Short answer: Emanuele Melis says AI One targets high-complexity, low-judgment use cases. Instead of piling up rules over years, he says agents handle nuance and edge cases much more quickly than a set of rules.
Watch this answer from Episode 46 (starts at 10:38) · Episode 46 notes and transcript
From Episode 46: We Tested Claude 4 Fable and Deployed Enterprise Agents Without a Single Data Migration, starting at 10:38.
Sam Nadler [10:38]: Perfect. So are there two questions for you. Are there a particular I think you mentioned client onboarding, but is there a particular like process or workflow that as of now is just seems to be a sweet spot? It's just working incredibly well. And in addition to that, has it is there a particular industry where you've gained a lot of traction or is it just it's useful for any in enterprise of a certain size or who's who has certain similar processes?
Emanuele Melis [11:03]: So I would say the size of the enterprise is is usually where when we see that need. But what we target is mostly high complexity, low judgment use cases, which basically means something that is very difficult but does not require the human judgment to be completed. So that's when you keep that is when you can put an agent. Now if you think of the evolution about, computer science and how processes were completed. Right? You have computers. Computers failed, and then you put a rule to address the failure. But now that rule itself would fail, then you put another rule, And that one rule will fail. And then ten years down the line, you have 150,000 rules all to fall back on the process. Right? Which is where we're that actually with GenAI, Procedure specifically, you don't need to do that cascade of scenarios and we're finding that the agent is much quicker at dealing with the Nuance and all those edge cases than a set of retail rules would do.
