AI Readiness in Project
Planning: What’s Real, What’s
Not, and What to Do Next

The gap between what leaders want from AI and what teams are currently equipped to deliver is one of the most urgent challenges in cost, schedule, and risk planning.

In the 2025 survey, 57% of respondents said AI is either “somewhat important” or “very important” to their long-term estimation strategy. This reflects the strong executive belief that AI can improve accuracy, accelerate decision-making, and surface risks earlier in the project lifecycle.

But belief is not the same as readiness.

Only 12% of professionals surveyed said they are “very confident” in the accuracy of their organization’s current cost estimates. A majority, 54%, said they are only “somewhat confident.” This cautious posture highlights an underlying truth. AI strategy may be taking shape at the leadership level, but it has not yet reached a point of operational maturity.

That disconnect comes with consequences. When executives assume AI is delivering better forecasts, but estimation teams are still using manual tools or inconsistent data, the risk of misalignment grows. Projects are more likely to exceed budgets, miss timelines or experience change order volatility. These problems are not caused by a lack of interest in AI. They are symptoms of unclear priorities, outdated workflows, and gaps in communication between strategic and technical teams.

The survey also revealed that confidence in estimates did not correlate directly with tool availability. In some cases, even teams that had access to advanced tools lacked the training or governance needed to use them effectively. This reinforces the idea that tools alone do not improve estimation. What matters is whether the organization has invested in enabling its people to use those tools in the right context, with the right data, and toward clearly defined goals.

Strategic intent must now be matched with operational execution. AI cannot close the confidence gap on its own. It must be part of a larger shift toward consistent practices, shared metrics, and real-time collaboration across teams. Leaders who understand this distinction will be better prepared to turn their AI strategy into measurable project outcomes.

Most organizations are not rejecting AI. They are struggling to implement it effectively. The issue is not awareness or curiosity. It is a series of structural barriers that prevent estimation tools from delivering value.

According to the 2025 survey, professionals identified five main blockers to improving cost and schedule estimation:

Each barrier touches a different layer of the organization. Data issues prevent AI tools from generating reliable outputs. Integration gaps isolate information across departments, preventing coordinated planning. Strategic misalignment leads to confusion about where to apply AI, and outdated tools introduce friction and manual rework into otherwise scalable processes. Finally, training shortfalls leave teams unprepared to interpret or act on AI-driven insights.

The survey’s open responses confirmed that these problems are not isolated. Many professionals reported limited support for training, lack of role-specific guidance, and widespread use of legacy platforms that require manual data cleanup. Others described confusion over leadership priorities or frustration with tools that failed to match actual workflows.

Taken together, these issues form a kind of invisible ceiling on AI performance. Even when new tools are available, they cannot operate effectively without clean data, well-integrated systems, a clear use case, and a skilled workforce. Most organizations today lack some combination of those elements.

The implication for leadership is clear. If these barriers are not addressed directly, AI will remain on the margins of project planning. It will be used for isolated reporting tasks or dashboard enhancements, rather than becoming a driver of forecast accuracy, risk modeling, or decision support.

Organizations that want more from AI must invest first in the conditions that allow it to succeed.

The demand for AI in cost, schedule and risk estimation is not driven by hype. It is driven by pain points. Most teams are not looking for futuristic features or speculative capabilities. They are asking for targeted, practical support that improves day-to-day planning.

These are not abstract goals. They reflect operational gaps that exist today. Manual tasks continue to dominate estimation workflows, forcing teams to spend time on data entry, formatting, and reconciliation. Real-time data remains out of reach for many, with updates flowing slowly across disconnected platforms. Predictive modeling is used inconsistently, if at all, and simulation-based estimation is still considered aspirational in most environments.

The survey responses reveal a workforce that is not resistant to AI — it is underserved by existing systems. Teams are asking for tools that help them react faster, model uncertainty more effectively, and reduce the risk of late-stage surprises. These needs are tightly linked to business performance. When estimates are accurate and dynamic, projects are more likely to stay on budget and on schedule.

The fact that automation and integration top the list is especially important. It suggests that AI is viewed not as a solution in search of a problem, but as a way to solve problems that have already been defined. Estimators and program managers do not want AI to take over. They want it to reduce low-value effort, close visibility gaps, and support better judgment.

What is missing is not demand. It is delivery.

Most current platforms do not offer these capabilities out of the box. Generic tools may support trend analysis or provide high-level visualizations, but they are rarely designed with cost breakdown structures, risk contingency modeling, or program phase alignment in mind. As a result, the most valuable features remain difficult to access or scale. 

For AI to move from experimental to essential, organizations will need to invest in platforms that reflect how teams actually estimate. That means solutions tailored to real planning logic, integrated with upstream and downstream systems, and supported by teams who understand both the data and the domain.

Leaders believe AI can transform how cost, schedule, and risk are managed. But without deliberate action, that transformation will stall. Teams need more than software. They need direction, infrastructure, and support from the top.

These steps do not require an overhaul. Most organizations already have the urgency and partial infrastructure in place. What is missing is coordinated ownership of security, workflows, and workforce enablement.

Leaders who act now can embed AI into the operational fabric of project planning. Those who wait may inherit gaps in trust, transparency, and accountability.

AI adoption in project environments is not binary. It is a maturity journey. Some organizations are still evaluating. Others are piloting tools with limited use cases. A few have reached integration, using AI to improve estimation speed, accuracy, and transparency.

This checklist is not a technical audit. It reflects how prepared your organization is to move beyond experimentation. The goal is not to check every box on day one. It is to understand where you are today and where to invest next.

Organizations that treat AI as a strategic capability will make faster, more confident decisions. Those that treat it as a plug-in will struggle to demonstrate value.