Artificial Intelligence in manufacturing

There was a moment, around 2020, when artificial intelligence in manufacturing was considered an interesting opportunity for large companies with dedicated budgets. Today, in 2025, that window has closed: AI is no longer a competitive advantage, it is a prerequisite for survival.

The three phases of AI adoption in manufacturing

The first phase (2018-2022) was the pilot phase: small predictive maintenance or computer vision projects, often never scaled. The second phase (2022-2024) saw the spread of AI-as-a-service platforms. The third phase, the current one, is normalization: AI is integrated into processes the way PLC automation was in the 1990s.

The applications that make the difference

Predictive maintenance: downtime reduction of up to 50%, ROI in 6-18 months. AI quality control: 99%+ accuracy, 5,000+ inspections/hour. AI energy monitoring: -12-20% in consumption. Demand forecasting: ±5% accuracy versus ±15-20% for traditional methods.

77% of manufacturers have introduced AI solutions, but only 39% see concrete results. The gap is not technological: it is methodological and organizational.

How to make the difference

Key4 starts from the process, not the technology. First, the production process is analyzed, bottlenecks are identified, and the improvement potential is quantified. Then the appropriate AI solution is chosen and implemented with an iterative approach. The result is measurable within 90 days.