Arpy Dragffy Guerrero

speaker

627 appearances 7 recordings 1 series first heard May 2026 last heard 6 Aug

Arpy Dragffy Guerrero’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 7 in all, peaking in Jun 2026 with 3.

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So this is proximity to workflows with clear evaluation criteria and the ability to write down what good work actually looks like.

So technical teams two plus years into this are in a different position than everyone else.

They've put in the cycles.

They know what these tools can do and can't do.

They've iterated through enough failure to understand where the value sits, and that's compounding.

It's widening the gap every month.

The rest, especially non-technical teams, are still trying to map legacy workflows into models that reward structure.

So these models are extraordinarily good when evaluation criteria are explicit and written down.

When the quality bar lives in someone's head, the model cannot access it.

So creative work, program management, relationship-intensive roles, none of that gets easier just because the model gets smarter.

The model can't guess how to make your workflow work ready.

You have to document it.

And even when motivation is high and talent is there, most organizations are not set up to succeed.

Teams are siloed, data is scattered, key decisions get bottlenecked.

Institutional knowledge locked in individual brains with no documentation that AI can work from.

And divisions don't share KPIs or terminology or data sets.

You can't build effective AI workflows on that substrate.

And this is why adoption and token usage are the wrong things to track.

They measure consumption, not like how much a tool was used, but they don't actually look at

Whether the work was good or if it was worth doing, or if it actually was faster.

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