AI in Construction: Why Visibility Drives Real ROI

AI has swiftly become a competitive necessity for construction companies. While the industry has historically lagged in tech adoption, 44% of construction entities now plan to increase AI investment. Early adopters are already seeing measurable gains in productivity, safety, and profitability.

But successful AI adoption requires more than buying new tools. It demands a clear strategy, strong data foundations, and a culture ready for change.

Why AI Matters Now to Construction Companies

Project management: Only 8.5% of construction projects are completed on time and on budget. AI can help streamline project schedules and resource allocation.
Labor and margin pressures: 94% of companies struggle to fill job openings; AI can help bridge the workforce gap and preserve institutional knowledge.
Competitive advantage: Early adopters are using AI for cost estimation, scheduling, safety, and quality control — unlocking new value and outpacing peers.

AI isn’t a Tool. It’s a Visibility Strategy

Many construction firms approach AI as a tool decision: estimating software, scheduling automation, or safety analytics. In reality, AI is most powerful when it’s treated as a visibility strategy.

AI doesn’t replace judgment. It improves leaders’ ability to see what’s happening across projects, crews, costs, and risk in time to act.

Take, for instance, a recent construction client who faced a growing volume of insurance claims data. With no easy way to move information between their CRM and claims management platform, re-entering project details became time-consuming and error-prone.

We developed a custom intelligent automation solution that:

Pulled data from their CRM, including photos and documentation
Automatically pushed it into the claims system for submission
Enabled faster, more accurate claim processing

This automation reduced friction in a revenue-critical workflow and gave the company visibility to enable scalable growth.

Here are the first steps to take toward AI Adoption:
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Barriers to AI Adoption

Despite the hype, most construction firms face three main hurdles:

Lack of clarity on where to begin
Security and governance concerns
Data quality challenges

Survey results showing top AI adoption challenges: 45% of business leaders lack clarity on where to begin, 21% report security and governance concerns, and 17% face data quality challenges.

Where Do We Start? Is the Wrong First Question

There is so much AI can do for an organization. It’s no wonder 45% of our survey respondents cite lack of clarity about where to start as a key hurdle to AI adoption.

Asking “Where do we start?” leads to experimentation.
Asking “What problem are we solving?” leads to results.

AI initiatives succeed when they are tied to long-term business goals. Start by defining two to three outcomes AI can support, such as reducing project delays, improving safety, or automating estimates. Involve people across your organization, so priorities reflect real needs.

Next, assess your technology infrastructure. Many construction companies underinvest in IT, making it hard to scale AI. In fact, only one in four businesses is confident that their infrastructure can support AI at scale, according to an IBM survey. If that’s a gap for you, address it early.

Data Quality

Data is a serious hindrance to any AI implementation. Forty-three percent of construction companies believe they could improve their business with access to real-time and historical project data. And the lack of clean, robust data is costing construction firms significant time and money: 18% of project time is spent searching for data, while 13% of total construction project spending could be saved with efficient data standardization.

Here’s the reality: AI is only as good as your data.
Without clean data, no AI project can succeed.

Focus first on making existing data usable, not perfect. Identify the systems where project, cost, and safety data already live, clean the fields most critical to your chosen use case, and ensure teams are capturing data consistently. Centralization and standardization can come later — value can be unlocked sooner than many firms expect.

Dive Deeper: Blueprinting a Better Data Strategy in Construction

Security and Governance

Data security is top of mind when it comes to IT challenges. And with how much AI relies on data, it’s important to keep that data protected across security providers, platforms, and more.

Investing in cybersecurity infrastructure and implementing a strong AI governance model will help safeguard your construction firm.

Start by defining who owns AI-related data and decisions, then align security controls to your highest-risk workflows. As AI use expands, formal governance ensures innovation doesn’t outpace trust or compliance.

Dive Deeper: How Leading Construction Firms Turn Risk into a Competitive Edge

A Strategic Roadmap for AI Adoption

Treating AI as a one-off initiative limits its impact. Construction companies that see consistent returns embed AI into long-term operational, financial, and growth strategies with prioritized governance models.

Here’s how leading construction firms are building AI maturity:

Risk Management and Governance

Starting point: No established security posture, and loosely managed data positioning and access.
Next step: Formalize risk definition, measurement, and monitoring. Begin tracking regulatory and industry compliance.
Mature state: Continuous threat monitoring and real-time optimization. Compliance embedded into every AI workflow, not just annual audits.

Business Impacts

Starting point: Limited change management and minimal workforce education. Governance is handled project by project.
Next step: Implement proactive change management and widespread education. Move governance toward automated, board-visible assurance.
Mature state: AI integrated across departments, driving revenue, reducing costs and risks, and tracked through ongoing ROI measurement.

Technology & Team Composition

Starting point: Fragmented data and ad hoc tools managed by individuals.
Next step: Build curated, validated, and secured data pipelines. Begin integrating tech stacks and team roles.
Mature state: Scalable, AI-first organization with fully integrated technology and specialized teams.

The Future of AI in Construction

AI is transforming construction from the ground up. The construction companies that succeed will be those that align technology with business strategy, invest in data quality, and build a culture ready for change.

At Eide Bailly, we help construction firms transform disconnected efforts into integrated, scalable performance. Let’s build smarter together.

AI Adoption in Construction FAQ

How do construction companies start with AI?

Construction companies should start with a business needs assessment, not a technology purchase. The most successful AI initiatives begin by identifying where improved visibility would have the greatest impact on project scheduling, safety performance, cost control, or billing accuracy. Once those priorities are clear, organizations can assess whether existing data and systems can support those use cases and address gaps incrementally.

Why is “Where do we start?” the wrong first question when it comes to AI in construction?

Asking “Where do we start?” often leads to disconnected pilots and short-term experimentation. A better question is “What business outcome are we trying to improve?” AI delivers value when it’s aligned to long-term operational and financial goals, not when it’s deployed as a standalone tool. Firms that anchor AI initiatives to strategic priorities are far more likely to see measurable ROI.

What does AI readiness look like for contractors?

AI readiness for contractors depends on three core elements: strategy, data, and governance. Organizations need clearly defined business goals for AI, access to reliable and usable project data, and governance structures that guide adoption responsibly. Readiness isn’t about being perfect — it’s about having the foundational capabilities in place to scale AI as needs evolve.

How can contractors tell if their data is AI-ready?

Data is AI-ready when it’s accessible, accurate, and consistently captured for a defined use case. Contractors don’t need perfectly centralized data to begin; they need usable data in the systems that support their highest-priority workflows. Improving data quality incrementally allows firms to unlock value faster while building toward more advanced AI capabilities over time.

What is construction AI governance, and why does it matter?

Construction AI governance defines how AI-related decisions, data access, and risk controls are managed across the organization. Effective governance ensures automation improves trust, safety, and financial control — rather than introducing new risks. As AI adoption expands, governance helps align innovation with compliance, cybersecurity, and operational accountability.

What are the biggest risks of AI adoption in construction?

The most common risks include poor data quality, unclear business objectives, lack of change management, and weak cybersecurity controls. AI initiatives often fail when they move faster than governance and workforce readiness. Addressing these risks early allows construction firms to scale AI with confidence instead of reacting to issues after the fact.

How should construction firms measure AI ROI?

AI ROI should be measured against the specific business outcome the initiative was designed to support — such as reduced project delays, improved safety metrics, fewer cost overruns, faster billing cycles, or time saved on manual processes. Measuring success this way keeps AI investments grounded in performance, not activity.

Will AI replace jobs in construction?

AI isn’t replacing construction jobs — it’s reshaping them. By automating repetitive and administrative tasks, AI allows teams to focus on higher-value work such as decision-making, coordination, and problem-solving. In a tight labor market, AI helps construction firms do more with the workforce they already have.