Why do in-house AI pilots plateau at 70-80% accuracy?

In short.
A scenario keeps repeating itself in companies: the internal document AI pilot impresses, then stalls at between 70 and 80% accuracy. The reason: the first 80% are easy, carried by the simple cases; the last few points are exponentially expensive, because the remaining errors live in the difficult cases, scanned appendices, nested tables, cross-references, ambiguous wording. And below roughly 95%, experts have to review everything, and the gain disappears.

The long tail of difficult cases

The accuracy of a document system does not improve linearly with effort: the remaining errors are concentrated in cases that are rare individually but frequent collectively. Fixing them means identifying them (fine-grained measurement), annotating them (bringing in domain experts), and checking that each fix does not break another (replaying full benchmarks). Every point of accuracy gained costs more than the one before.

The economic threshold is unforgiving

At 80% accuracy, one answer in five is wrong, and nobody knows which one: review has to be total, and the gain is limited to typing time. Value is only unlocked when measured reliability allows a review targeted on the answers flagged as fragile. It is a change of regime, not a gradual improvement. Hence the paradox of pilots: they succeed in their demonstration (which lives in the easy 80%) and fail at scale-up (which lives in the remaining 20%). The governance question before any deployment: “What measured accuracy, on which benchmark, and what costed plan to reach the targeted-review threshold?”

The Optivalue.ai approach

Getting past this wall is Optivalue.ai’s ongoing investment: models specialised by function, continuous evaluation, and confidence scores that organise targeted review instead of total review.

Why do the last few points of accuracy cost so much?
Because they live in the long tail of rare cases, each of which requires identification, annotation and non-regression testing.

‍Is 80% accuracy a failure?
‍Economically, yes: review remains total, and the return on investment disappears.