The real cost of an AI-generated answer: the review

In brief.
When a company evaluates an AI that drafts its answers to questionnaires and tenders, it looks at drafting speed. The real cost lies elsewhere: in verification. If every generated answer has to be reread and checked by an expert, the gain is limited to typing time: a few minutes, when verification takes fifteen. The equation only turns positive once the system itself qualifies its reliability, answer by answer, and allows a targeted review.

The calculation, to run for yourself

Take a 200-question questionnaire, by way of illustration. Without AI: 10 minutes per answer on average, research included, around 33 expert hours. With a general-purpose LLM: drafting drops to almost zero, but every answer has to be verified, with sources to be found manually, say 7 minutes per answer, 23 hours. Real gain: 30%, far from the promises, carried by the same experts who are already stretched. With a system that sources and scores every answer: the 80% of answers with a high score are checked in a minute (the source is provided, it just needs confirming), and 20% take up the experts’ attention. The same questionnaire comes down to around 8 to 10 hours.

The evaluation criterion missing from comparisons

The exact figures vary; the structure of the calculation does not. It explains why so many general-purpose AI deployments disappoint despite enthusiastic users: time has shifted from drafting to verification, without decreasing. At the demo, don’t ask “show me a good answer”, ask “show me how I will know which ones to check”.

The Optivalue.ai approach

This is the architecture of Optivalue.ai: every answer comes out with its source, down to the document and page, and its confidence score from 0 to 100, which turns a full review into a targeted review. The gain does not come from writing speed, it comes from measured confidence.

Why is the gain from general-purpose LLMs disappointing?
Because time shifts from drafting to verification instead of decreasing.

‍What is a targeted review?
Focusing human expertise on low-score answers and gaps, and approving the rest quickly.