Buildots Launches AI-Powered Intelligence Lab to Bring Data-Driven Decision Making to Construction

The construction industry has no shortage of data, but turning that information into actionable intelligence has long remained a challenge. While sectors such as finance, manufacturing, and logistics rely on standardized performance benchmarks, construction teams have traditionally depended on fragmented reporting, manual observations, and experience-based decision making.

Buildots is aiming to change that with the launch of the Buildots Intelligence Lab, an AI-powered research hub designed to provide construction professionals with free access to objective industry benchmarks, performance metrics, and operational insights derived from real-world projects.

The initiative represents an evolution for Buildots, extending the company’s construction intelligence platform beyond individual project optimization toward industry-wide research and benchmarking.

From Jobsite Data to Industry Intelligence

Buildots has built its platform around AI-powered reality capture. Workers wearing 360-degree helmet-mounted cameras continuously capture site conditions, while artificial intelligence compares those images against BIM models, construction schedules, and project plans. The platform automatically measures progress, detects deviations, identifies delays, and highlights emerging risks before they become costly problems.

Rather than relying on manual status reports, project leaders receive near real-time visibility into what has actually been built, allowing teams to make faster, evidence-based decisions. According to the company, projects using its construction intelligence platform have reduced delays by as much as 50% through earlier risk detection and more proactive project management.

The new Intelligence Lab builds on this foundation by aggregating and anonymizing data across Buildots’ global customer base, transforming project-level insights into industry-wide research.

Addressing Construction’s Data Gap

For decades, construction leaders have struggled to answer surprisingly fundamental questions with confidence.

How productive should a particular trade be under normal conditions? How does one project compare with similar projects elsewhere? What constitutes realistic weekly output for mechanical, electrical, and plumbing (MEP) work?

Until now, most answers have been based on historical assumptions, limited internal data, or anecdotal experience.

“The construction industry has always lacked a source of macro-level truth,” said Roy Danon, Co-founder and CEO of Buildots. “We believe this is a core factor holding back performance and a key contributor to stagnating productivity.”

The Intelligence Lab seeks to fill that gap by publishing freely available research built around three core pillars:

• Metrics: Standardized measurements designed to replace subjective reporting with consistent performance indicators.
• Benchmarks: Global comparisons across project types, regions, and construction trades.
• Insights: Data-driven analyses that uncover patterns, bottlenecks, and early warning signals that may otherwise remain hidden.

Unlike traditional industry surveys, the findings are based on continuously collected operational data from active construction projects.

Early Findings Challenge Long-Held Assumptions

The Lab’s first research publications highlight several trends that could reshape how construction teams evaluate project performance.

Among the most notable findings is the discovery that data center projects experience a 20% to 50% gap between planned weekly MEP output and actual delivery. Given the growing global demand for AI infrastructure and hyperscale data centers, this gap may help explain why many large-scale facilities struggle to stay on schedule.

Another finding points to what Buildots calls the “long tail” effect. The final 20% of many construction activities accounts for roughly 27% of the overall task duration, suggesting that late-stage slowdowns are a structural characteristic of projects rather than isolated exceptions.

The research also found significant differences in trade productivity. Top-performing MEP teams complete work at up to three times the pace of average crews, highlighting substantial opportunities for improving execution through better planning and coordination.

Project type also appears to have a measurable influence on schedule performance. Healthcare construction achieved the highest average schedule adherence at approximately 65%, while data centers averaged around 57%. Commercial and industrial projects clustered in the low-to-mid 40% range, with education projects showing the lowest adherence at under 39%.

These kinds of comparative benchmarks have historically been difficult for contractors and owners to obtain, making cross-project performance evaluations largely subjective.

Expanding the Role of AI in Construction

The launch reflects a broader trend toward AI-driven operational intelligence across the construction industry.

While many organizations initially adopted AI for document automation, design assistance, or scheduling support, increasing attention is shifting toward continuous project monitoring using computer vision, digital twins, and predictive analytics.

Buildots’ platform combines reality capture with artificial intelligence to create a continuously updated digital representation of construction progress. By integrating imagery, schedules, BIM models, and workforce information, the system identifies deviations, forecasts risks, and supports faster decision making throughout the construction lifecycle.

The Intelligence Lab extends that capability beyond individual organizations by creating a shared knowledge base built from anonymized industry data.

According to Buildots, participation remains privacy-focused, with project information aggregated and anonymized before inclusion in research datasets.

A Collaborative Research Model

Although the Intelligence Lab operates within Buildots, the company says it functions as an autonomous research unit focused on industry advancement rather than commercial objectives.

Construction professionals, academics, analysts, consultants, and media organizations are encouraged to submit research questions and hypotheses for future analysis. Rather than publishing proprietary reports behind a paywall, the Lab will make its metrics, benchmarks, and insights freely available.

As AI continues transforming industries that have historically relied on manual processes, initiatives like the Buildots Intelligence Lab illustrate how aggregated operational data can become a shared resource rather than remaining isolated within individual organizations.

For an industry that has long relied on intuition, historical averages, and disconnected project reporting, objective benchmarks grounded in real-world execution data could become an increasingly valuable foundation for improving productivity, reducing delays, and making more informed construction decisions.