AI + The Built Environment Delivering Where the Sector Needs it Most

The buildings and construction sector accounts for around 37 per cent of global energy related carbon emissions, according to the UN Environment Programme. While much of the infrastructure that will define that footprint over the coming decades has already been built, one of the many challenges is no longer simply about building better, but about making what already exists perform more efficiently, more cleanly and more intelligently. Increasingly, that shift is being enabled by artificial intelligence.

Rather than operating as a standalone solution, AI is emerging as a connective layer across the built environment, enhancing how buildings are designed, constructed and managed. From optimising energy intensive systems and reducing waste on construction sites to improving the performance of low carbon materials, its impact is being realised through practical, measurable applications rather than theoretical promise.

What distinguishes the current wave of innovation is its focus on deployment. Across the sector, AI is moving beyond pilot projects into real world use cases that deliver immediate operational and environmental gains. At the same time, investors and industry networks are placing greater emphasis on evidence, scalability and integration, raising the bar for what constitutes meaningful climate impact.

Optimising existing assets at scale

Operational energy use remains one of the largest sources of emissions in the built environment, with heating, ventilation and air conditioning systems accounting for a substantial share of building energy demand. According to James Strickland, Chief Operating Officer of Verv, HVAC alone is responsible for roughly 40 per cent of energy consumption in many buildings, yet inefficiencies often develop gradually and go undetected. “HVAC systems run for years and they degrade over time,” he says. “Fans work harder than they should, compressors begin short cycling and filters become clogged. Individually these issues rarely trigger alarms, but over time they lead to significant wasted energy.”

The company’s approach focuses on analysing the electrical behaviour of equipment rather than relying solely on traditional building management data. Motors, pumps and compressors all produce distinct electrical signatures as they operate. When those signatures begin to shift, it can indicate that equipment is under stress or performing inefficiently. “The electrical behaviour of a system is the fingerprint of its mechanical performance,” Strickland explains. “When that signature changes, it tells us something operational has shifted.”

This kind of analysis can reveal inefficiencies that conventional building management systems may overlook. While building management system platforms typically monitor temperatures, schedules and set points, they do not always detect whether the underlying assets are operating efficiently. By identifying anomalies in electrical signals, AI driven monitoring can flag issues such as airflow restrictions, abnormal cycling or refrigerant leakage before they escalate into larger problems. In this way, building performance becomes something that can be continuously interrogated and improved rather than periodically checked.

Crucially, Strickland argues that improving operational performance may be one of the most immediate ways to reduce emissions from the built environment. “Most of the buildings that will exist in 2040 have already been built,” he says. “If we want meaningful carbon reduction in the near term, we have to focus on improving the performance of the equipment that is already there.” This places existing assets at the centre of decarbonisation efforts, shifting attention towards optimisation at scale.

Making construction accuracy a carbon issue

In the construction phase, inefficiencies such as rework remain a significant but often underreported source of embodied carbon. Chris Davison, Chief Executive Officer of NavLive, highlights the scale of the issue. “Rework is one of construction’s dirtiest secrets, not just financially, but environmentally. When a structural element is built out of spec, you’re not just spending money to fix it…. you’re spending carbon. The materials, the transport, the labour, the disposal, all compounds into an embodied carbon hit that rarely shows up in any project’s sustainability reporting.”

NavLive’s approach focuses on improving visibility of what is actually being built on site. “What NavLive enables is for teams to capture accurate site conditions quickly during a simple walkthrough, then compare what has actually been built against design intent or previous project stages,” Davison explains. “Because our scanner captures millimetre-precise spatial data in minutes (simply by walking a site) teams can verify as-built conditions against design intent at any stage of a build, not just at practical completion.” This allows discrepancies to be identified earlier, reducing the risk of costly and carbon intensive rework later in the project lifecycle.

The technology also addresses long-standing challenges around access to usable spatial data on site. “The problem with spatial data on construction sites today isn’t that it doesn’t exist, it’s just that it arrives too slowly, requires specialist equipment, and sits in formats that most site teams can’t easily act on,” he says. By enabling more frequent and accessible high precision scanning, teams can move towards a more continuous approach to quality assurance and project monitoring.

Looking ahead, Davison sees AI enabled site intelligence becoming embedded within standard construction workflows. “When building intelligence management was first introduced, it was seen as a specialist capability for large projects and forward-thinking firms. Today it’s a contractual requirement on major public sector projects and an expected baseline across the industry. We believe spatial intelligence, continuously updated, AI-processed, and accessible to the whole project team, is on the same path.”

“The conditions for that transition are already happening. Hardware costs are falling, AI processing is moving on-device so there’s no bottleneck on connectivity, and the integration pathways into existing BIM and project management platforms are becoming standardised. NavLive is actively building those integrations, with Trimble Connect, Autodesk, and others, precisely because we see our role as providing a spatial data layer that sits underneath the tools teams already use, rather than replacing them.”

Rethinking environmental control on construction sites

Beyond carbon emissions, construction sites are also a major source of airborne particulate pollution in urban environments. Dust generated during earthworks, demolition and material handling can affect air quality for workers and nearby communities. Traditionally, suppression has relied on periodic manual spraying, which often proves inefficient. “Operators typically spray water once or twice a day regardless of real time dust conditions,” says Nishika Mehta, Chief Operating Officer at Mistify AI. “Dust often returns within hours while large volumes of water are wasted.”

The company is applying artificial intelligence to transform this process from a reactive task into a continuous environmental control system. Mistify’s platform combines environmental sensors, machine learning models and adaptive misting hardware to monitor particulate levels, weather conditions and site activity in real time. Based on these inputs, the system can automatically adjust spray timing, intensity and droplet size to suppress dust at the point where it forms.

Early testing suggests that this targeted approach can deliver substantial environmental improvements. Controlled trials conducted by the company have recorded particulate reductions of up to 84 per cent for PM2.5 and 73 per cent for PM10, two categories of airborne particles that can affect air quality and human health, while also significantly reducing water consumption compared with conventional suppression methods. According to Mehta, the key shift lies in moving away from static spraying towards intelligent, responsive site management.

Looking ahead, she believes AI driven monitoring could become a standard component of construction site management as environmental scrutiny increases. “Construction projects are under growing pressure from regulators, investors and communities to demonstrate measurable environmental performance,” she says. “AI driven environmental monitoring provides real time data, automated control and transparent reporting, allowing contractors to move from manual compliance to continuous environmental management.” As expectations tighten, the ability to demonstrate environmental performance in real time is becoming a defining requirement rather than a differentiator.

Where AI is delivering real value in the built environment

The growing adoption of these technologies reflects a broader shift in how artificial intelligence is positioned within the sector. Hannah Scott, Chief Executive of Oxfordshire Greentech, argues that its value lies in how it enhances existing innovations rather than competing with them. “The built environment is a complex, high emissions system, and AI has an important role in helping us optimise that system more intelligently across design, construction and operations,” she says.

What is changing, she notes, is the transition from theory to deployment. AI is now being embedded within practical applications that deliver measurable outcomes, from improving the efficiency of low carbon material production to optimising building systems and reducing waste on construction sites. In this context, AI acts as an accelerator, helping proven hardware technologies scale more quickly and perform more effectively in real world conditions. “We don’t see AI climate tech companies replacing hardware companies,” Scott adds. “Both work in step with each other, and both require investment to help them scale.”

However, not all companies are making the transition from pilot to scale. According to Scott, those that succeed tend to focus on clear operational problems and immediate value creation. “The companies that scale are solving defined industry pain points in a way that fits into existing workflows and delivers value quickly,” she explains. This often means demonstrating both environmental and commercial benefits, whether through reduced energy use, improved asset performance or lower operating costs. Solutions that cannot integrate easily or fail to show a clear return on investment continue to struggle to move beyond demonstration.

Investor expectations are also evolving alongside this shift. While AI enabled climate technologies are often perceived as faster to deploy and less capital intensive than earlier cleantech models, scrutiny has increased. Investors are looking for robust data, credible performance in live environments and solutions that can scale across portfolios without unnecessary complexity. “AI has changed perceptions around speed to impact,” Scott says, “but the companies that stand out are those that can combine that speed with credibility and a genuinely scalable decarbonisation pathway.”

How investors are assessing AI driven climate solutions

Artificial intelligence is also positioned as an enabler within a wider decarbonisation strategy.

This dual track of physical and digital innovation is already beginning to take shape, although adoption dynamics differ. “We’re seeing encouraging progress on both fronts,” Astone notes. “On the materials side, lower-carbon specifications are becoming easier to procure and assure, but adoption still relies on supply readiness, robust standards, and client confidence. On the digital side, AI solutions can often be layered onto existing workflows and building systems; even so, investors and clients should insist on good data governance, careful integration, and transparent measurement so that claimed benefits are real, durable, and net of AI’s own footprint.”

From an investor standpoint, the most compelling opportunities are those tied to clear operational and delivery outcomes. “The most promising opportunities today are practical ones,” he says. “In operations, AI can help design teams and facilities managers deliver performance that aligns with in-use targets rather than model-only assumptions. In project delivery, better forecasting and quality assurance can reduce rework and programme risk, with knock-on benefits for waste and carbon. And as the industry continues to industrialise production and assembly, data-led design for manufacture can support cost, quality, and whole-life outcomes.”

Ultimately, Astone argues that credibility will determine whether AI becomes a meaningful decarbonisation tool. “Ultimately, what matters is evidence. Where solutions are aligned to recognised standards and measured against clear baselines, AI becomes a meaningful accelerator of decarbonisation rather than an abstract promise.”

Evidence will determine what scales

Taken together, these perspectives point to a maturing role for artificial intelligence in the built environment. Its impact is not defined by novelty, but by its ability to deliver measurable improvements across existing systems, reduce inefficiencies at scale and support more informed decision making throughout the asset lifecycle. As pressure mounts to decarbonise quickly and credibly, AI is emerging not as a silver bullet, but as a practical and increasingly essential tool in accelerating that transition.