By: Garima Bharadwaj, Co-founder and CTO, Enlite
For most of the last century, a building was judged the way you judge a bridge. How much weight it could bear. How long it could stand before something gave way. Infrastructure was measured in endurance, not intelligence. That idea is starting to crack open. Buildings, grids and industrial systems are no longer content to just stand there and hold their load. They are starting to notice things. They sense, they adjust, they occasionally even warn you before something breaks. Somewhere in that shift, a new economy has quietly switched on, and I have spent the last several years building the wiring for it, sometimes literally.
When buildings stopped being furniture
My work lives in the layer most people never think about, the systems deciding how a structure breathes, cools itself and stays alive. For decades, that layer was practically mute. A switch could turn something on or off. It could not tell you why it mattered, or that a compressor two floors up was about to fail on a Tuesday afternoon.
What changed was not one breakthrough. It was the economics quietly lining up behind the scenes. Sensors got cheap. Connectivity got dependable. Computing got strong enough to chew through building-level data without breaking a sweat. Put those three together and you get something buildings have never really had before, the ability to understand their own behaviour. An intelligent BMS today can flag a failing part before it fails, shift energy use around actual human presence instead of a fixed clock, and strip out the kind of wiring that used to snake through kilometres of ceiling space just to make a room listen to a switch.
Why this is an economy, not a trend
It is easy to lump this into the general AI noise everyone is talking about right now. I would resist that. What is happening inside buildings is not AI being sprinkled onto infrastructure for effect. It is a genuinely new kind of value being created, infrastructure that starts paying for itself instead of simply sitting there depreciating.
Look at what intelligent infrastructure actually hands back. Lower energy use is money saved, not a nice-to-have footnote. Predictive maintenance turns downtime from an accepted cost of doing business into something you catch early and sidestep. Retrofitting intelligence into old buildings stretches their useful life instead of demanding new construction, which matters enormously somewhere like India, where most commercial real estate already exists and is not going anywhere. Every one of these outcomes carries a number attached to it. That is what makes this an economy rather than a passing wave of enthusiasm.
We have watched this unfold firsthand. Digitizing tens of millions of square feet and deploying thousands of intelligent controllers across more than a hundred clients was never an abstract upgrade. It showed up as real cuts in energy waste, material use and operational overhead for the buildings we touched. The wider market tells the same story from a different angle, with the smart building sector on a steep multi-year climb and data centre power demand rising fast enough that intelligence, not just raw capacity, is starting to look like the only real answer.
The part nobody wants to engineer
Here is the truth about this work: the AI model is rarely the hard part. The hard part is everything underneath it. Most buildings were never designed to sense anything. Retrofitting intelligence into structures like this means wrestling with patchy power, ageing wiring and systems that were never built to speak to each other, let alone cooperate. That demands a different kind of engineering than building software in a clean lab environment. It has to fail gracefully, earn the trust of a facility team that has seen plenty of technology promises fall flat, and keep working when conditions are nowhere close to ideal.
This, I think, is the useful correction the built environment offers to the broader AI conversation happening everywhere else. Software alone does not create lasting value. Intelligence only matters if it lives inside a physical system that behaves predictably and safely for years, not just for the length of a demo. Engineering for constraints instead of ideal conditions is what will separate technology that genuinely transforms the built world from technology that just gets bolted onto it for a headline.
What happens next
The next chapter here will not be written by a single dramatic breakthrough. It will be written in scale, in how much of the built world we already have gets retrofitted with intelligence instead of torn down and replaced. Most of the world’s buildings, grids and factories are not disappearing anytime soon. The infrastructure economy of the next decade will largely be built on top of what already stands, taught to respond rather than rebuilt from nothing.
That is the work in front of engineers in this space. Not dreaming up an entirely new built world, but making the one we already have smart enough to carry the next few decades. Every building we retrofit, every controller we deploy, every line of code that teaches a system to notice something before it breaks, is quietly becoming the infrastructure economy of tomorrow.