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

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.