How AI Is Changing R&D Tax Credit Documentation

AI in R&D tax credits is changing how companies document their claims. For decades, most R&D credit studies relied on memory-based interviews, spreadsheets, and late-stage data collection. With the IRS demanding more detail through updates to Form 6765, that approach is becoming harder to defend.

Artificial intelligence offers new possibilities. It can scan large volumes of technical data, identify potential qualifying research expenses (QREs), and even draft preliminary documentation. But AI isn’t a silver bullet. Without professional oversight, it can introduce new risks.

In this article, we’ll explore how AI is transforming R&D tax credit documentation, where it works, and why SMEs still play a central role.

Why Documentation Is Central to the R&D Credit

The research credit is built on proof. To qualify, companies must show they performed activities that meet the four-part test, incurred costs directly tied to those activities, and kept records to support those claims. Documentation is not just recommended—it is required.

Traditionally, companies relied on engineer interviews and after-the-fact surveys. While these methods can work, they create weak spots: memories fade, details are lost, and the IRS increasingly expects contemporaneous evidence.

At MASSIE, we’ve written about the importance of process design in optimizing your R&D tax credit process using today’s technology. AI is the next step in that evolution.

The Traditional Approach

For many companies, R&D documentation has looked the same for years:

• Interviews: Engineers are asked to recall which projects involved uncertainty.
• Spreadsheets: Finance teams manually track expenses across projects.
• Reports: Tax teams draft narratives that summarize activities and link them to costs.

This process works, but it is slow, resource-heavy, and prone to error. Most importantly, it leaves companies vulnerable if the IRS asks for contemporaneous records.

How AI Changes Documentation

AI offers new ways to capture, organize, and analyze data in real time.

Real-Time Data Capture

AI tools can pull records directly from systems like Jira, GitHub, or ERP platforms. Instead of asking an engineer what they worked on last year, the system can show ticket history or design revisions as they happened.

Natural Language Processing

By scanning logs, emails, and project notes, AI can flag language that indicates experimentation or technical uncertainty. For example, terms like “prototype,” “iteration,” or “failed test” often suggest qualified activities.

Categorizing Costs Automatically

AI can tag wages, supply costs, or contractor invoices as likely QREs. These preliminary classifications save finance teams hours of manual work and highlight where more review is needed.

Risks of AI-Only Documentation

While AI speeds up data gathering, it cannot replace human judgment.

• Misclassification: AI may confuse routine work with experimentation.
• Lack of context: AI cannot determine project intent, which is critical to the four-part test.
• Audit risk: IRS agents expect real evidence, not just an AI-generated summary.

In short, AI helps find the data, but it doesn’t prove eligibility on its own.

AI + SME Oversight = Defensible Process

Subject matter experts (SMEs) remain central to defensibility. Engineers and technical leads must confirm whether flagged activities truly involved eliminating uncertainty.

This is why training SMEs matters. In fact, we’ve shared best practices in our piece on training SMEs on R&D documentation. AI surfaces the data; SMEs validate it.

At MASSIE, we view AI as one more tool within a defensible process. Every AI-tagged record is reviewed by professionals. Our defensible R&D credit process combines technology with layered review, ensuring claims meet IRS standards.

Key Takeaway

AI is reshaping R&D tax credit documentation. It makes data collection faster, reduces manual work, and improves audit readiness. But AI alone cannot create a defensible claim. Tax teams must balance automation with professional oversight to meet IRS expectations.

For companies navigating this shift, the best approach is hybrid: AI plus human expertise.

Want to talk about how to use AI within your tax team? Reach out. We’d love to chat.

John Hoang, CPA

As Director of Tax Technology at MASSIE, John plays a pivotal role in revolutionizing the approach to R&D tax credits. He specializes in providing clients with innovative and forward-thinking technology solutions for their R&D tax credit c...