our internal customers to show a little patience.

Can you describe your typical day?

Well, I’ve tried, but I haven’t been able to think of a typical day! A typical day would involve being surprised by the great ideas of our internal customers and surprising them in return by explaining how AI could meet their needs (or not…but this is very rare), what involvement they would have in a joint project (and this is where we really understand that AI can’t be done without “experts in the job”), and then how we can persuade the company to release the resources needed to implement an adequate project.

To sum up, it involves a lot of education and a lot of optimism, while remaining realistic and being able to see both the short- and long-term perspectives.

What career path is required to reach your position and what are the possibilities for development?

My answer to this question will only be true for the next few years: we are in transition on AI and people who could reasonably and effectively do my job are people who combine a lot of experience in the field (bancassurance in this case), a background in data close to IT issues (we often talk of CRM) and very sound training in maths.

The important thing is to be able to link the challenges faced by the company (expressed macroscopically) to the algorithmic solutions that might help but which have to be realistic in terms of cost and time frame. Project management and budget management are essential! At this stage, the difficulty is knowing what I might do after this job: at the moment, no-one would be interested in me doing anything else. And no-one would understand me wanting to do anything else: people often say that this is “the sexiest job”, so why change it?

I think the solution will come when the scope of these types of jobs shifts towards fully centralised data functions (“classic” data, AI and data governance).

What advice would you give to someone thinking of taking up a similar position?

Strangely, I’m rather inclined to advise a great deal of caution: I’ve seen many colleagues take up similar roles without meeting the conditions for success, either because they or their bosses have simply given in to the buzz surrounding AI. As there are very few of us who really understand the underlying aspects of AI (which is not what you read in the press), there aren’t many people able to assess the validity of an application for this type of role.

So my advice would be to discuss it with experts in a managerial position and…listen carefully to them before taking the job. And believe me, you will be warmly welcomed: we love people with a sincere attitude and we want more of us in these jobs, but we want to be legitimate…

How do you see the company’s progress in terms of gender equality?

It’s making progress! Of course there’s still a long way to go, but the desire is there and it’s becoming automatic. It will necessarily progress much faster if women and men are encouraged to change company within the CA Group (where this type of mobility is strongly facilitated): in effect, entering a new environment allows for much greater openness.

It’s when we change environments that we rely more on the women and men that make it up, and we soon wave goodbye to our old prejudices, believe me! In the CA Group, mobility between the entities has changed substantially recently, and you can see attitudes starting to relax as that happens.