Six Steps to an AI Maintenance Strategy That Actually Works

AI Strategy for Maintenance: A Practical Playbook for Executives

There's pressure to "have an AI strategy," but few teams are confident in theirs. A 2025 study reported that just 1% of executives describe their AI rollouts as mature, and only a quarter have a clear roadmap. That gap is your opening.

Maintenance is where AI can pay for itself fast-less downtime, smarter parts spend, safer operations. Below is a simple, six-step framework your operations and maintenance leaders can execute without stalling out.

Source: McKinsey's State of AI (2025)

Six steps that turn AI from theory into uptime

1) Pick the right use case

Don't start with a tool. Start with a problem big enough to matter and small enough to win.

• What problems need solving? Identify the top three drains on cost, uptime, or safety. Think chronic unplanned downtime, PM overload, stockouts, or compliance misses.
• What reliable data can you access? Use data you already trust-CMMS work orders, PM history, parts transactions, failure codes. Clean it, structure it, and ship.
• What workflows can AI slot into? Improve how people already work: suggest PM intervals during planning, flag risky assets in the daily review, auto-draft work orders for supervisor approval.

2) Connect AI to business outcomes

Frame AI as a fix for a costly status quo. Show the leak; then show the patch.

• Executive objectives: Reduce unplanned downtime by 15%, cut parts working capital by 10%, lift PM compliance to 95%, improve OEE by 3 points.
• Cost-of-failure view: Prioritize assets where downtime is expensive or a safety risk. Lead with numbers, not hype.
• Narrative: "We're losing X per month to avoidable outages. Here's how AI-optimized PMs and smarter parts planning reverse it in 90 days."

3) Build a phased plan you can execute

Ship value early. Expand only after you prove it.

• Phase 1: Quick wins (0-90 days)
  • Use work order history to optimize PM frequency and scope for top 10 assets.
  • AI-assisted procedure drafting (techs approve before use).
  • Dashboards for failure patterns and MTTR outliers.
• Phase 2: Scale (90-180 days)
  • Forecast parts usage from work orders and optimized PMs to cut stockouts and excess.
  • Auto-generate work orders for recurring issues; supervisors review and publish.
• Phase 3: Predict (180-365 days)
  • Add IIoT sensors on the asset with the highest cost of failure.
  • Predictive maintenance: anomaly alerts routed into your CMMS with human-in-the-loop validation.

4) Set clear goals and KPIs

Avoid the "silver bullet" trap. Define outputs and the actions that turn them into results.

• Outputs: Optimized PM templates, prioritized asset list, AI-drafted procedures, parts forecasts, anomaly alerts.
• Actions: PMs published and used, procedures approved by leads, forecasts driving buys, alerts converted to work orders.
• KPIs: Unplanned downtime (-15%), MTTR (-10%), planned maintenance percentage (+20 pts), PM compliance (≥95%), stockouts (-30%), parts turns (+15%), OEE (+2-3 pts).

5) Identify risks and how you'll mitigate them

Executives fund plans that handle risk head-on. Name the pitfalls and show your guardrails.

• Data quality: Data hygiene sprints; standardize codes; change requests flow through a data owner.
• Model reliability: Start with interpretable rules + simple models; monitor drift; quarterly recalibration.
• Safety and compliance: Human-in-the-loop approvals for any AI-generated procedure; traceability for audits.
• Change management: Pilot charters, tech champions on each shift, short "how-to" sessions, and visible wins.
• Integration risk: Use APIs your CMMS supports; sandbox first; staged rollout by site or line.
• Overpromising: Cap targets per phase; tie funding to delivered milestones.

6) Call the decision points and act now

Make the decisions that keep momentum.

• Decision points: Budget owner, success metrics, priority assets, data sources, vendor vs. internal build, approval workflow, privacy policy.
• Next 30 days: Confirm use case and KPIs; clean and label core data; draft pilot plan; pick champions; set baseline metrics.
• Days 31-60: Configure models and dashboards; publish optimized PMs for first asset group; enable approvals; report first deltas.
• Days 61-90: Expand to next asset group; turn on parts forecasting; formalize governance; present results and request Phase 2 funding.

Where to go from here

Most companies don't need more experiments. They need a strategy that the C-suite and the shop floor both trust. Pick problems that move the P&L, phase your roadmap, measure what matters, and keep the loop tight between insights and action.

If you want a concise overview of industry adoption patterns and pitfalls, this summary is useful: McKinsey's State of AI. If your leadership team needs focused upskilling by role, browse curated options here: AI courses by job.