Author:
R&D Tax Advisors
Role:
CPA
Publish Date:
Nov 10, 2025
The Question
“What can go wrong in an R&D tax credit study?”
More than most realize.
The R&D credit can be one of the most valuable incentives available to U.S. companies — but it’s also one of the most misunderstood, inconsistently applied, and frequently challenged by the IRS.
When the process isn’t handled correctly, companies risk more than just missing savings. They risk overstating credits, failing audits, or losing credibility with their CPA, investors, or the IRS.
Understanding where things go wrong is the first step toward making sure they don’t.
The Short Answer
Most R&D credit issues come down to one of four breakdowns:
Misunderstanding what actually qualifies
Weak or missing documentation
Over-reliance on templates or automated tools
Poor communication between technical and finance teams
Each one can undermine the accuracy or defensibility of a claim — even when the underlying work is legitimate.
The Deep Dive
1. Misunderstanding What Qualifies
The most common problem is conceptual — companies claim work that doesn’t actually meet the IRS definition of qualified research.
Common examples include:
Routine QA testing or bug fixes
Cosmetic UI adjustments
Data entry, deployment, or production support
Business process improvements unrelated to technology
The IRS’s four-part test defines qualified R&D as work that:
Has a permitted purpose (creating or improving a product, process, or software),
Is technological in nature,
Seeks to eliminate uncertainty, and
Involves a process of experimentation to do so.
Failing to apply that framework consistently — or stretching it too far — is one of the fastest ways to invite scrutiny.
2. Weak or Missing Documentation
The IRS doesn’t just want numbers; it wants evidence.
A strong R&D credit claim needs to show what was done, why it was uncertain, and how it was tested.
The most common documentation failures include:
Missing design docs or technical specifications
No contemporaneous records of testing or iteration
Payroll allocations without project context
Reliance on employee recollection long after the fact
When documentation is reconstructed retroactively — months after the work happened — accuracy drops and audit exposure increases.
Best practice: Capture documentation as work happens.
Sprint notes, Jira tickets, architecture diagrams, and Git commits can all serve as contemporaneous proof if tied to specific projects.
3. Over-Reliance on Templates or Automation
Automation can help gather data faster, but it often misses the nuance that determines eligibility.
Many companies (and even some providers) rely on standardized surveys or algorithms that assign blanket percentages to job titles or departments.
The result?
Activities that don’t qualify get swept in.
Others that should qualify get missed.
Documentation looks formulaic — something IRS agents spot immediately.
Automation can supplement human review, but it can’t replace a structured analysis that ties specific projects to the credit calculation.
4. Poor Communication Between Technical and Finance Teams
This one’s subtle but critical.
Finance teams understand tax requirements; engineering teams understand the work. The credit depends on both sides speaking the same language — and too often, they don’t.
Without structured communication:
Engineers undersell the technical challenges they solved.
Finance overgeneralizes activities to fill gaps.
The story connecting projects to tax criteria gets lost.
A successful R&D credit study translates technical problem-solving into the language of tax law — without losing accuracy or credibility.
5. Provider or Methodology Risk
Even when the company’s work qualifies, problems arise if the methodology behind the study isn’t sound.
Common pitfalls include:
Contingent-fee arrangements that encourage inflated credits
Incomplete project sampling that skews results
Inconsistent year-over-year methodologies that create red flags
Providers who disappear during audits — leaving companies to defend a report they didn’t fully understand
The best safeguard is to know how your study is done — not just the number it produces.
The Real-World Consequences
When an R&D credit study goes wrong, the fallout can be costly:
Audit adjustments and repayment: The IRS can disallow all or part of a credit and assess penalties or interest.
Amended return fatigue: Refiling to fix errors consumes months of time.
Lost credibility: Investors and auditors may scrutinize future filings more closely.
Opportunity cost: Teams lose faith in the process and stop claiming credits entirely — forfeiting future savings.
These aren’t rare outcomes. Most could be avoided with clear documentation, consistent methodology, and proactive communication between stakeholders.
How to Avoid These Problems
Document continuously. Capture what’s being developed, by whom, and why it’s technically uncertain.
Connect technical and tax teams early. Don’t wait until year-end to interpret the work.
Ask your provider to explain their methodology. How do they determine qualifying activities and percentages? Who conducts interviews?
Insist on audit-ready documentation. The deliverable should be more than a credit amount — it should tell the story behind it.
Review your process annually. Consistency year over year builds credibility with the IRS and state agencies.
The Takeaway
R&D credits aren’t lost because companies didn’t innovate — they’re lost because the story wasn’t told clearly enough to stand up to scrutiny.
A good study doesn’t just calculate; it documents, explains, and sustains.
That’s how you turn the R&D credit from a risky one-time win into a reliable, long-term advantage.
