Sudden factory breakdowns have been a problem for centuries. But with agentic artificial intelligence (AI), mobile robots, and immersive tools, factories aren’t waiting for breakdowns to happen anymore. These tools are turning maintenance into a mostly autonomous workflow that predicts issues, schedules interventions and executes them with minimal disruption to production.
For example, at Beko, a machine-learning control system that adjusts parameters in real time has led to a 12.5% material cost savings. A decision-tree model that flags clinching risks from sheet-thickness variation cut defect rates by 66%, while a convolutional-neural-network valve-gate controller analyzed more than 150,000 data points to optimize plastic injection and improve cycle time by 18%. Even cleaning-cycle design moved faster. Advanced machine learning (ML) reduced time to market by 46% and achieved a 99% optimization in cleaning performance. Those gains didn’t happen in isolation; Beko logged 3,160 training hours in six months to upskill operators and engineers, then scaled wins through a global automation program and a plan to establish a lighthouse factory for each product group.
The pharmaceutical industry has also benefitted greatly. For example, AstraZeneca uses AI to simulate how drug-substance properties and process conditions interact with product quality. In parallel, predictive modeling in development cut lead times by 50% and reduced the use of active pharmaceutical ingredients in experiments by 75%, freeing constrained assets and lowering waste. These have cut end-to-end manufacturing lead times from weeks to hours. The same digital tools shorten paperwork. GenAI-human workflows cut time to create some regulatory documents by more than 70%, indirectly improving maintenance by reducing idle time waiting for approvals. As the company targets net-zero, AI tools interrogate life-cycle data to spotlight emissions “hotspots,” so utilities maintenance can retune boilers, compressors, and HVAC systems before inefficiencies harden into higher energy bills.
Chemicals show how predictive maintenance scales across diverse assets. Jubilant Ingrevia connected its plants to an Operational Data Lake, deployed soft sensors, and ran Internet of Things (IoT) based monitoring with predictive analytics. The result: Process variability down 63%, downtime down by more than 50%, and Scope 1 emissions down 20%. The company plans 10–12 new deployments across 50 plants this year and next. A “JUMP” model plant functions as a digital lighthouse to perfect models before rollout, while a Digital Centre of Excellence and “Digital 101” training embed new practices.
Robotics has also fastened the maintenance processes. In Siemens’ Electronics Factory Erlangen, AI-enabled pick-and-place slashed automation costs by up to 90%, while machine-learning-optimized test procedures boosted first-pass yield. The same infrastructure keeps maintenance algorithms trustworthy, detects drift early, and enables quick rollbacks. On the floor, autonomous mobile robots shuttle parts and tools to scheduled jobs while vision-guided robots handle repetitive inspections, torque checks, and lubrication to spec every time. Mount thermal cameras, ultrasonic probes, or hyperspectral sensors on autonomous mobile robots (AMRs) or drones and you get safe, frequent inspections of hot zones, tall racks, and confined spaces that human crews struggle to reach. More inspections mean tighter condition baselines, which mean better predictions and fewer surprises.
Consumer goods and food illustrate how maintenance touches the whole value chain. Mengniu’s AI for procurement and cyclic delivery automated supplier scheduling and vehicle dispatching, lifting inventory turnover by 73% and improving operational efficiency by 8%. That agility feeds back into maintenance because parts arrive when the agent schedules the job and not after a line sits idle. In the lab, neural-network image recognition and reinforcement-learning scheduling replace manual testing, shortening diagnostic loops so assets return to service faster.
As maintenance becomes software-defined, cybersecurity has become more important than ever, relying on signed model artifacts, identity-first access to operational technology (OT), network segmentation, and anomaly detection on sensor baselines. Siemens’ approach, i.e., automated training, deployment, and monitoring, offers a practical template for trust at scale. The same pipelines that boosted first-pass yield also ensure maintenance agents don’t act on stale or compromised models.
The shift from reactive to predictive, and now autonomous, maintenance is rewriting the playbook for industrial operations. With AI, robotics, and secure digital pipelines, factories can not only prevent costly breakdowns but also unlock efficiency, sustainability, and agility across the value chain. From pharmaceuticals to chemicals, consumer goods to electronics, early adopters are already proving the impact with measurable gains in cost savings, uptime, and emissions reductions. The future of maintenance isn’t just about fixing what breaks—it’s about building intelligent, resilient systems that learn, adapt, and evolve in real time. In this reinvention, maintenance becomes more than a support function; it becomes a strategic driver of competitive advantage.