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.
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.