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[VOICEOVER] Special edition, BFM Business Files. “AI Uncovered: Driving the AI revolution” with Frédéric Simottel.

[Frédéric Simottel]
Hello and welcome to this special edition of AI Uncovered, in partnership with Sopra Steria Next. We are exploring the behind-the-scenes of generative AI and its impact on businesses. Over the past few months, you have been able to watch replays covering sectors like luxury, industry, and banking. Today, we are focusing on a truly strategic domain – submarine cables – with our guests. Let me introduce them: Fabrice Asvazadourian, hello.

[Info bar: Time for industrial 5G]

[Fabrice Asvazadourian]
Hello, Frédéric.

[Frédéric Simottel]
Managing Director of the consulting firm Sopra Steria Next. More than 4,000 consultants today, and of course, a subsidiary of the ESN Sopra Steria, which we know well. And with us, Christophe Bejina.

[Christophe Bejina]
Hello.

[Speaker bar: Christophe Bejina: Chief Information Officer of the ASN (Alcatel Submarine Networks) group]

[Frédéric Simottel]
Hello Christophe. CIO of ASN Group. People now just say ASN, but let’s clarify – Alcatel Submarine Networks. Otherwise, there are plenty of acronyms! Christophe, let’s start with you. ASN is an industrial company, a giant in submarine cables. For over five years you have significantly digitalised the group. Your digital transformation is well underway: a complete overhaul of your infrastructure, integrating industrial 5G. We’ll also talk later about this important year for you, as the French state has taken back 80% of ASN’s capital. But first, tell us about this industrial 5G.

[Christophe Bejina]
Of course. Industrial 5G is part of a digital transformation programme that began five years ago, as you mentioned. It’s a four-phase programme. The first phase was to lay the foundations of this transformation – starting with network infrastructure and data centres, hence 5G, and setting up a partnership ecosystem to support us on this journey.

The second phase was implementing or integrating an enterprise architecture – an information system architecture geared towards Industry 4.0, while also putting in place principles of securing industrial IT systems, which is becoming increasingly important.

[Frédéric Simottel]
So, it really was necessary to renovate, to start from scratch and rebuild.

[Christophe Bejina]
Exactly. Renovation was crucial, firstly because this is a historic industry with legacy systems that had to evolve. Secondly, there is a boom in the business, and the nature of contracts and projects has reached a level of complexity that can no longer be handled with the old tools.

So, this was a major imperative – a fast-paced digital transformation.

The third and fourth phases involve deploying what we call data and AI.

[Frédéric Simottel]
That’s right, because above this infrastructure – you’re building it in parallel – there’s a whole data infrastructure as well.

[Infographic: Alcatel Submarine Networks (ASN)
 • 2,000 employees
 • 7 vessels
 • 410,000 km of cables under maintenance
 • 180 systems installed]

[Christophe Bejina]
Exactly. We need to refine the data, expose it securely, and use it for AI. The final phases we are in now involve deploying sovereign computing capacity – that is, computing power within our own data centres – to gradually train AI models on our data without exposing them.

That is the roadmap: implement the digital transformation so that, in the end, we can fully exploit data and enable the advent of AI.

[Frédéric Simottel]
And this industrial 5G – it’s now a key element for you?

[Christophe Bejina]
Absolutely. It is progressively becoming the data backbone of our factories. More and more, we are connecting to 5G rather than fibre. All industrial security systems will eventually be connected via 5G. It becomes the nervous system enabling IoT deployment, smart systems, new robots, energy control systems, and other intelligent devices across our plants.

[Frédéric Simottel]
We are seeing this everywhere. Fabrice Asvazadourian, just a few days ago was the Mobile World Congress in Barcelona. There was a lot of talk about 5G and industrial 5G. I’ve always felt that operators somewhat mis-sold 5G to the general public, while in industry things are really picking up speed.

[Fabrice Asvazadourian]
Absolutely, Frédéric. I believe 5G is not simply the next step after 4G. It was designed first and foremost to meet industrial needs. What does it bring? In factory environments, it ensures there are no more network problems, delivers much faster bandwidth, drastically reduced latency – enabling true real-time operations.

It allows decentralisation within factories, and coupled with edge computing, it brings computing power closer to the data. This helps to move faster and meet the extremely low-latency demands of industrial processes. Additional security layers are also crucial. So 5G combined with edge computing becomes the enabler of Industry 4.0, multiplying sensors and IoT, allowing machines to communicate, and optimising flows and supply chains – even between suppliers. This is a major productivity driver.

[Frédéric Simotel]
Exactly – Christophe, ASN had a significant year, as I mentioned. The state took back 80% of your capital, because ASN is clearly a strategic industry. When we talk about AI, we talk about data – and when we talk about data, we talk about sovereignty. Does state ownership heighten the focus on sovereignty issues?

[Info bar: AI: the challenge of sovereignty]

[Christophe Bejina]
Yes, absolutely. Being state-owned – or at least having the state as majority shareholder through the APE – strengthens the sovereignty focus. But this was already a priority when we began our digital transformation. This simply accelerates the process.

Sovereignty brings two key points of focus: First, when deploying AI and 5G, we must remember that everything runs on data. Data from a strategic industry like ASN cannot be shared or exposed. So, the first priority is to work with the right partners to build this data and AI ecosystem – ideally French or European partners, since we also have plants in Norway and the UK.

The second point is building a sovereign infrastructure: ensuring computing power is located in our own or our French partners’ data centres, and that applications are hosted on-premises, with data remaining inside our systems and not exposed externally.

[Frédéric Simottel]
That sovereignty criterion is important – and not just for strategic industries, given the current geopolitical climate.

[Fabrice Asvazadourian]
Exactly. Three or four years ago, this was an intellectual discussion – except for defence, the army, etc. – but sovereignty was not really a key decision factor. Today, every CIO, across all industries, considers sovereignty when choosing cloud providers, AI solutions, and other technologies.

France is, in my view, ahead of the curve here. Europe has put in place defensive regulatory mechanisms, but perhaps we are not yet offensive enough. Hopefully, the billions announced just last month will help us take a more proactive stance. The President’s strong emphasis on Mistral, for instance, should prompt French and European companies to think carefully about how to make sovereignty a lived reality – because ultimately, it’s about making choices.

[Christophe Bejina]
Yes, and it is a constraint.

[Frédéric Simottel]
On AI specifically, could you share one or two projects that have scaled successfully?

[Info bar: AI: scaling up]

[Christophe Bejina]
There are several, but I’ll highlight two or three. First, in our R&D teams, we already use machine learning to develop our systems. Ultra-high-speed optical networks are very complex, particularly in terms of resource management and filtering capacity.

We have AI compute capacity – using those famous Nvidia GPUs – to solve these extremely complex problems in our R&D products. These systems process 60–70 terabytes of data – so we are operating at a whole new scale.

A second example: we run highly complex marine operations. Cable deployment is not just maritime transport – these are factory-ships travelling back and forth. We faced serious planning complexity for these operations. This year we implemented an AI-based system using metaheuristics to handle marine planning – a task that had become too complex for human intelligence alone.

[Frédéric Simotel]
Fascinating. AI is clearly a productivity driver – and even a driver for reindustrialisation.

[Info bar: AI becomes a productivity driver]

[Fabrice Asvazadourian]
Absolutely. Eighteen months ago, together with ASN, we were proud to win a major innovation award for deploying 5G in industry. Because ultimately, the goal is competitiveness.

The combination of technologies – 5G, AI – allows us to deliver higher performance. One interesting example is using AI to improve circular manufacturing: reusing waste as an input for production, rather than relying on chemistry or raw materials sourced from far away. AI models help boost performance in ESG objectives, sovereignty, and competitiveness simultaneously. The reindustrialisation of France will necessarily rely on these technologies to restore competitive advantage.

[Frédéric Simottel]

That’s the positive message we wanted to share, particularly in these times of geopolitical tension. Our industry is strong – and will become even stronger thanks to AI. That’s what we’ll keep promoting on our programmes, alongside industrial, service, and consulting players.

Thank you for joining us. See you soon for another episode of AI Uncovered. And remember, you can watch all the other episodes on replay with our partner Sopra Steria Next. Have an excellent day.

[VOICEOVER]
Special edition, BFM Business Files.

[BFM Business Logo, text appears: “Special Edition, BFM Business files” then “AI Uncovered: The leaders driving the AI revolution“]

[Voiceover]
Special edition, BFM Business files. AI Uncovered: The leaders driving the AI revolution. With Frédéric Simotel.

[Frédéric Simotel]
Welcome to our show AI Uncovered, where we explore how business leaders driving the AI revolution, in partnership with Sopra Steria Next.
We’ll be talking about the implementation of generative AI and trustworthy AI within companies. And specifically, we’ll take a closer look at AI within FDJ United. You probably know the name—it’s La Française des Jeux, but today it operates internationally, present in ten countries, with a strong focus on redesigning the customer experience.
We’ll discuss this with our two guests: Sébastien Rosanès, hello.

[Sébastien Rosanès]
Hello.

[Frédéric Simotel]
Sébastien, thanks for joining us. You’re the Digital, Data and AI Director at FDJ United. And Fabrice Asvazadourian, hello Fabrice.

[Fabrice Asvazadourian]
Hello Frédéric.

[Info bar: FDJ United – AI for Safer Gaming]

[Frédéric Simotel]
Managing Director of consulting firm Sopra Steria Next. So, Sébastien, let’s start with you. FDJ United—every year, you’ve got 33 million players. I guess we’re all part of that, some more often than others, depending on the jackpots.
You’re already using AI in your organisation—for fraud detection, customer experience, and what you call responsible gaming, right?

[Sébastien Rosanès]
Exactly. AI is a key element of our long-term growth strategy.
We use it for marketing, targeting, better customer knowledge, and optimising game creation.
We’re even exploring how AI can help us design new games, imagine new ones.
And of course, we also use it to protect players. Gaming has addictive risks, and AI helps us be much more precise in detecting and deciding when to intervene or stop play, or support players.

[Frédéric Simotel]
And not just checking the birth date to see if someone’s over 18.

[Sébastien Rosanès]
Exactly. That’s just the basic requirement. But beyond that, we look at how many games are played, how much money is spent, and so on.
AI helps detect patterns and even suggest more personalised ways to intervene instead of sending a one-size-fits-all alert.
And behind that, humans step in to take over and have real conversations with players.

[Infographic: FDJ United]
• 5,000+ employees
• Presence in 10+ countries
• 33 million players
• €3B in revenue (2024)
• Digital = 30% of activity

We regularly make thousands of calls to some of our online players, with whom we’ve built long-term relationships.
It’s important for us to rely on insights flagged by AI.

[Frédéric Simotel]
And that’s the paradox—you want people to play, but not too much. Because of the addictive side. You’re aware of it, you set the right alerts, and AI helps you do that.
This customer relationship, this personalisation, is one of the main successful uses of AI across sectors.

[Info bar: Customer personalisation, key to AI success]

[Fabrice Asvazadourian]
We see it a lot, especially in fraud, risk, and customer support with a responsible approach.
I believe companies that push AI too far for short-term revenue gains may pay a high price for irresponsible AI later.
That’s why FDJ United’s initiative is remarkable. It’s a big issue.
We see companies hesitating between ultra-personalisation—which AI allows—but also worrying about data quality. Because personalisation only works if based on accurate data. Otherwise, you risk fake personalisation that customers quickly dislike.

On the other hand, proper personalisation helps retain and support loyal customers over time.

[Frédéric Simotel]
Sébastien, can you explain how this AI integration works at FDJ United—both in terms of your Data & AI team organisation and your infrastructure, since you’ve chosen a cloud-first approach?

[Info bar: Strong impact on data & AI teams]

[Sébastien Rosanès]
Sure. Both go hand in hand. First, to scale AI, teams must be more autonomous, thinking strategically about their AI products, developing them, and operating them.
So we’ve grouped all these skills together under one unit, under my direction, to give teams autonomy.
We call them “squads.” It’s a model inspired by tech companies like Amazon or Google, now spreading into larger organisations like FDJ United.
But beyond organisation, we also need flexible tools that support autonomous work.
That’s where the cloud is powerful—it gives teams the ability to build, test, and deploy directly, right up to customer-facing environments.

[Frédéric Simotel]
So what does being cloud-first bring you?

[Sébastien Rosanès]
It lets teams control their applications directly and push them to the cloud.
They also benefit from the latest AI innovations. And it avoids the usual inertia of big IT departments managing heavy projects that can’t pivot quickly.
For instance, when GPT-5 came out this summer, or with Microsoft Copilot—the generative AI tool in Word, Excel, PowerPoint, and Teams—it became available within days to all FDJ United employees through the cloud, with zero IT intervention.
That’s a revolution. Because in AI, every 2–3 months, a major innovation reshuffles everything. Having access to the best tools as soon as they’re released is transformative.

[Frédéric Simotel]
That’s what fascinates me about IT today. How do you keep up with that pace of innovation, when before cycles were so much longer?
And Fabrice, with FDJ United, we clearly see a balance between the Data-AI team structure and their infrastructure. Many companies struggle with that.

[Fabrice Asvazadourian]
Yes. Many clients have well-thought-out organisational setups.
But fundamentally, we’re technologists. Ideas only matter if they go beyond POCs into real-world deployment.
That’s why investment in data platforms and cloud is critical—to move from lab thinking to industrial thinking.
Many clients feel frustrated after the initial hype phase and now face the challenge of scaling.
When you have 33 million customers, like FDJ United, a pilot that reaches 50 people is useless. It has to scale to everyone.
That’s where infrastructure investments come in—beyond organisation and governance.

[Info bar: Industrialisation and AI culture, the keys]

[Frédéric Simotel]
Exactly. Scaling isn’t just inflating a small project—it’s a complex industrialisation process.
And successful AI projects combine that scale with employee culture and adoption.
How did you ensure this transformation was well received? Because it changes jobs, and people need to be brought along.

[Sébastien Rosanès]
Absolutely. My view is that AI actually means more human interaction.
AI takes over repetitive tasks, freeing people to focus on more human, value-added ones—like spending more time with clients or colleagues instead of doing back-office work.
So we train and upskill employees. We built a Data and AI Academy with two goals.
First, to train all employees to use AI daily—using tools like Copilot, for example. It sounds simple, but prompting properly is a real skill.

[Frédéric Simotel]
And they need to make it a habit.

[Sébastien Rosanès]
Exactly. It becomes second nature. Our goal is to make 100% of our employees AI-literate, with basic skills and data-savvy analysts in every department.
The data analyst role is spreading fast—it’s no longer a separate job. It’s becoming a core skill. Marketing? You’re also a data analyst. HR? You analyse employee data.
That’s reflected in our hiring too. For example, we launched a graduate program this year to train new hires in all AI product roles—data engineering, data science, etc.—so they gain a 360° view.
And for existing Data & AI teams, we help them evolve too. Ten years ago, we hired statisticians. Today, those roles are becoming data scientists. We’re helping them upskill into the AI era.

[Info bar: AI – Getting teams on board]

[Frédéric Simotel]
And how do you make sure it’s well received?

[Sébastien Rosanès]
It starts with understanding: your job isn’t being replaced, it’s being augmented.
If you embrace AI, your work becomes more interesting and higher value.
So, we conduct a fairly detailed analysis of each of the professions to imagine the jobs of the future, how they will change and tell employees that we are not here to replace them. We’re replacing tasks, not people.
We analyse each role to map its future evolution and show employees we’re replacing tasks, not people.
I’m convinced AI won’t replace employees—but employees with AI skills will replace those without them.

[Frédéric Simotel]
Fabrice, that’s where mapping tasks and anticipating change is key, right?

[Fabrice Asvazadourian]
Exactly. Every leader must commit to bringing people along—it’s their duty.
We’ve seen many transformations before; this one is no different.
We now have enough data by job role to model how fast and how much AI will help, so we can plan ahead and manage the change.
The key is anticipating, not reacting.

[Frédéric Simotel]
That’s the perfect closing line. Thank you both, Sébastien Rosanès from FDJ United and Fabrice Asvazadourian from Sopra Steria Next. See you soon for another episode of AI Uncovered – Leaders driving the AI revolution.

[Voice-over] BFM Business special feature “AI Uncovered, driving the AI revolution” with Frédéric Simottel.

[On-screen caption: Implementing trusted AI – how?]

[Frédéric Simottel]

Welcome to this special feature produced in partnership with Sopra Steria Next. We are going to talk about the implementation of generative AI and trusted AI within companies.

Several episodes are already available to watch on replay, featuring senior executives who have joined us. Today, we are focusing on AI within the Orange Group, and more specifically on identifying key projects that are scaling up or already in the process of doing so.

How do you engage employees? There are many themes to discuss. With us is Alexis Trichet. Hello Alexis.

[Alexis Trichet]

Hello everyone.

[Frédéric Simottel]

Director of Data and AI at Orange France, and with us as well, of course, Fabrice Asvazadourian. Hello.

[Fabrice Asvazadourian]

Hello Frédéric.

[Frédéric Simottel]

Fabrice, thank you for being with us, Managing Director of the consulting firm Sopra Steria Next.

Alexis, you have been working on AI at Orange for several years now, four or five years, and things have clearly accelerated given the structure of the Orange Group.

[On-screen caption: 10 AI domains identified at Orange]

One might have feared a somewhat fragmented approach. There are engineers everywhere, everyone wants to move forward. So what are the main areas you are focusing on today?

[On-screen caption: Alexis Trichet – Director of Data and AI, Orange France]

[Alexis Trichet]

That could have been the case, but in fact, in 2020 we realised we needed to structure things.

We could see that everything was starting to take shape. At that time, we carried out a fairly simple assessment. We already had several hundred people at Orange working on these topics, close to the business teams, which was an asset.

However, we struggled to have an overall view of what they were working on, and we also had some difficulty, we have to admit, in using standardised methods to assess the value being generated. So we decided we needed to focus on two or three major priorities.

**[Infographic: ~300 AI use cases at Orange France

55% under review
 45% deployed or being deployed
 Source: Orange France, September 2025]**

We realised we could not leave the situation as it was, and that we needed to organise things. This was before the arrival of GPT, at the end of 2020.

We decided to identify the key domains where we were willing to make significant bets, building on existing strengths, with business teams working closely with data scientists who had already begun exploring these areas.

[Frédéric Simottel]

So what are these areas? Network, customer relations, marketing?

[Alexis Trichet]

Exactly. The major domains are fairly classic, but they make sense for a telecoms operator: customer relations, marketing, communication and the network.

There is also a very important domain for an operator called field operations. These are the employees and partners we send either to customers’ homes or into the network to deploy or maintain services.

These are the small orange vans you see everywhere, carrying field technicians. This is therefore a major area for us.

[Frédéric Simottel]

So you focused on a small number of major themes. Fabrice, this is often the challenge highlighted throughout AI Uncovered: identifying which use cases will truly create value. How should companies approach this?

[On-screen caption: AI – identifying high-value use cases]

[Fabrice Asvazadourian]

I like what Alexis said: they made bets. We see many companies spending countless hours building Excel business plans. When it comes to AI or generative AI, I am not sure that is the best use of time.

You need convictions, but also the ability to regularly challenge them. And before trying to invent entirely new use cases, we should draw inspiration from what already exists.

Orange’s network operations, marketing and customer relations are well-known domains, and many companies communicate extensively about their AI initiatives. Identifying promising use cases that have already been tested elsewhere builds confidence.

At the start of the year, we had already deployed more than 500 use cases, which we can share with our clients. That does not mean they will work everywhere, but it shows it is possible.

Finally, companies need to be modest and ask whether the conditions for large-scale deployment are already in place. Too often, organisations wait too long before asking whether a successful proof of concept is ready to scale, and that is where things get stuck.

[Frédéric Simottel]

In other words, we need to see whether service quality has improved and assess the ratio between revenue generated and savings achieved. These indicators matter, Alexis.

[Alexis Trichet]

Absolutely. Let me give you a few examples.

The first is a marketing use case, still ongoing today: personalisation. When you log in, if you are an Orange TV customer, what you see is not the same as what I see. The same applies on the website, the app, customer service calls or in-store interactions. Employees have information tailored to each customer’s situation.

This is a classic use case, as Fabrice said, but in 2020 we decided to bet on it. We already had rule-based technology, but we felt it was time to move to state-of-the-art AI.

We built a system that clearly delivers between 5% and 10% additional value, depending on the case. These projects also involve the CFO, whose role is sometimes underestimated in AI initiatives.

**[Infographic: Orange objectives

Revenue → Savings (OPEX, CAPEX) → NPS → Revenue]**

Finance and management control play a crucial role. From the outset, senior management legitimately asked: what return are we getting?

So we invested a lot of effort in measuring value consistently, using sophisticated methods and control groups to ensure the value generated by these systems was real.

**[Infographic: Contribution of AI projects to Orange Group financial results

~€200m generated in 2024

€300m expected in 2025
 Source: Orange Group financial results 2024]**

[Frédéric Simottel]

Another key topic we often discuss is data management. At Orange, there is a major overhaul of your data assets.

[On-screen caption: Reinventing the data estate for AI]

[Alexis Trichet]

Yes, data management is essential. We handle tens of petabytes of data, tens of thousands of gigabytes. It is a massive data estate.

You cannot tackle everything at once. Our rule is simple: start where the priority use cases are. That is what we are doing now.

Structuring, documenting and sometimes discarding data takes time. What we underestimated was the effort required to explain where data is located and to change habits. Change management costs more than expected.

[Frédéric Simottel]

This brings us to data governance, which may sound abstract but is critical. Fabrice?

[Fabrice Asvazadourian]

It is extremely important. Many companies have already made progress with golden sources, data owners and governance frameworks.

Generative AI adds complexity with unstructured data and rapidly evolving technologies. Today, however, the real challenge is investment in data platforms. These investments may be invisible, but they are indispensable.

[Frédéric Simottel]

Has the data governance stage been completed?

[Fabrice Asvazadourian]

Completed? No. But companies like Orange are well equipped and moving fast. The frameworks, ethics and training are in place. Now they need to be fully implemented.

[Frédéric Simottel]

What are the key best practices for scaling AI?

[On-screen caption: Industrialising AI – best practices]

[Alexis Trichet]

The good news is that we are scaling. Across the group, AI use cases generated €200 million in value in 2024, and more than €300 million is expected in 2025.

There are two main dimensions. First, project and change management must be flawless: clear problem definition, strong sponsorship, robust governance, KPIs, and involving end users.

Second, you need the right technical stack to scale under the right conditions. When both are in place, scaling is possible, and that is what we are doing every day.

[Frédéric Simottel]

We do not often talk about social dialogue, but it is crucial when transforming jobs.

[On-screen caption: AI and transformation – social dialogue is key]

[Fabrice Asvazadourian]

Workforce planning needs to integrate AI. The major employment transformations will come around 2030 and beyond, but preparation must start now.

[Frédéric Simottel]

Thank you both. Alexis Trichet, Director of Data and AI at Orange France, and Fabrice Asvazadourian, Managing Director of Sopra Steria Next.

See you very soon for another BFM Business special feature: AI Uncovered, driving the AI revolution.