There is no shortage of content about what AI could do for business. What is harder to find are concrete examples of what has actually been done with AI. Organizations in manufacturing, logistics, and e-commerce make daily decisions that drive cost, service levels, and revenue. We have helped them improve those decisions with AI solutions. Here are three use cases from that work, each with a problem, an approach, and what it produced.
Industrial AI agents cut downtime and improve maintenance decisions
Production lines generate data continuously. In practice, it sits in silos: SCADA systems, maintenance logs, PDF manuals, and shift handover notes. When a line stops, finding the root cause takes hours or days. That time is a direct cost.
AI and AI agents bring this scattered information together. They interpret process manuals, maintenance logs, and operational data in combination, and give operators a concrete answer in their own language.
For an industrial client, we combined a generative AI model with a machine learning model trained on maintenance logs. Operators received a plain-language explanation of what caused the downtime and what the downstream effects were without needing a data specialist in the loop. These critical component inspections previously took weeks but with the AI agent now take days.
What was achieved with AI:
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Downtime decreases.
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Maintenance shifts from schedule-based to need-based.
- Overall equipment effectiveness is improved as AI identifies the problems before they stop the line.
Read how AI agents work in manufacturing
AI and digital twins optimize routing and planning across logistics networks
A logistics network spanning multiple countries means dozens of daily decisions on routes, transport modes, inventory levels and capacity. What is the right route next week? Where is the real bottleneck? What happens if demand grows 15 % next month? Answering those questions traditionally takes weeks of analysis.
With AI and digital twins, the entire network is modeled in real time. Digital twins automate routine decisions and make scenario analysis possible in minutes, such as the cost impact of adding a route, driver requirements for next year and production planning optimized against energy prices.
What was achieved with AI:
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Planners’ time shifted from routine decisions to the ones that require human judgement.
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Globally optimized routing directly reduced operating costs and improved delivery reliability.
Read how digital twins optimize logistics
AI agents in e-commerce improve search and inventory planning
The marketing budget goes into advertising, traffic follows, but somewhere between arriving and buying the customer disappears. They searched, could not find what they needed, and the cart stayed empty. This is a pattern many online retailers recognize.
The problem is often search. A customer types ”warm winter jacket that fits over a suit” and a traditional search engine returns every winter coat or nothing at all, while an AI-powered search understands context, intent and natural language and thus returns results that match what the customer needs.
The same capability applies to the data retailers already have. Customer reviews, product images, and return data are unstructured data that carry commercial signals that most retailers leave unused. The solutions we build are purpose-trained AI agents that connect this unstructured data with structured data from their existing systems, such as CRM and finance systems. One example is a retailer that combines sales data, customer reviews, and trend signals to decide what to stock for the next three to six months.
What was achieved with AI:
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The right products are available when customers look for them, informed by trend signals, reviews, and sales data.
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Marketing spend goes further because targeting improves and the product range is built on what customers need.
Read how agentic commerce is reshaping e-commerce
In all three cases, the data existed but sat in separate systems with nothing to connect it. AI agents made that connection between them. With help from AI agents’ , downtime shortened, routing sharpened and customers found what they needed. What in your business is still waiting for that connection?
