Commercial lending moves fast. Deals need to close, capital needs to deploy, and everyone wants efficiency. In this environment, it’s tempting to turn to general-purpose AI tools to generate loan documents, promissory notes, security agreements, or guaranties. These tools can produce polished-looking drafts in seconds. But commercial lending documentation is not ordinary paperwork; it’s the transaction’s legal infrastructure. Errors here can create unenforceable terms, delayed closings, disputes, or personal liability for those involved.
At Farley Law, we see the intersection of law, lending, and technology as a place to build better outcomes. This article offers a practical look at the real risks of relying on general-purpose AI for lending documents, where those tools help, and where specialized approaches make a meaningful difference.
AI Predicts Language But It Doesn’t Reason About Law
General-purpose AI models are trained using vast amounts of text and excel at predicting what words should come next based on patterns. This makes them useful for brainstorming, summarizing, or generating first drafts of routine correspondence. Still, AI models don’t “understand” legal requirements, negotiate risk allocation, or ensure enforceability under applicable law. When applied to commercial loan documents, AI’s predictive nature creates predictable failures. Let’s look at some.
The Hallucination Problem
One of the most documented issues with large language models is the confident generation of false information, known as hallucination. In lending documents, this appears as:
- Invented case citations or statutory references that don’t exist.
- Misstated rules for remedies, notice periods, or default triggers.
- Incorrect descriptions of governing law or venue provisions.
An attorney or lender who hands a client an AI-generated promissory note containing nonexistent legal authority has created real exposure. Courts and regulators expect professionals to verify accuracy. A borrower who later discovers flawed language may claim the documents were not properly prepared, potentially complicating enforcement or triggering professional liability claims.
Consistency Across a Document Package
A typical commercial lending transaction involves multiple interrelated documents: a credit agreement, promissory note, security agreement, guaranty, subordination agreement, and more. Defined terms, cross-references, and conditions must align perfectly.
AI tools often generate each document, or even each clause, in relative isolation. The result? Inconsistent use of defined terms (e.g., “obligations” defined differently across agreements), mismatched interest rate calculations, or conflicting amendment provisions. These inconsistencies create ambiguity that sophisticated parties or their counsel can later exploit, or that regulators may flag during review.
Jurisdiction and State-Specific Requirements
States heavily regulate commercial lending. Usury limits, notice requirements, confession of judgment rules, and secured transaction formalities vary significantly by jurisdiction. General-purpose AI tends to produce a “national average” version of legal language that may be invalid or suboptimal in a specific state.
For example, a document drafted with AI may overlook Minnesota’s specific requirements for certain security interests or fail to include required consumer protection notices if there is any ambiguity about the borrower’s status. These gaps are not always obvious.
Missing Transaction-Specific Customizations
AI-generated documents often appear complete but lack tailored provisions that experienced lending counsel would include based on the deal’s unique facts. For example:
- Specific representations and warranties tied to the borrower’s industry or assets
- Financial covenant structures calibrated to the company’s cash flow
- Intercreditor or subordination terms reflecting the actual capital stack
- Exit or prepayment mechanics that protect the lender’s economics
A generic document may close quickly but leave value on the table or expose parties to unnecessary risk.
The Review Burden and Professional Responsibility
Who’s responsible when AI gets it wrong? The answer is clear under legal ethics rules: the attorney (or the lender relying on counsel) is fully responsible. “Adequate review” means more than a quick review. It requires independent legal analysis, verification of citations, cross-checking consistency, and customizing for the transaction and jurisdiction.
This reality often eliminates much of the promised time savings. What starts as a fast AI draft can become a time-intensive editing project, sometimes more laborious than starting from a proven template.
AI Drafting Tools vs. Purpose-Built Document Automation
There’s an important distinction between general-purpose generative AI and attorney-supervised document automation systems designed especially for lending. Tools built for this domain (such as those developed with deep input from experienced lending counsel) can incorporate:
- Jurisdiction-specific clause libraries
- Consistent defined-term management across document packages
- Built-in logic for common transaction variables
- Audit trails and version control suited to regulated environments
These systems don’t replace attorney judgment. Rather, they amplify it. They reduce mechanical errors so legal professionals can focus on the high-value work of structuring the deal and advising clients.
BankDocs Pro represents one example of this approach in practice: technology that supports, rather than substitutes for, rigorous legal oversight in commercial lending.
Practical Guidance for Users of AI Tools
AI is not going away, and it has legitimate roles in commercial lending workflows. Here’s a balanced framework for using it responsibly:
- Use AI for early-stage ideas, not final drafts. Generate outlines or alternative clause language for discussion, then build the actual documents from vetted templates.
- Limit AI to non-substantive components. It can help with recitals, signature blocks, or standard exhibits, provided everything is verified carefully.
- Always maintain human accountability. Have experienced lending counsel perform a substantive review. Document the review process, especially for high-value or complex transactions.
- Test for jurisdiction and consistency. Cross-reference every statutory citation and defined term. Run documents through jurisdiction-specific checklists.
- Consider the full cost. Factor in review time, potential rework, delay risk, and liability exposure when comparing AI-only approaches to purpose-built solutions.
Business owners and founders should ask their counsel or lenders about their document generation processes. Lenders and loan officers should understand the limitations of the tools their teams use. Attorneys in other practice areas venturing into lending matters should approach AI-generated first drafts with healthy skepticism.
Better Infrastructure Produces Better Outcomes
Commercial lending documentation isn’t a commodity. It allocates risk, protects economic expectations, and provides a roadmap for what happens if things go wrong. Cutting corners on quality, whether through over-reliance on general AI or outdated manual processes, creates downstream friction that can sour relationships and harm deal economics.
The most effective practices combine experienced legal judgment with modern, purpose-built technology. Farley Law is focused at this intersection: delivering faster, more reliable, and more enforceable lending documentation so deals can close confidently and perform as intended.
Business owners, founders, lenders, and counsel who want to discuss how these issues affect their transactions or explore more robust approaches to commercial lending documentation are invited to reach out. The right infrastructure doesn’t just reduce risk; it enables better deals.
Farley Law and BankDocs Pro are at the intersection of law and technology, building practical solutions for commercial lending. This article is for informational purposes and does not constitute legal advice.