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Using AI for Your R&D Tax Credit? Don’t Forget the Documentation

Posted in Blog, R&D, on

AI can help companies analyze R&D activity faster than ever. It can sort through Jira records, Git repositories and other project data, connect employees to projects and help identify activities that may qualify for the R&D tax credit.

But when it comes to supporting your credit, identifying potentially qualified activity is only part of the job.

You still need to be able to show why the work qualifies.

A Jira Ticket Isn’t Necessarily R&D Documentation

Project-management systems are designed to help your teams build products and manage work. They weren’t designed to document a tax credit.

That distinction matters.

A Jira ticket might tell you that an engineer worked on a particular feature or issue. It may provide dates, assignments, comments and technical details. What it often doesn’t provide is the full information needed to demonstrate how the activity meets the requirements for qualified research.

For example, project records may not clearly document the technical uncertainty the team was trying to resolve, the alternatives considered or the process of experimentation used to reach a solution.

AI can help organize and interpret the information that’s there, but it doesn’t automatically turn operational records into complete R&D tax documentation.

Additional project details, employee input and supporting documentation may still be needed, particularly if the credit is later examined.

Keep Track of How AI Reached the Answer

There’s another documentation issue that’s unique to AI: reproducibility.

AI systems are generally probabilistic. Depending on the model, instructions and context provided, the same underlying information can potentially produce different results.

That makes it important to preserve more than the final spreadsheet or credit calculation.

Consider retaining the source data used in the analysis, the prompts or instructions provided to the AI system, the criteria used to define qualified and non-qualified activity, and the basis provided for individual classifications.

If a question comes up later, you want to be able to explain how you got from the original company records to the final credit calculation.

You don’t want the answer to be, “The AI said it qualified.”

Protect the Information You’re Sharing

R&D credit studies can involve some of your company’s most sensitive information. Jira exports, Git repositories and similar datasets may contain product roadmaps, proprietary technology, development issues, employee information and other confidential material.

Before providing that information to any AI platform, understand how your data will be handled.

Review the provider’s security controls and data-retention practices. Determine whether your information may be used to train models. Understand who can access the data, where it is stored and what happens to it after the analysis is complete.

The efficiency gained from AI shouldn’t come at the expense of protecting valuable company information.

Build a Credit You Can Explain

The real test of an R&D credit process isn’t how quickly the calculation can be produced. It’s whether you can explain and support the result.

That means connecting employees and wages to qualified activities, documenting the projects behind the credit and maintaining enough evidence to demonstrate why those activities meet the applicable requirements.

AI can make parts of that process significantly easier. It can analyze more information, identify relationships that might otherwise be missed and help your tax team focus its attention. But technology doesn’t eliminate the need for good records, thoughtful review and sound tax judgment.

If you’re considering an AI-based approach to your R&D credit, start by looking at the information your company already captures. Understanding the strengths and gaps in that data can help you determine where AI can add real efficiency and where additional documentation will still be needed.

The goal isn’t simply to automate your R&D credit study. It’s to use the right combination of technology, documentation and expertise to build a credit you can stand behind. Start a conversation with our team today.

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