AI and the IRS: What Tax Teams Need to Know

AI in R&D tax credits is gaining attention. Tax teams are exploring tools that can scan project records, tag qualified research expenses (QREs), and even draft documentation. But there’s a question every tax leader must ask: How will the IRS view AI-generated documentation?

The IRS has not released specific guidance on AI, but its expectations for R&D documentation remain clear. Agents want verifiable evidence, alignment with the four-part test, and proof that qualified individuals performed the work. AI output alone cannot deliver that.

In this article, we’ll explore how the IRS may approach AI in audits, the risks of over-automation, and how tax teams can prepare.

Why the IRS Cares About AI in R&D Credits

The IRS has one goal in reviewing R&D claims: ensure taxpayers comply with the law. AI introduces both opportunity and risk in that process.

On one hand, AI can strengthen claims by producing contemporaneous documentation and reducing human error. On the other, it can generate summaries that lack the detail auditors require.

The IRS is watching this trend closely. With Form 6765 updates requiring more granular reporting, companies must provide more detail than ever before. That means AI-driven shortcuts will not pass muster if they fail to show uncertainty, experimentation, and qualified activity.

Audit Expectations

The IRS does not relax its standards for new technology. Whether records are AI-generated or human-written, they must meet the same requirements.

Detail

Auditors expect clear descriptions of what was tested, how it was tested, and what uncertainty was resolved. Generic summaries—AI or otherwise—will not suffice.

Evidence

The IRS favors contemporaneous documentation. AI can help collect this, but teams must retain the original project records, not just the AI-processed output.

Four-Part Test Alignment

Every activity must tie back to the statutory test: permitted purpose, elimination of uncertainty, process of experimentation, and technological nature. AI tools cannot apply this framework on their own.

Risks of Over-Automation

Tax teams that rely too heavily on AI face several risks.

The first risk is false confidence. AI output can look authoritative without actually meeting IRS documentation standards. Tax teams that treat AI summaries as proof, rather than as a starting point for human review, are vulnerable when examiners ask for underlying source records. The second risk is misclassification. AI tools that flag activities based on keyword patterns may surface routine tasks as qualified research, creating exposure that is difficult to unwind during an audit. The third risk is documentation gaps. IRS agents frequently request original evidence, including tickets, lab reports, test results, and time records. AI summaries that do not tie back to those sources will not hold up under examination.

At MASSIE, we’ve seen companies lean too heavily on automation, only to face difficult conversations during IRS examinations.

How IRS Auditors May Treat AI Output

The IRS has not taken a formal position on AI, but the audit behavior of examiners points in a clear direction. Auditors treat AI as a tool for organizing data, not as a source of proof. When examiners request original evidence, they want the underlying records: tickets, lab reports, test results, and time logs. A generated summary, however well structured, does not satisfy that request. AI output that appears generic or disconnected from actual project facts can also increase scrutiny rather than reduce it. For companies that want to establish clarity before filing, the IRS Pre-Filing Agreement program offers a path to resolve questions proactively rather than under examination pressure.

Building Defensibility with AI Tools

The best approach is hybrid: AI plus human oversight.

SME Validation

Engineers and technical leads must confirm whether flagged projects truly qualify. AI can surface the data; SMEs provide the judgment.

Documentation Standards

Tax teams should retain original records, even if AI tools generate summaries. These records prove the claim meets IRS requirements.

Governance Framework

Establish policies for how AI is used, how outputs are validated, and how source records are preserved. This reduces audit exposure and demonstrates control.

At MASSIE, we apply AI thoughtfully within the MASSIE Method. Every AI-assisted document goes through SME review and professional validation before it is filed.

The MASSIE Perspective on Audit Readiness

AI can make R&D tax credit work faster and more efficient. IRS scrutiny, however, is not decreasing. It is increasing. For tax teams exploring AI, the path forward is not full automation. It is a deliberate balance: AI captures and organizes data, and qualified professionals interpret, validate, and defend it. That combination reduces risk and produces claims that hold up when the IRS asks hard questions. If your team is evaluating how to structure AI use within your R&D documentation process, MASSIE can help. We work with tax departments and CFO offices to build AI-assisted workflows that meet IRS standards and hold up under examination.

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...