Frontline AI implementers at ordinary companies (logistics, accounting) are already seeing clear day-to-day ROI, even though this hasn't yet shown up in the aggregate numbers that top executives rely on.
Pash recounts meeting non-Silicon-Valley operators at the AI Engineer World's Fair — a Midwest logistics CTO and an accounting firm — who are seeing falling exception rates and rising handling rates from internally-built AI systems, even as C-suite leaders further from the front line remain skeptical about ROI. ✦ AI generated
Pash · The Cognitive Revolution · 2026-07-02 · original ↗
starts at this moment · 42:24
the exception rate is falling the handling rate is going up and and they're seeing that on a day-to-day basis right so this is I think where we are uh the guys who are actually implementing and close to the implementations are actually seeing the results it hasn't really filtered up into the, you know, top layer of the enterprises yet, but the CEOs who are who are AI pilled kind of know what's going to happen.
verbatim transcript · starts at 42:24
42:24don't actually know what's going on very closely. They're just looking at the numbers and by the numbers the token spend is going up but you know are you really seeing the return on investment but if you go down to that working level and you see day-to-day on like the customer calls coming in and whether they're getting handled the handling rate and the exception rate the exception rate is falling the handling
42:42rate is going up and and they're seeing that on a day-to-day basis right so this is I think where we are uh the guys who are actually implementing and close to the implementations are actually seeing the results it hasn't really filtered up into the, you know, top layer of the enterprises yet, but the CEOs who are who are AI pilled kind of know what's going to happen and they're kind of
43:08they've they've made the commitment and they're making the investment. The CEOs who are not are kind of sitting by and like, oh, you know, we're going to wait around. We're going to see what happens, like, etc., etc. That's really where we are. Um, and I think it's not it's not re really vis like it wasn't visible to me until I went to this AI engineers worldfare. Like I I think of AI people
43:30as just the people on Twitter, right? Like that's you know for for some reason my world has gotten constrained into this like small tiny world and like you know the bubble and I don't really have this good sense of what people outside the bubble are actually doing or thinking. But you go there and you meet these implementers and then you realize like all of the stuff that we talk about
43:50and we're producing in the bubble is getting used outside. People are learning how to use these tools and people are deploying and people are using them. People are seeing the return on investment but it's at the very like micro granular level right now. It's going to take some time for the numbers to filter upwards. So coming back to the two paradigms, a lot of what you're describing there
44:19sounds to me like people are successfully implementing the workflow paradigm if they can measure things like the handled rate and the exception rate and so on. >> Yeah, >> that by kind of definition means they're doing this more controlled structured workflow style buildout. I still kind of wonder, you know, for me that work is very quickly becoming Claude's work. So, we're we're in this weird spot again