AI in Construction Finance: A Framework for Real ROI

There’s no shortage of AI use cases in construction right now. Vendors will happily present you with dozens of them. The problem isn’t the volume of options. It’s that most of them have no clear line to a financial outcome.

Finance doesn’t buy potential. It buys return.

The construction companies generating real ROI from AI are the ones that started with a disciplined way of evaluating which problems are actually worth solving with AI, and which ones sound compelling in a slide deck but stall in implementation.

Start With the Three Outcomes That Move the Needle

Point your AI resources at the three financial outcomes that construction finance leaders actually care about.

1. Time-to-cash. In construction, cash is the oxygen the business runs on. The faster work gets invoiced, approved, and paid, the less working capital the business needs to fund operations. AI use cases that compress billing cycles, accelerate pay application approvals, or surface retention release opportunities directly affect cash position. These are among the highest-value priorities in the industry.

2. Leakage prevention. As covered in a previous post in this series, financial leakage through unbilled work, duplicate payments, mis-coded labor, and aging retention is a silent margin killer. AI that systematically catches what humans miss at scale pays for itself and generates a measurable, easy-to-calculate return that’s hard to argue with.

3. Cycle-time reduction. Manual processes in finance are slow, error-prone, and expensive in labor and opportunity costs. And construction finance is no exception. AI use cases that shorten approval cycles, automate document processing, or eliminate manual reconciliation steps free capacity for higher-value work. The productivity gain is real and quantifiable.

Any AI use case worth pursuing should connect to at least one of these three outcomes. If it doesn’t, the ROI conversation gets muddled very quickly.

The Four-Factor Evaluation Model

Once you’ve identified use cases that connect to real financial outcomes, assess them through four perspectives.

1. Business Value

What’s the dollar impact if this application of AI works?

Be specific. “Better insights” is not a business value. A 15% reduction in “days sales outstanding” is a business value. An estimated $400,000 in recovered unbilled revenue is a business value.

If you can’t put a credible number on it, the use case isn’t ready to evaluate.

2. Readiness

Does the organization have what this use case requires?

The two most common readiness gaps are data and integration. If the data feeding the AI model is fragmented, inconsistently coded, or siloed across systems that don’t communicate, AI is more likely to amplify the problem than solve it. If the integration architecture required to make the use case work doesn’t exist, it must be built before AI can deliver value. And that entails its own timeline and cost, which must be considered.

3. Risk

What happens if it doesn’t work as planned?

Some AI failures are recoverable. For example, a pilot that generates less uplift than expected can be refined. But others are more costly. A model that introduces errors into billing data or payment workflows can create downstream problems that take months to identify and then unwind.

Higher-risk use cases require stronger governance frameworks and more rigorous testing before deployment. This can also add to deployment costs.

4. Complexity

How much does this require before it delivers?

Use cases that require extensive custom development, heavy pre-build integration work, or deep organizational change management before generating any return are poor candidates for early investment.

Complexity isn’t a disqualifier, but it is a variable that directly affects the payback period. And the payback period is what the CFO will ask about.

What Good Looks Like

The use cases worth prioritizing share a recognizable profile with four characteristics.

• A clear financial outcome
• Data that’s clean enough to work with today
• A contained scope
• A realistic path to ROI payback within approximately six months.

That last criterion is more important than it sounds. Why? Because an AI investment that takes 18 months to pay back requires sustained organizational commitment through a period of uncertainty. Organizations typically don’t do well with uncertainty. Commitment waivers, while an investment that pays back in a single quarter builds confidence, creates advocates, and generates the internal momentum to fund the next initiative.

Watch for These Red Flags

It’s equally important to know what to avoid. Three patterns consistently signal a use case that isn’t ready or real.

1. It requires perfect data before work can start. If an AI vendor tells you the system needs a full data cleanup initiative before it can deliver any value, that’s not an AI problem. That’s an integration architecture problem that needs to be solved first.
2. Several integrations are needed to ensure results. Heavy pre-integration requirements front-load cost and time while pushing value downstream. Use cases that can demonstrate early return on clean data subsets are more manageable and more fundable.
3. This use case will give you better insights. This is the vaguest promise in enterprise technology, and it keeps showing up in AI conversations. Insights without a defined KPI and a clear action they enable aren’t a business case. Push past the insight narrative to the specific decision or workflow the insight is meant to improve. Then measure that.

AI as a Foundation, Not a Point Solution

The firms seeing the most durable returns from AI aren’t treating individual use cases as standalone projects. They’re part of building an integration and data layer that makes each new AI capability easier and cheaper to deploy than the last. They’re a foundation.

This is the approach that separates organizations that get one good AI outcome from organizations that build an ongoing competitive advantage. The first use case, which pays back in six months, funds the data improvements that make the second use case faster to implement. The second funds the integration work that unlocks the third.

That’s the logic of compounding as it applies to AI. It’s about making AI structural to the business rather than a series of experiments.

The Prerequisite: Connected, Reliable Data

The three outcomes that “move the needle” share a common prerequisite: connected, reliable data. AI can’t compress billing cycles if billing data resides in a system that doesn’t talk to the project management platform. It can’t catch duplicate payments if AP transactions aren’t synchronized in real time across ERP and subcontract management. It can’t reduce cycle times if approvals still require manual handoffs between disconnected systems.

This is where Boomi fits into the construction finance equation.

The Boomi Enterprise Platform integrates the ERP, project management, document management, and financial systems that construction firms depend on, which creates the connected data layer that makes AI-driven outcomes possible. Pre-built connectors for platforms like Procore and NetSuite mean that integration doesn’t require months of custom development before value is realized.

For time-to-cash, Boomi automates the data flows between field systems and billing platforms, ensuring that completed work is reflected in invoices without manual reconciliation. For leakage prevention, Boomi’s real-time data synchronization enables AI models to monitor transactions continuously rather than in periodic batch reviews. For cycle-time reduction, Boomi Flow can automate approval workflows and document routing. This eliminates the manual handoffs that slow down every step from pay application to retention release.

Without integration, AI will simply not deliver the results senior business leaders expect — and boards of directors demand.

If you need help using AI to boost ROI from your AI use cases, contact our team of Boomi experts to discuss how Eide Bailly and Boomi can help your organization.

This is the third in a four-part series on the use of AI in the construction industry, featuring Eide Bailly Technology Consulting, a Boomi partner and business advisory firm specializing in the implementation, customization, and integration of leading ERP, CRM, and cloud technologies.