Manufacturers are under increasing pressure to improve their efficiency, resilience and responsiveness in the face of ongoing supply chain volatility and increasing operational complexity. Against this backdrop, AI at production scale is positioned as an operational necessity.
It is estimated that 94% of manufacturers plan to increase their investment in AI throughout 2026, expecting a return of $2.86 for each invested. It is evident that priorities have evolved from experimentation to production execution.
At Hannover Messe 2026, Lenovo, in collaboration with NVIDIA, will demonstrate how manufacturers can close the gap by deploying proven AI solutions at scale in their own global manufacturing operations, delivering measurable improvements in delivery times, costs, quality and productivity.
«Manufacturers do not need more AI pilots. “What they are looking for are AI solutions that can run at scale in production environments,” said Jonathan Wu, head of smart manufacturing technology at Lenovo. “At Lenovo, we have already deployed these systems in our own global manufacturing and production operations, achieving significant improvements in timelines, costs and productivity. At our largest facilities in North America, delivery times were reduced by 85%, logistics costs by 42%, and productivity increased by 58% by deploying solutions enabled by traditional and generative AI. “This is the experience we bring to our clients.”
Improve quality and performance through AI in connected production systems
Improving quality in manufacturing is no longer just about isolated inspection points, but about connected data and decision making across the entire production system.
Lenovo applies AI in production environments to enable real-time detection, rapid root cause analysis, and continuous improvement. By combining computer vision, edge AI, and digital twins, manufacturers can identify defects as they occur, reducing variability and responding immediately to any issues before they can impact other operations in the production chain. These capabilities extend beyond individual production lines, connecting quality insights with material flow, equipment performance, and initial inputs to generate a more adaptive and resilient manufacturing system.
In its facilities in Brazil, Hungary and Mexico, Lenovo has deployed its Automatic Quality Inspection Robotic Cell, obtaining measurable improvements in quality, consistency and production efficiency.
Maintain production flow through autonomous internal logistics
Production performance depends largely not only on what happens on the lines, but also on the effectiveness of the flow of materials as they transit through the factory.
Lenovo’s multipurpose robots facilitate real-time, adaptive automation in workflows such as line supply, order preparation (picking), component grouping (kitting) and movement of materials between production stages.
By improving material flow and reducing reliance on manual processes, manufacturers can maintain more stable production, increase the overall effectiveness of their equipment and better align their operations with changes in demand, while also optimizing production continuity.
Strengthen the resilience of supply chains
Leveraging its experience deploying AI in manufacturing and production environments, Lenovo is also applying these capabilities in broader operational ecosystems, from supply chain coordination to real-time systems monitoring.
Connected supply chains through Lenovo iChain. Lenovo iChain connects suppliers, logistics partners and manufacturing operations through secure, real-time data sharing. This improves coordination between material sourcing and production planning, increases visibility across multi-tier supply chains, and helps manufacturers respond more effectively to fluctuations in demand. Lenovo’s leadership in supply chain operations has been recognized in the Gartner Supply Chain Top 25 ranking of 2025, in which the company has placed eighth.
Operations monitoring powered by AI. To improve the way issues are identified and resolved, Lenovo offers AI-powered monitoring solutions to maintain stable production environments and reduce the risk of unplanned outages. Electronics manufacturer Hisense has implemented this technology in its operating environments to improve system visibility and response times.
Scale AI to production through comprehensive and proven execution
Most AI initiatives in manufacturing stall before reaching the production phase. This is not due to a lack of tools, but because those tools have not been designed or tested for operation in real, highly complex production environments.
Lenovo bridges this gap by offering AI solutions that are already being applied at scale in its own global manufacturing and production operations. This experience translates into faster deployment, reduced execution risk, and measurable business impact from day one in production.
Lenovo’s Hybrid AI Advantage platform combines infrastructure, data, models and services in a single integrated environment that spans the edge, cloud and on-premises systems. Even more importantly, this technology has been designed for real-world conditions, allowing manufacturers to move from pilot testing to production environments with greater speed, confidence and control.
Validation before deployment: Lenovo ThinkStation PGX powered by the NVIDIA GB10 Grace Blackwell superchip offers secure testing and simulation capabilities like NVIDIA Isaac Sim, ideal for training and validating robotic systems before deployment, improving the accuracy and reliability of autonomous machines, and simulating complex industrial workflows to accelerate automation and production projects.
By combining computer vision, edge AI, and digital twins, manufacturers can identify defects as they occur
Deploy at the point of action: Lenovo ThinkEdge can drive use cases such as visual inspection, predictive maintenance, and autonomous systems. Processing data and running AI models at the point of action facilitates real-time decision making, boosting production efficiency, while allowing data to be stored locally to meet regulatory and sovereignty requirements.
