Maintenance used to be viewed as a necessary expense – something to minimize. In capital-intensive industries (like paper and packaging), it’s increasingly a value lever: better reliability lifts throughput, sharper execution reduces labour cost, smarter spare-parts decisions free working capital, and healthier assets reduce capex needs.
The value is bigger than most teams assume
Research on maintenance-focused transformations shows measurable operational impact, even before major capex:
- +1 to +2 percentage points OEE from reduced failures and downtime – often enough to move EBITDA meaningfully when valued at contribution margin.
- −5% to −15% mean time to repair (MTTR), and +15 percentage points “tool-in-hand” time, translating into ~17% to 23% lower maintenance cost per ton.
- Spare-parts inventory reductions of ~20% to 40% where inventories haven’t been optimized recently.
These gains are increasingly achievable because sensor costs have dropped and AI tools can now sit directly in the flow of work (not just in engineering teams).
Why AI changes maintenance now (and why “predictive” is only part of it)
AI is hitting maintenance in two complementary ways:
- Traditional machine learning improves quantitative problems (failure prediction, anomaly detection, optimization).
- Generative AI improves human-machine work (copilots that guide technicians in real time using manuals, logs, and sensor signals; faster documentation; better troubleshooting).
The biggest shift is that you can improve performance with little to no capex compared with classic reliability programs – especially when you start by fixing execution and information flow.
The three “pillars” of smart maintenance (where value really comes from)
1) Asset strategy and management: from preventive → predictive → prescriptive
Predictive programs often start with threshold alerts, but those can create noise (false positives). More advanced “prescriptive” approaches combine sensors with algorithms that learn from feedback, improving signal quality over time.
Where to focus first
- High-impact, high-failure assets (rotating equipment, engines, critical sets) that justify deeper sensing + models.
2) Work productivity: attack the “two-thirds problem”
Many sites discover a hard truth: only about one-third of maintenance time is spent doing maintenance; the rest is admin, walking, parts retrieval, permits, and coordination.
That’s why “tool-in-hand time” (wrench time) becomes the primary productivity KPI – and why gen AI can be so powerful (e.g., voice-based documentation that reduces form-filling and rework).
3) Spare-parts optimization: free cash without raising risk
Spare parts are often the most neglected lever. Done well, analytics improves service levels and lowers inventory by linking part criticality to failure patterns and lead-time realities.
Even basic process redesign can matter: one example described changing logistics so storerooms deliver parts to work areas instead of technicians fetching them – boosting productivity.
The real blocker isn’t the model – it’s the data ledger
A common “silent killer” is inconsistent master data and mismatched records across systems (e.g., ERP maintenance module vs replenishment module), forcing months of data cleansing before analytics delivers reliable outputs.
This is also why many predictive maintenance efforts stall at pilot stage: scaling requires standard data, repeatable workflows, and an operating cadence – not isolated models.
A practical 60–90 day playbook
Days 1–20: Build the maintenance value map
- Baseline OEE loss from downtime and failures
- Measure tool-in-hand time (even a short observational sample is enough to reveal the “two-thirds problem”)
- Identify top 20 assets by criticality and failure impact
Days 21–50: Prove value with 2 “thin-slice” deployments
- GenAI documentation copilot (reduce admin time; improve work order quality)
- Predictive model on one critical asset class (start with a narrow, measurable scope; build feedback loops to reduce false positives)
Days 51–90: Lock the operating model
- Establish weekly governance: reliability, maintenance, ops, stores, and production together
- Stand up a spare-parts analytics routine (criticality tiers, min/max reset, service-level guardrails)
- Create a repeatable deployment template for the next asset classes
The bottom line
Maintenance becomes a high-return “cash program” when it’s treated as a system: better asset intelligence, more productive work execution, and smarter spares – all reinforced by clean data and a tight operating cadence. That’s how AI stops being a pilot and becomes a durable performance advantage.
