Why Human-Centered AI Is Changing R&D Credit Defensibility

Modernizing the R&D credit process does not mean shifting responsibility to engineering teams or relying on stand-alone software to generate conclusions. Approaches that overautomate or push documentation downstream often increase disruption without improving audit readiness.

As Form 6765, “Credit for Increasing Research Activities,” expectations continue to shape IRS examinations, a smarter approach integrates technology and AI in a human-centered way – using it to surface issues earlier, improve consistency, and focus professional judgment where it matters most. This approach allows for more targeted SME engagement, greater context, clearer analysis, and documentation that is better positioned to withstand scrutiny in today’s examination environment.

Where traditional R&D tax study models start to strain

For many taxpayers, the structure of an R&D tax study has remained largely unchanged, even when the R&D activities have materially evolved. Studies still commonly are performed after the fact and under deadline pressure, limiting consistency and weakening alignment to how development activities actually occur.

That gap has become more consequential under today’s examination environment. Recent case law and revisions to Form 6765 have increased IRS demands for business component-level nexus, traceability of activities, and support for time and costs – placing greater emphasis in IRS examinations on how positions are developed and documented, not just the resulting credit amount.

As scrutiny increases, traditional R&D study models are more prone to business disruption and examination risk, particularly when taxpayers and advisers identify issues late in the R&D study process. By using technology and AI to surface gaps, inconsistencies, and higher-risk areas earlier, taxpayers can take a smarter, more proactive approach – one that allows experienced professionals to focus on the right issues and produce documentation that is better positioned to hold up under IRS examination.

Modernizing without creating more work

A human-centered, technology-enabled approach can materially improve the defensibility of R&D credit claims in the following ways:

• Reducing early study disruption through informed analysis. Using AI to review technical records before engaging SMEs allows initial conversations to move quickly past basic explanations of how teams work and allow SMEs to focus instead on evaluating qualifying activities and business components. This reduces back-and-forth requests with advisers and allows professional judgment to be applied where it adds the most value.
• Strengthening business component definitions through cross-evidence analysis. Using AI to analyze and compare technical documentation, cost data, tracking systems, and product information helps identify misalignment in how business components are supported across evidence. Addressing these inconsistencies early allows professionals to strengthen nexus and documentation proactively, reducing the risk of the IRS proposing material adjustments to the credit during examination because of fragmented support.
• Preserving institutional knowledge beyond individual contributors. Using AI to analyze technical and operational data created in the normal course of business allows critical context about development activities to be captured independent of the activities of specific individuals. This strategy reduces reliance on interviews and memory years later and provides more consistent, defensible support when R&D claims are reviewed long after teams or personnel have changed.
• Improving precision and responsiveness during an examination. Human-centered AI helps locate and validate relevant technical support across large and varied data sets, such as patents granted after the R&D study period or extensive engineering documentation. This focus allows for more targeted responses and stronger positioning without pulling internal teams into prolonged, reactive efforts to respond to the IRS during an examination.

What feels different in practice

As IRS scrutiny continues to increase, claiming and sustaining R&D credits claimed requires a more disciplined approach. The bar for documentation, linkage, and consistency is higher. Outdated and retrospective R&D study models are less likely to meet those expectations reliably. Technology and AI, when applied thoughtfully, no longer are optional. They are necessary to generate the level of insight and documentation required to support R&D credits during an examination.

For taxpayers, this shift is not about producing the same R&D study faster or cheaper but about producing a study that holds up when examined by the IRS. It is about doing smarter work – focusing effort where risk is highest, producing clearer and more defensible documentation, and positioning the credit to withstand sustained scrutiny.

Crowe observation

Organizations that adapt their approach will be better positioned to continue claiming the R&D credit with a higher degree of confidence; those that do not are likely to experience increasing friction and risk during IRS examinations.

Looking ahead

A sustained emphasis on business component-level nexus and contemporaneous technical documentation should be the expected baseline for any R&D tax study. Credits built solely on retrospective reconstruction and inconsistent support will face increasing pressure as IRS scrutiny of R&D credits increases.

Taxpayers are best positioned to withstand IRS scrutiny by adopting smarter, more disciplined approaches using technology, AI, and disciplined automation to improve focus, consistency, and insights. When applied intentionally, this approach can strengthen documentation, reduce reactive examination burden, and support defensible positions when credits are challenged.