Artificial intelligence (AI) has arrived in facilities management with impressive speed. Across buildings, the technology is surfacing early warnings, spotting unusual patterns, and recommending fixes long before a human would. However, what hasn’t caught up are the workflows around it.
Facility leaders need a much clearer view of where AI should lead, where humans must intervene, and how to prepare teams for a faster, more alert-driven way of working.
The New Sequence of Decisions
AI is now a trusty companion in building operations. Instead of waiting for something to drift out of range or fail and then taking action, modern systems watch equipment performance in real time and can forecast issues before they surface.
Rather than going out to find the issue, operations teams start by validating it. This update changes the rhythm of work as alerts arrive earlier and faster, meaning teams need stronger triage skills. Staff begin work with model assumptions they must confirm, adjust, or reject. Therefore, planning begins with a machine’s hypothesis, not a technician’s observation, and needs context layered onto it.
For example, AI forecasts a potential pump failure within 72 hours. Before the team mobilizes resources, a supervisor compares the prediction against service logs, known irregular sensor behavior, staffing constraints, and building occupancy. In this collaboration, AI didn’t replace anyone. It simply reshaped who started the action and how quickly the team can organize a solution.
Because the sequence of decisions has changed, facility teams need clarity on three things:
- When AI recommendations arise, should the system alert on every anomaly or only after a specific threshold is crossed?
- Which human roles validate those recommendations?
- What triggers action, delay, or escalation: Which signals demand immediate intervention, and what can wait until a scheduled review?
Clarity around the decision sequence helps managers avoid mistakes and confusion about who acts when. But sequence is only half the equation. Facility teams also need safeguards to ensure AI alerts are interpreted and escalated correctly—especially when the system gets it wrong.
Designing for Friction, Not Failure
Even the best AI solutions misfire. A spike in humidity can distort a sensor reading, and a temporary occupancy surge can trip a false alarm. But the problem isn’t the misfire; rather, it’s the lack of a framework built to catch mistakes.
Teams need to identify errors before they cascade into wasted labor, unnecessary work orders, comfort complaints, or ESG setbacks. That requires intentionally building safeguards into the workflow. Verification checks, governance boundaries, and escalation pathways prevent automated recommendations from being accepted blindly.
For instance, there’s a harmless HVAC fluctuation misread as a failing component. On its own, it’s not a major issue. The problem arises when technicians don’t have a structured way to question or validate the alert. Without that framework, a minor mismatch can easily turn into an avoidable cost or compromise occupant comfort.
Verification checks are the first line of defense. When AI identifies an anomaly or issues a recommendation, technicians and supervisors should confirm the signal against operational content like occupancy patterns and recent maintenance activities. This prevents phantom faults from triggering work orders or equipment shutdowns.
Next, governance boundaries prevent AI from acting outside its safe lane. These limits ensure that automated systems support decisions without overriding critical safeguards. So, while AI may recommend but not autonomously execute equipment shutdowns, ventilation adjustments must require human approval when safety is at risk. All automated actions must be logged with clear “who approved what and why.”
Escalation pathways define when AI recommendations move from suggestions to action. Not every alert requires instant attention, but some should be delayed until the next operation checkpoint.
Preparing People for AI: Redefining Roles
As AI becomes more embedded in everyday operations, roles across facility teams are shifting. The technology isn’t creating entirely new job roles but reshaping how existing roles function, and this isn’t exclusive to facilities management. PwC reports that skills sought by employers are changing 66% faster in jobs “most exposed” to AI.
The impact on roles is where teams feel the shift most directly. Instead of just changing when decisions start, AI is reshaping how different members of the team contribute to those decisions. For example:
- Supervisors now spend more time interpreting AI-generated recommendations than manually tracking down problems.
- Technicians move from reactive diagnosis to confirming predictions early in the cycle.
- Operators focus less on passive monitoring and more on managing decision logic—sorting which alerts matter, which can wait, and which require escalation.
But essentially, the core responsibilities remain human. Human teams must still decide which alerts make sense in the context of the building, how to prioritize interventions when staffing, budgets, and tenant needs conflict, when a shutdown or airflow adjustment is safe, and when to override the system entirely during anomalies or unusual patterns.
To help facility employees thrive in their evolving roles, training models must fit facilities management constraints. Long upskilling programs or one-off workshops don’t match the pace or structure of facility operations. Teams need training tools that can be woven into daily routines.
A practical upskilling model could include quick simulations during shift handovers where teams walk through a faulty alert. Error-chasing exercises are also helpful, as teams intentionally hunt for model mistakes to help staff build pattern-recognition skills. Reviewing weekly override situations is also useful for documenting what happened and feeding that insight back into governance and model refinement.
These exercises build confidence and ensure that teams aren’t just using the tools but actively shaping how the technology works inside their buildings. However, the shift in roles only adds value if the impact is measurable. Facility leaders should track performance indicators that capture both system accuracy and human judgment by tracking override accuracy, reduction in misaligned work orders, occupant comfort, and technician time reclaimed from manual monitoring. Leaders who monitor these metrics gain a clear view of whether AI is improving operations or simply getting in the way.
AI can enhance facility operations, but only when the human side is structured to meet it. Clear workflows, firm safeguards, and practical training give teams the confidence to guide the technology, not be guided by it. With the right balance, AI becomes a reliable partner in running smarter and more efficient buildings.
Gaku Ueda is CEO of MODE Inc., an AI solution provider for buildings.