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AI and automation offer unprecedented solutions for boosting business performance. A race to adopt these technologies is underway in the supply chain—as in virtually every other field—amid full awareness of the risks posed by uncontrolled, large-scale deployment to the world of work as we know it.

While our companies are fighting to keep growing despite repeated crises, people are worried about the future of their jobs and their children.

Two issues that may seem contradictory at first glance but can be addressed through new technologies and digital solutions.

Here is a preview of one of the two panel discussions on the agenda for the upcoming RISC 2026, courtesy of our main sponsor: GENERIX.

Reducing physical strain helps protect workers’ health. Reducing the mental strain of repetitive tasks and boredom helps protect their well-being at work.

Here, technology enhances human capabilities by relieving people of certain tedious tasks.

Let’s take the example of Computer Vision , which enables continuous monitoring of the integrity and compliance of incoming pallets.

This task is so tedious, and the resulting quality so poor, that it is sometimes eliminated altogether, leading to gaps in traceability and associated costs. While AI handles the tedious part of the inspection and identifies errors, humans focus on resolving them. This is a far more satisfying and motivating role.

Well-designed digital tools make the rules of the game clearer: transparent objectives, shared standards, and metrics that are understood and accepted. They enable employees to understand the impact of their actions, measure their contribution, and make progress. Technology then becomes a driver of engagement, rather than a tool of control that is imposed on them.

Here, technology enhances human potential by giving meaning to people’s work.

Let’s take the example of performance analysis and activity reports. With AI, the role of the manager is transforming. Today, managers are essentially Excel experts who spend a significant portion of their time chasing down information and processing it to present it in a clear and coherent way. With AI, they become the architects of an efficient and fast information system. They connect more information sources, streamline and standardize input data, and build reports that are more readable and robust—which enhances their value. They save time and can better focus on identifying new performance drivers. They make the results that inform their decisions more transparent and strengthen their oversight, leading to greater engagement among their teams.

Example of how generative AI can be used to support maintenance technicians (best practices guide from the Digital Techno LAB)

Gain insights from a wider range of data and information from various sources, both internal and external.
Be able to simulate numerous scenarios to compare results by varying assumptions at breakneck speed. Decision support is a highly valued application of AI in supply chain operations, particularly in execution, which must constantly adapt to all kinds of unforeseen events.

Which orders should I prioritize when I don’t have time to process the entire order book? How will this delay affect my schedule tomorrow?

Do I need to hire additional staff? How much will that cost? Who can I call on at the last minute? How can I get started quickly?

With specialized AI agents or models that are well-fed with data, even a junior team leader can fully control their schedule and budget. They can ensure the best work environment and level of engagement among their team members.

Here, technology enhances human capabilities by helping people make better decisions more quickly.

With the advent of technology, the role of the manager is evolving. It is no longer just about “getting things done,” but about “fostering growth”: developing versatility, supporting skill development, and safeguarding critical human know-how and expertise. Managers are learning to assign tasks to different types of resources (technological or human), creating mixed teams and integrating AI agents as members of their teams. Of course, they are training themselves and their teams in “prompt” usage.

Here, technology enhances human capabilities by helping people manage more effectively.

Human review and approval required

An AI system that is well-designed, well-trained, well-fed, and well-monitored should make very few mistakes. In supply chain management—and often in other fields as well—most AI systems do not yet have the necessary conditions to solve complex problems completely on their own.

To address this challenge, Breton Yann LeCun has announcedAdvanced Machine Intelligence ( AMI) Labs, which is capable of understanding the “real world”—that is, the context of a problem and its environment—thus overcoming the limitations of current AI models, which merely manipulate language.

Understanding AI Results

Understanding the decision-making process remains essential when entrusting AI with certain tasks that carry high risks to human health, for example. We want to understand how and why an AI system arrives at a specific result. However, Large Language Models ( LLMs ), Deep Learning (deep neural networks), complex Machine Learning, and quantum computing remain black boxes. At best, they provide a confidence score or warn of limitations regarding the results they deliver. Humans therefore remain in charge, and AI serves as a tool and an aid.

Set decision-making priorities

AI generates results based on a specific request and context. For example, when conducting decision simulations, a human will guide the AI based on their preference between economic performance, service quality, and environmental impact. Or between robustness and hyper-optimization in an uncertain context. This precise framework ensures the relevance of the AI’s responses.

A supply chain powered by augmented human capabilities promises to be more responsive, more profitable, and more sustainable. It puts people back at the heart of the system’s intelligence and embraces technology while adhering to humanistic goals that ensure the long-term viability of the solutions implemented.

To learn more about the full program

"I'm not a data specialist, it's not my problem," "There are so many tools, I don't know where to find my customer data," "Trust is one of our company's values, we don't need a complicated authorization system." Often heard in companies, these phrases reflect a reality: data is widely discussed, but its management often remains scattered. We therefore decided, together with our partner Citwell, to dedicate a webinar to this topic , as it can be costly for a company. Here is a summary of this live event, which will help you understand the pillars of this essential business organization through real-life examples and numerous expert recommendations.

Roles and responsibilities

Who is responsible for what?

During this webinar, Saâd Kadioui, Partner/Head of IS Transformation at Citwell, emphasized thatthe business is primarily responsible for data quality. He warns organizations that dilute this responsibility by entrusting it to a data entry department that adds no value: "The real impact of this type of organization is that itdilutes responsibility for data quality. The business doesn't feel responsible because it's not the one entering the data, and the department that enters the data isn't responsible either because it's not the one providing the data."

To structure governance, he introduced key roles: the Data Owner, a business person responsible for the content and quality of data, and the Data Steward, who oversees the creation and modification processes. The whole process is orchestrated by a Chief Data Officer.

Illustration of an organization structured around data

Olivier Weiss, S&OP Business Development Manager at Renault, illustrated this point with the example of vehicle coding at Renault Trucks, where the coexistence of old and new codes can lead to errors if the business does not ensure reliability. "In the current 7-digit coding system, each digit is meaningful and allows us to identify the type of silhouette, the type of engine, the type of emission standard, etc. However, some old model codes still exist, and if we use the new coding system with these old model codes, we end up with incorrect data." Responsibility or proximity to the business is essential here because it is the business that will be able to say , "Watch out, we have a potential discrepancy here."

Data mapping

Where can the data be found and who owns it?

Given the multitude of tools available (ERP, PLM, etc.),there is a clear need for a"data GPS ." Saad explained that there is no such thing as a universal ideal urbanization (data mapping) solution, but rather one that is tailored to the context and uses of each company. The key is to define where data should be created, modified, and stored, following a strict master-slave relationship (a single master for modification, multiple slaves for consultation) in order to guarantee integrity.

Remember that in urbanization, you first need to think about uses and the complexity of use. "For example, if I am in a company that produces standard machined parts, with little documentation and extremely slow product life cycles, then I may not need PLM (Product Lifestyle Management), and perhaps an ERP workflow would suffice for the new products I create."

Data security

What is the level of confidentiality of my data and how can I protect it?

The speakers addressed the need to secure access to data. Saad advocated finding a pragmatic balance in access management so as not to create an overly complex system, while highlighting the immense risks (data leaks, loss of integrity, GDPR non-compliance) of lax management, particularly during project go-lives. "What will really push us to be pragmatic here is the level of maintainability and confidentiality." 

Olivier shared an example in which cross-referencing a chassis number with geolocation data borders on the concept of personal data: "All vehicles are connected, we receive information on journeys and registrations, it's an important source of business. Couldn't I identify the driver? Sensitive data can be hidden at levels we don't necessarily suspect."

Data management process

Which data is mandatory versus optional?

To counter the use of parallel Excel files due to a lack of confidence in the systems, it is crucial to manage the data lifecycle (creation, life, death) through robust and documented processes. Saad emphasized "frugality": structuring and maintaining only those data whose value to the business exceeds the cost of management.

To do this, it introduced a distinction between a priori controls (blocking, to guarantee quality) and a posteriori controls (non-blocking, for fluidity). The application of these controls depends on the criticality of the data, which is classified into four groups: critical data, operational performance data, reporting data, and strategic management data.

What about performance?

Monitoring and ensuring data governance performance revolves around two main areas:

On the one hand, the initiative aims to guarantee the quality, speed of availability, and accuracy of data from the moment it is first produced. The associated key performance indicators (KPIs) are time to data (the average time between the request and the availability of data) and first shot accuracy (the rate of data that is correct from the first version), reflecting the efficiency of processes and their ability to avoid subsequent corrections.

On the other hand, content responsibility is based on five essential dimensions, managed by data owners:integrity (consistency and existence of values), completeness (exhaustiveness of records), relevance (adequacy of values to business rules), freshness (regular updating of data), anduniqueness (absence of duplicates). These dimensions ensure that data is reliable and usable.

Q&A with the Audience

The last part is devoted to questions from participants, which focus on very concrete implementation issues.

The data governance is a powerful lever for improving performance, securing operations and preparing for the future from Supply Chains (AI, automation, compliance, etc.). To reach this level of maturity progressive digitizationof the Supply Chain is essential. Discover in this article the work of the members of LAB Digital et Technologies who who have been working on the subject since 2024, to provide accessible tools even with limited and expertise.

The story began a year and a half ago, with the design of a "digital maturity grid". This resource enables us to define a company's transformation potential a company's transformation progressin complete autonomy (link at end of article).

The major finding of the discussions and design of this 1st guide was: no maximum digital maturity, no logical Event Driven logic (i.e. an augmented operator and a system that acts) without complete complete mastery of its data.

The "maturity grid" squad was transformed in 2025 into a new squad with a focus data governance.

This new group of members is now publishing its practical guide. 

Guide reserved for association members

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Saad KADIOUI

Partner / Head of IS Transformation CITWELL

"Data quality is key to supply chain efficiency chainData quality is key to supply chain efficiency, but I find that many of my customers have poor control over it. This is precisely where governance plays a central role: although it is often neglected as being restrictive and not very visible in the short term, it is the essential lever for this control. Without it, it's impossible to guarantee reliable data, and the the company misses out on the real opportunities for excellence provided by new data technologies (data science, data platform, AI, etc.).. "

Philippe GOURBEYRE

Supply Chain Owner - Michelin

"Faced with the rapid proliferation of data, the need for responsiveness in decision making, compliance in an increasingly large ecosystem, and opportunities with new technologies (Artificial Intelligence, Machine Learning, ...), Data transformation requires well-defined roles and responsibilities. Setting up governance will enable everyone in the company to understand their role in the data lifecycle and assume responsibility for it."

The 2 publications follow the same same guiding principle: to aim for operational practices that are easy to implement. With these tools, the members demonstrate that digitizing the supply chain is accessible even with limited resources, as it is a gradual process (we have included a glossary for the uninitiated).

Two guides offering a pragmatic, step-by-step approach step-by-step, pragmatic approach to structuring yourto structure your practices around pillars.

The 3 pillars of Supply Chain digitalization :

When we talk about alignment with the field, we're referring first and foremost to tests and implementations of the grid carried out by other squads and members on topics such as S&OP, data governance for CO2, bonded warehousing or AI GEN.

In the context of data governance, recommendations can seem very theoretical and rigid, but here we remind you of the importance of taking into account the reality on the ground. This guide and its pillars must be adapted to your reality.

Being concrete and actionable has always been a priority for LAB. This is also reflected in the availability of techniques and methods methods (diagnostic tables, concrete examples, diagrams) to facilitate implementation in your context.

Let's talk to the guide's editors to understand the importance of this project for organizations.

"In Business Development activities within the S&OP department at Renault Trucks, being able to rely on quality data, i.e. data in which there is a high level of confidence, is essential. This is all the more true where the concept of "Citizen Development" is concerned (i.e. user departments develop their own tools on the basis of the data models made available to them). 

Raw or, more often than not, transformed, data is the indispensable cement in the management of our supply chain , and in the decision-making process. That's why data governance processes, which are increasingly solid, structured and documented, are becoming more and more important, enabling us to make better use of our data on a daily basis. 

"When we think of customs, the first thing that comes to mind isn't necessarily data. However, we are also impacted by digitization, which is changing our practices and professions, and this is only the beginning. We are at the dawn of a data-driven revolution, which is redefining not only our tools, but also the interactions between public and private players. That's why I wanted to take part in this work. 

In the customs field, this transformation is accelerating thanks to the transposition of the Union Customs Code (UCC) into data and the imminent arrival of the European Data Hub. These developments mark a strategic turning point: data is becoming an essential lever for performance, compliance and anticipation.  

For customs authorities, the challenges are many and varied. Firstly, to intelligently target goods flows by detecting anomalies and fraud risks, such as undervaluation or inconsistencies in declarations. Improving service performance, by providing an overview of an operator's exchanges. And combat illicit trafficking by exploiting massive data with the help of artificial intelligence. 

For economic operators, data is also becoming a strategic tool. It enables them to anticipate protectionist measures and adjust sourcing strategies. It facilitates the integration of customs regulations and associated costs into the supplier selection process. And it helps secure supply chains and optimize international flows.  

It's always a great pleasure to share and exchange ideas on key subjects, and I hope that this collaborative work will be of use to other professions." 

The adventure continues - contact us to find out more!