April 2026
Artificial intelligence (AI) is dominating conversations in construction, from industry conference topics to executive leaders exploring AI for operational efficiency and risk management. The promise is real: better cost forecasting, earlier warnings and smarter use of project controls data.
One thing standing in the way of that promise is a lack of data readiness. Because AI is only as good as what you feed it.
That’s a challenge for many megaprojects, despite the massive amounts of data they generate. The solution? Start preparing your project controls data now so you’re ready to fully leverage AI when the time comes.
Why Project Controls Data Is Rather Messy
What’s behind the lack of data readiness? One common reason stands out: megaprojects are complicated.
We’re all familiar with why: Many teams, each with their own systems. Multiple contract structures. Layers of weather or political disruptions. Intricate supply chains. Cost details that live separately from schedule data. Inconsistent cost code structures and data entry practices. And, as a McKinsey report has noted, megaprojects have been growing in size. Their data sets have also gotten bigger, more dispersed and therefore harder to mine for valuable insights.
These factors have created a frustrating reality for anyone relying heavily on project controls data. But this isn’t just about inconvenience. AI needs data that’s complete, consistent and standardized so it can deliver meaningful cost, schedule and risk insights worth acting on. Without that foundation, even the most sophisticated tools will underperform. No wonder data-related challenges consistently rank among the biggest obstacles to AI delivering on its promise in construction.
What “AI-Ready” Data Actually Means
AI-ready data doesn’t have to be perfect. But it does have to be governed—meaning it is guided by rules on how data is collected, structured, accessed and used. This is critical because AI’s success depends on it.
That governance is what makes the data usable: structured and reliable enough for AI to deliver meaningful insights and forecasting. So what does “usable” look like?
- It’s connected, so that cost, schedule and scope data function together rather than existing in separate systems that have to be manually reconciled.
- It’s consistent, using cost codes, terminology and units of measure that follow the same structure across every project, enabling cross-project comparison and portfolio-level analysis.
- And it’s current, reflecting actual project status rather than estimates or data entered after the fact. AI-driven cost projections are only as reliable as the actuals and progress data feeding them.
Even with these fundamentals in place, AI can also play a role in strengthening them—helping identify gaps, inconsistencies and misaligned data structures that might otherwise go unnoticed, and making it easier to pinpoint where data governance needs to be reinforced.
Think of it this way: trying to run analytics on fragmented project data would be like navigating with a map where some roads are missing, and the rest have different names depending on who drew them. You might get somewhere, but not quickly or confidently.
When those three data characteristics are in place, you can trust what your AI-driven analytics are telling you.
The Foundational Steps to Get There
Getting data to that state isn’t so much a technology problem as a process problem. It starts with a few foundational steps.
Know your data landscape. AI tools can only work with what they can access. Map every system that captures project controls data—cost, schedule, procurement, field reporting—and identify where data is siloed or missing entirely. You can’t prepare data for AI if you don’t know what you’re working with.
Create a common cost language. AI-driven analytics depend on pattern recognition across projects. That’s impossible if every project uses different cost codes, categories or naming conventions. A consistent cost breakdown structure (CBS) creates the common language that allows AI to compare, learn and surface meaningful trends, not just within a single project, but across your entire portfolio.
Build governance into the process. AI amplifies whatever it’s given, good or bad. That makes governance non-negotiable. Define who enters data, when and in what format. Build in validation checkpoints so errors such as mismatched cost codes or discrepancies between schedule progress and cost actuals are caught before they reach reporting. Formalizing this process helps improve data quality and analytical output—exactly the foundation AI needs to perform.
Get cost and schedule data talking. AI-driven forecasting and risk analysis require both data streams working together. When cost and schedule are managed in separate systems that don’t communicate, the gaps between them become gaps in your analytics. And AI tools will surface incomplete insights at best, misleading ones at worst.
What This All Enables
Connected, consistent and current project controls data is what makes advanced analytics and credible forecasting possible in the first place.
With that foundation in place, AI-driven tools can do what they’re designed to do: recognize patterns across projects, surface cost trends before they become problems and generate forecasts grounded in actual project data rather than estimates and assumptions. The insights become reliable enough to act on. And they get sharper over time as more clean, consistent data accumulates.
That’s the real payoff of data readiness. The organizations that will get the most out of AI-driven analytics are the ones that do the foundational work first and have the right tools to support it.
One such tool to consider is Contruent Enterprise. It’s a robust, lifecycle cost management software designed to handle the complexities of megaprojects and the data they generate—now and for your AI-driven future. It’s fast to implement, easy to adopt and proven to help improve outcomes across the project lifecycle. Learn more or request a demo today.