Article
Construction audit trends for 2025 and beyond
May 6, 2025 · Authored by Robert J. Zellmer and Blair Brown
As we progress through 2025, the construction industry faces a landscape marked by volatility and rapid change. There are several emerging trends that are reshaping capital construction projects and audit programs. In this article, we’ll delve into three of these trends, offering strategic guidance to navigate the complexities ahead — and highlighting why engaging a construction business advisor can be a valuable step.
1. Economic and regulatory uncertainty
New policies on rebalancing trade have introduced significant uncertainty in the construction sector. As a result, the construction industry is grappling with fluctuating material pricing and unpredictable supply chains. Tariffs on key materials like steel and aluminum are driving up costs and causing delays. Executives must place greater emphasis on risk assessments, inventory management and supplier reliability. Contracts should include escalation clauses to manage price increases and ensure transparency in cost adjustments. Developing contingency plans and maintaining sufficient budget allowances are critical to absorb unforeseen fluctuations.
Regulatory rollbacks, policy shifts and potential freezes on new regulations are creating a dynamic environment. Federal funding priorities are shifting towards traditional infrastructure and energy sectors, potentially reducing support for green projects. Project professionals must stay informed about these changes and adjust their strategies accordingly.
2. Technological advancements and data security
The adoption of advanced technologies, including artificial intelligence (AI) and cloud software, is transforming the construction industry. While these technologies enhance efficiency and productivity, they also introduce significant risks, many of which are uncharted with little to no precedent on mitigation strategies. Cyberattacks and data breaches are major concerns, as construction companies handle vast amounts of sensitive data. Ensuring the integrity and security of AI systems is crucial, as biased or poor-quality data can lead to inaccurate predictions and decisions. Clear guidelines, robust data security measures and intentional human oversight are essential to mitigate these risks.
