Losing the Human Element: Why AI Alone Fails the Audit Test

AI is changing how tax teams prepare R&D credit claims. It can organize data, draft narratives, and even suggest ways to fill in gaps. But when companies let AI take over completely, they create serious AI vs human R&D audit problems. The IRS doesn’t audit software. It audits people. And people, especially subject matter experts (SMEs),  still make or break an audit.

Why Human Context Matters

IRS auditors want to understand the real technical challenges your team faced. They ask detailed follow-up questions and expect answers that align with your project records. AI can mimic technical language, but it can’t provide the first-hand knowledge that comes from doing the work.

Without SME input, narratives become vague, repetitive, or disconnected from actual testing logs. That disconnect makes it harder to defend your claim during an audit.

If you want to see how SME involvement transforms documentation, check out our guide on training SMEs for better R&D tax documentation.

The Audit Gap

An AI-generated narrative might pass an initial review, but in an audit, the IRS will:

1. Ask how your team identified uncertainties.
2. Request specific examples of testing methods and results.
3. Compare your descriptions to contemporaneous documentation.

If the person answering can’t explain the details beyond the AI’s generic wording, credibility drops fast. This is especially risky in today’s climate of increased IRS scrutiny on R&D tax credits.

When AI Replaces, Instead of Supports, SMEs

AI is best used as a support tool, not a replacement. Problems arise when:

• Teams rely on AI to write all narratives without SME review.
• Narratives use technical terms incorrectly because no one checks them.
• AI merges multiple projects into one generic story, losing important distinctions.

We’ve outlined in our optimizing your R&D tax credit process with technology post how automation should work alongside, not in place of, expert review.

Why IRS Auditors Prefer Human Interaction

Auditors know AI can generate convincing text. That’s why they focus on live conversations during audits. They want to see if the technical lead or SME can:

• Explain project challenges in their own words.
• Provide specific examples that match documentation.
• Show how decisions evolved over time.

AI can’t handle those live, nuanced conversations. And it certainly can’t answer questions about why the team chose one testing method over another.

The Risk of Generic Answers

When AI writes without SME oversight, it often creates answers that “sound right” but don’t hold up under scrutiny. For example:

• Generic statement: “The team conducted experiments to improve efficiency.”
• Strong human answer: “We ran three iterations of the heat exchange process, adjusting the input temperature by 5°C each time to measure throughput changes. The third test achieved a 12% improvement without increasing energy consumption.”

The difference is detail, credibility, and audit defensibility. This kind of specificity is what keeps claims strong — and it’s also the reason we stress having a defensible R&D credit process.

How to Keep the Human Element in Your AI Process

1. Use AI for structure, not substance. Let it organize your ideas, but provide the content from real project records.
2. Involve SMEs early. They should help shape the initial narrative, not just review the final version.
3. Train your SMEs for audits. They need to explain technical challenges in IRS-friendly language without losing accuracy.
4. Link AI outputs to documentation. Every claim should tie back to contemporaneous records.

Case Example

A software company used AI to draft all its R&D narratives for Form 6765. During an IRS audit, the lead engineer struggled to explain the “uncertainty” section because the AI had described it in broad terms. The narrative didn’t match the team’s actual work. The IRS disallowed two projects due to insufficient technical detail.

After that audit, the company shifted to an SME-led process with AI only providing draft structure. Their next claim passed with no IRS follow-up questions.

Key Takeaways

• AI can speed up narrative drafting, but it can’t replace human expertise.
• IRS audits depend on specific, first-hand technical explanations.
• SMEs must remain involved to ensure accuracy and audit readiness.
• AI works best when it supports — not replaces — people.

Your R&D claim is only as strong as the people who can explain it. AI is a great assistant, but it’s not the storyteller the IRS wants to hear.

Want to discuss R&D and AI? Reach out! We’ll help you build a process that blends technology with human expertise for audit-ready results.

Stephen Whiteaker, CPA

As MASSIE’s Director of Controversy, Stephen specializes in audit risk assessment and quality review. His proficiency in navigating complex tax law makes him an essential resource in addressing tax technical challenges for clients. His exten...