AI has become a shortcut for speed. Faster reports. Faster summaries. Faster output. Arguably a great outcome.

But speed alone is not progress, especially when the underlying process has not changed.

Across many industries, AI is being used to generate narratives from existing assumptions. Interviews are still conducted. Estimates are still made. The difference is that a language model now drafts the report.

That approach feels modern. It is not rigorous.

At SPRX, we believe AI should eliminate estimation and statistical sampling in the tax credits, not automate the writing of them.

Automation That Produces Text Is Not the Same as Automation That Produces Truth

AI-driven automation is often framed as a productivity win. McKinsey estimates that automation can reduce operating costs by up to 25 percent.

That only holds when automation replaces manual judgment and approximation.

Using generative AI to draft reports does not improve the quality of inputs. It accelerates output based on whatever assumptions were fed into the system. If the process still depends on interviews, sampling, or same-as-last-year logic, AI has not improved it. It has only made it faster.

True automation analyzes underlying activity directly and applies consistent logic at scale. That is where accuracy improves and risk declines.

Interviews Are a Technology Limitation, Not a Best Practice

Interviews became standard because there was no scalable way to analyze complex work directly. For decades, that limitation shaped how studies were conducted.

That limitation no longer exists.

AI can now analyze large volumes of documentation, structure unstructured data, and evaluate activity without relying on memory or retrospective estimates. Continuing to depend on interviews is not conservative. It is outdated.

Replacing interviews with narrative-generating AI does not solve the problem. It preserves it.

Generative AI Is Not the Same as Evidence-Based Analysis

Not all AI is built for the same purpose.

There is a fundamental difference between using AI to write a report and using AI to determine what the report should say.

At SPRX, AI is applied upstream. Our systems analyze real work activity and supporting evidence directly. Conclusions are derived from data, not drafted first and justified later.

Speed that comes from narrative automation is easy to achieve. Speed that comes from eliminating estimation is much harder, and far more valuable.

Better Decisions Come From Evidence, Not Stories

Decision-making improves when inputs improve.

Harvard Business Review reports that organizations using AI in decision-making see significantly better outcomes because AI processes full datasets instead of selective summaries.

When conclusions change depending on who was interviewed or how questions were framed, the process is not objective. AI should make outcomes repeatable, consistent, and defensible.

That is only possible when evidence, not recollection, is the foundation.

Innovation Begins Where Approximation Ends

Estimates smooth over reality. They hide edge cases. They miss opportunity.

Organizations that see strong returns from AI do so because AI reveals what approximation obscures. Not because teams worked harder, but because assumptions were removed from the process.

AI does not replace expertise. It exposes the limits of guesswork.

Why SPRX Exists

The R&D tax credit industry still runs on interviews, statistical sampling, and same-as-last-year assumptions. These methods persist because they are familiar, not because they are accurate.

SPRX was built to replace them.

By applying AI to analyze real work activity and supporting evidence directly, SPRX removes estimation from the R&D credit process. Reports are delivered quickly because the system is precise. Defensibility improves because conclusions are grounded in documented evidence, not narrative generation.

If your R&D credit relies on interviews or AI-written reports, the process has not been modernized. It has only been repackaged.

If you're looking for a better way, get in touch with our team.