For more than 30 years, R&D tax credit studies have relied on the same tool: interviews with subject matter experts.

The goal of the interview is simple. Answer one question:

How much time do you spend performing qualified research activities?

But the process used to answer that question is anything but efficient.

Why Interviews Became the Default

When the R&D credit gained traction in the early 1990s, most technical documentation existed only on paper. Reviewing thousands of pages stored in banker boxes was slow and impractical.

Interviewing the people closest to the work became the fastest way to estimate qualified research time. Out of necessity, the R&D credit interview was born.

A Process Frozen in Time

Despite massive changes in technology, the interview process has barely evolved.

Most interviews follow the same script. A short tax rule explanation. A walk through the employee’s job description. Then the real ask: estimate how much of your time qualifies based on a tax concept you just heard explained.

The result is a 60 to 90-minute meeting built on guesswork.

I once had an interviewee ask if this was my full-time job. When I said yes, he responded, “I would kill myself if that was my full-time job.”

The reaction is not unusual. Employees routinely avoid these interviews, delay scheduling, skip meetings, or claim they do not perform research at all.

The Hidden Cost

The true cost of an R&D credit interview is not the meeting itself.

It starts when the calendar invite arrives. Employees spend time preparing, worrying, and coordinating schedules. After the interview come follow ups, document requests, and clarifying questions.

When you account for all of it, a single interview typically costs about three hours per employee.

All of that effort to answer one question.

A Better Way Forward

Today, nearly all technical documentation exists in digital form. Modern AI tools can read and understand this data at scale.

AI models can identify qualifying projects, determine who worked on them, and estimate time spent performing qualified research activities based on evidence, not memory.

That changes the role of the interview.

Instead of guessing first and validating later, AI produces predictions up front. The interview becomes a short review to confirm or adjust the results.

What once took hours now takes minutes.

The outcome is not just faster. It is more precise, more defensible, and far easier on the people involved.