How the lack of data standardization slows down AI in construction

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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-powered applications promises record-breaking efficiency, a fundamental limitation, still unseen by most, is already preventing the industry from scaling these advances.

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

The standardization bottleneck

Construction data is inherently cross-functional, but construction companies’ 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 or ERP software don’t match what’s in their BIM model, so any information the AI ​​could or should provide is essentially trapped and provides no value.

This is a significant problem even for an individual project, but imagine that the same company now wants to measure the breakdown of costs over time across its entire portfolio. The issue becomes immeasurably more important.

The company will need business data, tasks, and due dates from its scheduling software, as well as timesheets and labor reports from software like Procore or Autodesk Construction Cloud. In addition, the company will need progress, logistics and procurement data from its ERP system.

Each system will have different naming conventions for the entities it covers, as well as different data structures. This makes it extremely difficult, if not impossible, to connect the dots between all systems for information.

These problems are not just a minor inconvenience. They create a fundamental barrier to progress. You can’t improve what you can’t measure, and you can’t measure what hasn’t been standardized.

While individual departments or projects can achieve pockets of consistency, companies lack the critical mass of standardized data needed to derive value and scale all the AI ​​technologies that they already exist and are poised to transform the construction industry.

Taking the front seat

The way forward requires what I call “active governance,” which is a fundamental shift in how construction leaders approach data. It’s not about becoming a data scientist or building a technical infrastructure, it’s about taking control of your data instead of waiting for technology providers to figure it out for you.

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

• Measure the set. Launch business initiatives to capture and integrate data across the organization. Stop treating each project as an isolated event. When each project uses different metrics, information structure, and reporting methods, you’re managing a collection of one-off instances rather than running a portfolio. Standardization allows you to finally see patterns: which regions consistently struggle, which types of projects 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 QA must follow a consistent format that stores and models data in a way that machines can read and humans can understand.
• Reject chaos. Stop accepting digital waste—those unstructured PDFs, inconsistent spreadsheets, and siloed datasets—as final deliverables. Insist that structured data is as non-negotiable as security protocols. Data is now a critical asset for construction and must be treated with the same rigor as any other critical asset.

Build the database

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 fulfillment or reporting. Critical knowledge remains buried in spreadsheets and email threads, and project knowledge evaporates when teams disperse. Meanwhile, the lessons learned never really transfer to the next job.

Fragmentation goes deeper than format. Project teams often develop their own vocabularies, naming conventions, metrics, and ways of categorizing work. This further prevents meaningful comparisons or learning between projects.

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

A call to active leadership

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