The landscape of utility construction finance is rapidly evolving, shaped by shifting regulations, economic pressures, and increased demand for sustainable infrastructure. Rising interest rates, tariff uncertainty, and inflation have inflated capital costs, straining project financing and long-term debt strategies.

Meanwhile, federal and state programs like the Inflation Reduction Act and the Bipartisan Infrastructure Investment and Jobs Act are injecting significant funding into water, energy, and broadband projects, prompting new financial strategies for public-private partnerships and municipal bond issuances.¹

As utilities incorporate renewable energy; grid modernization; and reliability, resiliency, and rate affordability efforts, innovative funding tools like bonds and performance-based financing are becoming essential.

Financial leaders in this sector must juggle cost control, compliance, and technology adoption to meet the demands of modern infrastructure delivery.

Historically, construction financial professionals, CFOs, COOs, and CEOs have leaned on retrospective data for budgeting and forecasting. But with today’s projects producing immense volumes of real-time data across labor, supply chains, and materials, those lagging tactics fall short.

Emerging research shows that artificial intelligence (AI) could boost construction profit growth by as much as 71% over the next decade.² This article examines how applying the Pareto Principle (the “80/20 rule”) can guide companies to deploy AI where it delivers the greatest impact — helping to reduce risk, streamline operations, and accelerate digital transformation.

Challenges in Construction Finance

Despite the growing industry dialogue about AI, its real-world impact on construction finance remains limited. Common barriers include:

• Inaccurate project setup: Errors in capturing initial data, such as purchase orders and project codes, lead to downstream invoicing and payment issues.
• Delayed invoicing: When the project setup is flawed, and time entries and expenses are categorized incorrectly, extensive manual corrections are required. Incorrect data is fed to the client, resulting in delays in invoice approval and submission.
• Late detection of deviations: Mismatches in time and expenses often go unnoticed until billing cycles end, wasting critical lead time.
• Manual overload: Correcting mistakes in spreadsheets and emails can overwhelm finance teams, delaying more strategic tasks such as risk assessments and cash flow optimization.
• Poor data management: Data and analytical skills are critical factors for the success of construction finance teams. Bad data leads to poor decision-making and outcomes. Decisions made using bad data are estimated to have cost the construction industry $88.69 billion in rework alone.³
• Potential skill gaps: From a construction operations execution perspective, AI integrations may require workers with specialized skills, creating a need for training field personnel, which will have an additional cost affixed to the project.

Applying the 80/20 Rule to AI Adoption

The Pareto Principle states that 80% of outcomes often stem from 20% of causes.⁴

In the context of construction finance, this typically means a small number of processes generate the majority of cost overruns and/or data errors. Focusing on these key issues first — rather than attempting to automate everything at once — enables quick, high-impact results and builds the organizational confidence needed for larger-scale transformation.

Case Study

A utilities infrastructure solutions provider specializing in electric utility operations across 14 U.S. states, routinely logs hours, expenses, and field observations that feed into payroll, customer invoices, and regulatory reports.

Projects are assigned sporadically and often in real time as crews arrive at the jobsite, and they can last days, weeks, and sometimes months. Work scopes are changed as projects progress, making project timelines and accuracy paramount to ensuring smooth billing and collections.

However, the company noticed recurring data latency issues:

1. Project setup errors: Inaccurate and inconsistent project setup caused cascading mistakes in time/expense tracking and invoicing, including duplicate projects, project numbering that did not match client-required numbering schemes, and poor project metadata that did not adequately characterize the project details.
2. Time-tracking errors: Project setup errors drove inconsistent time tracking, entry to multiple project numbers for the same project, and failure to enter hours in a timely manner.
3. Invoicing delays and rework: Project setup flaws and time entry errors led to time-consuming research by the finance team, often resulting in multiple emails and/or calls to both employees and clients, invoice adjustments, and re-bills after the period ended. These delays increased unbilled cycle times and contributed to higher working capital.
4. Delayed detection: Finance teams received cost data weeks after field activities, making course corrections difficult, and sometimes impossible, due to client restrictions on billing closed or expired projects.
5. Manual investigations: Teams had to sift through emails and spreadsheets once errors were identified to find the root cause. This often involved speaking directly with project employees and client construction managers days or even weeks after invoices were rejected.
6. Quality control and assurance 
   issues: Deviations or defects from plans were detected after the fact, and additional resources, time, and costs were needed to remedy them. In some cases, these issues occurred after project completion, which damaged the client relationship.
7. Supply chain and inventory management: Project schedules on construction projects change routinely, and in the field, procurement of materials and equipment is critical to the success of a project to avoid delays or waste in the system.

Focused AI Rollout

The company used the 80/20 rule to identify processes most prone to errors, such as project setup discrepancies, timesheet anomalies, and real-time field updates that directly affect payroll, billing, and project reporting.

By fixing these high-impact areas, they aimed to prove AI’s value and pave the way for more robust digital transformation. The result was a more robust, real-time data collection process that included an AI “watchdog” to maintain pre-defined project parameters.

Key AI-Driven Improvements

The following highlights some key AI-driven improvements:

1. Accurate project setup: AI scanned both active and new work orders for accuracy and duplication, ensuring each project was assigned to the correct purchase orders and key cost parameters.
2. Automated timesheet and expense tracking: Crews entered time/expense data daily into a user-friendly, cloud-based platform that also worked offline — critical for remote sites. Project selection of time input was also curated to limit the number of active selectable projects by employees, further reducing input and cross-project errors.
3. Anomaly detection and alerts: The platform flagged irregularities, such as sudden overtime spikes or mismatches in job codes in near-real-time, speeding up corrective actions.
4. AI interface: A built-in AI interface allowed users to ask natural-language questions, such as “Compare this week’s overtime hours to last week’s overtime hours on Project X,” and receive instant insights.
5. AI-powered voice notes: Field teams began recording voice updates to their field notes, including Spanish-to-English translations, which fed immediately into a shared platform used by field managers and back-office staff. This fostered collaboration between office and field teams and provided near-real-time project updates.
6. Improved payroll and invoicing: Cleaner data led to more accurate timesheets, significantly reducing payroll processing and rework. Most important, it provided more accurate and timely invoices, enhancing cash flow and trust with clients.
7. AI inspection systems: Field personnel used AI to inspect construction quality with image recognition tools, spotting defects or deviations from contractors’ plans in real time.
8. AI-driven procurement: The tool assisted in predicting material needs based on project schedules and changes to adjust purchasing to ensure materials arrive on time.

By implementing AI, the company’s process improved in several ways, including:

• Faster financial closes: Month-end surprises dropped significantly because most questions were resolved earlier in the cycle. The billing cycle became a perpetual process throughout the period, reducing the size and complexity of month-end accruals.
• Reduced working capital requirements: Streamlined invoice generation and shortened billing cycle times led to less cash tied up in administrative delays.
• Proactive profit center management: Managers quickly compared actual performance against forecasts, intervening before costs escalated. This added visibility-empowered managers to drive accountability and accuracy down to the field level.
• Lower administrative burden: Data entry and reporting were largely automated for field staff, allowing more time to focus on higher-value tasks.
• Enhanced visibility: Cross-functional dashboards gave a unified view of project performance, boosting communication across teams. Leadership staff now had access to more timely data and enhanced reporting dashboards.
• Operational efficiency and human performance improvement: There was a significant reduction in manual labor hours in the field, thereby increasing the speed of the construction process and reducing human error.
• Improved safety and risk mitigation: Through real-time monitoring of workplace hazards, injuries and near-miss events decreased while working conditions improved.
• Mergers and acquisitions (M&A): M&A helps integrate an acquired company in a seamless manner from an operational, IT, and finance perspective.