Industrial AI: A Strategic Challenge to Avoid Failure

AI: A Major Industrial Turning Point

Artificial intelligence (AI) is emerging as the new great industrial revolution, succeeding mechanization, electrification, and automation. Companies are placing high hopes on AI to transform data analysis, intelligent machine control, and automated quality inspection. Intelligent automation could even enhance the competitiveness of production sites in developed economies.

However, the tangible benefits of AI in business are increasingly being questioned. After a phase of enthusiasm, AI seems to be approaching the "valley of disillusionment." A study from MIT indicates that 95% of AI projects fail, generating no return on investment despite significant investments.

AI Adoption: A Challenge for Industry

MIT has analyzed the relationship between adoption and disruption in AI projects, ranking different sectors on a scale from 0 to 5. Media and telecommunications score 2 out of 5, while the industrial sector only reaches 0.5 points. This reflects the complexity inherent in industrial environments, where the integration of AI faces much stronger constraints than in other sectors.

In other sectors like software development, media, and telecommunications, standardized AI tools available today already deliver impressive results. However, in industry, the reality is quite different. Production sites are often unique, built over several decades, and are now at the intersection of IT and OT. Internal processes have adapted to this complexity, making the adoption of generic AI solutions largely ineffective.

The use of general-purpose AI tools by support functions does not change the operation of production lines. The integration of AI is often confused with the adoption of such tools. While this logic can create value in certain sectors, it remains largely disconnected from industrial realities. The main challenge is therefore to integrate AI into deeply rooted individual processes to create real value.

Strategies for Successful AI Integration

Governance and Transparency

Successful AI projects always begin with clear guidelines, transparent decision-making processes, and early consideration of compliance issues. In industry, AI models directly influence physical processes such as equipment availability, product quality, and workplace safety. Clearly defined responsibilities, explainable models, data traceability, and compliance with regulatory and normative requirements are essential.

Economic Operationalization

Many AI applications in industry start as promising proof of concepts. However, value only becomes apparent when these solutions operate reliably, maintainably, and reproducibly in the production environment. This requires robust IT/OT architectures, standardized interfaces with industrial protocols and ERPs, as well as clear processes for operating, maintaining, and evolving models.

Intelligent Automation

The integration of AI does not aim for maximum automation at all costs, but rather an intelligent distribution of roles. It provides precise analyses, pattern recognition, and real-time forecasting, while employees leverage their experience, process knowledge, and judgment. This complementarity allows for increased equipment availability, reduced waste, and sustainably improved operational efficiency.

Training and Employee Involvement

In software companies, enthusiasm for AI is naturally more pronounced than in industry. This is why raising awareness and involving teams is essential. Employees must understand how AI systems work, the decisions they support, and their limitations.

The "Human + AI" approach is therefore central: it is about empowering teams to interpret AI results, critically evaluate them, and integrate them into their daily decisions. This requires targeted training, transparent change management, and a culture that views technology as a lever rather than a constraint.

Scalability and Adaptability

To achieve quick results, AI projects often start with targeted use cases chosen for their immediate productivity gains. However, once validated, their extension to other areas remains complex, as conditions vary widely from one process or site to another.

It is therefore essential to define a long-term roadmap from the outset, incorporating scaling challenges as well as the evolution of technologies and processes.

Even though integrating AI into production environments generally requires more effort than in other sectors, companies have no choice but to persevere. Because in the long run, it will be difficult, if not impossible, to remain competitive without intelligent automation. For its adoption to be strategic, successful, and sustainable, AI in industry must be structured and gradual—a process that ultimately proves to be sustainably rewarding.