How to get ROI out of AI in the Construction Industry

Construction finance professionals face the ongoing challenge of managing tight profit margins, evaluating project risks, and keeping operations running efficiently. Between reviewing contracts, monitoring project expenses, and preparing competitive bids, these responsibilities require significant time and attention. Artificial intelligence (AI) offers an opportunity to simplify many of these processes while helping organizations make more informed financial decisions.

In a recent industry conference presentation, Dr. Tim Oates, Co-founder and Chief Data Scientist at Synaptiq, explored how AI and machine learning can help construction companies address these challenges. Through examples ranging from predictive equipment maintenance to automated document review, he demonstrated how organizations can use their existing data to improve efficiency, anticipate potential problems, and protect profitability.

Let's explore some of these applications and how construction finance teams can begin putting AI to work.

Understanding AI's Role in Construction Finance

Before exploring specific applications, it's helpful to understand the distinction between predictive machine learning and generative AI. Although both fall under the broader umbrella of artificial intelligence, they serve different purposes.

Predictive machine learning uses historical data to identify patterns and anticipate future outcomes. For construction companies, this might mean analyzing previous projects to predict whether a new project will meet its target profit margin or determining when a piece of equipment is likely to fail.

Generative AI, on the other hand, is particularly useful when working with text and documents. Large language models (LLMs), such as those powering ChatGPT, can summarize contracts, extract information from payment applications, and assist with proposal development. Rather than requiring employees to manually search through hundreds of pages, these tools make it possible to locate and organize information much more efficiently.

While these technologies have different capabilities, both offer opportunities to address some of the construction industry's most persistent financial and operational challenges.

Predicting Problems Before They Impact Profitability

One of the most valuable applications of machine learning is its ability to recognize early warning signs that might otherwise go unnoticed. In construction, where unexpected delays and equipment failures can quickly affect project budgets, identifying potential problems in advance can make a significant difference.

Consider equipment maintenance. Traditionally, companies have relied on routine maintenance schedules or addressed mechanical issues after they occur. Predictive maintenance takes a different approach by analyzing sensor data to identify unusual patterns that may indicate an impending failure.

During his presentation, Dr. Oates discussed Synaptiq's work with Disney's Splash Mountain attraction, where machine learning was used to distinguish normal equipment behavior from early signs of malfunction. Similar techniques can be applied to construction equipment, allowing companies to anticipate maintenance needs and potentially avoid costly interruptions.

The same predictive capabilities can also help construction companies protect project profitability.

In one project, Synaptiq worked with a construction company to identify early indicators of margin fade, a decline in a project's expected profit margin. Experienced project managers often recognize subtle warning signs, such as delays in important scheduling milestones, but this knowledge isn't always formally documented or shared across an organization.

By combining insights from these experienced employees with historical project data, Synaptiq developed a machine learning model that achieved 86% accuracy in predicting whether projects would meet acceptable profit margins.

This approach makes valuable institutional knowledge more accessible while giving project teams an opportunity to investigate potential financial risks before they escalate.

Making More Informed Bidding Decisions

Beyond identifying risks within existing projects, AI can also help construction companies make more strategic decisions about future opportunities.

Preparing a competitive bid requires balancing several factors, including project costs, expected profitability, and the likelihood of winning the contract. Bid too high, and a company may lose the opportunity. Bid too low, and it risks leaving money on the table.

Machine learning can help navigate this challenge by analyzing historical bidding information, project characteristics, and competitor behavior.

Dr. Oates shared an example of Synaptiq working with a construction company to predict which competitors were likely to submit bids on upcoming projects and estimate how much they might bid. By combining historical bid records with project schedules and financial data, the company could gain a clearer understanding of the competitive landscape.

These insights don't eliminate the need for experienced estimators. Instead, they provide additional information that can support more informed bidding decisions and help companies pursue opportunities with greater confidence.

Simplifying Financial Documents with Generative AI

While predictive machine learning focuses on anticipating future outcomes, generative AI can help construction finance professionals manage the substantial amount of information they work with every day.

Contracts, payment applications, project specifications, and subcontractor agreements often contain important financial details buried within lengthy documents. Locating these details manually can be time-consuming, particularly when information must be compared across multiple sources.

Generative AI can streamline this process by extracting relevant information, summarizing documents, and identifying potential inconsistencies.

For example, Dr. Oates demonstrated how AI could review a payment application and flag discrepancies between the percentage of work billed and the project's reported progress. In the example, the payment application reflected 85% completion while the project schedule indicated only 40%. The system also identified a difference between the retainage percentage listed in the payment application and the amount required by the subcontract.

Rather than manually reviewing every line of every document, finance teams can use AI to identify potential issues that warrant closer examination.

These capabilities extend to proposal development as well. During his presentation, Dr. Oates used generative AI to analyze a 106-page request for proposals (RFP) for a $32 million project with a 12-day response window. The system organized the document into a checklist of important deadlines, submission requirements, and contractual considerations.

With access to previous proposals and relevant company information, generative AI can also help prepare initial proposal drafts, reducing the administrative work involved in responding to new opportunities.

Of course, AI-generated information isn't always accurate. Dr. Oates emphasized the importance of verifying results, reviewing supporting documentation, and maintaining human oversight when financial or contractual decisions are involved.

Turning Existing Data into Measurable Business Value

AI's potential to improve financial performance isn't limited to construction-specific processes. Predictive analytics can also help organizations improve broader financial operations, including accounts receivable management.

Dr. Oates highlighted Synaptiq's work with Iron Mountain, a document storage and information management company that wanted to improve its collections process. With a large number of customers, the company needed a more effective way to determine which accounts would benefit from direct outreach.

Synaptiq developed a machine learning model that used historical customer information and payment behavior to predict where collection efforts would be most effective.

The results were significant: a 40% reduction in time to settlement, with collections occurring two to four weeks faster.

This example illustrates an important principle of successful AI implementation. Organizations don't necessarily need to undertake massive technology transformations to see meaningful results. Sometimes, applying machine learning to a specific, well-defined problem can create substantial value.

Getting Started with AI in Construction Finance

For construction companies interested in adopting AI, knowing where to begin can be one of the biggest challenges. Financial information is often distributed across project management platforms, accounting software, spreadsheets, and other systems, making it difficult to determine whether the necessary data is readily available.

However, Dr. Oates cautioned against waiting until every data source has been consolidated before pursuing AI initiatives.

Instead, he encouraged organizations to start by identifying a recurring business problem and determining what information would be needed to solve it. If the goal is to predict margin fade, for example, a company might begin by combining project schedules and historical financial performance rather than attempting to integrate every system across the organization.

It's also important to establish how success will be measured. Whether the objective is reducing document review time, improving bid accuracy, or identifying financial risks earlier, organizations should understand their current performance and define what meaningful improvement would look like.

Starting with a focused, measurable project allows companies to evaluate AI's effectiveness, build internal confidence, and expand their capabilities over time.

Final Thoughts

From predicting equipment failures to improving project profitability and simplifying financial documentation, AI offers construction finance professionals new ways to work more efficiently and make better use of their existing data.

As Dr. Oates demonstrated, the greatest opportunities often come from applying these technologies to familiar business challenges rather than pursuing AI for its own sake. By combining the knowledge of experienced employees with the analytical capabilities of machine learning and generative AI, construction companies can identify risks earlier, reduce repetitive work, and make more informed financial decisions.

Interested in exploring how AI could improve your organization's financial operations? Synaptiq works with businesses to identify practical AI opportunities and develop solutions that deliver measurable results. Assess your construction company's AI and data readiness by taking 5 minutes to take Synaptiq's construction-specific AIQ assessment.

Let’s Chat. Contact me if you have a problem to solve, a process to refine, or a question to ask.