Garbage In, Garbage Out: Why AI Can’t Fix Bad R&D Data

AI tools are only as good as the data they’re given. In R&D tax credit work, that truth becomes critical. If your inputs are incomplete or inaccurate, you’re setting yourself up for serious AI data quality R&D problems. Instead of fixing weak records, AI will simply produce polished but wrong narratives — and that can put your claim at risk.

The Myth That AI “Cleans” Data

Some teams believe AI can take messy or incomplete data and transform it into something accurate. It can’t. AI doesn’t know if your expense codes are wrong, if your time entries are outdated, or if a project’s scope has been misclassified.

All it can do is take what you give it and present it more neatly. That’s dangerous because it creates a false sense of security. The output looks professional, but it might be reinforcing — even amplifying — your original errors.

If you want to see an example of how software can introduce risk, read our breakdown of a controlled group Form 6765 software error that cost companies valuable credits.

Why Data Quality Matters in R&D Claims

In R&D tax credit compliance, every claim stands or falls on documentation. If the inputs are bad, the risk rises in three ways:

1. Misclassified expenses — AI will happily include non-qualified costs in its calculations.
2. Vague activity records — AI will rewrite them in better English but still fail the IRS detail test.
3. Missing contemporaneous evidence — AI can’t invent a timestamped record that didn’t exist when the work was done.

This is why having a defensible R&D credit process matters more than any automation you use.

Common Bad Data Scenarios

We see certain problems come up again and again:

• Old time-tracking systems that don’t capture the level of detail the IRS now expects.
• Project codes that don’t match actual work performed.
• Expense records lumping R&D costs with unrelated activities.
• Incomplete testing documentation, especially in projects that pivot midstream.

If your process has these weaknesses, AI will only make them look cleaner — not fix them. For ideas on tightening your documentation, see our tips on training SMEs for better R&D tax documentation.

How AI Amplifies Bad Data

AI operates on patterns. If you give it incomplete activity notes for a software project, it might “fill in” missing context with generic industry language. That might sound better, but it’s still wrong if it doesn’t reflect what your team actually did.

Worse, AI can carry forward those errors into multiple project narratives, multiplying the risk. This is especially dangerous if you rely on AI for high-volume claims across business units.

The Human Role in Data Quality

AI doesn’t replace the need for human review — it increases it. SMEs and tax teams need to:

• Verify data accuracy before feeding it to AI.
• Flag potential issues in activity records.
• Compare AI’s output against actual project logs and testing notes.

For more on how AI and human review can work together, see our piece on AI in R&D tax credit qualification.

The Four-Part Test and Data Quality

The IRS four-part test is unforgiving when the underlying data is sloppy. For example:

• Permitted purpose: If your records describe “product upgrades” without technical details, AI will repeat that — and fail to show qualified purpose.
• Process of experimentation: If your testing notes are missing, AI might default to vague descriptions that the IRS rejects.
• Technological in nature: Without the right technical inputs, AI can’t produce language that meets the definition.

Improving Data Before AI Gets Involved

To make AI work for you, clean up your data first:

1. Audit your time tracking. Ensure it captures enough detail for each qualified project.
2. Review expense coding. Separate qualified from non-qualified costs clearly.
3. Update project records. Fill in testing details, decisions made, and alternatives considered.
4. Train SMEs. Give them the tools and guidance to log activities in IRS-friendly formats.

Once this foundation is solid, AI can help organize and format it — but it should never be the one deciding what’s true.

Real-World Example

A manufacturing client fed AI their R&D time sheets and testing notes. The tool created sharp, concise narratives for each project. On review, the SME team realized several problems:

• Two projects listed as new products were actually routine quality control work — not qualified R&D.
• Testing methods were described generically, leaving out specific measurement criteria.
• Some expenses tied to non-qualified marketing research had been included in totals.

If they had filed without human review, the IRS could have challenged those projects — and the claim’s credibility.

The Bottom Line: AI Needs Quality to Deliver Quality

When your inputs are accurate, complete, and verified, AI can make the process faster and more consistent. When your inputs are sloppy, AI just helps you get to a bad result faster.

The smartest teams treat AI as a formatting and organization tool — not a truth-finder. The truth still comes from your people, your records, and your process discipline.

Key Takeaways

• AI doesn’t fix bad data — it amplifies it.
• Strong inputs come from disciplined documentation, not automated guesses.
• Human review is essential before and after AI is used.
• Data quality directly impacts how well you meet the IRS four-part test.

If your team is thinking about using AI for R&D tax credit documentation, start with your data. Clean inputs are the only way to get safe outputs.

Want to discuss R&D and AI? Reach out, and we’ll help you pair quality data with the right technology for stronger, audit-ready claims.

Jason Massie, CPA, Esq.

As the Founder and President of MASSIE R&D Tax Credits, Jason is responsible for the quality, consistency, and integrity of our services. His strategic vision and leadership drive innovation within the company, encouraging collaboration and ne...