The Missing Link In AI-Powered Facility Management
By Tea Rajic
Across buildings large and small, digital tools are becoming powerful assistants to facility managers, from tracking equipment performance to indicating when parts of a building are in use or idle. As AI sweeps through facility management, it’s clear that these systems still need a guiding human hand. AI can interpret patterns faster than humans can, yet it can’t fully understand the daily realities of running a building.
Facility leaders must combine the precision and speed of AI with the operational insight of the teams who keep buildings running. The partnership between algorithms and human expertise is where greater and sustainable performance gains will come from.
AI’s Advantages And Where It Still Needs Human Judgment
Facility-management teams are increasing their usage of AI tools to handle the heavy lifting of building monitoring and optimization. AI systems can analyze live data, from chiller temperatures to occupancy patterns, and recognize anomalous behavior. Its predictive models can forecast when a pump might fail, meaning maintenance teams can be proactive rather than reactive to inconvenient breakdowns.
AI’s job is to provide the signal, not strategy. It increases visibility and reduces guesswork, but buildings are messy, context-rich environments. AI may analyze the data, but it doesn’t always comprehend the underlying story behind it.
For example, an occupancy-based system flags a meeting room as under-occupied. It switches the HVAC to low-energy mode, but unbeknownst to the algorithm, the leadership team of a later meeting is arriving ahead of schedule for a board briefing. The result is a suite that feels uncomfortable just as senior leaders walk in. On an AI dashboard, it appears to be efficient, but this minor adjustment turns into an obvious service issue that reflects poorly on facility managers, not the algorithm.
Humans bring contextual prioritization, like staffing availability, budget constraints, tenant expectations, scheduled events, or contract obligations. They also understand the “why” behind the numbers and can apply risk assessment and safety judgment to situations. They ask the important questions, such as, is it worth reducing airflow in a zone if health and safety or comfort might be compromised?
AI is a powerful instrument, but one that thrives when paired with human judgment. The division of labor is clear: let AI handle high-volume signal detection and baseline optimization; let the human team review, prioritize, contextualize, and make decisions. With that structure in place, facility teams can embody strategic management.
How To Govern AI Responsibly
The more AI integrations affect energy spending, occupant comfort, compliance, and even safety, the more governance becomes as crucial as the technology itself. A robust framework ensures that AI enhances operations, rather than introducing risks or inefficiencies.
A facilities-ready governance plan should include several key components, including transparent decision logic. Teams need visibility into how AI arrives at its recommendations. This revolves around:
- Accessing the original data source, such as IoT sensors.
- Understanding what the model prioritizes, such as cost savings and comfort.
- Identifying any existing limitations.
Without transparency, teams are stuck responding to black-box alerts they can’t contextualize or trust.
Auditability and documentation are also crucial, as every AI-driven action requires human oversight and verification. Facilities must keep records of what the system did, why it did it, and who approved or overrode the decision. This helps prevent finger-pointing during failures and ensures compliance with safety and building-management regulations.
In facilities, seemingly minor errors can snowball into cost or comfort issues. So facility managers must check for bias and hallucinations. A hallucinated urgent fault could trigger a misplaced work order, so regular validation ensures the system is grounded in real operational data.
Automated systems also need boundaries. A governance plan should define clear escalation rules for events that AI can resolve autonomously and those that require human review or immediate attention. This prevents AI from overreaching and ensures humans remain firmly in the loop.
AI improves the fastest when it learns from the teams who know the building and the people inside it best. Therefore, operators’ corrections must be incorporated into the system. These inputs help refine processes so that errors aren’t repeated and the system accurately reflects how the building operates on a day-to-day basis.
Building A Team That Can Work With AI
As AI becomes part of everyday facility operations, the skill sets required on the shop floor and in the control room are shifting. These tools don’t replace technicians or supervisors but change how decisions are made. Because of this workflow change, facility management teams need to understand what the system is telling them in order to be able to trust it.
Skills That Support Better Decision-Making
The most effective AI programs are supported by teams that can interpret and question outputs. These skills are extensions of what good facility teams already do well, including:
- Data literacy basics: reading basic trends, spotting outliers, and understanding what the system is measuring and why.
- Analytic reasoning: comparing automated recommendations with known operational patterns.
- Recognizing model limitations: knowing that an alert isn’t the law, and an optimization suggestion may clash with comfort or maintenance schedules.
Make Training Hands-On And Scenario-Based
Theoretical and slide deck training can’t truly prepare teams for AI-supported work. What teams need is exposure. They need to learn how to respond to simulated equipment failures and walk through false alarms and misclassifications. Hands-on exercises build confidence and help teams understand how their judgment complements automation.
Track Performance On Both Sides
To know whether AI is actually improving operations, facility leaders need clear metrics that reflect real outcomes, such as:
- Reduction in nuisance alerts.
- Energy efficiency improvements, measured in consumption and cost.
- Fewer unplanned maintenance events.
- Faster response times to issues.
- Higher accuracy in triaging alerts and knowing when to escalate.
- Better occupant comfort metrics, including NPS and satisfaction scores.
When teams understand the tools and the tools learn from the teams, both sides improve and the building benefits.
AI is now part of modern facility operations; there’s no doubt about that. But it delivers its most significant value when paired with human judgment. Strong oversight and capable teams must ensure the technology stays useful, grounded, and aligned with how buildings truly operate.
Rajic leads marketing at MODE, a system integration platform for commercial real estate (CRE).