AI in Manufacturing: Use Cases from Start-Up Week Aachen

During the 4th Start-Up Week in Aachen (June 30–July 4, 2025), several AI start-ups shared how their solutions are already delivering value across Europe’s manufacturing sector. From predictive maintenance to knowledge management, here are five practical ways manufacturers are applying AI today.

Use Case 1: Democratizing Data Access and Sales Insights

For many manufacturers, especially SMEs, a major barrier to becoming truly data-driven is access. Data is locked in complex systems, requiring IT expertise to extract and analyze.

AI tools using natural-language querying and semantic layers can change this. Essentially, what this means is that anyone in the organization, given user rights to the application, will be able to talk to their database and create dashboard formats within seconds. 

A pump manufacturer’s sales team used an AI-driven interface to ask questions of their sales data in plain language. This enables them to identify upsell opportunities, understand buying patterns over time, and forecast demand more accurately, without waiting for specialist reports.

Felix Beissel, Co-Founder of Scavenger AI, notes: “It takes less time to reach insights when users explore data themselves. This pushes data owners to make datasets more business-oriented, since business colleagues, not just technical teams, query them. So we see explainability as a long-term benefit while speed is immediate.”

This approach democratizes data access, empowering commercial teams to make faster, better-informed decisions.

Use Case 2: Predictive Maintenance and Condition Monitoring

Another high-value application of AI in manufacturing is predicting when assets will fail, turning maintenance from reactive to planned.

In one example, a windmill operator, working with a tech consultancy, trained an AI model on “good state” data to predict part failures. The system provided forecasts down to the day and hour, helping to avoid unplanned downtime.

In another example, in a factory setting, AI-based ultrasonic devices monitored rolling bearings in real-time. By analyzing sound error patterns, the system flagged early maintenance needs, reducing the need for manual inspections and preventing failures.

These predictive approaches reduce downtime, cut costs, and extend asset life, key priorities for operations leaders.

Use Case 3: Improving Supply Chain Transparency and Inventory Management

AI also enhances visibility across disconnected systems, enabling manufacturers to consolidate data and identify trends more quickly.

One example highlights how an AI-enabled interface helped a mid-sized planning and forecasting provider make complex supply chain data instantly searchable and visualizable, without relying on IT. With natural language input, users could generate line charts, identify sales trends, and compare SKU performance across time periods. What once took hours or even days to process now happens in seconds, improving both responsiveness and decision-making across supply and production planning.

“Natural language search brings data to a wider audience and makes it part of daily work,” Beissel explains. “If human understanding takes too long, data just gets deprioritized. It’s critical not just to show data, but to make it more explainable and anchor it in daily interactions.”

Use Case 4: Automating Customer Service and Support

Beyond production, AI is also improving service operations, a key area for manufacturers managing parts sales, warranties, and distributor relationships.

Generative AI can help automate responses to repetitive enquiries, freeing staff to focus on more complex, high-value tasks.

A large transportation operator used generative AI to support customer service teams, which handle thousands of daily queries. Employees could more easily search internal databases for answers, while repetitive questions could be answered automatically.

Bastian Maiworm, Co-Founder and CRO of amberSearch, shares: “When employees can independently access information, it saves time, boosts productivity, and fosters a sense of empowerment, leading to better decision-making and collaboration. This autonomy enhances job satisfaction and drives innovation across teams.”

While this example came from transportation, the same logic can help manufacturers improve response times and consistency in spare parts support or distributor communications.

Use Case 5: Knowledge Management

AI is also proving valuable in the area of knowledge management, particularly for manufacturers dealing with hybrid work, decentralized teams, and increasing volumes of internal data. 

“Manufacturers struggle with fragmented knowledge spread across multiple systems and silos,” says Maiworm. “This challenge is worsened by retiring employees and the loss of institutional knowledge.”

At one mid-sized engineering company, employees were routinely spending several minutes and multiple searches to locate critical information across different systems. To address this, the company implemented an AI-powered enterprise search solution that enabled staff to retrieve documents, chat messages, and multimedia content from a single central interface. The result: smoother coordination, reduced internal queries, and faster onboarding.

Maiworm explains, “AI centralizes knowledge retrieval, enabling employees to find information across systems quickly and intuitively using natural language queries.” He continues, “It improves efficiency, respects access rights, and enhances decision-making by delivering precise, context-aware results.”

As manufacturers continue to digitize, AI-supported search is emerging as a practical solution for enhancing information flow and operational efficiency.

Data Privacy and Security Considerations

While use cases demonstrate real value, there are critical considerations around implementation, especially data privacy, security, and regulatory compliance, that shouldn’t be overlooked. Beyond technical possibilities, it is crucial to address these issues, especially in manufacturing environments that handle sensitive IP and customer data.

For AI projects to succeed, manufacturers need to be clear about:

• Where and how data is stored and processed
• Compliance with local and international regulations (GDPR, EU AI Act, industry standards)
• Ensuring transparency and explainability of AI models

Manufacturers seeking to adopt AI must plan for robust data governance strategies in conjunction with technical implementation.

Change Management and Skills

Just as important to bear in mind is that AI adoption isn’t only a technology project but also a people project.

Successful adoption depends on equipping employees with the skills to use AI tools confidently, ensuring that data and information flow across departments, and managing cultural change while building trust in AI outputs.

Manufacturers considering AI should also plan carefully not just for the technical implementation, but for the human factors as well In a recent report by BCG, they identified that those leading in AI adoption have adopted a 70-20-10 rule: this means that they have actually allocated more of their effort on people and processes (70%) than technology (20%) and algorithm (10%). 

Generating Real-Life AI Value

Across all these examples, one pattern stands out: AI delivers value when it solves real business problems.

Whether it’s providing sales teams with better access to data, predicting maintenance needs with precision, streamlining inventory visibility, or automating customer service, successful AI projects begin with a clear goal and end-user in mind.

For manufacturing leaders exploring AI, the message is simple: solve actual problems that remove friction from everyday work, instead of chasing hype. Focus on practical, high-impact use cases that address genuine operational challenges and deliver measurable results.