«AI uncovered: The leaders driving the AI revolution», the new programme from BFM Business & Sopra Steria Next

[Appearance of the programme’s - "AI Uncovered: the leaders driving the AI  Revolution”. Voice over] 
Special edition. BFM Business Files.  "AI uncovered: the leaders driving the AI Revolution" with Frédéric Simottel. 

[Frédéric Simottel] 
Welcome to our programme "AI Uncovered” in partnership with Sopra Steria Next. We're going to talk to you about the implementation of AI, particularly generative AI. This AI of trust also within companies. So this week we're going to take a look at the jobs of lawyers with our two guests. Fabrice Asvazadourian 

[Fabrice Asvazadourian] 
Hello, Frédéric! 

[Frédéric Simottel] 
CEO of the consulting firm Sopra Steria Next, more than 4,000 people. It's obviously a subsidiary of the ESN Sopra Steria, which we know well. And also with us is Jean-Godefroy Desmazières, good morning. 

[Jean-Godefroy Desmazières] 
Hello Frédéric 

[Frédéric Simmotel] 
Jean-Godefroy, thank you for joining us. You are Deputy Managing Director of the law firm Fidal. You are specifically in charge of digital transformation. Good timing, we're going to talk about it. I'd like to remind you of Fidal, a law firm that's been around for 102 years, with 2000 lawyers, 90 sites and a turnover of 300 millions of euros. It's one of the finest law firms in France and I'm going to start with you. Jean-Godefroy. Your job undoubtedly involves a certain amount of rigour when it comes to using data, and in particular how you work with your tool, which is called Fidal IA, isn't that right? 

[Jean-Godefroy Desmazières] 
Yes, we haven't been very original.  It's the same for everyone! Maybe two subjects. There's the subject of data as such in the firm. In fact, we're as old as we are, so we have a lot of data. And over the last few years, we've collected an enormous amount of digital data. Which we store in the practice. This data needs to be organised and, above all, secured because it can be sensitive. We have a number of clients in the defence, energy and health sectors. So this is highly sensitive data that we need to secure. Then, how do we exploit this data with the tool we are building? For practice 2,000 professionals, it is by applying a maxim that is, in the end, quite simple, that you become what you eat. And what is true of human biology is also true of artificial intelligence. We make sure that we feed the AI with quality data, good quality data, and by sorting out what constitutes relevant documents that we will give to the AI, and by avoiding feeding the AI with documents that are copies, documents that are not finalised and that could lead to poorer quality results. 

[Frédéric Simmotel] 
And here we are in one of the pillars for artificial intelligence to work in a company, which is the quality of the data. Right, Fabrice? 

[Fabrice Asvazadourian] 
Absolutely. I think Jean-Godefroy said it very well: no data, no AI. Or in any case, ‘Bad data, no AI’, that's for sure. Let me remind you of an interesting statistic: we only have one algorithm out of seven that continues to have the same level of performance when it goes into deployment mode, because, among other things, we don't manage to have sufficient data quality when we're in industrial mode. So I can see three data challenges. The first is what data? And I think that today there is a real challenge for a lot of companies, especially companies that don't have a lot of data. There are the big banks, they have a lot of data, but many industries are entitled to ask ‘Where can I find data?’ That's the first question. Now there are lots of sources of data that we didn't think of. Let me give you an example. 

[Frédéric Simmotel] 
Or do you think they're all good? For example, in a law firm, there was no loss, I imagine? every piece of data counts. That's what Jean-Godefroy has just said. 

[Fabrice Asvazadourian] 
Secondly, there's the issue of data quality. And the good news is that there are AI solutions to check the quality of the data and help us to continue to improve on the subject of data quality. And then there's the third subject, which is, let's say, a bit old-fashioned, but which needs to be brought up to date with the issues specific to AI. It's all about the governance that a company has to put in place to do what people see. It means sorting, selecting, making sure there are data owners, and so on. Who are the people who have to ensure that the data remains of high quality? 

[Frédéric Simottel] 
So how do we work around all this? Jean-Godefroy, at FIDAL, I said it's 2,000 lawyers, there are more senior people and also young lawyers who arrive and who have different attitudes to AI. And then you really put... one of your priorities is learning around all this? 

[Jean-Godefroy Desmazières] 
Yes, it's experimentation and learning.  Ultimately, we're working with a tool that's going to change the day-to-day lives of our professionals in general, and in different ways for different categories of professionals.  A lawyer who is more senior, who is a partner in the firm, will be able to use this tool but won't necessarily trust it directly.  When you use AI to produce content.  In the end, you're going to have a level of efficiency that may be a little lower than when you work on pre-existing documents.  We also have young professionals to train, and these young professionals have been training for years, for decades. In other words, we ask them a question. They were going to look for answers, they were going to get lost, they were going to be able to come up with solutions. They would come up with an answer that we would fine-tune for our customer. Today, we have a slightly different system. In other words, when we ask a question to one of the young professionals who work with us, they will be able to ask this question to the tool and get an answer immediately. And with that answer, rather than looking for an answer they don't yet know, they'll try to validate the answer we've given them. This is an absolutely central element, as it means that professionals will no longer lose their way, in other words, they will no longer have the opportunity to invent and create the law of tomorrow. AI works on documents that already exist, on data that already exists. So AI is looking in the rear-view mirror. What we are interested in for our clients is also inventing the law of tomorrow, developing case law, giving advice that is of a different quality because it is off the beaten track. And to do that, we need ingenuity, we need inventiveness. So we're going to have to train our professionals in AI, and also train our young people to develop a critical mind despite AI. 

[Frédéric Simottel] 
Yes, it's easy on the one hand, but it's not easy on the other.  

[Jean-Godefroy Desmazières] 
Exactly. 

[Frédéric Simottel] 
This side is important. And when it comes to learning tools, there's an interesting initiative that Jean-Godefroy and I were talking about when we were preparing this programme, a prompt library, Fabrice?  

[Fabrice Asvazadourian] 
Yes, that's right, it's a very good practice that we recommend because I see it with my customers and I see it with my consultants. Initially, everyone wants to have their own FIDAL IA or ‘X-GPT’. And then they deploy it, install it and all that, and then they look at it. And then there are those who use it every day. Because they naturally want to, they're resourceful. There are those who don't use it much, sporadically, for very specific things. And then there are those who refuse, or who haven't got the hang of it. Or don't consider a use of AI to be part of their life. It's not a way of saying ‘there's a tool out there, use it’, it's a way of saying ‘we have specific uses, here's how to get into them’. And then you can re-prompts, refining and specifying. And so we very strongly recommend that our customers, in order to anchor their employees' daily practices, have libraries of prompts and then let the creativity of the in-house communities enrich, enhance and complete them. These prompts. You have to crutch the entrance. For the vast majority of employees, you need a crutch.  

[Frédéric Simottel] 
I found this initiative interesting, because what we often wonder about is the front door. Once you've asked a question, you've got the answer, but have you really asked the question in the right way? And I thought that this library of prompts was an initiative that you at FIDAL could certainly emphasise. All these uses are also transforming the profession and the business model of your firm. But also of the legal profession. Generally speaking, Jean-Godefroy Desmazières 

[Jean-Godefroy Desmazières] 
Yes, absolutely. In fact, this AI approach means that we can go much faster in aggregating knowledge that was previously scattered. So that leads us to two things. It leads us to question our business model. I'll come back to this point. It also leads us to ask ourselves what the profession of a business lawyer really is. Because when it comes down to it, a lawyer's added value when it comes to aggregating scattered knowledge is not very high. And because it's not very high, we're going to find it hard to sell it to our clients. Until now, and it's been like this for years in most law firms, our profession, our turnover, has been a matter of adding and multiplying, of adding time and multiplying it by hourly rates and finally arriving at a value. But it is almost exclusively in these professions that the value of a service is linked to the time taken to produce it.  If you look at a mobile phone, you don't ask yourself how long it took to build it.  We look at how useful it is, how effective it is, how innovative it is. That's exactly what we're doing, using time-saving tools. In the end, we spend less time on our customers' questions and if there is less inventiveness, this multiplication of less time by a rate leads to less sales. So the question is either Yes, or we have a lot more customers and we can get by. If the modernisation of our businesses results in a drop in sales, then we're missing the point. So we want to provide a high-quality customer service that's as innovative and modern as possible, while at the same time focusing on the finer points of value for our customers. And this value is served by AI, but it's not just AI, so it's a tool for our professionals. 

[Frédéric Simottel] 
And so today, are you reviewing your model in relation to the use of this AI?  

[Jean-Godefroy Desmazières] 
Today, we work with our customers on the value that is provided, regardless of the time taken to provide the service. And do you already have customers who come to you and say, ‘Look, generative AI is the same as what I've been able to do as a customer’, and then come back to you and say, ‘There are easier things to do’?  It really depends on what the customer or our customers want. We have 69,000 customers, so we don't have all the same 69,000 points of view, but it's still a significant number. Some of them come to a law firm to get perfect service.  Today, AI helps us, but it doesn't provide a perfect service. So when we produce content, a document or a deed, we use digital tools, whether they be tech contracts, AI with Fidal IA, or other tools. In reality, we always have to rework the whole thing to ensure that it's as perfect as possible, i.e. in line with the rules, the rules applicable to the law, case law, etc. But above all, it has to be tailored to our client's needs. So obviously, our clients are going to tell us you're going to save time on certain tasks. Low added value. It's up to us to demonstrate that 100% value is something that matters to us.  

[Frédéric Simottel] 
And here we come to the theme of our show: trusted AI. As Jean-Godefroy Desmazières has just said, every company is becoming a trusted player for its employees and customers. 

[Fabrice Asvazadourian] 
Yes, AI will be trusted or it won't be. So this is a critical issue. At the same time, it's not a new issue, because companies have always known that if they don't have the trust of their employees and customers, they'll have trouble surviving. So... But here, the stakes are a little higher, since... 

[Frédéric Simottel] 
Yes, now we have a tool that sometimes does our thinking for us... Yes. So we have to recruit. So, there is, there will be the compulsory floor and the AI act will create. The seven dimensions of trusted AI that everyone will have to face up to. They're common sense. I don't know if I can name all seven off the top of my head, but they're common sense. They're going to become obligations. So we're going to have to, we're going to have to demonstrate that we've effectively put in place within our organization the capacity to manage a trusted AI. And then, I think there's a second stage where we'll go beyond that and say, “How can we really implement trusted AI in these critical business processes? We're taking part in a consortium called Confiance.AI, alongside seven other major partners, which we're working on. How will we be able to handle business-critical processes in the future with trusted AI and with confidence in relation to an environment, sensitive data, etc., etc.? 

[Frédéric Simottel] 
Well, thank you both for coming to talk to us about all this, about this trusted AI. Fabrice Asvazadourian, CEO of consulting firm Sopra Steria Next and Jean-Godefroy Desmazières, Deputy Managing Director of law firm FIDAL, and then in charge of this digital transformation, this AI transformation. Thank you both for sharing your stories with us. We'll be back very soon for a new episode in our series “AI Uncovered” 

[Voice-over] 
Special edition. BFM Business Files.

[Appearance of the programme’s - "AI Uncovered: the leaders driving the AI  Revolution”.] 
[Voice over] 
Special edition. BFM Business Files.  "AI uncovered: the leaders driving the AI Revolution" with Frédéric Simottel. 

[On-set cameras] 

[Frédéric Simottel] 
Welcome to our programme "AI uncovered: the leaders driving the AI Revolution", in partnership with Sopra Steria Next, where we're going to talk about the implementation of generative AI and trusted AI within companies. As you know, if you watch the replays, we're interested in industry, banking and insurance, and then we're going to take a look at sales departments and customer services within the La Poste group with our guest Pierre-Etienne Bardin. Hello ! 

[Pierre-Etienne Bardin] 
Hello.  

[Fréderic Simottel] 
Thank you for joining us, Chief Data Officer at La Poste Group. So, more than 15 billion objects distributed around the world every year, a turnover of 34 billion euros in 2023, 230,000 employees, 500 experts in data and AI and a group that is truly innovative in both the digital and AI fields. We're going to talk about it with you and then with us. 

[Appearance of a "Deploy AI with confidence" banner] 
Fabrice Asvazadourian, hello! 

[Fabrice Asvazadourian] 
Hello Frédéric  

[Frédéric Simottel] 
Fabrice, thank you for joining us. CEO of the consulting firm Sopra Steria Next. It has over 4,000 consultants. And it's a subsidiary of the well-known ESN Sopra Steria! Pierre-Etienne We're going to start with you on the ideas for use cases. I imagine there's no shortage of them at La Poste group. You must manage them and see them arrive on your desk every day, especially with generative AI. So you had to prioritise and you thought, well, maybe there's an area where we can get started. Well, there are several, but this is one where we might see some applications straight away. It's for sales departments and customer services. 

[“AI: how to prioritise projects" banner] 

[Pierre-Etienne Bardin] 
So let's go back a bit, let's go back a bit. We've all been victims of this generative AI wave. And when I say we've all been victims, I'm talking in inverted commas because it's not as dramatic as all that. And all the business lines have taken an interest in these transformations, these innovations, and the IT teams have also taken an interest, particularly in understanding and knowing how to use these new technologies. So we decided to filter the use cases and take those with the biggest impact. 

[More than 15 billion objects distributed 233,000 employees (more than 179,000 in France) €34.1 billion turnover in 2023 More than 500 data and AI experts]. 
We could have taken the easiest, the simplest ones. No, we wanted to take the ones with the biggest impact, to demonstrate that generative AI was really something that was a major breakthrough compared with traditional AI. We developed an in-house solution, a single point of contact, a single point of entry, based on an LLM. We didn't want to use ChatGPT for this solution, so we called on our experts to build this solution, this in-house solution, to meet our needs based on our data. This is 'La Poste GPT'. That's its name. 

[Frédéric Simottel] 
Oh, I don't think anyone is original in this field! 

[Pierre-Etienne Bardin] 
So La Poste GPT means using generative AI technologies on our knowledge bases, on postal data. That's what was important for us, and it's a first. And it's an assistant. And indeed, when we chose this use case, we favoured the one with the greatest impact, and therefore for the sales teams and customer relations teams, to make it easier to respond and to speed up the responses we give to customers, whether they are major accounts, business customers or individual customers. That's what was important to us. We're now entering a phase of industrialisation and scaling up. We've been experimenting for a year now, testing different solutions and choosing certain algorithms. We've also done a lot of work on RAG technologies, i.e. technologies that enable us to understand our knowledge bases and structure them in such a way that they don't hallucinate. And that they respond correctly to needs. Because that was very concrete for us. 

[Frédéric Simottel] 
what does this mean in practical terms for users?  

[Pierre-Etienne Bardin] 
In practical terms, what does this mean for these users? Today you have very complex knowledge bases, hundreds of products, and so a user logs on to Poste GPT and has a response to make to a customer, either a commercial response or a customer relations response. So they log on, chat to Poste GPT in natural language and ask questions about a particular product, a particular customer is interested in a particular feature, and what Poste GPT does is connect to all these product sheets and summarise the information, still in natural language, but with links - and this is very important if we are to gain credibility for the solution - by linking all the product sheets that were used to produce this response. 

[Frédéric Simottel] 
By smiling at all the information?  

[Pierre-Etienne Bardin] 
Here, smiling at his answers. 


[Frédéric Simottel] 
You could criticise ChatGPT... 

[Pierre-Etienne Bardin] 
You could criticise it for giving us an answer and we didn't know where it was coming from. So to reassure users, we're saying, this is a summary of all the product sheets in relation to this customer request. But these are the files that correspond to this response. Of course, you can access these files via hypertext links, and then you can access the database that is the source of the LLM. So we rely on our in-house experts and that's how we can speed things up. 

[Frédéric Simottel] 
Fabrice, it's important to start with a POC, of course, but also to have something of a flagship application. We say, ‘Look, it's working here, now we're going to look at other directions.  


[Fabrice Asvazadourian] 
Exactly. I think we came up with a point of view at the beginning of the year on how to deal with generative AI. And we said ‘2024, acculturez, acculturez, acculturez’. So we had and we saw this era of POCs left and right, with the positive side of POCs, which is that it allowed us to get a bit closer to the real thing, and then the sometimes disappointing side of POCs, which is that it is ultimately difficult to move from in vitro to in vivo. So we told them that 2025 would be the time for flagships. Choose major areas in which you won't take the subject in bits and pieces, but from start to finish, because that's how you calculate a real business case. Because these days you can calculate ROI on anything. Which, at the same time, Excel knows how to do a lot of things, but it doesn't generate any gains that you can see afterwards in the profit and loss accounts. And so we can see that in the world of industry, the flagships today are more on the supply chain, if we want to see them it's more on supply chain optimisation. We talked about this on a previous programme. In the world of services, there's a lot of focus on assistants who are making productivity gains for employees. That's an interesting example from La Poste. Yes, these seem to me to be two moments, two major subjects that we're seeing industrialised at the moment.  

[Frédéric Simottel] 
And behind that, Pierre Etienne, you have to involve everyone in these projects. So how do you do that? Because there's the fantasy that ‘it's going to replace me’ and so on. So today we're saying ‘no, it's those who are going to use AI who are going to replace you. So go ahead, concentrate on ‘how did you manage to get everyone on board and then take AI to other jobs? 

[Pierre-Etienne Bardin] 
At La Poste, we've been working on AI for ten years now, and our approach is to say that we're all players and we're all affected by this transformation. We have 75,000 people certified in AI within the group, so it's something that was taken very seriously very quickly. And it's true that over the past year, we've seen an explosion in requests for acculturation and training to understand what this generative AI means and how I use it. And is there a risk to my job? And so two thousand training courses have been launched in one year, which is quite a lot. We worked a lot on acculturation. We are now moving on to more serious training, prompt techniques and hackathons on certain professions. And what we wanted to explain during these training sessions was to reassure people that the jobs at La Poste are very complex and very diversified. We're not talking about jobs that are going to disappear, but rather about activities within these jobs that are going to be automated and simplified, and that's the message we wanted to get across. And the other message we also wanted to get across is that there are a lot of opportunities behind it. Generative AI is an opportunity to access information more easily. And so with the example I gave. 

[Frédéric Simottel] 
Yes, having the flagship a bit 

[Pierre-Etienne Bardin] 
Before, it was complicated to access knowledge bases, it took up a lot of my time and sometimes I couldn't find what I was looking for. I had to call in an expert. But now I feel empowered because I'm the one who can access information quickly. So we have this opportunity. The second opportunity is for those professions that were looking at AI from a distance, saying ‘it's not for me’, but in fact they've been caught up by the patrol and we've now been able to launch programmes to accelerate the transformation of these professions. And the third opportunity is to say that we are all capable of creating content, we all have this possibility in a fairly simple way, to create documents, to create computer code, to create images, to create videos. I'm not saying that we all become artists, but in a way we all have this capacity to be augmented by this generative AI. So these are the three opportunities we're trying to push. These are the messages we're sending out. And obviously we won't be replaced by AI, but by those who know how to use it.  

[Frédéric Simottel] 
And be bold, that's what a study by Sopra Steria Next says. And it's about increasing the number of employees, showing that it's a disruptive innovation, but also an incremental innovation, Fabrice? 

[Fabrice Asvazadourian] 
Absolutely. I think that when we look at how we can convince employees to use AI, first of all, we need to do what you did at La Poste, which is to train them, because there's nothing worse than not knowing how to resist. So that's the first step. We see many of our customers using and supporting the deployment of AI through training in empathy and postures. Ultimately, the time that will be saved on tasks that can be automated needs to be redeployed to create more of a bond, more human warmth if you like. And as we've just come out of a fairly long period where we were driven by Compliance, high compliance, the softer qualities of the relationship need to be reasserted. And so we are seeing customers providing support, to demonstrate the full added value of the human element, when augmented by these AI solutions.  

[Frédéric Simottel] 
And on these programmes, at “AI Uncovered” we are also interested in trusted AI. And that's where you were able to talk about ethics, to say that we're here to support you. That's what we said. But what is your definition of trusted AI within the La Poste group? 

[Banner ‘AI: the ethical challenge’] 

[Pierre-Etienne Bardin] 
For us, it's actually three dates: 2016, 2022 and 2024. 2016 is two years before the RGPD and it's the creation of a data charter that sets out the group's values. And to ensure that we have an ethical approach to the use of data. 2022 is two years before the RIA, and it's the same approach, but on AI and trusted AI. In 2024, these two charters will be merged to form an AI data charter that explains to employees and also to our partners how we use artificial intelligence. It's a set of principles. It was defined by a team of experts, a cross-functional team that brought together all the Group's business lines, including banking, insurance, logistics and support functions. It is a system of governance that makes it possible to control and verify the correct application of these principles. A governance structure that I co-chair with the Group's CSR Director, Stéphanie Dupuy-Lyon, which brings together the DPOs (the people in charge of compliance), data experts, legal experts and external experts who provide us with a viewpoint, a vision, a perspective on what is being done in other groups to help us go even faster and even further. We also have a whole range of control systems that will review projects involving the use of artificial intelligence to check that they comply with these principles and meet compliance requirements. But it also involves acculturation and training, and an analysis grid to check the risks associated with AI, because that's what the RIA is all about. There you have it. 

[Frédéric Simottel] 
And so this need to be a trusted player for the company. Just a word on that, Fabrice? 

[Fabrice Asvazadourian] 
So we know that this is a major issue and I think that Europe is ahead of the game in this area. We have to say so, because people often say that we are not ahead of the game, but on this subject, we are very much ahead of the game and we have to continue to stimulate the world to maintain a trusted AI environment. 

[Frédéric Simottel] 
Thank you both. Pierre-Etienne Bardin, Chief Data Officer, La Poste Group, Fabrice Asvazadourian, CEO of  the consulting firm Sopra Steria Next. See you soon for a new programme entitled “AI Uncovered”. 

[Voiceover] 
Special edition. BFM Business Files 

[Appearance of the programme’s - "AI Uncovered: the leaders driving the AI  Revolution”. Voice over]  
Special edition. BFM Business Files.  "AI uncovered: the leaders driving the AI Revolution" with Frédéric Simottel.
[Frédéric Simottel]
- Hello and welcome to this special issue "AI uncovered: the leaders driving the AI Revolution.” And we're going to be talking about AI and luxury today. But you can already replay our three interviews with Michelin, Crédit Agricole and CNP Assurances. Now we're taking a look at all these sectors with our partner Sopra Steria Next. This week, it's all about luxury, with lots to see! Improvements in shops, in the supply chain... We'll be looking at all of this with our two guests. Franck Le Moal - Hello Franck
[Franck Le Moal]
- Hello
[Frédéric Simottel]
- Thank you for joining us as LVMH's IT and Technology Director. And hello Fabrice Asvazadourian!
[Fabrice Asvazadourian]
- Hello Frédéric
[Frédéric Simmotel]
- CEO of the consulting firm Sopra Steria Next
[Appearance of a banner entitled "Artificial Intelligence at the service of luxury”] 
So Franck, I'll start with you. So AI is appearing in many areas at LVMH. It's true that the list, we have a time limit, but it's quite long. You've trained a very large number of employees and then, when we were preparing this programme, you said "Hey, there's an interesting use case, it's in designer workshops".
[Franck Le Moal]  Yes, that's right. So, first of all, you're right, we've trained a lot of employees in the LVMH group, since we have around 10,000 employees trained in AI and GenAI.
[Banner " Franck Le Moal, Director of IT and Technology at LVMH "]
And we're continuing with an approach that is increasingly segmented by profession, by geography, because what's important now is to bring AI and GenAI into the professions so that they can add even more value. We're slowly getting into the workshops, but when we say workshops, we mean workshops. We have a vision which is extremely important in luxury and at LVMH, and that is that we don't want AI and GenAI to replace our creators and designers, they are there to help and support. But behind the process of creating our products, our shops, our windows, there will always be men and women with the passion and aestheticism of their creations. Indeed, GenAI will add to them. Here are a few examples...
[Frédéric Simottel]
- Yes, very concretely, we're going to make a prompt saying "Here, I need this kind of thing" And then the Creator is going to work from these images?
[Franck Le Moal] 
- So, for example, at Louis Vuitton, we've set up a little tool called the "AI Atelier", which will give style assistants and assistant designers a kind of wall of inspiration.
[Banner " Franck Le Moal, Director of IT and Technology at LVMH "]
Shapes and colours can help them in their research process. Trigger a little inspiration. The same thing happens in shops. As you know, we have very beautiful, very luxurious shops. So, for example, at Christian Dior Couture or Fendi, by the by the window designers, the merchandising designers, who will draw inspiration from the shapes, colours and atmospheres. So this will help to feed the creative process, without replacing it.
[Frédéric Simottel]
- Fabrice That's what we always say, it's not AI that will replace us, it's the person using AI who risks replacing us. We have a very concrete example of this. AI won't kill creativity, on the contrary?
[Fabrice Asvazadourian]
- Not at all. First of all, generative AI is not, it does not create. It's highly advanced statistics that allow you to
[Banner " Fabrice Asvazadourian, Managing Director of Sopra Steria Next "]
save a lot of time on inspirational tasks. But creation, the human process of creating generative AI, is incapable of this, so we won't be replacing creators, real creators, any time soon. On the other hand, generative AI can already help them a great deal. And that, in the end, is always about keeping the human being's greatest added value in focus. And in the case of designers, the aim is to lighten them, to multiply them, to increase them as they say, thanks to AI. And that's what it's all about.
[Frédéric Simottel]
- I like the idea of a trigger. We all want to write a post or an article, we're looking for inspiration, we're thinking, I've got some ideas, but I can't find the beginning. You do your little prompt and you come up with two or three beginnings. You say ah yes, here, I'll use this one. And then we unroll the whole thing. Frank, AI can be found in the boutiques, as you said, in the design department, but also, I imagine, in customer relations, with customers who now, even in the luxury sector, it has to be said, go online, use social networks and also go into the boutique.
[Franck Le Moal]
- Of course Frédéric, you've hit the nail on the head, First of all, it's in the world of the shop and the salesperson that we're making the most progress. GenAI can be very useful. Exactly, as for creators and designers. We're not there to replace the salesperson, we're there to help him, to enhance him and to give him a new lease of life.
[Banner " Franck Le Moal, Director of IT and Technology at LVMH "]
at the heart of an even richer relationship, with an even stronger experience, with its customers. So we really want AI and GenAI to enable our sales staff to focus on customer knowledge before our customers come into the shop. And when the customer arrives in the shop, they walk through the door, and there they are, with these tools. And I'll give you two or three examples, we're going to have another sales assistant who's going to support and enhance the experience and the dialogue with the customer. So what do we do? We try to find out what our customer is doing. So, of course, we're very respectful of our customers' data, but we know that our customers go online, they use digital platforms, they look at social networks, they call our call centres. So we're going to have a mass of information that is extremely interesting, and we're going to make it available to our sales people so that they can synthesise their value proposition even better. So that's one of the first use cases. We have deployed it on a large scale at Tag Heuer, at Bugari, at Tiffany, at Vuitton, at Christian...
[Frédéric Simottel]
- Yes, because every house has its own particularity... 
[Franck Le Moal]
- But at the same time we're trying to re-use these cases. And the second point is that once the customer has come into the shop or left the shop, we're going to further enrich our exchange with them. For example, we're going to synthesise all the purchase history and comments that a customer may make, and our sales assistant, who will have his iPhone with his application, will be able to use the GenAI prompts that are integrated into the application to propose messages that are more qualitative and more personalised. That's what we're doing.
[Frédéric Simottel] 
- I'm caricaturing here a bit, but how do you manage to strike the right balance, so that the sales assistant doesn't suddenly feel too... I'm caricaturing here a bit, but too much in front of his screen, so that he has all the data on the customer?
 [Franck Le Moal]
- So we're working hard on ergonomics, we're working on applications that are going to be extremely streamlined and extremely fast. We're actually starting to test GenAI engines where the seller will speak and the iPhone will respond. These are the tests we're carrying out and the implementations we're in the process of making. So we're trying to make sure that during the act of buying, the experience and interaction with the customer are as seamless as possible.
[Banner " Franck Le Moal, Director of IT and Technology at LVMH "]
He has prepared, he quickly looks at his iPhone because the applications are very well designed and so I think that when we talk, if you have the opportunity to go to the Dior Couture boutique in Montaigne, for example, you will see a very fluid experience with the mobile phone which is always there, but in an extremely light way and in the discussion and exchange with our customer and our sales advisor.
[Frédéric Simottel]
- Because that's one of the difficulties. We may come back to the question of prioritising projects. How can we be sure that this type of application - AI in the service of the customer, customer personalisation, customer relations - will generate this value, Fabrice?
[Fabrice Asvazadourian]
- So that's the whole question. And Franck has already highlighted the key points. The first thing is that, thanks to the arrival of generative AI, we are now able to combine predictive AI, which is not new (companies have been doing it for years), to produce the famous 'Best next actions'. What is the next product or service that I can sell to my customer?
[Banner " Fabrice Asvazadourian, Managing Director of Sopra Steria Next "]
The problem was that it was quite cold, so now we're able, as Franck was saying, to contextualise it so that the advisor, the sales person, can not just come and push a product, but come and engage a customer.
[Frédéric Simottel] 
- In fact, putting the relationship into context...
[Fabrice Asvazadourian] 
- So that's a quantum leap in efficiency. The only thing is that for it to be a leap in efficiency, humans still have to use it.
[Frédéric Simottel]
- Yes, we're all familiar with those big customer relations projects that were set up a few years ago, but...
[Fabrice Asvadourian]
- Yes, and it's clear that working on the employee experience, on how employees will use it, on ergonomics, is what's going to make it a success, and I think that today, we're sometimes too much on the technical side of AI and not enough on the experiential side. In my opinion, this is what's going to make the big difference between the companies that make money out of it and those that make models, which is not quite the same thing.
[Frédéric Simottel]
- Yes, Franck also pointed out that. We're really going to have this exchange which will further enrich the relationship between the employee, the tool and the platform.
[Fabrice Asvazadourian]
- Yes, and less is more. We're going to have to hide the complexity for the user.
[Frédéric Simottel] 
- So, Franck, we've seen the boutiques, we've seen the design, we've seen the customer personalisation, and what's also important is AI serving the supply chain. Because obviously in the luxury sector you have very demanding customers. So they want their product as quickly as possible, in the right place, at the right time. We're also involved in CSR, and that's important too, because it allows you to work your way up the chain and ensure that all the criteria currently applied in the company are respected.
[Franck Le Moal]
[Banner " Franck Le Moal, IT and Technology Director, LVMH "]
- Completely, Frédéric, because in fact the second very important subject for us is the supply chain. But more than that, it's also production. In fact, you are right to sum up our customers' expectations. But we must never lose sight of the fact that we are the luxury industry and this industry is a bit different from others. We're not a mass production industry. We handle and use expensive and rare materials in our products. That's all there is to it. We have men and women in our workshops who produce the smallest possible quantities on a just-in-time basis. And so, of course, AI and algorithms, especially on AI, are going to play a very important role. So, how we can be as relevant as possible in assessing the expectations of our markets ? So everything we do is geared towards optimising sales forecasts, so that we know exactly where the demand is, and where we need to send the right product to the right shop. So this is a very important subject. We have houses with completely different activities, we have a wealth of products and collections that means we have to get the right product to the right shop. I'm not even talking about seasons. We have the northern hemisphere and the southern hemisphere. Summer in Greece versus winter in Argentina. So these are extremely important issues, how best to prepare our product ranges and, above all, how to produce rare products. So producing at the right level, producing effectively to avoid transfers, not overproducing with extremely expensive materials, is very important. So anything that optimises the connection between sales and the signal we send to our workshops to produce the right quantities is absolutely essential. And, of course, to provide our customers with the best possible service in terms of what they expect, with the best possible experience. And that's where you're right, in an extremely strong way, we're going to have a model, AI models that will contribute to this phenomenon of sustainability and the environment, because what we don't produce is materials that we don't spend and above all products that we send to the right place. These are products that we're not going to move again, so they immediately have an impact on the environment and on supply chain performance.
[Frédéric Simottel]
- And we know that it's the supply chain
[Franck Le Moal]
- So the supply chain and production are extremely sensitive environmental factors. So that's our focus in most of our companies.
[Frédéric Simottel]
- It's many, many... a multi-part equation, not all unknowns because, thanks to AI, they are less and less unknown, but there are many parameters to take into account. Fabrice, through Franck's testimony, we see all this maturity with regard to AI. We're in a fine company like LVMH, a major group that has been assimilating all these technologies and this digital transformation for some time. And how can we ensure that this value-performance-investment ratio is taken into account in the best possible way elsewhere?
[Fabrice Asvazadourian]
- So that's a bit of a frustration for a lot of our customers, who say to themselves, well, we're doing POCs, we're doing POCs and then we fall into the curse of POCs that never get deployed.
[Banner " Fabrice Asvazadourian, Managing Director, Sopra Steria Next Consulting "]
We need to focus on use cases that are very mature today. Of course you have to devote perhaps 20-15% of your budget to investment and innovation, but if you put 70-80% of your budget into AI, in things where you know that solutions exist, you know that there is a key business value and that you just have to land it at home, which is already not bad, and secondly, land it at home. The first is no good data, no good AI. So how do I modernise my data platform to bring it up to the same standards as AI? That's a massive challenge. And secondly, how do I get AI algorithms into my IT system today? We have a figure: one AI algorithm in seven continues to perform satisfactorily when it goes into production. In other words, the transition from in vitro to in vivo is currently six out of seven that don't survive.
[Frédéric Simottel]
- Oh yes, so it's in your interest to calculate the cost from the outset.
[Fabrice Asvazadourian]
- Work on industrialisation right from the start. I think that's going to be the big challenge over the coming months. Industrialise AI.
[Frédéric Simottel]
- Yes, because everyone has ideas, but you have to prioritise in relation to the value you create. But obviously, industrialisation is the key to this work, to scaling up, and we've seen that clearly. Thank you again to Franck Le Moal for coming to talk about this. So much for these different aspects. I would also like to thank Fabrice Asvazadourian, Managing Director of the consultancy firm Sopra Steria Next, for joining us. There you have it, I hope we've shed some more light on AI. And as we can see from the examples here, it works, but there are a whole host of things to think about well beforehand, not least the data. Thank you for joining us, and we look forward to a new edition of AI Uncovered.
[Voice-over]
- Special edition. BFM Business Files.

Voice over: Special edition. BFM Business Files.  "AI uncovered: the leaders driving the AI Revolution" with Frédéric Simottel.

Frédéric Simottel: Welcome to our programme "AI uncovered" in partnership with Sopra Steria Next. We're going to talk about the implementation of AI, generative AI. We've already done two programmes with leaders in the digital word, from major companies and with you Fabrice Asvazadourian, hello! 

Fabrice Asvazadourian: Hello!

Frédéric Simottel: Good morning and thank you for joining us. You are the CEO of the consulting firm Sopra Steria Next, which currently employs over 4,000 consultants. Of course, behind Sopra Steria Next, there is Sopra Steria. You are an entity of Sopra Steria group. And with us also today Hervé Thoumyre. Hello ! 

Hervé Thoumyre: Hello. 

Frédéric Simottel: Hervé, thank you for joining us, Director of Customer Experience, Digital Services and Data and member of the Executive Committee of CNP Assurances. So we're starting to see more and more, as each month passes, we gain a little more perspective on  Generative AI that is developing everywhere. Let me start with you, Hervé. We're talking a lot today about the benefits of AI in winning new customers. How is that working out for you at CNP Assurances? 

[Banner " AI to attract new customers "]

Hervé Thoumyre:  At CNP Assurances it's happening by Firstly, simplifying the daily lives of our employees, our partners, but also our customers, and finally, that's nothing new because we've been investing in AI for at least ten years. 
Frédéric Simottel: Yes, it's true that we're talking about Generative AI because it's just arrived, but AI is something that everyone is working on, including you. 

Hervé Thoumyre: In fact, I think we're more of a pioneer in this field. We set up our Data Lab in 2015.

Frédéric Simottel: Yes, indeed.

Hervé Thoumyre: And I have to say that today it's also materialised by the fact that we are convinced that AI has definitely become a lever to ensure that we create a competitive advantage, with our old partners. I would say the historical partners, the banks but also the new partners. 

Frédéric Simottel: Yes, it's true that this is in your DNA. You have this ecosystem of partners around you. 

Hervé Thoumyre: Completely. We're really in a multi-partner model, both with banks and with new sectors. Let me take the example of the retail sector. Last year we sealed a partnership with Carrefour, for example. So we're in this model. And to stand out from the crowd, the challenge is to offer insurance products that meet our customers' expectations, of course, but also to offer them services and ensure that the process, in particular, is as simple and as straightforward as possible. So, let's take a simple example of credit insurance. When we take out a loan for a house, we like to know very quickly whether our loan is covered by insurance under acceptable conditions. 

Frédéric Simottel: Yes, that's always the sticking point. 

Hervé Thoumyre: Well, today this process is completely automated using artificial intelligence so that in 85% of cases, we give an immediate response to our customers, as well as to the advisor looking after the customer. And I can tell you that in many cases, they're very happy to have this level of service. 

Frédéric Simottel: It also makes negotiations in this area a little more transparent. Fabrice, from Sopra Steria Next, How is CNP Assurances exemplary? It's a data industrialist.  Obviously, when you're in the world of finance and insurance, you have an enormous amount of data. So how are they exemplary when it comes to developing new businesses? As Hervé was saying, we've been working on this for several years, but we're raising the bar a little? 

Fabrice Asvazadourian: Yes, we are. Hervé's testimony clearly shows that mastering data is almost a competitive advantage for CNP Assurances.  It allows them to create seamless customer journeys, It allows them to make productivity gains in their middle and back office and it probably allows them to refine value propositions for particular segments or types of customer. 

Frédéric Simottel: And then you have to see if we don't have data, a certain data hygiene. In any case, there's no point in embarking on AI projects. 

Fabrice Asvazadourian: Absolutely. In the end, the rule is quite simple: If you don't have data, it's going to be hard to get AI to work properly, and we've been at this since this year, and Hervé is right, we need to be looking at ten years rather than two. Even if generative AI is changing the game. We need to feed these machine learning and deep learning models with massive amounts of data and with data from increasingly varied and different sources. It can be text, it can be images, and it all becomes data. And so for managers, we need to make sure that we regularly update these data platforms so that we can put this data to work for our business and facilitate interactions, in the case of CNP Assurances, with our partners. Because in the end, the data initially resides with the partner. We need to get it to CNP Assurances in a way that protects its partner's property, the rights of its partner's customers and secures all that. 

Frédéric Simottel:  So, it is precisely this data that comes back from partners and that goes back to all CNP Assurances employees. Now, as soon as we talk about AI, generative AI, we talk about augmented employees, assisted employees with more automated tasks. A lot is being done around this employee. Hervé, how will this augmented collaborator be able to add more value to the service provided to the customer? 

Hervé Thoumyre:
[Banner « Hervé Thoumyre, Director of Customer Experience, Digital Services and Data & Member of the Executive

Committee of CNP Assurances]

[Banner « AI at the service of the “augmented collaborator” »]

 Well, first of all, I would say that AI serves the employee and the human being in general, not the other way round. And in reality, and that's a very important conviction, which is really part of who we are, our raison d'être. In other words, we want people to be at the heart of our business model, at the heart of everything we do every day. So if I come back to the question, what this means in particular is that what we're trying to do first and foremost is free up time for our employees and ensure that they can express their expertise as fully as possible. Here are a few examples. Today, we know how to read the beneficiary clause of a life insurance policy and extract the key data from this clause to prepare an inheritance file. Secondly, in terms of fraud detection, we use AI technologies that enable us to detect fraud on identity card or bank details. Another example is e-mail processing. We know how to automate mail processing in order to direct them and organise the work of our teams. 

Frédéric Simottel: Now that's almost traditional AI. But today, we're talking about generative AI. When we talk about customer service, generative AI often means, for example, we have all these verbatims from people who write or call. So, there you have it, there's a whole host of data sources coming in and generative AI can perhaps give us a better sense of what the customer is feeling, of what's going wrong. 

Hervé Thoumyre: That's true.  And since the beginning of the year, we've been using a platform that allows us to analyse and process customers' feedback, everything they tell us, so that we can respond, particularly if there's an emergency. The objective is also to learn from everything they tell us so that we can improve our customer experience. 
Frédéric Simottel: This data comes from our customers. But there's also what we call synthetic data, Fabrice, which plays an important role. This is data created by other data. 

Fabrice Asvazadourian: Exactly. First of all, what is synthetic data?  It's data that was created by an algorithm and that aims to reproduce, initially, what happened two or three years ago, existing data. This means that you can take data from your customers or your operations and transform it into data that has the same statistical properties, but which is no longer real data. This means that data confidentiality is totally protected and that data sharing between partners is automated. And what we're now seeing thanks to generative AI is that not only does it reproduce existing data, it now creates new data, and this has led to the advances we've seen in pharmaceutical research, for example, where we've been able to drastically reduce the time it takes to decide which type of molecule to use. Because by creating data that generates new data, we've been able to reduce the time needed for experimentation and the creation of new data. That's the great advance, The great revolution. 

Frédéric Simottel:  Yes, we can even see this in the polling institutes, which sometimes use this synthetic data to get slightly more accurate figures. 

Fabrice Asvazadourian: Exactly.

And when you consider the cost and time it can take to create databases of the right size for models that are increasingly demanding, it's clear that this is an asset in terms of productivity and automation for companies.
 Frédéric Simottel:  Hervé Thoumyre of CNP Assurances, another important subject, is this ethical and inclusive AI.
[Banner "Towards ethical and inclusive AI?"]

We talk about it a lot, but we were talking about trust earlier, about being able to inspire this culture of trust that was between customers and CNP Assurances agents.  But this trust must be everywhere.

[Banner " Hervé Thoumyre, Director of Customer Experience, Digital and Data of CNP Assurances "]

Hervé Thoumyre: That's true. And I think it's based on three essential points.  The first is to have a charter.  Any company that wants to embark on AI must have an ethics charter and associated governance to really put it into practice.  The second point is training, making employees aware of the use of AI.  As part of the La Poste group, 70,000 employees have been trained in the use of AI technologies. And then, the third essential element is data sovereignty.  We were just talking about it.  And this sovereignty is demonstrated by the fact that we are in the process of migrating all our data platforms to the sovereign cloud Numspot, of which the major French public financial centre is one of the main shareholders and of which we are a part. 

Frédéric Simottel:  Yes, particularly with Docaposte, it's an offering that can be managed from that side. But it's true that this sovereignty of data is becoming, and I think we understand it a little better, I find, often when we talk about sovereignty, rather than talking about the cloud, it's the sovereignty of data. It's a little easier to understand what we're working with here. Regarding this ethically, sustainable AI, how does CNP Assurances seem to you today, Fabrice? Compared to the other companies you see? Are we in line or are we a bit ahead? 

Fabrice Asvazadourian:
[Bandeau « Fabrice Asvazadourian, CEO of the consulting firm Sopra Steria Next »]
I think that CNP and Sopra Steria share the same high standards on this subject, because AI will either be sustainable, or it won't be. So, I can see that European companies are setting the bar higher than others on these issues, on this sensitivity. It's both about environmental issues. Today, generative AI, we know, is not sustainable, but a lot of billions are invested... 

Frédéric Simottel:  Perhaps, we're thinking ahead more than the Americans, if I take their example? It's when they're confronted with it that they react. We try to anticipate a little more. 

Fabrice Asvazadourian:  In any case, it's a subject that is one of the challenges for a manager.  It's a question of "how can I still move forward? "  Because, in my opinion, not moving forward is not the right solution, while at the same time, keeping an eye on all the new solutions that are emerging and that will enable me  to reduce and control areas of risk.  When you look at the billions and tens of billions of euros of investment being made by Google, Amazon, Apple, Nvidia and others, you know that the solutions are coming.  But they are not all there. So, as a manager, how can I move fast without going too fast? And how can I be agile enough to integrate these new solutions when they will provide me models that consume less energy and water, models that will allow  better control of data, of risks, of bias, etc. There are many dimensions in AI. 

Frédéric Simottel:  Yes, when we talk about 'responsible AI', there's the ethical part and then there's the sustainability and environmental part. Well, thank you for coming here enlightening us on these subjects. Hervé Thoumyre, Director of Customer Experience, Digital Services and Data, member of Comex, CNP Assurances  where AI has been in use for quite a few years and where generative AI is accelerating in many areas.  And thank you Fabrice Asvazadourian, CEO of the consulting firm Sopra Steria Next. See you soon for a new programme entitled  "AI Uncovered". We've already got two or three on Replay, in the world of industry, in the world of banking, and today, in the world of insurance. We promise you plenty more throughout the year.  Have a great week on BFM Business. 
Voice over:  Special Edition. BFM Business Files. 

Special Edition, BFM Business Files. "AI uncovered, the leaders driving the AI revolution", with Frédéric Simottel. 

- Welcome to our programme "AI Uncovered", in partnership with Sopra Steria Next, where we'll be talking about the implementation of generative AI in companies, the transformation of organisations, cultural transformation, the transformation of businesses... and also, new forms of collaboration or how to avoid bias. We're going to talk about all this with our two expert guests. Fabrice Asvazadourian  

- Hello 

- Hello Fabrice, CEO of the consulting firm Sopra Steria Next. It currently represents 4000 consultants and is a subsidiary of Sopra Steria. And also with us is Jean-Paul Mazoyer. Hello Jean-Paul 

- Hello 

- Thank you for joining us. You are the Deputy Chief Executive Officer of Crédit Agricole, with particular responsibility for digital technologies and payments.  You are also Chairman of the bankcard group. So, we have enough distance today to talk about AI, because it's true that it's been a while, this generative AI in business. And my first question, Jean-Paul, is how, at Crédit Agricole, when you're a manager and you see new projects coming up all the time, how do you prioritise them? Then the one we're going to scale up. 

- I used to say that, to quote Bill Gates, we tend to overestimate the impact of technologies in two or three years' time, and underestimate them in ten years' time. But that's not why we shouldn't jump on the bandwagon, we shouldn't be inactive. I think that for AI and generative AI, we're going to have to face up to this. So there's a craze out there today, and it's important to follow it, but obviously remain measured in what we do, 

- Yes, and to encourage 

- Yes, to encourage. So I think we're witnessing a change of period. There are a lot of initiatives in companies today, but there are also a lot of POCs, which are arriving all over the place, but there aren't many experiments that have been thought through on a large scale and that have already been deployed with a significant impact, either on the company's NLP, or on the number of employees affected. In my opinion, it's important that today's generative AI projects are driven and supported by the business. It's not just about technology, it's not just about choosing the right LLM, it's about thinking "OK, I'm the boss of a business, what's it going to change in my business? We need this business-driven approach. Obviously, we need to have technological control over what we do, and we need human support. Which means that these generative AI projects need to be managed at the highest level of the company. And so that's at Comex, it's by the boss. You know, we in the Crédit Agricole Group are natively decentralised, so it's important that every manager in each of our entities feels fully invested in the need to understand these technologies and to support them. 

- So you set these criteria, you said to each of the business line managers "Here, give me one or two projects, then we'll see how we get on". 

- Exactly. 

- So, we have to change our way of thinking, our software, if we stay in the IT world. Fabrice, do less experimentation, fewer POCs, and scale up too. And it has to be good. As we've just said, it's your job to manage projects. We keep saying it, we say it for other IT projects. But in the case of generative AI, it's even more important. 

- Absolutely. I think we're seeing it clearly with generative AI, but also with other technologies such as 'low code, no code'. We're moving towards decentralisation, as close as possible to the users, of computing. In a way, natural language is almost becoming the programming language, and this gives business people much greater capacity to use technology to solve their business problems. Technology is only useful if it solves problems. That's what we see, and what we recommend to our customers. With regard to your question about mastering the time dimension, perhaps it's a question of thinking in three time horizons. It seems to us that there are areas in which we now have sufficient evidence to say that we can move into deployment mode. I'm thinking of virtual assistants, I'm thinking of digital marketing, I'm even thinking of tools for computer engineers. We know today that some use cases, not all, but some are mature and so it's important for a company to move quickly. 

- Yes, with productivity gains from automation... 

- That's it. We need to prepare for wave two, which will focus on, probably, more cross-functional uses in HR, finance, etc., but which will require companies to mobilise their proprietary data even more in order to refine the AI models that will come from Big Tech or start-ups. And then there will be the third phase, but we're probably three years or more away from really applying AI to companies' core businesses. Before we get there, we still need to have levels of confidence and reliability that are a certain number of steps ahead. 

- And then levels of acculturation. Jean-Paul Mazoyer, we can see that generative AI raises a lot of questions about its uses. With its prompts, it is accessible to everyone. So we really need to look at whether it's accessible to anyone in the banking world, whether it's someone in a branch or someone who's a telephone operator like the top executive. So that's what's changing today with this technology. For you, it's information, it's acculturation, it's all quite technological, it's very important. And then relearning how to work, or relearning how to work collaboratively, everyone already works together a lot, but there are a lot of things we need to relearn. 

- I share this opinion. I believe that generative AI is going to have an impact on all business lines for a company like a bank, with a very large majority of employees who are going to have to learn to live and work with AI on a daily basis. It's not AI that will replace employees, it's employees who will know how to use AI, who will replace employees, whatever their level, whatever their job.  So all the men and women need to be trained, to be acculturated, to understand the uses, to understand what it can enable them to do. So a lot of support is needed, both for employees and for managers. A lot of support, a lot of training, a lot of deployment. A few years ago, we deployed collaborative tools like Teams, and when you ask people how they use it, they say it's a video tool. And few have really transformed the way they work to move towards collaboration. It shouldn't be the same with generative AI tools. You have to ask yourself, "What's this going to change?" "How can I reinvent my way of working, my way of connecting with others using these tools? 

- I imagine that you've launched policies and training programmes for each profession. How are things going? 

- Yes, that's exactly it. We're going to look at each business and each function, and the rate at which tools are deployed, because it depends on the tools you're going to deploy. Let's take some very concrete examples, like when you deploy Copilot for office automation tools. This can have a major impact very quickly on the transcription of a meeting, a report, a translation, whatever you want. People still need to be able to use it as easily as possible. When you deploy GitHub Copilot for IT teams, it can also have a very significant impact. But you still need to provide support, because there's always the necessary critical eye on the part of the employee about any potential hallucinations. So it's important that we have employees who are able to use it, who understand it, but at the same time are able to take a critical look at what's being produced. 

- What's more, we know that we're going, that no matter what happens, we'll be moving towards these technologies. Fabrice, we need to think about a certain industrialisation of AI, because there are lots of things we need to push on. You have to be able to bounce back and forth between the proximity of each of the business divisions, the use cases, the effects of scale, the technologies too, because we haven't gone into detail here, but obviously there's Mistral, OpenAI and Google Gemini, and then there's a whole bunch of others coming along. After that, you sometimes have to combine that with the processors behind it. There are a lot of things that need to be taken into account. 

- You're right, Frédéric, the industrialisation of AI is the theme of 2024. And with us, all the requests from our customers are "Help us to industrialise" "How do we go about taking POCs, which have demonstrated in POC mode that they bring benefits, and deploying them at scale?" Four questions generally come up? The first, as we've already discussed, is "how do I prioritise?" I can't do everything at once. So how do I prioritise? The second is "But for there to be AI, my data platform has to be up to scratch". The level of requirement for AI is much higher than it was before. So how do I modernise my data platform? There's the subject to talk about AI industrialisation again. "I have a POC, how can I deploy it while maintaining the quality level of the results?" Because we realise that there can be a gap between the protected mode of the POC and the deployment mode. "How do I integrate these AI solutions into all my industrial IT?" 

- Yes, especially as there are some that need to be kept within a perimeter and others that can be opened up a little. 

- Yes, they have to feed off data and interact with other tools. And the IT of a large group like Crédit Agricole or others is the IT industry. So what works on a small scale has to work on a much larger scale. And then the fourth question. Is the question "Does performance not deteriorate over time, or does it improve? "Doesn't a new technology replace the choices I made a month ago? So it's this maintenance in operational condition. And that's it. And all this leads companies to ask themselves questions in terms of organisation. I don't know if it's a question of scale, but it's more a question of learning effects. Today, it's a question of "how can I learn faster? You're right, you have to choose technologies, partners... 

- Who move every three months! 

- Which change... And you have to define the frameworks within which you want your employees to be able to use AI with confidence and security. So there are a number of issues that need to be addressed, and the answers will vary depending on the group's culture. Some are natively more decentralised; Crédit Agricole is an obvious example, and so we see central teams defining standards, rules and choices, and then leaving a lot of freedom to the business lines to produce their solutions locally. Others are building AI factories, bringing together people from the tech world with specific skills, and then people from the business who, for a year or a month, will step outside their operational role to build solutions. There are these two models. And then all the shades of grey in between. Every company needs to know itself. 

- What would terrify me if I were in the shoes of a manager like Jean-Paul, or advising managers like Fabrice, is the speed of innovation, saying "Wow, in three months we've already got a new version of ChatGPT out, etc.". Then there's another point, which is all about ethics, ethical AI, and I imagine that in banking, of course, you have it in every business. But there you have it, there's a new indicator that needs to be put in with this artificial intelligence and linked to this ethics. 

- Yes, at Crédit Agricole we have two convictions in this area. The first is that we're going to have to make massive use of AI and generative AI tools, with all that Fabrice has just mentioned. So there's no question of missing out on this revolution. We need to give ourselves the means to consider it properly, to organise ourselves properly to be able to deploy it on a massive scale, because it has a massive impact on customer relations and internal organisation. The second conviction is that we are convinced that all of this must be at the service of people. Our logic is that these tools should enhance the human element, but they should also be enhanced by human responsibility. In other words, if we have this direct interaction with customers, we must always have the possibility that a woman or a man from Crédit Agricole can take over and complete the relationship with customers. So the first rule, in a way, is to say that responsibility must remain with the men and women of Crédit Agricole. We've been aware of these ethical issues for many years, and we already had scoring tools that we were obliged to be careful about. So we have these issues, but it's becoming much more complex with generative AI to be able to understand what data has been used, whether there are any biases, and that's what we're going to have to control. And we're obviously going to need to have a position on these biases or algorithms and therefore be able to work in confidence with certain LLMs to be sure that the data used are the right ones. There's one final conviction Yes, it's about electricity consumption. We know that AI and generative AI consume a lot of electricity. 

- Yes, and that's the big issue that's going to come up in the next few months. 

- Exactly. And we already know that the biggest AI companies are the biggest consumers of electricity. We're going to have to ask ourselves the question of, not just LLMs, but SLMs, or Small Language Models, so that we can be more economical, more frugal in our use of technology. 

- We'll certainly have an opportunity to talk more about this with Fabrice at an upcoming  “AI Uncovered “ program in partnership with Sopra Steria Next. Thank you both, Jean-Paul Mazoyer from Crédit Agricole and Groupement Cartes Bancaires and Fabrice Asvazadourian from Sopra Steria Next. Thank you for following us, and we look forward to a new programme, "In search of AI", very soon. 

- Special Edition, BFM Business Files 

Special edition. BFM Business Files. AI uncovered: the leaders driving the AI revolution With Frédéric Simottel. 

Welcome to BFM Business in our Special Edition program "AI uncovered" in partnership with Sopra Steria Next. Today, we’re going to talk about Digital Manufacturing, that is the application of digital technologies in industrial production. We’ve been talking about industry 4.0 , robotics, Supply Chain improvement, smart and connected machines for a while. But now, we'll take a closer look at this. We’re going to look at data, AI and their impact in factories. To talk about it, today, we have two guests with us, Fabrice Asvazadourian, hello! 

Hello Frédéric 

Thank you for being with us. You are the CEO Of the consulting firm Sopra Steria Next. It represents more than 4000 consultants. This is the Consulting Division of the well-known group Sopra Steria. We have also with us Yves Caseau, hello! 

Hello 

Thank you for being with us. You are the Group Chief Digital and Information Officer of Michelin To remind you, Michelin is a group with a turnover of over   28 billion euros and that employs more than  132,000  people who need to be trained in AI, We’ll come back later to this topic. Michelin also owns 121 production sites, 9 R&D centers, ... Yves, let’s start with you like all of us, you have been surprised by the evolution, the speed of AI democratisation, especially generative AI. So very concretely, AI, what does it look like in factories? 

If you visit a factory, you'll see screens that have appeared everywhere, in addition to machine tool displays. These screens allow operators to make their own applications Data-driven visualisation and optimisation. To what purpose? To optimise their process, to see if there is something that needs to be settled and to reduce material losses. So, it brings a lot of value. According to the German academy, Digital helps us to see better, to understand better, to predict better, and then to adapt better. And, that’s what Michelin is looking for. These machine tools are fully automated with sensors and data flows. So, AI helps to optimize. Let me give you a second example, about the optimisation of electricity consumption. We have a partnership with Microsoft and we use all these flows to improve the way our machines operate. It works very well. Let me give you a third quick example: in a factory, everything is constantly changing. We change dimensions, we change material, we are constantly adapting. For us, AI is not artificial intelligence, it is more like augmented intelligence.  And we perceive this intelligence as tools that help us to change and make a transition from a model to another more easily. So, it is a progress that can be shared. One of the benefits of AI is to have tools to adapt better and then, you can share with others when you see something that works well in a factory. 

To describe that, I'm using your term “toolbox”. It means that the work is done a lot upstream, and it represents a change in processes, a transformation of professions. How does Michelin deal with it ?  As I said, Michelin represents 132,000  employees. There are a lot of people in the factories, and in all of Michelin's activities in general. All of these people have to move forward, and be onboarded. 

AI, here, is the accomplishment of Digital transformation, of the digitalisation of all flows, of Software-based control. During the Covid pandemic, Michelin's Director of Manufacturing said we were able to restart factories much faster thanks to the ten years investment in digitalisation. Well, for the moment I've only shared the big advantages with you, but it's important to know that it's an in-depth work. And there is no AI without data engineering and software engineering. They form the basic framework.  And then, as you also said, it's not just machines and systems, they are also the people behind the screens, and they need to make it their own. But if I took the example of screens which are used by operators to steer. This is in line with the trend of citizen development. It means that a significant portion of the value is generated when operators adopt the tools and invent local solutions for their specific business problems. 

Yes, that's often what we hear in your profession, Obviously, in the industrial profession, this phenomenon is very present. When we talk about training, we tend to think of managers who must have the ideas. But ideas also come from the field, and it is these people who need to be listened to the most, in order to get the right ideas, to automate, to understand the operational tasks and processes. That's the idea. 

That's right.  

I'd now like to turn to Fabrice. So, AI is used at different levels, in the supply chain efficiency, to facilitate the work of employees.  But above all, it has been quoted by Yves just before, it’s data.  You need a rigorous architecture of data, a data hygiene, I like to use this term. 

That’s right Frédéric. Maybe, you're familiar with the saying “garbage in garbage out". Data is at the heart and is essential to use AI solutions.  Over the past years, the amount of data stored by companies  has doubled every  years. It’s huge. In order to cope with this rise, all the major groups have set up governance, management models,  tools, solutions, to be able to manage that.  What's new, with generative AI, is that text, image, and other unstructured information  have become data and must become Data for Businesses. This change requires us to review everything we've built and talked about up to now. We need to invest in new solutions to handle and give meaning to data.  

Data Lake is no longer enough? 

There are different kinds of data. Storing 0, 3, 7 is different from storing sentences and images.  Last year, 90% of the new data stored was unstructured, it’s a lot. For companies, it's imperative to be able to fully exploit new data to take advantage of generative AI. 

And for you, Yves Caseau, from Michelin, is this a worry for your company?  Indeed, data from machines was already a challenge.  You also stated that you’ve been investing for ten years in all of these systems.  But I imagine that all this unstructured data, all these emails that are exchanged, these technical documents, …  

So no, it’s not a worry, we have data lakes at different levels  in our factories and even beyond. We have data lakes in the cloud, special or private.  We have a data architecture that enables us to absorb what you're talking about Fabrice, the complexity and the heterogeneity of data.  But then why isn't it a worry?  The purpose of all this transformation is to be more efficient, more adaptable in an ever-changing world,  and to have a more pleasant job.  I can give you another example.  We've always done control quality with sophisticated machines. Now, we have put new generation robots with Vision machines and neural networks. Operators remain the experts. It's not the machine that decides, but the machine does 90% work, including the routine work. The operator's job of quality control becomes much more interesting.  That's why it's so valuable.  And we need to do this in order to recruit, in our factories, the talents of tomorrow.  Indeed, today, work in factories is not necessarily perceived as very attractive.  It is therefore important to invest in these solutions to make these jobs more exciting, to attract new talents, to make the tires of tomorrow. 

Yes, that's what you said, the transformation of professions is important. (engineers, technicians,)  Yves, AI is also used to invent the decarbonised products of tomorrow.  And at Michelin, do you work with this AI too?  As I said earlier, Michelin has 9 R&D centers. So, at Michelin, are you also using AI for it? 

Absolutely.  I will give you three examples.  First, we are moving towards a low-carbon world, which means that inputs will come from circular economy whether recycled or bio-based.  These new inputs have stronger dispersions than petrochemical products.  We need to adapt processes and to do it, AI is fundamental.  AI makes it much easier to manage these new products.  Then, we need to invent in these new materials and for this, generative AI is very useful, as Fabrice said.  It enables us to improve and develop our knowledge engineering, our ability to mix and match loads of data sources.  But generative AI isn't used for everything at Michelin.  We don't plan, we don't do forecasting with Generative AI.  However, to manage knowledge, share or refine it, Generative AI is very useful.  

Yes, it allows you to accelerate. 

At Michelin, our favourite slogan is that the tire is a high-performance composite.  In a composite, there are both structure and materials.  And you know it, we've already shown off our Vision tire 3D printed.  We also made Uptis which is a tubeless tire with a structure.  So, innovation and structure are fundamental.  To do it, we use hybrid AI where we mix  classic methods,  with AI and machine learning.  So why is it important?  It's because we're convinced that in order to invent tomorrow's low-carbon solutions, the possibilities are endless.  We need to explore with Digital Twins . Because, the world of tomorrow will be realised in a concrete way.  But it invents itself with simulation.  It invents itself with digital twins.  

Fabrice Asvazadourian from Sopra Steria Next, AI is at the service of sustainability.  However, AI is criticised for consuming a lot of energy.  What do you think about it? Yves has just shared some striking examples. 

So absolutely. First, I think all our customers know that many AI solutions are not sustainable today.  And all AI leaders, like cloud providers, solution providers are working to invent  new ways of doing sustainable AI, including generative AI.  And that's in front of us. It's progressing fast but it's in front of us. On the other hand, as you mentioned, AI allows us to improve our ability to be frugal, to optimise energy consumption, to optimise the use of inputs, to avoid waste.  All of this is made possible by AI. What I find exciting about the arrival of AI in factories is our ability to digitalise reality in order to simulate infinite scenarios  and enable faster learning. For example, Digital twins, Industrial  Meta Verse,…  We've reached a point where we have millions of sensors in our factories, enabling us to measure, but also tools enabling us to create scenarios, at levels never reached before.  

Exactly, we have minutes left to talk about this subject. How do we prioritise all these projects?  When you have, people with ideas.  These projects need to be prioritised; these costs must also be managed.  Even if you’ve invested for years in digital and everything is ready, you still need to know how to manage and pilot  this set of parameters. 

So yes, AI is not a destination, it's a journey.  It started years ago at Michelin.  Every generation, we learn from failures and previous successes and therefore in terms of investment and piloting, we are working at a loss acceptable. That is to say, we’re trying things, but fortunately We've had successes that made us want to go for it. As we are big, we have a hybrid structure with a center and locations called “Hub and spoke”. It allows us to try to find the right compromise between innovation close to the field, as we said, widely distributed across continents, with the capitalisation through a hub. And also, there are some difficult areas such as machine learning with reinforcement, for processes, for things where we're going to create very specialised teams.  And we're not going to create that everywhere.  

And as a consulting firm, Sopra Steria Next's aim is to help companies prioritise their projects.  And one of the most important criteria for making projects a success is controlling costs. That's right. So, first, the costs of IT will continue to increase.  No one believes that they will decline in the coming years.  We've seen this with the Cloud.  Secondly, what we recommend to our customers is to keep in mind projects where AI is mature enough to be deployed at scale, under 12, 18, 24 months and to be able to deploy.  With this criterion of speed and getting out of these POCs failures: these POCs that fail and are never deployed.  And besides, as Yves said, you must have a budget envelope to make real innovation on things we don't know if the value is going to be there.  Often, we are asked to make business cases very early on.  But sometimes there are situations where to make a business case, so everyone in Excel can do a lot of things, but, at the end, it's not necessarily what's needed.  But what is very important for our customers is to separate well where they are in logic of time to market,  obsession with time to market and where they are in a logic of exploration.  It's important to separate them. 

I hope you've been able to find some tips for your AI projects.  Thank you to both of you, Fabrice Asvazadourian, CEO of the consulting firm Sopra Steria Next and Yves Caseau, Group Chief Digital and Information Officer at Michelin.  Thank you for giving us the opportunity to hear your testimony.  See you soon for a new special edition.  On this topic "AI uncovered”  

Special edition, the BFM Business files.