Our Perspective: AI Is an Important New Building Block, but Not a Silver Bullet.

There is intense cost pressure across both manufacturing and administrative functions. AI can certainly help by providing automation, advanced analytics, and improved forecasting capabilities. However, our consulting experience at Bronk & Company shows that traditional approaches, such as lean management, process discipline, and organizational simplification, often lead to greater short-term efficiency improvements. These established methods also create the conditions necessary for successful AI adoption by standardizing processes, increasing transparency, and improving data quality.

The same principle applies to the flexibility that companies urgently need. Data-driven and AI-enabled solutions support more agile decision-making and execution. However, they only realize their full impact when embedded within robust management systems that include clear governance structures, transparent performance metrics, and well-defined responsibilities. AI does not replace these proven management tools; rather, it builds upon them and enhances their capabilities.

At the same time, fundamental strategic issues are growing in importance. Companies must deliberately choose which customer segments to serve, which markets to operate in, and which industries to focus on in the future. They should also consider broader diversification, including opportunities beyond their traditional core business, as a potential lever for growth and resilience.

In parallel, active strategic risk management is becoming increasingly important. This involves reducing dependencies on specific customers, industries, and regions while proactively managing supply, pricing, and procurement risks. These challenges require clear strategic direction, consistent decision-making, and disciplined execution throughout the organization. AI can support this process by providing advanced analytics, forecasting, and scenario modeling. However, management remains responsible for setting priorities and ensuring implementation of actions.

One frequently underestimated prerequisite for successful AI deployment is: AI requires end-to-end digital processes and reliable, high-quality data. Across the metals industry, significant gaps in this area still exist. Manual records, incomplete datasets, and poor data quality are common and do not provide a sufficient foundation for meaningful AI applications. Furthermore, AI implementation requires a certain level of organizational maturity. Companies must be able to systematically develop digital solutions, integrate them into existing processes, embed them in day-to-day operations, and continuously improve them over time. Many industrial companies, including those outside the metals sector, lack this level of maturity. Reaching this stage requires technological capabilities, as well as cultural and organizational change. Employee acceptance is particularly critical. AI must be perceived as a tool that supports employees rather than being a threat to their jobs.

Europe cannot afford to fall behind in AI development – that is beyond dispute. However, an excessive focus on AI could divert attention away from the industry’s fundamental structural challenges. Companies that rely solely on AI without simultaneously addressing capacity adjustments, reducing complexity, increasing transparency, standardizing processes, and making clear strategic choices will not solve underlying problems.

The key takeaway is therefore straightforward: AI can help, but it will not save the European metals industry on its own. Long-term success depends on decisive action to address structural challenges rather than on the hope that technology will provide a shortcut.