Admissibility in the AI Era: How to Ensure Your AI-Powered Valuations Meet Frye and Daubert Standards

Artificial intelligence is rapidly reshaping the valuation landscape, offering speed, consistency, and deeper data access than ever before. But amid the excitement, one question looms large for valuation professionals whose work must hold up under legal scrutiny:

Will your AI-assisted analysis stand up in court?

Too often, the conversation around AI in valuation is framed as a binary: either you avoid AI altogether to stay “safe,” or you adopt black-box tools that raise serious admissibility risks. This is a false choice. The future of valuation lies not in avoiding AI, but in applying it with precision, discipline, and transparency.

A Quick Refresher: Frye and Daubert in Plain English

To understand how AI intersects with legal admissibility, let’s revisit the two key standards courts use to decide whether expert testimony and the methods behind it can be admitted:

• Frye Standard (used in some state courts): Is the methodology you used generally accepted by professionals in your field?

• Daubert Standard (used in federal courts and most states): Is your method reliable? Judges evaluate this using several factors:

  • Can the technique be tested?

  • Has it been peer-reviewed?

  • Is there a known or potential error rate?

  • Are there standards and controls in place?

In both cases, the courts are asking: Is this expert using a method that is not only professional, but also provable?

The New AI Risk: Less About Hallucinations, More About Persuasive Inaccuracy

Thanks to newer model architectures and techniques like Retrieval-Augmented Generation (RAG), large-scale AI hallucinations (where the model fabricates data) are less frequent than they used to be.

But they haven’t disappeared. They’ve evolved.

According to the 2025 AI Index Report from Stanford’s Institute for Human-Centered AI, today’s top models are more likely to present errors that are subtle and stylistically convincing. In other words, the AI might reference a real document but subtly misinterpret it, or confidently summarize information in a way that distorts its true meaning. These kinds of inaccuracies are harder to catch and more dangerous.

Worse still, human evaluators (including professionals) are shown to prefer these confident, polished, but less accurate AI answers in study after study. The AI isn’t just wrong; it’s persuasive in its wrongness.

This reality raises the bar, not lowers it. As models become smoother, the responsibility shifts to the expert to prove that the conclusions are anchored in traceable, verifiable evidence.

Traceability: The Linchpin of a Defensible Methodology

For an AI-assisted valuation to be admissible under Daubert, the key question becomes: Can someone outside your firm retrace your steps and understand how your conclusion was reached?

Let’s look at the Daubert factors through this lens:

• Testability: A “black box” tool is not testable if there’s no way to recreate or audit how the result was produced. A reliable process shows the full path from prompt to output to final application.

• Error Rate: With AI, the question isn’t just “how accurate is the model?” It’s “how do we measure accuracy when the model is invisible?” A traceable system shifts this focus back to the underlying source data, which is something valuation professionals already know how to evaluate.

• Controls and Standards: In valuation, standards like SSVS 1, USPAP, and NACVA Professional Standards help ensure process discipline. For AI, the equivalent is a transparent audit trail: one that logs inputs, outputs, and human verification steps along the way.

Best Practices: How to Use AI Without Undermining Yourself

Here are advanced but implementable best practices designed for valuators navigating the AI era:

1. Maintain a “Dual-Track” Audit File

Create a separate audit log for AI-assisted tasks. This file should include:

• The original prompt (exactly as it was entered)

• The raw AI output (unedited)

• The cited source (e.g., “2024 10-K, Item 7, Page 42”)

• Your verification note (confirming accuracy and how it was applied)

This protects you in case of challenges and helps you train junior staff more effectively.

2. Treat AI Like a Junior Analyst, Not a Magic Box

Instead of asking, “What are some good comps?”, try:

“Using only the attached Excel file, identify all transactions in SIC code 7372 over the past 18 months. Exclude deals under $20M. Extract revenue, EBITDA, and deal multiple from the filing, and cite the exact source page.”

This creates structured, traceable output, just like you’d expect from a junior team member.

3. Implement “Chain-of-Evidence” Reporting

Don’t bury source details in footnotes. Include an appendix that shows how key figures were derived. For example:

“The 2.8x revenue multiple in Appendix C was extracted from public filings (see AI Audit Log #V25-14a) and verified against the July 2025 10-Ks.”

That level of transparency is what Daubert demands and what clients, regulators, and courts increasingly expect.

4. Proactively Test for Failure Modes

Occasionally ask your AI tool to do something it shouldn’t be able to do. Try giving it a fake company name or a known non-existent transaction. Does it fabricate or flag the gap?

Documenting how your system handles these edge cases demonstrates procedural diligence, not just technological dependence.

But What About Time and Cost?

This is the question every managing partner, director, and COO is asking: “Is this going to create more work or eliminate it?”

In truth, traceability without automation can feel like a burden. That’s why at Ranger Docs, we believe the answer isn’t to sidestep documentation; it’s to automate it.

The ultimate goal is to make the entire valuation process transparent and defensible, from source document to final report. But we know that before you can audit the AI, you must be able to audit the data and documents used.

That is why our current development effort is focused on perfecting the bedrock of any defensible analysis: an intelligent, traceable document management system. This system creates an unbreakable "chain of custody" for every piece of evidence in your work file. Every source document from SEC filings to interview notes is logged, versioned, and can be linked directly to the analysis it supports. By solving for data integrity and traceability at the source, we are building the essential foundation upon which all future AI tools, including a fully automated audit log, will eventually be built. It ensures that no matter the tool, the evidence is traceable and the conclusion is auditable.

We’re not releasing this feature publicly just yet, but we are actively collaborating with senior leaders at valuation and M&A firms to ensure it fits real world workflows. If you're interested in shaping that next phase of tooling, we’d love to connect.

Closing Thought: The Role of the Expert is Evolving, Not Disappearing

As AI takes on more of the heavy lifting (data scraping, data analysis, draft generation) our role as valuation professionals doesn’t diminish. It sharpens.

In this new paradigm, the expert's value is no longer in the data processing, but in the intellectual architecture of the analysis, the rigorous validation of its inputs, and the ultimate application of seasoned professional judgment.

Tools will change. Court standards will evolve. But transparency, intellectual rigor, and defensibility will always be the bedrock of admissible analysis.

If this conversation reflects how you're thinking about AI’s role in professional valuation, we’d love to compare notes. Reach out at hello@rangerdocs.com or connect with us privately for early access discussions about ai automation.

Because in this new era of valuation, it’s not just what you conclude - it’s how you got there that matters.

This content is for informational purposes only and is not legal or financial advice. Please consult with a qualified professional for advice tailored to your specific situation.