u/ppezaris

We’re working on a ladder for individual AI proficiency and would love feedback on both the levels and the definitions.

L0 New: brand new to AI, or has not yet used it.

L1 Chat: simple prompt-and-response use. Work is serial: ask, wait for an answer, then ask again.

L2 Contextual Work: gives AI relevant documents, data, or workspace context so it can work within the actual artifact and produce a more useful result.

L3 Orchestrate: coordinates multiple agents or AI roles across independent workstreams, with work that may review, challenge, compare, or build on other work. This is not just for engineering.

L4 Automate: creates workflows that are triggered by business events and run without someone sitting at a laptop directing each step.

L5 Loop: feeds the output of those workflows back into shared knowledge or a company brain, so future workflows improve over time.

A few things I’d love your perspective on:

Are these the right levels? Are any of the names unclear or overlapping? What observable behaviors would you use to distinguish one level from the next? Does “loop” make sense as an individual proficiency level, or is it inherently a team or company capability? Is there a L6 and if so how would you define it? We’re trying to define these because, in customer conversations, we’ve found that people are not very good at self-evaluating their own AI proficiency. Frequent use often gets mistaken for proficiency. And being low on a ladder like this can feel like admitting you are falling behind, do not fit in, or are less secure in your job, especially for leaders expected to set the pace. We want a more objective, behavior-based way to distinguish the two.

TL;DR: Everyone is racing to go all-in on AI. Almost nobody can tell you if it's actually delivering value.

We're all AI-pilled here. The models keep getting more cracked, the demos keep melting faces, and every exec on earth has been told to go all-in. But we all know, most of them are flying blind. They can feel spurts of acceleration but can't see out the front windshield, let alone the rear-view mirror.

Here's what we heard again and again, across 75+ conversations with companies of every size and industry (anonymized, since a lot of this was said in confidence):

1. Nobody has solved ROI, and everyone knows it. This was our biggest observation. After headcount displacement everything else is murky. One CFO put it perfectly: he knows his ad spend to the dollar, every platform, CAC per channel, and he wants the exact same for AI. The line from "AI usage" to "business outcome" still isn’t easy to draw.
2. Spend is a black box, and it terrifies finance. CFOs usually have one line item for AI (or perhaps one per vendor), but it’s not broken down by team, project, or agent. Token costs get compared to cloud invoices in every conversation. The bills are opaque, hard to forecast, and hard to explain. Where discipline exists, and mostly it's improvised. We’ve all heard by now that Uber burned its entire 2026 AI budget in four months, and its own COO admitted he couldn't connect the spend to anything a customer would feel. (!)
3. Everyone defaults to the most expensive model because nobody knows which to use. At one fast-growing fintech, per-use-case model selection was the single highest-impact thing they asked us to build. Nobody knows which model fits which task, so they default to max. Latent waste is absolutely everywhere.
4. Everyone wants to go faster, they just don't know how. This one reframed the whole company for us. You don't put brakes on a car to make it slower. You put brakes on so it can go faster safely. Right now every company is either flooring it blind or riding the brake out of fear. Visibility is the brake that lets you actually floor it, the thing standing between "we're being careful" and "we're going all-in." It also lets you see through the front windshield, the rear-view mirror, and out the side windows, so you can avoid accidents and out-maneuver the other cars on the road.
5. The maturity gap between companies is enormous. One neobank has moved AI into mission-critical ops that were off-limits six months ago, and is running a hiring freeze against a 50-60% growth target on their internal assumptions about AI efficiency. On the other end, a Director of AI at a large agency told us they're 3-4 years from ready, which kind of blew my mind, an not in a good way.
6. The adoption honeymoon is over. Real fatigue is setting in. A sharp drop after the initial enthusiasm, and a "please stop talking to me about AI" mood. The gap between top-down mandates and non-engineers who can't name a single daily use case is everywhere. Recent grads are often anti-ai, as is the aging work population.
7. Buy broadly now, consolidate later. The enterprise playbook is to experiment widely, avoid lock-in, consolidate over time, betting that no single AI vendor wins the whole SaaS surface. Pricing is already moving past tokens toward outcome-based models. We’ve seen this in action ourselves as a month ago Fable was the darling, and today perhaps that’s Sol.
8. Governance is a tug-of-war between legal and innovation. Legal wants tighter restrictions. The tech org pushes back to keep room to move. In regulated verticals it's sharper: weekly administrative orders on AI disclosure, and discovery requests now pulling AI chat logs. The companies furthest along still can't answer "is this actually working?" So the winners of this era won't be the ones with the most AI. They'll be the ones who can measure it.

What we all need is a system of record for AI work. A live view of every tool and agent operating across a company: what they do, who owns them, what data they touch, what they cost, and which ones are producing results. Leaders finally see what exists, govern what matters, and go all-in on what works.

Happy to compare notes with anyone else building or selling into this. The discovery was eye-opening and I'm glad to share more. We're so early. Let's go. DM me to set up a call or just reply here.

obComment: although i did use Granola to extract themes from our customer calls, this was written by me, not AI.