When Will AI Replace Ingénieurs industriels ?

The impact of AI on industrial engineering is phased. By early 2026, routine tasks like data collection and basic time studies will see the first wave of automation. By mid-2028, approximately 55% of all industrial engineering tasks will be automated, driven by AI-powered optimization and simulation platforms.

The early wave, starting as early as 2026, targets junior industrial engineers. Roles focused on data collection, basic time studies, and standard process documentation are most vulnerable to immediate automation by AI tools.

By mid-2028, AI-driven digital twins and machine learning optimization will significantly automate mid-level tasks. This includes routine process analysis, production scheduling, and lean manufacturing calculations, impacting a broader segment of the profession.

Junior industrial engineers heavily involved in data collection, basic time studies, and standard process documentation are most at risk of displacement as early as 2026. AI tools are rapidly automating these foundational tasks.

Experienced professionals with cross-functional leadership, strategic supply chain management expertise, and complex problem-solving skills in unpredictable manufacturing environments are best positioned to extend their careers to 2030 or beyond.

While automation is a concern, experienced engineers focusing on strategic operations design, change management leadership, and AI-augmented decision-making will see their value increase, potentially leading to salary growth.

You have approximately 4 years to transition from routine analysis work to higher-value activities. Focus on developing skills in strategic operations design, change management leadership, and AI-augmented decision-making to stay ahead.

Embrace AI-powered tools. Platforms like Siemens Tecnomatix with AI-enhanced simulation, AnyLogic with machine learning optimization, and Plant Simulation AI-driven digital twins are becoming indispensable. Learn to leverage them.

The shift from manual process analysis to AI-driven optimization requires continuous learning. Invest in upskilling in areas where human expertise complements AI capabilities, such as strategic oversight and complex problem resolution.