The central question is not whether AI can be used in valuation work because it clearly can. Rather, the key question is how it should be used in a way that improves quality without compromising professional judgment, ethical obligations, or defensibility. The author shares his thoughts on the later question.
Artificial intelligence (AI) is quickly becoming part of the day-to-day workflow in business valuation. What began as a novelty for drafting and summarizing has matured into a practical tool that can assist valuation professionals with research, data organization, financial analysis, visual presentation, and administrative efficiency. The central question is not whether AI can be used in valuation work because it clearly can. Rather, the key question is how it should be used in a way that improves quality without compromising professional judgment, ethical obligations, or defensibility.
For valuation analysts, AI is best understood not as a substitute for expertise, but as an accelerator of certain parts of the workflow. It can function like a research assistant, a drafting aide, or a tool for organizing large volumes of information. It can summarize an industry report, identify recurring themes across financial records, suggest ways to visualize performance trends, or help prepare meeting notes and follow-up questions. Used appropriately, AI can reduce time spent on lower-value tasks and create more space for higher-value work: critical analysis, professional skepticism, and the exercise of judgment. That framing is essential, because the valuation conclusion must still belong to the analyst, not the software.
One of the most practical uses of AI in valuation is research and analysis. Valuation engagements routinely require the analyst to review economic commentary, industry reports, market observations, company documents, and other background materials. AI tools can help summarize these materials quickly and organize information into usable categories such as key trends, growth drivers, risks, and outlook considerations. This can be especially helpful at the early stage of an engagement, when the professional is trying to build an understanding of the subject company and its environment. Instead of replacing research, AI can make initial research more efficient by accelerating the first-pass review of large document sets.
That efficiency, however, only has value if the analyst verifies the result. A polished AI summary can create a false sense of reliability. The output may sound authoritative while omitting nuance, overstating conclusions, or incorporating unsupported claims. For that reason, an AI-generated summary should be treated as a draft or a starting point, not a final answer. The analyst must still check the underlying sources, confirm that the summary aligns with the actual report, and rewrite the content as needed to reflect the facts and the analyst’s own understanding. In other words, AI may help produce a first draft of understanding, but it cannot relieve the valuator of responsibility for accuracy.
AI also has a role in financial modeling support. In most engagements, the analyst still performs the underlying historical spread, ratio analysis, normalization work, and valuation methodology selection. But once the core analytical work is completed, AI may help explain model structure, identify trends in the data, and generate charts or other visuals that make financial performance easier to interpret. A visual representation of revenue, margins, leverage, working capital, or other metrics can sometimes help the analyst see the company more clearly and communicate the story more effectively. This is particularly useful where the objective is not simply to compute a value, but to explain the underlying economics of the business to counsel, clients, mediators, judges, or other readers.
Still, financial modeling is an area where overreliance on AI can become dangerous. AI tools can assist with explanation, formatting, and visual presentation, but they do not independently understand whether assumptions are appropriate, whether an input is flawed, or whether an output is economically reasonable. A valuation professional must continue to evaluate whether the company-specific facts support the assumptions used, whether the selected methods are appropriate for the engagement, and whether the conclusion is consistent with both the data and the standard of value being applied. AI can help tell the story of the numbers, but it does not replace the responsibility to understand what the numbers mean.
Administrative tasks present another productive use case. Many valuation and forensic accounting engagements involve large amounts of communication, interviews, document management, and note-taking. AI can assist by drafting e-mails, summarizing documents, transcribing meetings, and helping turn conversations into organized follow-up questions. In litigation-related matters, it may also help generate potential questions for management interviews, depositions, direct examination, or cross-examination preparation. These uses are attractive because they save time without directly displacing the analyst’s technical reasoning. They can improve workflow and responsiveness while leaving the substantive judgment where it belongs: with the professional.
The promise of AI in these areas rests largely on three strengths: speed, scalability, and pattern recognition. AI can process large volumes of text faster than a human, summarize recurring themes across multiple documents, and help surface patterns that might otherwise take longer to identify. In valuation practice, where time budgets are real and information sets can be extensive, those advantages are meaningful. They may improve turnaround times, support broader review of available information, and help practitioners communicate findings more clearly.
But those strengths must be balanced against equally important limitations. AI can be wrong. It can rely on incomplete or outdated information. It may embed bias, misread context, or present speculation in a way that sounds like fact. Most importantly, it lacks independent professional judgment. It does not know when an answer is inappropriate for a specific legal context, when a financial assumption conflicts with engagement facts, or when a technically plausible answer is professionally indefensible. Those shortcomings are not minor. In valuation work, where conclusions may affect litigation outcomes, transactions, tax positions, or shareholder disputes, even small errors can have significant consequences.
That is why human oversight is not optional. A valuation analyst must maintain independence, skepticism, and ownership of the work product. AI output should be reviewed, tested, and revised before it becomes part of a report, memo, or expert opinion. A defensible process requires that the professional understand what the AI was asked to do, what information it used, what limitations may affect the answer, and whether the output makes sense considering the facts. The more important the conclusion, the less acceptable it is to treat AI as a black box.
The opinion from Matter of Weber, 2024 N.Y. Slip. 24258 (N.Y. Surrogate’s Ct. Oct. 10, 2024), underscores the litigation risk that can arise when an expert uses AI without being able to explain the process. As noted in the opinion, the expert relied on Copilot but could not adequately explain the prompts used, the sources relied upon, how the tool arrived at its output, or whether important considerations such as fees and tax implications had been accounted for. Whether one views that case as a warning sign or an early example of a developing issue, the lesson is straightforward: if AI contributes to an expert’s work, the expert must still be able to explain and defend the work. The inability to do so invites criticism.
That leads directly to the ethical and professional dimension of AI use. Disclosure remains an evolving issue, and the right disclosure practices may differ depending on the nature of the engagement, applicable standards, the forum, and the extent of AI assistance. Still, one distinction is especially useful: there is a difference between AI assistance and AI authorship. If AI helps summarize background materials, organize notes, or generate a draft visual, that is materially different from allowing AI to supply substantive analysis that the professional has not independently verified. The closer AI gets to the heart of an opinion, the more important transparency and documentation become.
Confidentiality and data governance also deserve close attention. Valuation professionals routinely handle sensitive client information, including financial statements, tax returns, banking records, QuickBooks data, and litigation materials. Before uploading or entering any such information into an AI system, the professional must understand the security settings, the provider’s data usage policies, the firm’s internal rules, the engagement letter, and any legal or professional constraints that apply. A useful AI tool is not automatically an acceptable AI tool. Convenience should not override confidentiality.
For firms that want to use AI responsibly, practical safeguards are essential. At a minimum, firms should establish written AI policies, approve specific tools for business use, train staff on proper use and limitations, require human review of all substantive outputs, and preserve documentation sufficient to explain how AI was used. In some matters, that may also include retaining prompts, source references, and workpaper support showing how the analyst validated AI-assisted output. These safeguards do more than reduce risk. They help preserve credibility. AI should strengthen the quality of the practice, not dilute trust in it.
There is also a broader professional development issue. Valuation professionals do not need to become technologists, but they do need to become informed users. That means understanding what common AI tools do well, what they do poorly, and how to question their results. It also means recognizing that junior staff may be especially vulnerable to automation bias if they treat fluent output as inherently accurate. Training should therefore cover not only how to use AI, but how to challenge it. Good AI practice in valuation is less about prompt cleverness and more about disciplined review.
Looking ahead, AI will become more deeply integrated into valuation practice. It will likely improve document review, data extraction, financial visualization, and workflow coordination. Industry-specific tools may also become more capable in research, market synthesis, and preliminary analytical support. Yet even as these tools improve, the core professional functions of judgment, skepticism, independence, and accountability will remain non-delegable. Business valuation is not just a data-processing exercise. It is a reasoning exercise carried out in a professional context, often under scrutiny.
The most productive way to view AI in valuation, then, is as a force multiplier rather than a replacement. It can enhance efficiency. It can help organize and communicate information. It can improve workflow and, in some cases, deepen the analyst’s perspective by making more information manageable. But it must operate within a disciplined framework of human review, ethical awareness, transparency, and risk management. In the end, the value of AI in business valuation will not be measured by how much work it can do on its own. It will be measured by whether it helps valuation professionals do better work, more thoughtfully, and more defensibly.
Gregory M. Clark, CPA, CVA, MAFF, is the Managing Member of GMC & Company. He specializes in valuation, financial forensics, M&A advisory, forensic accounting, damages, and further business and commercial litigation in a diverse range of industries. Mr. Clark serves as an expert witness for complex financial matters related to valuation, marital dissolution, shareholder disputes, damages, business litigation, commercial litigation, lost profits, and further matters requiring financial, valuation, and/or forensic accounting expertise. He is a frequent author and speaker on valuation, litigation advisory, business management, and other financial topics.
Mr. Clark can be contacted at (219) 554-9700 or by e-mail to greg@gmcandco.com.
