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When your borrower runs on AI: 5 questions small business lenders need to ask

Kate Randazzo
October 19, 2026
0 min read

How to evaluate AI-driven business performance, operational risks, and repayment capacity during underwriting 

AI adoption by small businesses calls for thoughtful underwriting that accounts for technology's potential impact on cash flow, management quality, and business continuity.

This article covers these key topics: 

AI adoption is changing small business operations 

Imagine a small business owner applies for financing and presents an ambitious growth forecast. Revenue is expected to increase, labor costs are projected to decline, and profit margins are expected to improve. The reason? A new artificial intelligence (AI) tool the business recently implemented. 

Small businesses are increasingly incorporating AI into everyday activities, from marketing and customer service to bookkeeping, production workflows, and business planning. According to the Federal Reserve's Small Business Credit Survey, 71% of small business AI users reported increased productivity, while 31% reported higher sales.  

Financial institutions are also exploring automation in small business lending to improve internal processes. With technology becoming more integrated into both sides of the lending relationship, understanding its business implications is increasingly important. AI can create meaningful efficiencies for small businesses, but projected improvements still require the same financial scrutiny as any other business investment. 

The following five questions can help community banks and credit unions evaluate their borrowers’ AI adoption within their existing credit processes. 

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Where is the borrower using AI?

A business might use AI to develop marketing content, automate bookkeeping, respond to customer inquiries, or support inventory management. Other companies may integrate AI into core production processes, pricing decisions, or financial reporting. Lenders should ask borrowers to explain which activities depend on AI and to what degree. 

The distinction between supplemental and business-critical technology is important. A marketing tool that saves several hours each week may offer an efficiency benefit without introducing substantial operational risk. A platform responsible for processing customer orders or managing production could represent a more significant dependency. 

Understanding the technology's role helps lenders determine how much attention it deserves during underwriting. The greater its influence on revenue generation, operating costs, or service delivery, the more important it becomes to evaluate the associated risks and controls. 

What financial results can the borrower actually demonstrate?

A borrower who attributes improved performance to AI should be able to explain how those improvements appear in the business's financial results. Lenders should review relevant financial and operating information, including revenue trends, payroll expenses, productivity measures, customer retention, and changes in operating costs. 

For example, a business claiming that AI has reduced staffing expenses should be able to identify those savings in recent financial statements or operating records. If management expects higher sales from an AI-supported marketing initiative, lenders should distinguish between actual revenue growth and projected results. Implementation costs also deserve attention. Software subscriptions, employee training, integration expenses, and ongoing oversight can offset some of the anticipated savings. 

Documenting how performance compares with management expectations is central to sound credit analysis, regardless of whether the underlying investment involves AI, equipment, or another operational improvement. 

What risks does this technology introduce, and what controls are in place?

AI adoption can introduce risks that extend beyond the cost of the technology itself. Inaccurate outputs, cybersecurity vulnerabilities, data privacy concerns, intellectual property issues, and reliance on third-party providers can affect business operations and financial performance. These risks may be especially important when AI-generated information influences customer interactions, financial reporting, or management decisions. 

Lenders should ask how borrowers verify AI-generated information, protect sensitive data, and oversee technology providers. They should also determine whether employees understand the limitations of the tools they use and whether management has established appropriate review procedures. 

The regulatory environment provides additional reasons to consider these exposures. The Federal Reserve's January 2026 Senior Loan Officer Opinion Survey specifically examined how banks assess lending to businesses with varying levels of AI exposure. Respondents reported being more likely, on average, to approve loans to firms benefiting from AI exposure and less likely to approve loans to firms adversely affected by it. 

This suggests that AI-related opportunities and vulnerabilities are already influencing credit decisions. For financial institutions, AI in small-business lending should be evaluated against established credit risk management practices, with appropriate documentation of material risks and mitigating controls. 

Can the borrower continue operating if the technology changes or fails? 

Technology dependencies can become business continuity concerns when a critical tool experiences an outage or no longer performs as expected. Changes to the provider's pricing, functionality, access terms, or service availability could affect operating expenses and the business's ability to serve customers.  

For smaller businesses with limited staff, AI can play an especially important role in daily operations. However, these businesses may also have fewer resources to fall back on if the technology fails. A survey by American Banker in partnership with Abrigo found that the smallest businesses were more likely to report cash or capital constraints, potentially limiting their ability to absorb disruptions or invest in alternative solutions. Lenders should consider whether these borrowers have realistic backup plans for maintaining critical operations without AI. 

Relevant questions include whether the business maintains backup processes, provides employee training, and periodically reviews its reliance on outside vendors. A business that can explain how it would respond to a technology disruption demonstrates a level of operational planning that lenders can consider in their broader assessment of management quality.

Does the use of proceeds support repayment capacity? 

When a borrower seeks financing for an AI-related investment, lenders should evaluate the proposed use of funds using the same financial and credit standards applied to other business investments, considering implementation costs, expected financial benefits, the anticipated payback period, and the borrower's ability to service debt if the investment performs below expectations. 

For example, a business seeking financing to automate a production process may project lower labor costs and increased output. The lender should determine whether those improvements are supported by reasonable assumptions and whether the business can maintain sufficient cash flow if implementation takes longer than planned. 

Sensitivity analysis can help illustrate how delayed adoption, unexpected expenses, or lower-than-anticipated productivity gains could affect repayment capacity. The proposed investment should also align with the institution's credit policy, underwriting standards, and risk appetite.

How to incorporate AI into existing underwriting processes 

Establishing consistent expectations for evaluating technology investments helps support more informed credit decisions. Lenders can begin addressing AI in their small-business lending processes without creating an entirely new credit review process. A few targeted adjustments can that can help: 

  • Expand management discussions. Ask borrowers where they use AI, how it affects operations, and who oversees the technology. 
  • Document financial assumptions. Distinguish verified performance improvements from projected benefits in credit memorandums. 
  • Identify material dependencies. Flag borrowers whose operations rely heavily on AI for additional review or appropriate monitoring. 
  • Establish performance indicators. Track relevant financial and operating measures after closing, particularly those tied to anticipated AI benefits. 

As AI in small business lending becomes more relevant to credit decisions, the fundamentals of sound underwriting remain unchanged. Lenders still need to understand the business, verify financial assumptions, evaluate management, and determine whether cash flow supports repayment. Asking better questions about AI is one more way to put those principles into practice.

This blog was written with the assistance of an AI large language model. It was reviewed and revised by an Abrigo subject-matter expert.

FAQs

How should lenders evaluate AI adoption in small business lending?

Lenders can ask where a borrower uses AI, how much the business depends on it, what financial results it can demonstrate, and what controls and backup plans are in place.

What risks should lenders consider when a small business uses AI?

Potential risks include inaccurate outputs, cybersecurity and data privacy concerns, intellectual property issues, and dependence on outside technology providers. The level of review should reflect how central AI is to the business’s operations and financial performance.

How can lenders incorporate AI into existing small business underwriting?

Lenders can add targeted questions to management discussions, document verified results separately from projections, flag significant technology dependencies, and monitor performance measures tied to expected AI benefits. Existing credit policies and repayment analysis still apply.

How can AI affect a small business borrower’s repayment capacity?

AI may improve productivity, reduce costs or support sales growth. Lenders should verify those results in financial and operating records, account for implementation and oversight costs, and test whether the business can repay if expected benefits are delayed or smaller than projected.

Are small business loan approval rates improving?

Approval rates increased for both small and large banks in Q1 2026, according to the Kansas City Fed survey, though rates remain below prepandemic levels. Among applicants seeking a loan, line of credit, or merchant cash advance, 42% received the full amount sought and 36% received some or most. In other words, the majority who applied obtained at least partial funding, according to the SBCS data.

What technology do banks and credit unions need for small business lending?

Leading institutions are deploying loan origination software with configurable digital applications, automated document collection, AI-powered credit scoring, and no-code workflow automation. These capabilities allow lenders to match the speed of online alternatives while maintaining the relationship depth and pricing advantage that community institutions hold. Abrigo Small Business Lending is purpose-built for exactly this use case.

Win more small business deals and grow market share.

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The information, content and materials provided through this website are for informational purposes only and are not intended to constitute legal advice. Customers should consult with their legal counsel regarding the application of laws and regulations to their specific circumstances.

About the Author

Kate Randazzo

Senior Content Marketing Manager
Abrigo
Kate Randazzo is a Senior Content Marketing Manager at Abrigo, where she collaborates with industry thought leaders to develop digital content for banks and credit unions. Drawing on her background in strategic communications and content marketing, she translates complex financial topics into practical insights that help financial institutions better serve

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About Abrigo

Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo's platform centralizes the institution's data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth.

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