May 2026
Given the prevalence of delays and overruns in megaprojects, it makes sense that artificial intelligence (AI) holds appeal among those managing them. But actual adoption demands more than a platform decision. It demands a foundation. And knowing what that foundation looks like—and where the barriers tend to appear—is where conversations around AI-driven project management need to begin.
The Readiness Question
Are you ready for AI adoption? This isn’t as straightforward as it sounds. That’s because being able to answer “yes” requires alignment across project data, organizational structure and cultural readiness.
Think about how thin project margins are—and how a misaligned technology investment won’t just underperform, it’ll hurt the bottom line.
The right conditions have to be in place. This is where AI adoption shifts from a platform decision to a strategic one. What is your current program infrastructure delivering? What problems do you need AI-driven project management to solve? What value do you need it to deliver?
Answers to these questions may look a little different across program types and scales. And while they point to how readiness is unique to each program, the core requirements remain consistent.
Barrier #1: Data Readiness
AI-driven project management depends on continuous, structured, connected data. And this is where many capital programs encounter a serious challenge. Cost, schedule and risk data often live in separate systems with no way to connect them. Data entry processes can vary across teams and project phases, creating information gaps that limit analytical accuracy. Historical project data is frequently unstandardized, limiting AI’s ability to establish reliable baselines or surface meaningful patterns. Without consistent historical and real-time project data, AI also struggles to support predictive forecasting, early risk identification and proactive project controls decision-making.
Programs with integrated project controls environments are often better positioned for AI adoption because cost, schedule and risk data already operate within a connected framework. When project information is centralized and standardized, AI-driven analysis becomes significantly more reliable and actionable.
Data readiness also encompasses how data is captured—whether inputs are consistent, timely and structured in a way that supports analysis. It includes interoperability among the project controls systems managing cost, schedule, procurement and field operations. And it covers governance standards that define what gets captured, how and by whom, across every phase of the program.
When these elements aren’t in place, AI is likely to return incomplete analysis, miss early warning signals and produce insights that can’t be fully trusted. For a capital program operating on thin margins, that’s quite a costly gap.
These data challenges are a natural byproduct of the complexity that comes with capital programs. But they’re also the first barrier worth addressing. That’s because everything else AI-driven project management promises to deliver depends on getting this right.
Barrier #2: Structural Readiness
Organizational structure matters, too—specifically, the ability to act on the insights AI surfaces. Those insights only create value when they reach the right project controls decision-makers quickly enough to matter.
Programs built around lagging indicators like monthly cost reports aren’t structured to fully leverage AI’s capabilities. AI works by surfacing early signals before they become costly problems. Acting on those signals requires a reporting cadence and decision-making culture oriented toward what’s coming, not what already happened. So, AI is able to detect risks like procurement delays or emerging contractor performance issues weeks before they appear in traditional reporting cycles—giving project teams more time to respond.
Cost, schedule, procurement and field data are generated by different teams. When no one is explicitly accountable for the accuracy, timeliness and integrity of each data stream, AI has nothing reliable to work with. And when its findings are questioned, there’s no clear owner to investigate or correct the problem. That kind of ambiguity slows response time and weakens AI’s effectiveness at exactly the moments it matters most.
Programs that build these workflows before adopting AI are better positioned to use it effectively—and to see a stronger return on that investment.
Barrier #3: Cultural Readiness
Cultural readiness for AI-driven project management means building organizational confidence in AI as a decision-support tool, one that enhances experienced judgment rather than replacing it.
Capital programs have been managed by construction professionals making decisions based on industry knowledge, lived experience, traditional reporting and familiar digital tools. So, introducing AI to help with that decision-making can feel disruptive, even when the complexity of capital programs is putting that foundational experience to the test. Research supports this: a 2025 global survey of senior construction and infrastructure decision-makers found that organizational culture and mindset, not skepticism about AI itself, were the primary barriers to adoption. This is more about a shift in trust than a shift in technology. Because even with a sophisticated platform, there may be a struggle to get real value from AI if people aren’t open to change and processes aren’t purposely prepared.
It really comes down to people trusting the tool enough to use it—and using it well enough to trust it. Project teams need to understand what AI is actually doing because, unlike traditional forecasting tools, AI-generated forecasts and risk flags can be less transparent in how they arrive at a conclusion. Leadership has to model data-driven decision-making rather than just prescribe it. And the harder questions—who owns the outputs, who verifies them, who’s accountable when an AI-generated insight drives a bad call—need answers before they arise mid-project.
Readiness as a Strategic Priority
While addressing these three barriers prepares a capital program for AI, it also improves the program’s overall performance. Data discipline improves project controls visibility across cost, schedule and risk—whether or not AI is in the picture. Structural clarity accelerates decision-making at every level. And a culture of intentional technology adoption builds institutional confidence in its use and potential.
As AI-driven project management continues to mature, programs that have done this strategic, foundational readiness work will extract more value from it—earlier and more consistently than those that haven’t.
That level of operational readiness doesn’t happen automatically. Contruent Enterprise helps project owners and construction companies establish the connected project controls environment needed to support scalable, AI-driven decision-making. With centralized cost management, standardized workflows and integrated project data, organizations are better positioned to adopt AI with confidence and operational clarity.
Learn more or request a demo today.