The real stakes of artificial intelligence in Facility Management

In 2026, artificial intelligence is everywhere in facility management conversations. Predictive maintenance, smart buildings, automated energy optimization: the global market for AI applied to FM is expected to exceed $12 billion by the end of the year, growing at more than 33% annually. Analysts all agree on the same diagnosis: AI is no longer an experiment, it's becoming an operational foundation.

But behind this collective enthusiasm, one question remains largely overlooked: where does the data feeding these models actually come from?

The blind spot of AI transformation in FM

The promise of AI rests on a simple principle: the richer and more reliable the data, the more relevant the decisions it produces. Detecting an anomaly before it becomes an incident, anticipating a cleaning need before a user complains, adjusting a maintenance round based on a site's actual usage, all of this depends on one thing: field data that reflects reality, not an approximation.

Yet across much of facility management operations, this data simply doesn't exist in a usable form. Paper-based sign-in sheets, self-reported check-ins, incidents reported by phone or email: these practices are still widespread, and they produce information that's partial, unstructured, and often logged after the fact rather than in real time.

An AI trained on this kind of data doesn't produce better decisions. It produces faster decisions on a basis that's just as approximate.

Ground truth as raw material

This is precisely the missing link we address at Taqt. Our role isn't to build AI models. It's to make sure those models, whether ours in the future or our partners' and clients' today, have access to reliable raw material: field data captured at the moment the action happens, with no re-entry and no rough self-reporting.

In practice, this means four data streams our solutions capture in real time:

• Presence and rounds (via TaqtOne): reliable time-stamping of every intervention, replacing the paper sign-in sheet with usable digital proof.
• Secure check-in (via SafeQod): entries and exits guaranteed free of fraud, for reliable HR and operational data.
• Service requests: every issue flagged with a single tap becomes structured, time-stamped data, allowing response times to be measured and, eventually, needs to be anticipated before they're even reported.
• User satisfaction: feedback collected directly on site, allowing correlation between sentiment and visit frequency.

These streams, centralized and standardized through Ubiqod, are exactly the kind of structured database an AI model needs to produce reliable predictions, rather than approximations dressed up as algorithms.

From reactive to predictive: a trajectory already underway

This isn't a distant prospect. The shift from reactive maintenance, stepping in once a problem is reported, to preventive, even predictive maintenance, is already the direction our service-request solutions are heading. The difference between the two isn't about how sophisticated the downstream algorithm is; it's about the quality and continuity of the data captured upstream.

This is the conviction that led us to join the AEROSEED consortium, alongside players like ATALIAN, City One, Envisa, and XXII (a computer vision specialist), coordinated by Hub One DataTrust. This project, backed with €3.7 million under the France 2030 plan, aims to build the first sovereign data-exchange and data-valorization platform for major infrastructure: airports, train stations, ports, and high-traffic cultural sites.

In this ecosystem, where domain experts, AI specialists, and data players converge, our contribution is clear: bringing ground truth to the table. The most advanced computer vision or predictive analytics algorithms only create value when connected to a properly documented operational reality: cleanliness, security, maintenance. That's the bridge between human fieldwork and the broader stakes of data that AEROSEED is working to build, and it's the role we play in it.

What this means for FM decision-makers

If you oversee facility management operations and AI is part of your 2026–2027 roadmap, the question to ask isn't "which AI tool should I choose first?" It's "what data will my AI strategy actually be built on?"

An AI fed on estimated data will produce estimated recommendations. An AI fed on real-time measured data (presence, interventions, satisfaction) will produce decisions that genuinely reflect what's happening on your sites.

AI's transformation of facility management won't play out in the models alone. It will play out, first and foremost, in the reliability of what feeds them.

That's the conviction driving our work every day, and the one that led us to take part in a nationwide data infrastructure project.