Between hype and tangible impact: Where does AI stand in Manufacturing?
It’s Monday morning at a medium-sized machinery manufacturer: A supplier reports a delay on a component. A rush order is pushing its way into the production pipeline. Two other orders are competing for the same machine. An employee reconciles the situation across several Excel spreadsheets, calls the production department, and makes a decision that will already be outdated in two hours. Scenes like these are part of everyday operations at many manufacturing companies. And it is precisely for such challenges that artificial intelligence promises a solution.
AI already ranks as the number one technology in the manufacturing industry, according to a key finding in the Digital Excellence Outlook 2026* by the Handelsblatt Research Institute and valantic. Around 82 percent of survey respondents are convinced that AI will be the most relevant technology trend by 2030.
The potential of AI has been recognized, but priorities remain unclear
Expectations are high. Yet there is a significant gap between ambition and the productive use of AI: Although half of manufacturing companies view AI as a key strategic component, only 41 percent have so far implemented the technology comprehensively and strategically. The majority is experimenting with individual pilot projects in isolated departments.
This reality is also reflected in our conversations with customers and industry decision-makers. The question is no longer “What can AI do?” but rather “Where should we start to ensure that AI really pays off?”
The following assessment summarizes key findings from the study and prioritizes AI applications whose benefits already justify the investment today.
Where is AI most beneficial for the manufacturing industry?
First things first: The priorities of manufacturing companies in the study differ significantly from those of the overall market under examination. The clear focus in the manufacturing sector is on operational and product-centric use cases. Four areas in which the greatest AI potential is emerging:
Why AI maturity is more important than the model
The study’s most significant finding does not concern individual use cases, but rather companies’ AI maturity levels. Depending on how deeply the technology is integrated and embedded within an organization, it can yield very different results.
So far, according to their own assessments, only 30 percent of the manufacturers surveyed report a high level of AI maturity—significantly less than the overall market average of 36 percent. Yet among these few pioneers, the potential of AI is particularly evident:
In about 82 percent of cases, they report time savings, 76 percent report improvements in output quality, and 73 percent report increased efficiency. For companies that remain in the experimental middle ground, these figures drop to about 66 percent (time), 68 percent (quality), and 69 percent (efficiency).
The industry-wide average is dragged down by the large middle group. This is the key insight: Success is determined not by the AI model, but by the organization’s AI readiness. Data, processes, roles, and clear responsibilities must be prepared for AI.
Where hype outpaces utility
Three key findings from the study should also be mentioned in this holistic assessment:
- AI dominates in software and analytics and is also more widespread in manufacturing than in hardware and physical systems. The AI applications in the manufacturing industry that might seem the most obvious and most relevant to profit margins—robotics and smart, autonomous products—rank only third in terms of perceived relevance and even fourth in terms of actual adoption.
- Governance, data management, and integrated control mechanisms are essential for effectively harnessing AI’s potential. 82 percent of the industry leaders surveyed expect that companies investing in ethical, transparent, and well-managed AI will outperform those that focus solely on speed and automation.
- The hype will fade where the benefits are not measurable. 68 percent of respondents expect the AI investment boom to end before 2030 if no business value is evident.
How will the next AI project deliver measurable results?
The gap between expectations and reality is not due to technological limitations. It stems from structural bottlenecks: the depth of integration, prioritization, and decision-making authority within organizations.
Three key takeaways from the study that have also proven effective in real-world AI projects:
- Start small and specific
An analytically prioritized use case with quantifiable results is better than an ambitious large-scale project. Quick wins in the first 30 days build trust and free up budgets for the next steps.
- Data first, model second
A quality-assured, trustworthy data foundation is the decisive factor for success. 78 percent of industrial companies expect data quality to be more important than pure AI model performance by 2030.
- Involve teams
Acceptance comes not from theory, but from participation. Employees become decision architects who steer AI, interpret results, and evaluate proposals, rather than simply maintaining data.
Key takeaway for the manufacturing industry: AI readiness is the deciding factor
AI models are mature, many applications have been tested in real-world use, and success stories from industry pioneers show that the transition from pilot projects to full-scale production has long been feasible. What matters is not chasing the loudest trend, but analyzing and honestly weighing which use case is compatible with the available data, internal resources, and AI expertise—so that it can move beyond the experimental phase into productive, scalable deployment.
*The findings in Digital Excellence Outlook 2026, published by valantic in collaboration with the Handelsblatt Research Institute, are based on a quantitative survey of more than 1,000 executives from companies in the DACH region, including 312 decision-makers from the manufacturing and production sectors, supplemented by in-depth qualitative interviews with C-level executives from international corporations.
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