Five years ago, we talked about AI in hypotheticals. That is clearly no longer the case.

AI is changing the construction landscape quickly. The heavy construction segment is quickly realizing that AI can offer options to manage costs, forecast cashflow, and find meaningful ways to protect their margins on large projects.

In having a variety of conversations with financial leaders on AI, some of the savviest look at this as more than technology — they look at AI as a strategy. This strategy offers options to run more cost effective, safer, and compliant.

The industry has always come down to dollar per ton difference when moving dirt, but the opportunity is clear: start looking strategically at how to use these tools to make the profit difference. Your competition certainly is.

From Reports to Foresight

Since the inception of construction software, CFOs have relied on historical cost reports. Reporting where after-the-fact variance analysis is used to understand where a project stands, which is a very reactive model.

For Heavy Construction, this often means learning about challenges months after the occurrence.

AI can offer an alternative. There are opportunities to pull data from estimate, enterprise resource planning, scheduling, telematics, and a variety of other software. AI models can detect patterns from this data and forecast outcomes beyond what have been seen in the past.

Instead of “What happened?”, the question becomes “What’s is going to happen and what do we do now?” This is not completely dependent on AI deciding what should be warned about — it is an opportunity for your staff to interact with AI and confirm what is a warning sign.

If that does not have your attention, it should. This isn’t about efficiency, it’s about the chance to make decisions proactively that can reduce risk and relay true cost savings.

Forecasting Cash Flow

Heavy construction jobs can be multi-year endeavors. Mobilization costs for equipment, materials, and subcontractors can be costly. Managing capital over time is always a challenge.

AI can help CFOs forecast cash flow with greater accuracy by:

• Modeling historical burn rates for similar projects
• Integrating real-time data from the field
• Testing scenarios based on labor costs, fuel prices, or weather

We can now anticipate when a project may require greater cash expenditure and when. I don’t know about you but would always prefer to speak with a lender well before payroll day! 

Combining the Data: The Timeless Dream

Heavy construction is all about repeatable processes that drive consistent production. Over the years, I’ve seen many contractors try to capture lessons learned to avoid repeating the same mistakes.

Too often, those insights make their way to binders or get discussed in meetings long after the fact. AI changes this dynamic — data can be pulled from scheduling systems, 3D civil models, and project management software into a single source, patterns can be proactively surfaced, and real-time warnings can be issued. That means lessons from past jobs can directly inform today’s projects. Flagging scheduling risks, productivity gaps, or financial impacts now occur before they hit the bottom line.

What was once an exercise no one seemed to achieve properly is now becoming an attainable, proactive process that protects both schedules and margins.

Variance Analysis

Variance analysis is a time-consuming process by hand. Typically, multiple reports that someone compares by hand. AI can automate the monitoring of data to flag differences.

• Fuel usage does not correlate to production time.
• Equipment idle time anomalies.
• Invoices containing incorrect bill rates for a subcontractor.

No need to wait until the monthly meeting to review the challenges. These exceptions can be handled at a fraction of the time.

Organization Adaptation

Change management is as much a finance team’s responsibility as it is the IT team. Adoption cannot be rushed, and every organization needs to do its research and confirm that the tools align with IT standards. Make sure the AI is secure, repeatable, and supportable over the long term.

Successful adoption strategies include:

• Start with quick wins. Start small and build trust in confidence in the tools.
• Keep it explainable. No tool should give you an answer you are not confident with. Make sure you can prove your homework as my math teacher would say.
• Show the return on investment early. Highlight time saved, errors avoided, or costs reduced within the first few months to build momentum.

When adoption is framed as a path to both efficiency and growth; the resistance will fade and be replaced with trust.

Looking Ahead

The examples outlined (forecasting cash flow, lessons learned, variance analysis, and secure adoption) are just some of what has been seen. AI’s potential in heavy construction reaches far beyond finance.

Safety, asset management, workforce planning, and estimation will all be affected. And financial leaders do not need to wait for the next wave to start seeing results.

Five years ago, AI was spoken about in hypotheticals. Today, AI is changing how contractors protect margins and manage risk. The time to act is now: explore, experiment, and begin weaving AI into your strategy.

In a business where the difference is measured in dollars per ton, waiting on the sidelines is not an option — your competition may have already started adjusting how to measure.

If you’d like to explore these topics further, authors Ken Jones and Greg Brown of Crowe LLP will be presenting on this topic at CFMA’s 2026 Annual Conference in Exhibition. Join us there to dive even deeper into the strategies CFOs can apply today to prepare for what’s next.