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Glossary
Predictive Maintenance
What is Predictive Maintenance? How AI Keeps CRE Portfolios Running Smoothly
Key takeaways:
Predictive maintenance (PdM) uses AI and live building data to identify early signs of wear or imbalance before failure occurs.
For CRE portfolios, AI-driven predictive maintenance reduces downtime, lowers repair costs, and extends the lifespan of HVAC, electrical, and access systems.
Visitt’s predictive maintenance solution filters 90% of false alerts, prioritizes real issues, and helps property teams act early to maintain smooth operations across sites.
What is predictive maintenance?
Predictive maintenance (PdM) is a method of maintaining an asset’s equipment and building systems based on its real-time condition. AI enables predictive maintenance systems to simultaneously process thousands of variables, recognizing complex relationships that human operators or static thresholds might miss through:
- Continuous monitoring of mechanical, electrical, plumbing, and environmental parameters across all building systems
- Comparison of live operating data against learned baselines and historical performance trends
- Pattern recognition to identify deviations that suggest friction, misalignment, or early degradation
- Automated alerts or work orders that specify the likely issue, required parts, and ideal repair window
- Feedback loops that refine predictions after each completed repair or inspection
This makes it possible to isolate risks from normal fluctuations and proactively direct technicians toward the root cause, while reducing potential downtime, extending asset lifespan, and optimizing maintenance costs.
Predictive vs. preventative maintenance
Both predictive and preventative maintenance aim to keep building systems running reliably. And both approaches share the same goals: preventing failures and supporting tenant satisfaction. The main difference lies in how each determines when maintenance should happen.
How does AI predictive maintenance work in CRE?
When it comes to AI in commercial real estate (CRE), predictive maintenance software operates as a continuous feedback loop that observes every building system in motion. Behind this process is an AI-driven engine that learns how each asset behaves, filters out irrelevant readings, and builds a full picture of portfolio health. It works through a structured sequence of actions that transforms raw facility management data into proactive maintenance decisions:
- Every asset is mapped and digitized: HVAC units, elevators, pumps, and electrical panels are entered into the CMMS with details such as make, model, and known failure points, creating a digital record for every system in the portfolio.
- Operational data is collected and structured: When using a unified system that integrates with work order data, live readings from the BMS, meters, and controllers combine with historical work orders and inspection logs to form a single, organized performance record for each asset.
- Normal performance is analyzed and defined: AI models study building operations under different conditions, such as load, weather, and occupancy, to establish baselines for airflow, vibration, pressure, and power use.
- Abnormal behavior is detected and flagged: The system compares live data to these baselines and identifies small deviations, such as higher current draw or frequent compressor starts, that indicate early signs of wear or imbalance.
- Related alerts are grouped and filtered: The software connects multiple warnings that stem from the same cause, removing duplicates, prioritizing risk management initiatives, and helping teams focus on actionable issues.
And when using unified, proactive AI predictive maintenance software like Visitt, the process becomes even more powerful:
- Work orders are automatically generated: Detailed repair tasks with clear fault descriptions, required tools, parts, safety checks, and estimated time for completion are logged into work order management software, where they are monitored through completion.
- Repairs are scheduled and completed at optimal times: Maintenance is planned around technician availability, vendor contracts, and tenant occupancy to minimize disruption. Once completed, engineers confirm system performance has returned to normal, and the results are logged to refine future predictions and portfolio insights.
- Portfolio insights are reviewed and applied: Managers view dashboards showing asset health, maintenance trends, and vendor performance to plan budgets, replacements, and service strategies.
Why is predictive maintenance a must for CRE firms?
The future of predictive maintenance includes widespread PropTech adoption, with forecasts predicting growth from about $7.24B in 2024 to ~$79.7B by 2034 at ~27% CAGR, and with good reason. For CRE, predictive maintenance addresses a critical challenge: buildings generate massive volumes of operational data. AI converts that unstructured data into actionable predictions, bridging the gap between human oversight and machine precision.
When implemented across a CRE portfolio, predictive maintenance technologies deliver measurable results, including:
- Less downtime and fewer unexpected breakdowns
- Lower maintenance costs through precise scheduling and targeted part use
- Up to 40% longer asset lifespan with condition-based servicing
- Lower energy waste, as building systems operate within optimal performance ranges
- Higher safety and compliance, as electrical, access, and other faults are resolved before they escalate
- More consistent tenant experience, with reliable entry, lighting, and vertical transport across every site
How is AI predictive maintenance used across CRE?
AI in predictive maintenance applies data and machine learning to every layer of commercial real estate property management. It continuously learns how mechanical, electrical, plumbing, and environmental systems perform, using that data to keep buildings running and give managers visibility into where maintenance will likely be needed next, as opposed to where it’s overdue.
HVAC and mechanical systems
HVAC systems are among the most data-rich assets in any CRE portfolio. Predictive maintenance solutions monitor variables like airflow, coil temperature, and compressor current to identify subtle efficiency losses. A rise in current draw or uneven temperature distribution might suggest a developing issue in one unit, prompting service before energy consumption or tenant comfort are affected. The same data helps engineers compare performance across identical systems, ensuring the entire network runs efficiently.
Elevators and escalators
Elevators and escalators operate almost continuously in high-traffic buildings, so even short interruptions create inconvenience. With predictive maintenance, data streams from door sensors, braking systems, and traction motors are reviewed to detect irregular travel speed or minor timing delays that can escalate into shutdowns. Once a trend is confirmed, the software schedules maintenance during low-usage hours and logs the cause, reducing repeat issues across other elevators in the property.
Access control and security systems
Access control networks handle hundreds of daily transactions across card readers, controllers, and locks. Predictive maintenance analytics examine voltage fluctuations, communication latency, and signal integrity to find early electrical or mechanical stress. If a controller begins responding slower than usual, AI isolates the source, so it can be fixed before access reliability declines. Over time, managers gain a full reliability map of the security system, improving both uptime and audit readiness.
Electrical and energy systems
Predictive maintenance software continuously measures voltage, current, and heat across electrical panels and feeders. When insulation weakens or a connection begins to draw uneven load, the system detects the change and alerts engineers to the exact location. This early warning prevents faults that can trip entire circuits and helps track how equipment ages under different loads.
Portfolio-wide planning
Predictive maintenance platforms aggregate asset health data across buildings. The information helps CRE firms plan replacements, evaluate vendor performance, and align maintenance schedules with ESG and financial targets. In this way, it turns operational data into a long-term strategy, improving asset reliability and cost control.
What does predictive maintenance look like with Visitt?
Visitt brings predictive maintenance into daily property operations through a practical mix of data, automation, and AI. The platform studies information from building management systems (BMS), sensors, and even routine maintenance logs to spot the small signs that equipment may need attention. HVAC systems, elevators, and pumps are continuously monitored, with AI learning what’s normal for each asset and flagging when something begins to shift.
Instead of sending every minor notification to staff, Visitt uses large language models (LLMs) to filter out over 90% of the noise and highlight only what needs a response. When the same work order keeps appearing, or a pattern points to a deeper issue, the system escalates it before tenants even realize something’s up. The result is a clearer view of building health across the entire portfolio, leading to reduced downtime, longer equipment lifecycles, and lower costs.
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