Can buildings be too smart for their own good?

Over the past decade, the construction industry has embraced the idea of the “smart building” with almost evangelical enthusiasm. Sensors, automation, predictive systems and AI-driven controls are now routinely presented as the future of the built environment. From lighting that adjusts itself minute by minute to HVAC systems that learn occupant behaviour, intelligence has become a selling point in its own right. But a question is now starting to surface quietly among architects, facilities managers and building operators, writes John Ridgeway - can buildings become too smart for their own good?

At their best, smart buildings promise efficiency, comfort and lower operating costs. Energy use is monitored in real time. Maintenance issues are flagged before failures occur. Spaces adapt to occupancy and use patterns. On paper, it all makes sense.

In practice, however, intelligence often introduces friction. Facilities teams inherit buildings packed with interconnected systems, each supplied by different manufacturers, each running proprietary software, each requiring its own updates, licences and specialist knowledge. What was designed to simplify operations can instead slow response times and complicate decision-making.

If a heating system fails in a conventional building, the problem is usually mechanical and visible. In a highly automated building, the fault could sit in a sensor, a network switch, a software rule or a cloud-based platform located hundreds of miles away. The building still looks fine, but it no longer behaves as intended. So, the question becomes - when does intelligence stop helping and start getting in the way?

Who really controls a smart building?

Another uncomfortable issue is control. Smart buildings are often described as “self-managing”, but they are rarely self-explanatory. Control frequently shifts away from the people who occupy or maintain the building and towards external vendors, system integrators or remote platforms.

Facilities managers report situations where simple changes, such as adjusting lighting zones, modifying occupancy schedules, overriding automated responses, require calling in specialists or waiting for remote access permissions. In some cases, even basic fault diagnosis is locked behind dashboards that are not available on site. This in turn raises an important question - if a building cannot be understood or adjusted by the people responsible for it, is it truly well designed?

A building that needs constant external interpretation risks becoming fragile. When budgets tighten, staff change or software support ends, complexity does not disappear - it becomes a liability.

Buildings are long-term assets. Mechanical and structural elements are often expected to last 25, 40 or even 60 years. Digital systems, by contrast, operate on much shorter life cycles.

Control platforms, communication protocols and software interfaces can become outdated within a decade, sometimes far sooner. Operating systems change. Manufacturers withdraw support. Cybersecurity standards evolve. What was cutting-edge at handover may feel obsolete before the first major refurbishment.

This creates a mismatch between the lifespan of the building and the lifespan of its intelligence. When systems can no longer be updated or integrated, operators are faced with difficult choices - expensive retrofits, partial disablement or running outdated systems with increasing risk. Is a building, therefore, still smart if no one dares to touch its controls?

Smart buildings are frequently designed around idealised user behaviour. Sensors assume predictable movement. Automation assumes consistent occupancy patterns. Algorithms assume that past behaviour is a reliable guide to future use.

Real buildings are messier. Occupants prop doors open. Meeting rooms are booked, but not used. People override systems because they are uncomfortable or confused. Facilities teams develop workarounds that never appear in the digital model.

When systems become too rigid, users stop trusting them. Manual overrides multiply. Stickers appear on walls explaining how to “trick” the system into working. At that point, intelligence no longer supports human behaviour - it competes with it. The question worth asking is simple - does the building work for the people inside it, or are the people constantly adapting to the building?

Data without decision-making

Smart buildings generate extraordinary volumes of data. Temperature, humidity, occupancy, air quality, energy consumption and system performance are tracked continuously. Dashboards glow with charts and alerts.

Yet data alone does not guarantee better decisions. Many building operators report feeling overwhelmed rather than empowered. Without clear priorities and actionable insights, information becomes noise.

There is also a growing tendency to collect data simply because it is possible, not because it serves a clear operational purpose. Sensors are installed without a plan for how their outputs will be used. Metrics are tracked without thresholds that trigger meaningful action.

At what point then, does data collection become an end in itself rather than a tool for better building performance?

Highly optimised systems often operate close to their limits. They are tuned for efficiency under normal conditions, not for resilience under stress. Power interruptions, network failures or extreme weather events can expose hidden dependencies.

In some smart buildings, loss of connectivity disables systems that could otherwise function locally. Manual controls are absent or poorly documented because “the system handles that automatically”.

Resilient buildings, by contrast, tend to favour clarity, redundancy and the ability to fail gracefully. They may be less optimised on paper, but they remain usable under a wider range of conditions. So, another question emerges - should intelligence always prioritise efficiency, or should it sometimes step back in favour of robustness?

None of this is an argument against intelligent systems. Automation, monitoring and analytics have transformed how buildings perform and how energy is managed. The problem is not smart technology itself, but uncritical adoption.

A genuinely smart building is not the one with the most sensors or the most complex control logic. It is the one that balances intelligence with transparency, automation with human understanding and optimisation with long-term resilience.

That balance requires asking harder questions at design stage. Who will run this building in ten years’ time? What happens when software support ends? Can critical systems still function if parts of the intelligence layer are removed?

As the industry continues to chase smarter, more connected buildings, it may be time to pause and reflect. Intelligence should reduce complexity for users, not hide it. It should extend the useful life of buildings, not shorten it. And it should support human decision-making, not replace it entirely.

So, can buildings be too smart for their own good? The answer may depend less on the technology itself and more on whether we are brave enough to design buildings that remain understandable, adaptable and resilient long after the novelty of “smart” has worn off.