AI Maturity Audit: A Checklist for Launching Industrial AI Projects

Artificial intelligence is emerging as one of the most promising drivers of modern industry. This belief is so strong in the recruitment sector that between 2019 and 2023, job postings for AI-related executive positions in the metallurgy sector grew by 56%, compared to 13% for all job postings. Its applications are multiplying: predictive maintenance, automated vision-based quality control, intelligent logistics planning, energy optimization of processes, design assistance, and more. In factories as well as in engineering offices, AI has become an essential tool for anyone seeking to improve performance, reliability, and responsiveness. A study shows that the adoption of agent-based AI in factories will increase starting in 2026 (from 6% to 24% of manufacturers), enabling proactive maintenance, real-time optimization of supply chains, and a reduction in unplanned downtime.

Yet, despite this enthusiasm, the reality on the ground remains mixed. Many AI projects never make it past the experimental stage. Some fail as early as the deployment phase, while others fizzle out because teams fail to embrace them. The causes are rarely technical. They have more to do with corporate culture, the quality of available data, work organization, governance, or the clarity of assigned objectives. In other words, AI does not run up against computational limits, but rather limits of maturity.

The concept of an AI maturity audit addresses this observation. It is a structured preliminary assessment process that evaluates an organization’s actual capacity to integrate and leverage artificial intelligence. The audit analyzes various interdependent factors: strategy (alignment with industry priorities), organization (governance and management), personnel (skills and buy-in), the offering (value created in the production chain), technology (quality and interoperability of systems), and finally the environment (regulations, partners, ecosystem).

This approach is similar to a preparation checklist: before committing significant resources, it encourages you to verify that the conditions for success are in place. It provides engineering and management staff with a practical tool for moving from an opportunistic approach to a controlled one.

The AI maturity audit offers several benefits. It promotes a holistic view of the project by bringing technical teams and decision-makers together, and makes it possible to prioritize investments, identify skill gaps, and avoid redundancies across departments. It also serves as a bridge, translating the language of algorithms into metrics that everyone can understand. Finally, it establishes a culture of continuous improvement: maturity is not achieved once and for all; it is built through successive iterations....