AI in manufacturing is not a magic wand

The manufacturing industry is at a crossroads of innovation and tradition. Artificial intelligence (AI) is often presented as a revolutionary force that increases efficiency and reduces costs, but the reality is more nuanced. AI is not a magic wand that automatically leads to FTE reductions and productivity gains. On the contrary, implementing AI requires a thoughtful approach, focusing on supporting existing processes and creating a phased learning path.

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The reality of AI in manufacturing

Many companies in the manufacturing industry operate based on human craftsmanship and interaction. AI is often positioned as a means to replace personnel and fully automate processes. In practice, however, the promises of drastic cost savings and increased efficiency often go unfulfilled. This is because many manufacturing processes are complex, variable and heavily dependent on human expertise.
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Focus on supporting processes

We advise companies not to deploy AI directly in primary production processes, but to start with supporting processes. Consider the following:
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• AI can help monitor machines and predict failures before they occur, reducing downtime.
• Image recognition technology can detect defects that are difficult for the human eye to detect.
• AI can recognize patterns in inventory levels and help optimize the supply chain.
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By implementing AI in these support processes first, the organization can gain experience with the technology and learn how best to utilize it.

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Phased upgrade to primary processes.

After successful deployment in support processes, AI can be gradually introduced into core processes, for example:
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• AI can help analyze production data to identify inefficiencies.
• Smart robots can take over routine tasks, while human operators focus on more complex tasks.
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This step-by-step approach prevents failures and unnecessary investments.
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The future: AI agents and integrated systems

The next evolution is the use of AI agents: intelligent systems that make decisions and coordinate processes independently. Where traditional AI tools focus on specific tasks, AI agents can integrate and dynamically optimize multiple processes.

Again, our advice remains to start small. Start with AI agents in administrative or support roles, such as order processing or predictive analytics. Only when the technology is proven effective can it be expanded to production-related applications.
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A practical and realistic AI strategy

Implementing AI in the manufacturing industry should not be a leap of faith, but a controlled and iterative process. By starting with supporting processes and scaling up incrementally, companies can manage realistic expectations and avoid disappointment. A well-designed AI strategy ensures sustainable innovation and competitive advantage, without losing sight of the human factor.

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