The 2025 Industry Report on
Cost, Schedule, and Risk

This year’s survey paints a clear picture: cost management is no longer a routine function. It’s a high-stakes balancing act in an environment shaped by uncertainty. Most professionals we surveyed are struggling to maintain operational budgets—not because of internal inefficiencies, but because external forces are shifting faster than their tools can adapt.

Fluctuating material costs were the top challenge, cited by 65% of respondents. Logistics delays followed at 53%, with trade policy changes impacting another 50%. Additionally, 47% reported that data inaccuracy in forecasts is making it harder to plan and respond effectively.

The data suggests that organizations are doing their best to plan, only to be disrupted by changes they can’t control. And while no company can predict the future, the demand is growing for systems that help prepare for it. That means more than relying on static modelsEstimation or forecasting tools that use fixed assumptions or inputs, limiting adaptability to real-time changes. or quarterly updates. It means building flexibility and responsiveness into forecasting processes.

Many organizations are now seeking dynamic forecasting and scenario modelingA method that tests different “what-if” situations to assess how changing variables could impact outcomes. tools that support faster, better-informed decisions. These capabilities are no longer considered “advanced” they are increasingly becoming essential for navigating macroeconomic volatility and protecting profit margins.

While program managers expressed the highest confidence in their estimates, they also reported frequent budget overruns and schedule delays which indicates that overconfidence could be masking risks in project planning. This gap between confidence and outcomes suggests that economic uncertainty is not just a forecasting problem, but a leadership one. When leaders overestimate the accuracy of estimates, they may underinvest in contingency planning or delay upgrades to more adaptive tools. In today’s environment, the margin for error is shrinking and overconfidence can become a liability.

When it comes to digital readinessAn organization’s capability to adopt and integrate digital technologies into its operations., most organizations aren’t starting from scratch, but they aren’t fully connected either. This year’s survey revealed that fragmentation across systems and teams is a significant barrier to operational efficiency and responsiveness.

More than 55% of respondents reported gaps in cross-functional data sharing, a clear sign that many teams still struggle to access the information they need from other parts of the organization. Only 33% said their infrastructure is fully integrated. A further 11% described their systems as completely siloed, with little or no interoperability between departments.

While leadership teams report relatively high confidence in their level of system integration, operational teams such as cost engineers and project managers frequently cite fragmented data and disconnected tools as major obstacles to forecasting accuracy and workflow alignment. This disconnect between strategic perception and day-to-day experience points to a broader challenge. Integration efforts that appear complete from the top may not fully address the needs of those working closest to the data.

Disconnected systems create more than inconvenience. They slow down decision-making, make it harder to adapt to change, and prevent leadership from seeing the full picture. When data lives in separate platforms, updates are delayed, insights are incomplete, and coordination breaks down.

These inefficiencies ripple throughout the business. Operational planning becomes less accurate. Workflows remain fragmented. Teams duplicate efforts because they lack visibility into what others are doing. In a market that rewards speed and adaptability, this kind of friction is costly.

Building integrated systems isn’t just an IT goal; it’s a strategic priority. Organizations that unify their infrastructure and promote data sharing are better positioned to act quickly, respond to market shifts, and lead with clarity.

Disparate systems hinder an organization's ability to respond to market shifts, optimize workflows, and deliver timely insights to leadership.

As AI, automation, and analytics continue to advance, organizations are under pressure to modernize not only their tools but also their talent. Our survey reveals a widening gap: while digital technologies are evolving rapidly, the human capacity to apply them effectively is not keeping pace.

Lack of training and skill gaps emerged as the single most cited challenge to improving cost estimation processes in 2025, selected by 26% of respondents. Closely behind were data issues and integration challenges (21%) and technology limitations (20%). This ranking signals that for many organizations, the biggest barriers to effective transformation are not just technical, they’re human.

These skill gaps are especially problematic in cost estimation, forecasting, and risk modelingA technique used to identify, quantify, and simulate risks in order to understand their potential project impact., areas where accurate interpretation of tools and data is essential. When employees don’t understand how to use digital estimation tools or can’t trust the data inputs, confidence in outputs collapses. This may partly explain why only 12% of respondents said they are “very confident” in their cost estimation accuracy, while a majority, 53% remain only “somewhat confident”.

The underlying challenge isn’t a lack of tools, it’s a lack of enablement. Even when digital estimation platforms are available, inconsistent training and poor documentation often prevent teams from using them to their full potential. As one respondent noted, “We don’t have enough licenses and people to help guide us on how to make it better. There is no training for us normally.” Another respondent cited a broader organizational issue: “Lack of training by far. People aren’t experienced in doing the job or running the models.”

This shows a clear preference for hands-on, structured education that aligns closely with daily workflows. These formats offer opportunities for both skill-building and organizational alignment, especially when teams across departments must coordinate estimates based on shared tools and frameworks.

The result is a workforce that often relies on informal knowledge transfer or trial-and-error methods, approaches that introduce risk and reduce productivity.

Some respondents emphasized the need for more structured, scalable training: “Online courses with good manuals that include specific details and tasks” and “internal support groups” were mentioned repeatedly. Others called for direct vendor involvement in providing real-time assistance or walkthroughs: “Lunch & learns/digital office hours for folks to come and ask questions about how to use the tool for their day-to-day work.”

Some respondents emphasized the need for more structured, scalable training: “Online courses with good manuals that include specific details and tasks” and “internal support groups” were mentioned repeatedly. Others called for direct vendor involvement in providing real-time assistance or walkthroughs: “Lunch & learns/digital office hours for folks to come and ask questions about how to use the tool for their day-to-day work.”

Organizations that prioritize continuous learning, especially around estimation, forecasting, and analytics, will be better equipped to adapt to economic volatility, maintain competitive advantage, and retain technical talent.

In a digital-first environment, skills are the multiplier. Tools only deliver value when people are empowered to use them well. With stronger investment in training, today’s underutilized platforms can become tomorrow’s performance drivers.

Across industries, leaders are under increasing pressure to meet regulatory and environmental, social, and governanceA framework for evaluating an organization’s sustainability and ethical practices. (ESG) demands with fewer resources and less clarity. Compliance is no longer a back-office task; it has become a front-line concern that shapes project scope, cost, and delivery—and the stakes are growing.

42% of respondents in our industry survey said ESG considerations are now “extremely important” in their project frameworks. Another 45% said ESG plays a “moderate role,” influencing decisions and planning, even if not fully embedded. Meanwhile, 21% said regulations are playing a “major role” in their estimation processes.

This shift reflects something bigger than a checklist mindset. ESG is moving from guidance to governance. It is no longer just a part of risk mitigation. It is reshaping how organizations model time, cost, and risk across the project lifecycle.

But intent is outpacing capacity.

In open-ended responses, participants described the growing demand to:

Yet despite the urgency, many teams said they lack the systems and data needed to comply with confidence. Several themes emerged:

Organizations want to shift from reacting to regulations to building for resilience. But without the right infrastructure—such as scalable data models, automated tracking, and integrated estimation tools—that shift remains out of reach for many.

Regulatory complexity is accelerating. ESG expectations are no longer isolated to public companies or specific industries. They are increasingly written into RFPs, embedded in supply chain contracts, and scrutinized by investors.

This is not just a compliance issue. It is a delivery issue. Projects are being delayed by uncertainty. Budgets are inflating to account for unknowns. Estimation models are failing to reflect obligations until it is too late.

The result is a growing gap between project strategy and execution. While most organizations express a commitment to ESG, many are still improvising their response. This adds pressure not just to sustainability teams, but to anyone involved in cost, scope, or delivery.

Organizations that build ESG and regulatory intelligence into their estimation and planning processes will be better equipped to respond, plan, and report with confidence. Those that succeed won’t just be compliant—they will be more competitive, more resilient, and more future-ready.

AI is widely seen as a game-changer in cost estimation, forecasting, and planning, but most organizations are not yet realizing its value. Despite growing interest, adoption remains limited to small-scale pilots or isolated use cases.

According to our survey, 63% of respondents said they have not integrated AI into daily workflows. Even among those using AI, most are still limiting its scope. 45% reported automating only 26 to 50 percent of their cost estimates, while just 4% said they have achieved automation at the 76 to 100 percent level.

This cautious approach is also reflected in how teams feel about their current capabilities. More than half of respondents (54%) said they are “somewhat confident” in their cost estimation processes. Only 12% expressed strong confidence, while 14% admitted they are “not very confident".

Engineering teams have shown greater experimentation with AI tools, including predictive modeling and automated risk analysis. Cost professionals and project managers, however, report more limited adoption, with most still relying on manual methods or basic automation. This gap in experimentation suggests that technical exposure and tool familiarity may be key factors in shaping adoption trends.

Still, many organizations are actively exploring AI, but few have crossed the line from investigation to implementation. AI is often seen as promising but mysterious, offering potential value while also raising concerns about transparency, accuracy, security, and control.

Among those that have embraced AI, the benefits are already becoming clear. Respondents cited success in areas such as predictive analytics for cost forecasting, risk modeling and scenario planning, and process automation, particularly in scheduling and logistics. In essence, the value of AI is being found in the mundane and “boring” of workflows, processes, and accessing data while making it actionable.

The gap between AI’s promise and its limited adoption reflects a broader hesitation to commit. This moment marks a turning point. Companies that are willing to take informed risks, invest in targeted use cases, and move beyond isolated pilots are positioning themselves to lead their industries. Those that wait may spend years trying to catch up.

Promise vs. Practice

Although most leaders acknowledge AI's potential, adoption remains cautious

Confidence in current cost estimation capabilities is also mixed:

Those who have adopted AI often report success with:

Organizations across sectors are aligned on one point: the status quo is no longer sustainable. Fragmented data, rising costs, and underused technology are not just operational burdens; they are signals of structural risk. The following developments are expected to influence strategic planning in 2025. Each carries implications that will shape whether companies lead or fall behind.

2025 offers a chance to reset. Strategic planning grounded in evidence, supported by adaptable systems, and informed by emerging signals will define which organizations move forward with confidence. The cost of waiting is no longer measured in missed milestones; it is measured in lost ground.