How Industrial AI Adds Real Value – Eight Real-World Applications

AI is rapidly evolving into a crucial driver of competitiveness for German industry. So where does the industry actually stand? According to a recent survey by Bitkom, 42 percent of manufacturing companies in Germany are already using AI in their production processes – and another 35% have relevant projects in the pipeline [1].

A comparable trend is highlighted in a recent study by the VDMA, which examines the use of generative AI in the mechanical and plant engineering sector across the DACH region. The findings show that 79% of companies are either already using generative AI or have concrete plans to implement it, with 89% seeing it as vital to future profitability. [2].

Eight Leading Applications for Industrial AI

Many AI initiatives still fall short due to a lack of focus on high-impact scenarios. According to Bitkom, 42 percent of industrial companies report that they lack the necessary expertise to effectively integrate AI into existing processes.  Around half are taking a wait-and-see approach, observing how other companies implement AI — a clear sign of uncertainty and a lack of confidence when it comes to real-world application. Industrial AI, however, can be applied anywhere data flows, decisions are made, and processes interact — essentially throughout the entire industrial value chain. The following top 8 use cases demonstrate where companies are already generating tangible economic benefits through targeted AI use, as well as where the key levers for future value creation lie.

1. Improving Data Quality and Insights: A clean, consistent data foundation is essential for any AI application. AI technologies identify and cleanse erroneous, duplicate, or incomplete datasets, regardless of whether they are structured or unstructured. On this basis, analysis and visualization tools provide deep insights into the data landscape. Patterns, anomalies, and deficiencies become visible in real time, enhancing transparency and supporting well-informed decisions across departments.

2. Inventory Optimization and Material Planning: AI-powered systems analyze historical consumption data and identify seasonal trends as well as demand fluctuations.  This enables better planning of replenishment cycles and order quantities, reducing both excess inventory and shortages.  The result: lower storage costs, improved supply reliability, and enhanced liquidity. emz Hanauer, for example, uses the Proalpha Industrial AI Platform to reduce excessive inventories [3]. Over 1,000 parts were analyzed, consumption patterns identified, and the optimal ordering point calculated — resulting in measurable improvements in capital commitment and supply chain security [4].

3. Production Optimization: In manufacturing, Industrial AI detects inefficient processes and bottlenecks early on. By analyzing machine data and utilization rates, cycle times can be reduced and resource utilization improved. AI-powered dashboards consolidate relevant production data, enabling employees to respond proactively — resulting in greater flexibility, reduced downtime, and higher product quality.

4. Delivery Performance: The foundation of a stable supply chain is its predictability. Industrial AI helps to detect disruptions along the supply chain early and initiate proactive measures.  The systems analyze real-time data from logistics, procurement, and partner networks, supporting capacity planning. This improves delivery reliability, reduces delays, and strengthens the overall resilience of the supply chain.

5. Dynamic Supply Chain Monitoring: AI analyzes not only internal data in real time but also unstructured external information — such as news feeds, weather data, or social media.  This enables early detection of demand fluctuations, transportation issues, or geopolitical risks.  Practical recommendations can then be automatically integrated into planning processes.

6. Predictive Maintenance: Industrial AI can detect early signs of wear or failure based on machine data such as temperature readings and operating hours. This enables condition-based and efficient maintenance planning — minimizing downtime and unplanned standstills while extending the lifespan of equipment. Especially in manufacturing, this is a key factor in maintaining high productivity.

7. Analyzing the Carbon Footprint: Industrial AI enables real-time analysis and control of environmental impacts along the value chain — for example, by evaluating energy and resource data. This allows companies to visualize emissions, identify saving potentials, and make informed sustainability decisions. These include data-driven carbon footprint calculations, detection of major energy consumers, and optimization of individual process steps — from regulatory compliance to improved external perception.

8. Intelligent Customer Service: Standard inquiries such as returns or delivery status can be handled automatically, while Natural Language Processing (NLP) understands, categorizes, and routes customer requests to the appropriate departments. This reduces processing time and increases accuracy. In addition, AI enables personalized recommendations and proactive service that anticipates customer needs early — a clear competitive advantage in service-driven markets.

"The latest studies by Bitkom and VDMA make it clear: Only those who move beyond initial pilot projects and apply AI in areas where it creates real value will benefit in the long run,” says Christoph Kull, President Business Applications at Proalpha. “Industrial AI offers exactly that: it connects data intelligence with operational excellence — from the supply chain and production to sustainability. The use cases presented here show how companies can already become more productive, resilient, and future-ready today. Progress requires action — and that means integration, prioritization, and proof of benefit."

[1] www.bitkom.org/Presse/Presseinformation/Industrie-4.0-Unternehmen-KIProduktion
[2] www.vdma.org/documents/34570/4888559/Studie_GenAI-Implications_Web_DE.pdf/e56b0f7c-f0b8-2026-1ce3-40b8b4f88657?t=1743007089675?filename=Studie_GenAI-Implications_Web_DE.pdf
[3] https://www.proalpha.com/en/ai-hub
[4] www.nemo-ai.com/anwenderbericht/emz-hanauer