Why “AI-only” documentation raises audit risk—and how to build a defensible, human-in-the-loop process
The Problem with Letting AI “Do the Work”
AI is attractive for R&D credits because it promises to clean up messy documentation—pulling together notes, tickets, and costs into a neat summary. That sounds great… until you’re under IRS exam.
The IRS doesn’t award credits for polished narratives. It awards credits for facts tied to business components, activities, and costs—supported by contemporaneous records. When AI becomes the “witness” instead of the actual records (and the people who created them), your credit weakens.
In recent updates, the IRS has doubled down:
- Credits must be tied to specific business components.
- Refund claims must spell out who did what, when, and to resolve what uncertainty.
- Project-level detail is now required, not optional.
AI can help you find evidence—but without subject-matter experts (SMEs) validating intent and context, the result is fragile and easy to challenge.
Why AI Summaries Don’t Satisfy an Auditor
An IRS exam really boils down to three questions:
- Were there qualified activities under the four-part test?
- Are QREs tied to specific business components?
- Do contemporaneous records back up the activities and costs?
For amended claims and refunds, the IRS requires an even higher bar: taxpayers must name the business components, outline the activities, identify the people involved, and explain how uncertainty was addressed.
Key point: An AI-generated narrative is not evidence. At best, it’s a roadmap to the evidence. If you can’t produce the original artifacts—tickets, design reviews, lab notes, test logs—your claim won’t hold.
Where AI-Only Pipelines Fail
Five common failure modes show why AI alone can’t carry an R&D credit:
- Intent Blindness – AI finds verbs but can’t prove why work was done. SMEs must attest to intent.
- Business-Component Drift – AI tends to cluster across projects; the IRS demands component-level detail.
- Broken Provenance – Without IDs, timestamps, and source links, records lose credibility.
- Probabilistic Costs – “Likely QREs” are guesses. Misclassifications are common without SME review.
- Narrative Without Records – A pretty AI report with no attachments looks like advocacy, not evidence.
The Human-in-the-Loop Evidence Model (HLEM)
The alternative is a hybrid approach: let AI speed up the hunt, but keep SMEs and tax professionals in control. Think of it like internal controls for your credit.
- Evidence Ingestion (Tech-led)
- Direct connectors to systems (ERPs etc.).
- AI assists with deduplication and indexing.
- Rule: no data loses its source ID or timestamp.
- SME Validation (Engineer-led)
- SMEs confirm uncertainty, methods, and results.
- Deliverables: short attestations + annotated records.
- QRE Mapping (Tax-led)
- Costs tied to validated components/activities.
- Non-qualified items excluded with rationale.
- Binder Assembly (Controls-led)
- Each component has an Evidence Map, activity packet, cost tie-outs, and SME attestations.
- AI summaries serve only as an index, not a substitute.
Where AI Shines (with Guardrails)
- Recall & retrieval: find the right tickets or emails quickly.
- Entity linking: connect people ↔ components ↔ artifacts.
- Index drafting: create skeleton Evidence Maps.
- Variance scans: flag anomalies or missing data.
Rule of thumb: If AI makes the process faster but weakens traceability, it’s a failed control. Rework it until the evidence trail is stronger than before.
Red Flags That Scream “Audit Risk”
- One polished report with no underlying records.
- QREs claimed at department level, not tied to components.
- Missing SME attestations or uncertainty statements.
- Payroll allocations with no activity evidence.
- Broken provenance (“we can’t find that ticket anymore”).
- Overuse of generic terms (“innovation sprint”) without detail.
A Practical Checklist for Defensibility
People
- Assign a Component Owner for each project.
- Require SME sign-off on uncertainty, methods, and outcomes.
- Have tax leads document exclusions.
Process
- Lock Evidence Maps before filing.
- Keep a provenance log (who approved what, when).
- Document sampling if used.
Technology
- Use AI to index, not invent.
- Preserve IDs and timestamps.
- Automate linking, not conclusions.
How CTA Implements This Model
At Corporate Tax Advisors, we pair AI connectors with strict controls:
- Data layer: read-only connectors with immutable IDs.
- Review layer: SME attestations bound to artifacts.
- Tax layer: QRE mapping by component, exclusions documented.
- Binder layer: evidence packets finalized before Form 6765 filing.
The result: AI accelerates evidence gathering, while SMEs and tax pros ensure eligibility. That way, documentation matches exactly what the IRS tests.
Bottom Line
AI is a powerful accelerator—but not a witness. Use it to organize the trail, then rely on SMEs and tax professionals to confirm the details. That’s the difference between a fast claim and a defensible claim.
