Manufacturers have made headway against unplanned downtime. But with parts and labor costs rising and infrastructure aging, every minute offline is more expensive. According to the 2025 State of Industrial Maintenance Report, 74% of facilities saw the same or less unplanned downtime last year, only 20% saw downtime costs fall—and 31% saw them rise. With downtime already costing the average organization at least $25,000 per hour (and often far more at larger firms), business‑as‑usual maintenance and reliability is no longer enough.
To regain control over downtime costs, leaders need more than uptime—they need a data‑driven maintenance system that manages equipment, parts, and people together, from the shop floor to the boardroom.
Two AI capabilities matter most in maintenance: real-time insight into machine health, reliability, and lifecycle cost; and standardizing procedures, capturing institutional knowledge, and routing the right work to the right person at the right moment.
Here are five practical ways industrial leaders can use AI to turn maintenance from a cost center into a durable value driver:
1) Capture the data you already generate
Industrial operations generate vast amounts of data, but much of it goes unused. With 70% of manufacturers still relying on manual data capture—often via clipboards — information is easily siloed or overlooked. Unstructured inputs (notes, photos, text, audio) are are especially hard to manage and activate.
AI tools and handheld devices now let you capture digital data directly from frontline workflows. Work orders, notes, photos, inter‑team messages, and even voice memos can be collected, distilled, and organized into structured datasets. Done right, that becomes a living repository of operational intelligence that reflects how machinery and people interact across the organization.
Worker or engineer work with ai with graphic display in factory
getty
2) Turn data into insight
Data capture is only the first step—you also have to use it. Economy-wide, almost three-quarters of enterprise data goes unanalyzed, and many enterprise leaders say most data is, at best, used once and then forgotten about. This is where AI shines: it can turn large, messy pools of information into timely, actionable, insights. Just as importantly, it can connect the dots between hard machine data and technicians’ qualitative observations.
By uniting workflow data and sensor readings into a single insight engine, you can supercharge predictive maintenance. Technicians often notice early signals – sounds, vibrations, even smells – that are hard to quantify. When those cues are linked with metrics like temperature or RPM, you can detect faults before they happen. Executed well,preventive and predictive strategies can cut overall maintenance costs by as much as 9X, while sharply reducing total downtime.
3) Turn insight into action
Insights only matter when they reach the point of work. Today, most data‑driven decisions are made by plant managers or executives, yet only about one-third of industrial organizations put insights into the hands of frontline workers. That must change.
Modern tools make it simple. Instead of trawling spreadsheets, technicians can see likely root causes, relevant documentation, and recommended next steps right inside the work order. They can confirm job plans, auto‑generate checklists, and use intelligent inventory to ensure parts are ready when repairs or upgrades are needed.
4) Close the loop and continually upskill frontline teams
Together, these practices turn hard‑won tribal knowledge—which too often walks out the door when veterans retire—into an enduring asset for every worker. If an expert decides to spend more time fly‑fishing, you don’t lose their insights. Your AI tools will have learned from their work and distilled it into clear, step‑by‑step procedures others can use for years.
Even better, generative AI can deliver those insights at the exact moment they’re needed. A smartphone or tablet becomes a hands‑free mentor: ask in plain English and instantly get the technical guidance, parts information, or know‑how needed to bring equipment back online to quickly get machinery back online.
5) Win the talent market
Embedding AI into frontline workflows raises first‑time‑fix rates, reduces downtime, and makes the work itself more attractive. Digital tools give technicians clear skill paths, help them see the impact of their work, and recognize them as domain experts. As the line between so‑called blue‑ and white‑collar roles narrows, next‑gen workers get what they want: Tech‑forward jobs with mobility, mastery, and meaning. That’s how manufacturers attract and keep scarce talent—on the factory floor and in the field.
A smarter future for industrial teams
Minimizing downtime will always be a priority. But leaders should also focus on reducing the cost of downtime. That means managing parts, processes, and people more effectively—not just eliminating outages—to curb costs and build resilience across the organization.
We won’t get there by sticking with traditional maintenance alone. We need to connect senior leadership, frontline technicians, and real‑world execution with modern AI.
By building intelligent tools that technicians want to use—and designing them around the needs and potential of frontline workers—industrial leaders can create a new playbook for operations. Bring trustworthy, action‑oriented maintenance AI into the mainstream, and you’ll lay the groundwork for a smarter, more resilient future.
