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The construction industry's relationship with artificial intelligence sometimes feels like watching a sports car stuck in first gear. As a new generation of AI-driven applications promises record efficiency, a fundamental constraint — which most still don’t see — is already keeping the industry from scaling these advancements. 

That obstacle is a lack of standardized data. And not just any standardized data, but construction-specific data that reflects how we plan and build. Only that kind of clean, uniform data holds the potential to help us build better.

The standardization bottleneck

Construction data is inherently cross-functional, but construction company systems still capture it in silos that don’t align with how work is actually planned and delivered.

Here’s an example of why this can be so critical: A company uses an AI tool to capture progress, but the cost codes in their accounting software or ERP don't match what’s in their BIM model, so any insights AI could or should be providing are essentially trapped, and provide no value at all. 

That’s a significant issue even for one individual project, but imagine that the same company now wants to measure their cost breakdown over time across their entire portfolio. The issue becomes immeasurably more significant. 

The company will need trade, tasks and due date data from their scheduling software, plus timesheets and manpower reports from software like Procore or Autodesk Construction Cloud. In addition, the company will need progress, logistics and procurement data from their ERP system.

Each system will have different naming conventions for the entities it covers, as well as different data structures. That makes it extremely hard, if not impossible, to connect the dots between all systems in order to get any insight.

These issues don’t just present a minor inconvenience. They create a fundamental barrier to progress. You cannot improve what you cannot measure, and you cannot measure what hasn’t been standardized. 

While individual departments or projects may achieve pockets of consistency, companies lack the critical mass of standardized data required to get value from — and to scale — all of the AI technologies that already exist and are poised to transform the construction industry.

Taking the front seat

The path forward requires what I call “active governance,” which is a fundamental shift in how construction leaders approach data. This isn’t about becoming data scientists or building technical infrastructure, but rather about taking command of data rather than hoping technology vendors figure it out for you.

Active governance means expanding the executive mandate beyond traditional project delivery to include three critical responsibilities:

• Measure the whole. Launch business-wide initiatives to capture and integrate data across the entire organization. Stop treating each project as an isolated event. When every project uses different metrics, information structure and reporting methods, you’re managing a collection of one-offs instead of running a portfolio. Standardization allows you to finally see patterns: which regions consistently struggle, which project types erode margins and which sequences cause delays.
• Define the standard. Work with technology partners to establish ground rules for how data is collected, organized and modeled. This means evolving from simply “collecting files” to demanding “structured deliverables.” Every RFI, every progress report, every quality check should follow a consistent format that stores and models data in a way machines can read and humans can understand.
• Reject chaos. Stop accepting digital exhaust — those unstructured PDFs, inconsistent spreadsheets and siloed datasets — as final deliverables. Insist that structured data become as non-negotiable as safety protocols. Data is now an asset that’s critical to construction, and it should be treated with the same rigor as any other critical asset.

Building the data foundation

Some construction companies are beginning to treat data as a strategic asset, but this remains the exception rather than the rule. Too often, data is an afterthought — something grudgingly collected for compliance or reporting. Critical insights remain buried in spreadsheets and email threads, and project knowledge evaporates when teams disperse. Meanwhile, lessons learned never truly transfer to the next job.

The fragmentation runs deeper than formatting. Project teams often develop their own vocabularies, naming conventions, metrics and ways of categorizing work. This further prevents meaningful comparison or learning between projects.

The shift to active governance changes this dynamic. It positions construction leaders as the commanders of their data infrastructure, defining what matters, how it should be captured and how it’s going to help their companies thrive. Doing so ensures that the insights from today’s projects can systematically inform tomorrow’s decisions.

A call for active leadership

The construction firms that will thrive in the next decade won’t necessarily be the ones with the biggest technology budgets. They’ll be the ones that recognize data as a strategic asset and take active steps to capture, structure and leverage it.

This means moving beyond pilot projects and departmental initiatives to organization- and perhaps even industry-wide standards. It means refusing to accept chaotic, unstructured information as a final deliverable. Most importantly, it means that construction professionals themselves must take ownership of data strategy and implementation. 

The AI revolution in construction isn't waiting for better AI models or more powerful computers — what we have today is already quite advanced. It's waiting for us to provide the structured, industry-specific data that will allow these tools to truly understand how we’re building and how we ought to be building better.