Responded to the same sentiment elsewhere but my general sense is that for many use cases users simply do not care about high 9s accuracy/consistency. A 95% solution using AI is "good enough" if you can ship it quickly and give them the tools to iterate on that last 5%.

95% solution might work for small startup X or small biz y but at large company scale 5% is a huge deviation to correct on. Maybe just depends on the client and how touchy they are. At my company, we measure metrics in bps and moving something 50 bps is a huge win. 500 bps would be unheard of.

IMO it's less about the size of the company and moreso the nature of the integration. Users are more forgiving of 95% accuracy when it's used to enhance/complement an existing (manual?) workflow than when it's used to wholesale replace it. The comparison would be building an AI tool to make data entry easier/faster for a human employee (making them say, 2x as productive even at 95%) versus an AI tool that bills itself as a full replacement for hiring a data entry function at all (requiring human or superhuman accuracy, edge case handling, maddening LLM debugging, etc).

In the long run the latter is of course more valuable and has a larger market, so it's understandable large corps would try to "shoot for the moon" and unlock that value, but for now the former is far far more practical. It's just a more natural way for the tech to get integrated and come to market, in most large corp settings per-head productivity is already a measurable and well understood metric. "Hands off" LLM workflows are totally new and are a much less certain value proposition, there will be some hesitation at adoption until solutions are proven and mature.