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. ⁱ

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 leaders — including 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.ⁱⁱ 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.

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‍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.^vi  

• 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.

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Applying the 80/20 Rule to AI Adoption  

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

In the content 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.

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‍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 leaves a bad rapport with the client.

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.

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‍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.  

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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 importantly, 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.

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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.

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Road Map for Construction Leaders

‍Embracing AI is not just about adopting a new tool; it’s about transforming the entire approach to construction financial management.  

The following four-phase plan can help any construction company looking to adopt AI, including mid-sized and smaller contractors.  

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‍Phase One: Identify Business Priorities & Must-Haves

Define Problems Clearly

Pinpoint and rank urgent bottlenecks based on potential impact. Perform a deep root-cause analysis on errors, delays, and cost overruns, and identify gaps and weaknesses in existing processes and data tools.

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‍Assess Existing Tools

Determine if your enterprise resource planning (ERP) system and various time and project tracking tools can be enhanced or modified and where AI-enhanced tools might fill gaps.

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‍List Must-Have Features

If a vendor lacks key functionality (e.g., offline mode or easy user interface), then consider upgrading to a more robust system.

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‍Apply the 80/20 Rule

The 80/20 rule suggests that 80% of effects come from 20% of causes. By evaluating your business in phase one, it can help a company in establish the correct priorities.

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‍Phase Two: Establish a Strong Foundation

Engage Stakeholders

Involve field managers and information technology, finance, human resources, and operations teams in the needs assessment process and overall solution design.  

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‍Select the Right Software as a Service (SaaS) Platform

Look for a user-friendly system that integrates with current and future workflows and can scale with the business. It may be helpful to retain an experienced, brand-agnostic solutions consultant to narrow down the list of potential providers and guide you through the selection process.  

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‍Enhance Collaboration & Communication

Automating schedule updates, documents, and project status will improve collaboration and communication between the different teams involved, including clients, contractors, suppliers, engineers, and field teams.

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‍Phase Three: Pilot & Measure

Start Small

Pick a high-impact area, such as timesheet automation or daily field updates, to launch the pilot. Choose a scope that’s narrow enough to control but visible enough to demonstrate value. Diligently measure output to ensure alignment with overall objectives and uncover potential process gaps.

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‍Set KPIs

Track metrics such as time savings, error reduction, user adoption, and data accuracy. Clear, well-defined KPIs keep your efforts focused and make it easier to communicate results to leadership. Share early wins broadly to build buy-in and drive internal momentum.  

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‍Apply the 80/20 Rule

Focus on the 20% of tasks causing 80% of project disruptions — whether it’s project setup, field input, or invoicing. Prioritizing the biggest pain points increases the chance of early success, builds credibility for the initiative, and sets the stage for expansion.

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‍Phase Four: Scale Across the Organization

Expand AI Use Cases

Incorporate budgeting, forecasting, risk assessment, and procurement. AI can help identify patterns, reduce manual input, and flag potential issues early — allowing teams to make faster, more informed decisions. As adoption grows, these functions become key drivers of both strategic planning and operational efficiency.

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‍Embrace Continuous Learning

AI models and staff skill sets need regular refreshers. Integrate AI development learning tools into your learning management system. Continue refining your 80/20 priorities as your business evolves. Regularly solicit feedback from key stakeholders.  

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‍Fostering an AI-Friendly Culture  

To be successful when adopting AI in construction finance, companies must create an environment where innovation can thrive.  

Introducing AI is not a one-and-done process; it’s an organizational shift that will require:  
 

• Leadership advocacy: When CFOs, COOs, CEOs, and construction financial professionals champion AI, it underscores the technology’s strategic importance.  

• Collaboration: Cross-departmental workshops (finance, operations, field supervision) encourage solution-focused brainstorming.  

• Psychological safety: Teams should feel comfortable experimenting with new workflows and sharing lessons learned.  

• Tangible tactics: Offer hands-on training modules and set up “AI help desks” for field employees. Provide step-by-step guides, short video tutorials, and local champions to address tech-related questions.  

• External expertise: Partnering with specialized technology providers can accelerate innovation and broaden your organization’s knowledge base.  

For mid-sized or smaller contractors, consider starting with an affordable, off-the-shelf SaaS solution and limiting the pilot to a few high-visibility projects. This approach reduces upfront costs and complexity, enabling you to refine processes before rolling them out organization-wide or making large capital commitments prematurely.  

Conclusion

‍The future of construction finance belongs to companies that blend human insight with AI-powered innovation. This isn’t just about adopting tools — it’s about reimagining how we work, decide, and lead.  

If you haven’t yet embraced AI, then now is the time. Start small by setting clear KPIs, focusing on the field, and committing to continuous improvement.  

By targeting the 20% of tasks causing 80% of the friction, AI can help shift operations from reactive to proactive — digitizing workflows, reducing risk, and increasing profitability.

The path ahead is clear. The next move is yours.

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Hari Vasudevan, PE is the Founder and CEO of KYRO AI (kyro.ai) in Plano, TX. He is an entrepreneur and engineer at the forefront of AI, utilities, and construction management, driving transformative advancements in construction, vegetation, utility, and field services. Hari holds bachelor’s and master’s degrees in civil engineering, along with professional engineering licensure in multiple states. He also serves as vice chair and strategic adviser for the Edison Electric Institute’s Transmission Subject Area Committee. Hari can be reached at [email protected].

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Copyright © 2025 by the Construction Financial Management Association (CFMA). All rights reserved. This article first appeared in May/June 2025 CFMA Building Profits magazine.

Link to the original article

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Sources

[1] “Overview ofthe Bipartisan Infrastructure Law and the Inflation Reduction Act.” KansasLegislative Research Department. December 18, 2024. klrd.gov/2024/12/18/overview-of-the-bipartisan-infrastructure-law-and-the-inflation-reduction-act.

[2] “AccentureReport: Artificial Intelligence Has Potential to Increase CorporateProfitability in 16 Industries by an Average of 38 Percent by 2035.” Accenture.June 21, 2017. newsroom.accenture.com/news/2017/accenture-report-artificial-intelligence-has-potential-to-increase-corporate-profitability-in-16-industries-by-an-average-of-38-percent-by-2035.

[3] Agarwal,Rajat; Chandrasekaran, Shankar; & Sridhar, Mukund. “Imaginingconstruction’s digital future.” McKinsey & Company. June 24, 2016. mckinsey.com/capabilities/operations/our-insights/imagining-constructions-digital-future.

[4] “AI InConstruction Market Size & Share Analysis - Growth Trends & Forecasts(2025 - 2030).” Mordor Intelligence. mordorintelligence.com/industry-reports/artificial-intelligence-in-construction-market.

[5] “AccentureReport: Artificial Intelligence Has Potential to Increase CorporateProfitability in 16 Industries by an Average of 38 Percent by 2035.” Accenture.June 21, 2017. newsroom.accenture.com/news/2017/accenture-report-artificial-intelligence-has-potential-to-increase-corporate-profitability-in-16-industries-by-an-average-of-38-percent-by-2035.

[6] “Study fromAutodesk and FMI Finds Better Data Strategies Could Save the GlobalConstruction Industry $1.85 Trillion.” Autodesk. September 14, 2021. autodesk.com/blogs/construction/autodesk-fmi-study-global-construction-industry-data-strategies.

[7] Koch,Richard. “The 80/20 Principle: The Secret to Achieving More with Less.” 1997.

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