AI Predictive Maintenance in FM: 7 Research-Backed Moves for 2025

Facilities manager in a campus mechanical room reviews a tablet showing predictive-maintenance charts for chilled-water flow, boiler efficiency and failure risk; pumps, pipes and a technician are visible in the background.

These tips distil current best practice in AI-driven predictive analytics for facilities management. Use them to structure a low-risk, high-impact rollout that ties data quality and governance to measurable results, from uptime to cost and energy performance.

Start with a hybrid FDD stack: label faults with rules from historical BMS data, then train a gradient-boosted classifier (e.g., XGBoost), which shows top accuracy on building faults (Mazzetto 2025). MDPI

Pilot adaptive control (MPC/RL) in limited zones with rigorous M&V; reliable field demos report ~13% cost savings in commercial buildings when protocols are sound (Khabbazi et al. 2025). arXiv

Instrument critical assets and auto-route AI alerts to work orders; in healthcare environments, predictive AI cut HVAC failures ~40% and reduced O&M costs ~15% (NIH ORF 2025). Office of Research Facilities

Apply Remaining Useful Life (RUL) models to cold-chain and mission-critical cooling—prioritise anomalies from vibration, power draw and thermal cycling to pre-position spares and shorten MTTR (IFMA FMJ 2025). FMJ

Use market momentum to unlock budget: 84% of building decision-makers plan to increase AI spend in the next 12 months—tie proposals to measurable PdM outcomes (Honeywell 2025). Honeywell

Harden the data foundation: standardise schemas/semantic tags, enforce data-quality checks, and upskill FM teams in data literacy to raise PdM accuracy and trust (IFMA FMJ 2025). FMJ

Co-optimise energy and maintenance: fuse occupancy and weather feeds so the PdM stack also adjusts setpoints/schedules in real time to sustain savings (NIH ORF 2025; Khabbazi et al. 2025). Office of Research Facilities arXiv

After implementation, review outcomes on a fixed cadence, tighten thresholds and models, and strengthen integrations into work-order workflows. Keep stakeholders aligned with transparent M&V and targeted training so improvements are sustained and scalable.