AI and the Future of Lab Building Performance

Artificial intelligence is helping laboratory teams turn complex building data into more accurate forecasts of demand and performance across the facility lifecycle.

Laboratory buildings are among the most complex environments project teams and facility stakeholders are asked to deliver. HVAC systems must support stringent safety standards, evolving research programs, and often unpredictable future demands.

In that context, small inaccuracies in early estimating and planning can ripple forward, affecting performance, installation quality, and long-term operability.

Artificial intelligence (AI) is emerging as a practical tool to reduce that uncertainty. While often associated with automation or speed, AI’s deeper value in laboratory projects lies in its ability to structure, analyze, and carry forward building data in ways that support more accurate forecasting of demand and performance across the facility lifecycle.

Aligning systems more closely with design intent

At the earliest stages of a project, estimating accuracy plays an outsized role in shaping downstream outcomes, explains Abdul Zamerli, senior partner, AI & strategy at Artefact. That consistency has implications far beyond cost control, he says. “System performance becomes closer to ‘design intent.’ More reliable quantity takeoff and schedule interpretation reduces the risk of mis-sized air devices, terminal units, and ductwork elements that later force compromises (e.g., higher fan speeds, noise, unstable pressure control, or comfort issues).”

This means fewer late-stage adjustments that erode carefully calibrated airflow strategies. It also translates into fewer comfort complaints, fewer nuisance alarms, and more stable pressurization—especially critical in containment labs and facilities with strict environmental requirements.

Supporting scenario planning without overbuilding

One of the perennial challenges in laboratory planning is uncertainty about future use. Owners frequently hedge against unknowns by designing for maximum-case scenarios across an entire facility—higher air change rates, excess capacity, or oversized infrastructure. While understandable, this approach can drive up first costs and long-term energy consumption.

AI-supported takeoff and estimating tools offer an alternative: rapid, data-backed scenario modeling.

“AI-enabled takeoff allows teams … to quickly generate and compare multiple program scenarios—different lab densities, equipment loads, air-change strategies, or expansion assumptions—using the same underlying drawings while adapting schedules accordingly to the scenarios,” says Zamerli. “That speed enables owners and planners to ask better questions earlier, rather than defaulting to maximum-case assumptions across the entire facility.”

This capability reframes early conversations for lab planners. Instead of asking whether to design for the highest possible load everywhere, teams can test variations by zone or phase. They can explore how incremental changes in density or ventilation strategy affect quantities, budgets, and downstream systems. The result is more nuanced forecasting—balancing flexibility with right-sizing.

Bringing consistency to complex scope

Laboratory HVAC estimating is rarely straightforward. Schedules, symbols, and notes can be interpreted differently by individual estimators, leading to variability across bids and projects. That variability introduces noise into forecasting and makes benchmarking difficult.

According to Zamerli, “One is consistency across bids and projects. Laboratory HVAC scope is complex, and traditional estimating relies heavily on individual interpretation of schedules, symbols, and notes. Data-driven takeoff establishes a repeatable baseline, reducing variability between estimators and improving comparability between bids.”

That repeatable baseline becomes a more reliable data foundation. When structured information about air devices, terminal units, and system components is captured early, it can be carried forward into commissioning, asset tagging, and facilities documentation. The forecasting exercise is no longer a one-time snapshot; it becomes the start of a continuous data thread.

Reducing risk in renovations and phased projects

Many laboratory projects involve renovations in occupied buildings, where forecasting errors can translate into operational disruption. Understanding exactly what systems exist in each phase and zone is critical for planning shutdowns, temporary systems, and containment measures.

“AI-enabled takeoff tools help by accelerating early understanding of what is actually present in each phase and zone—air devices, terminal units, exhaust points, and system interfaces,” Zamerli notes. “That allows teams to plan shutdowns, temporary systems, and containment strategies with greater precision.”

Because outputs can be segmented by area or phase, project teams can align scope with construction sequencing more effectively. In active labs, where work must be coordinated around experiments and regulatory constraints, this level of data clarity directly supports operational continuity.

A signal of broader industry change

Beyond immediate efficiencies, the growing use of AI in estimating signals a broader shift in how laboratory projects are delivered.

“We believe AI adoption in HVAC estimating signals a shift toward data continuity across the lab lifecycle,” says Zamerli. “Over the next decade, we’ll likely see laboratory delivery move from isolated phases—design, estimating, construction, operations—toward connected workflows where structured data persists and evolves.”

He adds, “AI-enabled estimating is an early entry point because it sits at the intersection of drawings, cost, and constructability.”

That intersection is strategic. When building data is structured and preserved from the outset, it can inform commissioning plans, support more accurate performance verification, and improve long-term asset management. Forecasting becomes less about educated guesswork and more about iterative, evidence-based refinement.

In laboratory environments—where performance, safety, and adaptability are paramount—AI’s greatest value may extend well beyond speed. Its real impact lies in bringing structure and continuity to fragmented building information, creating a reliable foundation for smarter, more confident decision-making over time.