With artificial intelligence, automation and connected technologies advancing rapidly, the pressure is on to modernize operations and keep pace with rising expectations. Many organizations are embracing AI in hopes of gaining an edge, whether by streamlining procurement or forecasting costs and schedules. But technology isn't the issue. That's the easy part. The real challenge is the data, or lack thereof, behind the tech.

For all the excitement around AI, what often goes unspoken is how fragile our systems can be without the right foundation. AI models are only as effective as the quality of the data they're built on, and in the construction industry that data can be disorganized, incomplete or siloed. When inconsistent, outdated or disconnected information is fed into AI systems, the results are not just underwhelming; they're misleading and potentially risky. Without the ability to turn quality data into actionable insight — the overall strategy — even the most advanced AI tools fall short.

A robust AI strategy begins with the right data strategy.

For years, data strategies have focused on static dashboards and spreadsheets. AI is forcing us to think differently. An enterprise data strategy that utilizes clean, complete data is the key to looking forward, allowing owners and project teams to be predictive instead of reactive. The right data helps optimize schedules, navigate tariffs, anticipate supply chain risks, improve safety and deploy field teams when needed. Inefficiencies of the old model are being exposed, and those who can't connect and act on data in real time risk falling further behind.

Data, however, often stays confined to the office. To truly leverage AI, the focus must shift to collecting more data in the field — because when information flow breaks down, efficiency breaks down with it. Strategic data readiness requires an all-hands-on-deck approach: aligning tools, standards and expectations across jobsites, teams, subcontractors and systems. Organizations that treat data as a strategic asset, not a byproduct, will be better positioned to lead, as well as to meet increasing expectations.

Here's how to begin:

Standardize the approach. Data standards — not software — are the foundation of any effective AI strategy. Define what types of data matter most and how they should be captured and structured consistently. The goal isn't to limit flexibility but to create clarity so that project data can be trusted, connected and compared across teams.

Break down silos. The most valuable insights come when data flows freely among the office, field, subcontractors, partners and even municipalities. AI tools need access to the full picture, not fragments. Creating an environment that encourages cross-functional collaboration and open data exchange amplifies the value of systems and decisions. Getting data into a common format that can be accessed and utilized by all will drive future business.

Build a culture that values data. Standards won't stick without the right mindset. Teams need to understand why data matters, how their inputs affect project outcomes, and why data is no longer just paperwork. This requires leadership support, training and ongoing communication. When people see data as a tool (that makes job functions easier) instead of a task, adoption follows naturally.

Once these fundamentals are in place, the door opens to a more forward-thinking vision.

Consider the perennial challenge of materials management. Traditionally, tracking critical components from the factory to the jobsite has relied on a fragile chain of emails, phone calls and, again, spreadsheets. When a crucial shipment is delayed or materials are misplaced on a sprawling site, the ripple effect is immediate: Crews are left waiting, schedules slip, and costs mount. The data is siloed, out of date, and difficult to act on.

Now imagine a dynamic supply chain where every critical component is tracked with GPS or RFID tags. This creates a transparent, real-time data flow from the supplier to the subcontractor, accessible to everyone who needs it. Instead of reacting to a missing delivery, project managers can proactively see a shipment that’s behind schedule and reroute another truck to keep crews productive. They can locate a specific pallet of materials on a 100-acre site in seconds instead of hours. The value isn't in the tags themselves, but in the reliable, shared data that allows teams to make smarter, faster decisions.

This is where concepts like digital fabric come into play, blanketing jobsites with smart connectivity and sensor networks to capture real-time data on materials, equipment, tools and personnel. Though connectivity remains a current pain point on jobsites, this concept creates opportunities to track everything from PPE to heavy equipment and from safety events to materials flow — driving more informed decisions about site layout, logistics and procurement.

In a future shaped by AI, the strength of a contractor's data strategy could be the difference between a job that moves forward and one that falls behind.

Supporting such a progressive vision encourages moving data stewardship from a backend function to a core part of field operations. Perhaps on-site data stewards — human or robotic — emerge, seeing that data is captured at the source, structured correctly, and made available for broader systems and analytics. This on-site data agent could offer fewer surprises, faster reactions, predictable performance and better long-term decisions, which, in turn, make projects safer, smarter and more resilient. That's the beauty of quality data.

This isn't abstract or far off. It's a practical, necessary step toward realizing the real-world benefits of AI and understanding what it takes to make it work for project teams. When selecting a contractor, that foundation and understanding matter: knowing how they're capturing data on the jobsite, what standards they follow, and how that data connects back to project goals. In a future shaped by AI, the strength of a contractor's data strategy could be the difference between a job that moves forward and one that falls behind.

A robust AI strategy begins with the right data strategy. Clean, well-structured, consistently captured data is what turns information into insight — and insight into action. Those who prioritize data strategy today will be tomorrow's AI leaders because they've built the foundation for it to thrive.