Tea Rajic, leads marketing at MODE looks at how to successfully implement a smart building.
Walk through many modern facilities, and AI programs and algorithms are predicting maintenance needs, regulating airflow, and cutting energy waste. The global smart-buildings market is projected to jump from $29.5 billion in 2024 to $77.5 billion by 2029, proof that digital infrastructure is now business as usual.
Yet a problem persists. Adoption is moving faster than understanding. Too many projects automate first and strategize later, leaving buildings connected but not necessarily intelligent. For facility managers, the real question isn’t whether to use these tools, but how to do it responsibly—balancing innovation with reliability, transparency, and human oversight.
These three questions can help make sure that AI simplifies operations instead of complicating them.
Does our data create clarity or confusion?
Every smart building strategy begins with data. But not all data deserves to be used with AI. Faulty, siloed or incomplete information can turn intelligence into chaos. In 2025, the challenge is data quality rather than scarcity. Most facilities generate terabytes of information from HVAC systems, occupancy sensors, lighting controls, and security systems, yet it's often left in silos or incompatible. Research shows that about 95% of construction-phase data never makes it into operations, starving building AI of context.
Additionally, a 2025 review of AI-enabled building energy systems identified ‘data quality requirements’ and ‘integration complexity’ as persistent adoption hurdles. So when data isn’t aligned, AI can recommend incorrect outputs, like cooling a space it thinks is occupied or misjudging air quality because sensors weren’t calibrated.
This isn’t only an expensive operational issue, but it also affects compliance. Under the EU’s GDPR, organizations using automated decision-making must provide individuals with “meaningful information about the logic involved.” So if the data pipeline is patchy or opaque, organizations risk breaching privacy and safety regulations, too.
The solution is not to implement AI building-wide before you can audit it. Firstly, facility leaders should verify where the data comes from, how it’s structured, and whether it’s fit for purpose. This includes checking sensor maintenance records, validating metadata, and ensuring integration between systems like HVAC and lighting works. Otherwise, AI insights can magnify uncertainty instead of eliminating it.
Where does human validation stay in the loop?
AI without oversight is unsafe and shortsighted. In facility management, human validation remains the critical layer that interprets, contextualizes, and course-corrects.
As AI evolves, facility manager roles are shifting from manual intervention to analytical supervision. They’re becoming interpreters of data rather than executors of tasks, responsible for questioning anomalies and ensuring recommendations match safety codes and business priorities.
Imagine a hospital ward at 2 a.m.—the sensors register a sharp temperature drop. The platform can send an alert in seconds, but it can’t tell whether that signal means a faulty thermostat or a patient at risk. That’s where human experience matters most.
Keeping that partnership healthy means giving the tech its own oversight: regular audits, data-quality checks, and teams that connect IT, compliance, and operations. Smart systems assist; humans stay accountable.
How do we ensure transparency, interoperability, and measurable ROI?
When it comes to choosing an AI solution, buyers beware. AI vendors come in many different shapes and sizes and don’t take what’s written on the box at face value. Before signing any contract, facility managers should look beyond the demo screen. Ask vendors how their models learn, how data is validated, and how the system integrates with existing building-management software.
Transparency here isn’t optional. It’s the difference between a system that enhances performance and one that adds another layer of complexity. Under the EU AI Act, high-risk systems must be “sufficiently transparent to enable deployers to interpret the system’s output and use it appropriately.” Hence, vendors should provide documentation of how the system learns, where its data comes from, and how human oversight is built in.
Interoperability is another key area. The smartest PropTech should be invisible, weaving into the building’s systems rather than stacking onto another dashboard. But invisible doesn’t mean unsupervised. Look for solutions that deliver autonomous adjustments alongside clear human-action pathways, with open APIs and alert mechanisms for human review. A lighting system that adjusts in real time based on occupancy is ideal, but if the facility manager can’t see why it made that adjustment or override when something is wrong, then they’ve outsourced control.
Facility managers must ensure the technology is measurable. Every AI implementation should answer: what will this system improve next quarter, and how will we measure it? That means establishing clear ROI metrics from day one on energy savings, uptime improvements, occupant comfort scores, and maintenance cost reductions. Without clear outcomes and reporting, no building can be truly “smart.”
The race to modernize the built environment is already won, but doing so wisely is still a game of catch-up. For facility managers, the competitive edge lies in curiosity and due diligence: asking the right questions about data, transparency, and measurable outcomes. The future of smart buildings should be established on questioning what powers them.