Industries around the world are entering a new phase of transformation driven by data and artificial intelligence. Rising energy costs, supply chain disruptions, sustainability requirements, and a growing talent gap are forcing manufacturers to rethink how they operate and innovate.
In this conversation for The Bridge, Artefact’s Alexandre Thion de la Chaume, Managing Partner, and Florence Bénézit, Partner and Data & AI Governance Expert, explore the major challenges of AI in industry and manufacturing, and the conditions that need to be met for AI to become a real driver of performance, innovation, and resilience.
Global industry at a turning point
Today’s industrial landscape is highly fragmented. It includes large multinational manufacturers with global production networks, but also thousands of specialized small and medium-sized enterprises operating in niche markets. Despite their differences, these organizations face many of the same structural challenges.
“Industrial players are extremely heterogeneous in terms of size, maturity, and transformation progress,” explains Alexandre. “But they are all confronted with rising costs, supply chain complexity, and growing expectations around sustainability.”
Several trends are putting pressure on industrial value chains:
- Higher energy and raw material costs
- Increasing supply chain volatility
- A shortage of technical and digital talent
- Stronger sustainability and regulatory requirements
At the same time, manufacturers must respond to rising demand for more customized products. This shift toward personalization requires more flexible production systems and better data infrastructure. “Customers increasingly expect products tailored to very specific needs,” notes Florence. “That requires industrial processes capable of adapting quickly, and data plays a central role in enabling that flexibility.”
Modernizing factories and integrating data-driven decision making, therefore, becomes essential not only for competitiveness but also for attracting skilled workers who expect modern digital environments.
AI across the industrial value chain
Artificial intelligence offers powerful tools to address these challenges across the entire industrial value chain. Rather than being limited to production environments, AI can support decision-making in multiple areas of the business. “AI can be used to better predict demand and align the supply chain,” says Alexandre. “It can improve safety in factories, optimize operations, and enhance functions like marketing, legal, or HR.”
Historically, many industrial AI applications focused on areas such as forecasting, quality control, and energy optimization. These use cases remain important and are now widely deployed across many sectors. However, the arrival of generative AI has significantly expanded the scope of possibilities, enabling companies to:
- Accelerate product design and configuration
- Improve safety training and operational knowledge sharing
- Automate complex workflows and documentation
- Enhance B2B sales and proposal management
Florence points out how generative AI could reshape interactions between employees and industrial systems: “In the future, an operator might simply ask a bot for the safety procedures related to a workstation. AI will transform the way we interact with knowledge inside factories.” This evolution has the potential to simplify access to complex technical information while improving productivity and operational safety.
Proven use cases delivering measurable impact
While many AI applications are still emerging, some use cases are already delivering measurable results in industrial environments. One of the most mature applications is predictive maintenance. By analyzing machine data in real time, AI models can anticipate equipment failures and recommend maintenance before breakdowns occur. “Predictive maintenance can reduce maintenance costs and downtime by around 30 percent,” Alexandre notes.
Another high-impact use case involves the automation of operational workflows through AI agents. Industrial business processes often involve multiple data sources, interfaces, and sequential tasks. AI-driven automation can simplify these workflows and significantly reduce processing time. In some cases, companies have achieved reductions of 70 to 75 percent in process duration by combining automation, AI, and improved user interfaces.
AI is also transforming customer support in industrial sectors where products are highly technical, and catalogs may contain thousands of references. Traditionally, customer service teams had to search across numerous documents and databases to answer technical questions. Generative AI now allows operators to query product information through a single interface, enabling faster responses to customers and reduced callback rates, improving both efficiency and customer satisfaction.
Data: The foundation of industrial AI
Despite these opportunities, deploying AI in industry remains challenging. The main obstacle is not the technology itself, but the quality and availability of data. “In many industrial companies, data is fragmented, difficult to access, or poorly structured,” Florence observes. Two types of data are particularly critical:
- Product data, which describes product specifications and configurations
- Operational data, generated by machines, sensors, and factory systems
Product data is often managed through Product Lifecycle Management (PLM) systems, but these platforms are not always fully implemented or standardized across organizations. Factory data presents additional challenges. It is generated in real time, may follow different standards across production sites, and often requires large-scale data collection programs to become usable.
Security considerations add another layer of complexity. Industrial environments operate with a strong safety culture, and any digital system connected to machines must meet strict cybersecurity requirements. “There are people working behind those machines,” Florence emphasizes. “Security must always be the highest priority.”
Governance and trust in AI systems
Beyond data infrastructure, governance is another essential element of successful AI deployment in industry. Industrial organizations typically operate in environments where errors can have significant operational or safety consequences. As a result, they tend to adopt AI cautiously.
Generative AI systems, for example, may occasionally produce incorrect outputs. This may be acceptable in some digital contexts but it requires careful management in industrial settings. Governance frameworks help organizations balance innovation with risk management. This includes:
- Defining clear data quality standards
- Monitoring AI system performance
- Establishing processes to detect and correct errors
Florence also brings up the emerging challenge of agent quality. As companies deploy AI agents to automate processes, they will need mechanisms to evaluate the reliability and accuracy of these systems. “Just as we monitor data quality today, we will soon need to monitor the quality of AI agents,” she states.
Conclusion: Building the industrial AI foundation
Artificial intelligence is rapidly becoming a key driver of transformation across industrial value chains. From predictive maintenance to workflow automation and enhanced customer support, AI is already delivering measurable improvements in productivity and operational efficiency. At the same time, successful deployment requires more than simply adopting new technologies.
Industrial organizations must build strong data foundations, implement robust governance frameworks, and ensure that AI systems are deployed in a secure and controlled environment.
By investing in data quality, governance, and modern digital infrastructure, industrial companies can unlock the full potential of AI and build more resilient, efficient, and innovative value chains for the future.
Watch the original interview in French:
