Research
We scored every employee across six dimensions of AI capability. The full framework, the surprising findings, and a self-assessment prompt you can run with your own team.
Driving AI usage is table stakes. The harder question is whether any of it is actually compounding beyond the individual. We interviewed every person at our company, scored everyone across six dimensions, and built individual growth profiles. The biggest findings: technical background barely predicted success, the traits that mattered were operational instinct and willingness to sit with discomfort, and sharing was the weakest dimension in the entire company.
Every company right now is trying to get their people to use AI — and they're pulling every lever to do it. Friendly (or not-so-friendly) competitions, leaderboards, demo days, show-and-tells. Some are even optimizing “tokenmaxxing.” Driving AI usage is table stakes. You have to build the muscle. But once usage is climbing, how do you actually know if any of this is making the organization better?
There's no product on the market that answers this. AI vendors can tell you who's active, but they can't tell you whether that activity is compounding beyond the individual. They can't tell you whether the person building impressive automations has shared any of it with a single colleague. They can't tell you whether teams are building duplicate tools because nobody knows what already exists.
When we turned the lens inward at Tribe AI, we realized we had the same blind spot as everyone else: strong individual capability, no way to measure whether it was compounding beyond the person who built it. So we decided to measure it. The numbers were stark: 63% of people who built working AI tools had never deployed anything beyond their own laptop. 73% of tools built across the company were never used by anyone other than the person who built them.
“The tools were there. The talent was there. The connective tissue wasn't.”
The process had three steps.
First, everyone ran a self-assessment. We built a prompt that people dropped into Claude or ChatGPT — wherever they had the most conversation history. The prompt asked the AI to scan that person's chat history and assess them against six capabilities, using only concrete evidence from actual conversations. No vibes. No self-reported surveys. If the AI couldn't find evidence of a capability, it said so.
Second, one-on-one interviews. The self-assessment gave us a starting point, but it only captured what showed up in chat history. The interviews went deeper: what have you built? What's sitting unfinished on your laptop? What have you shared with anyone else? These weren't performance conversations — they were diagnostic.
Third, we scored everyone across six dimensions of AI capability. The dimensions weren't designed top-down. We started with 764 distinct observations from the interviews, consolidated and clustered them through multiple rounds of analysis, and let the framework emerge from what people actually do. From there, we built individual growth profiles — each person got a private page showing where they sit on each dimension, with specific recommendations for how to reach the next level.
Sanitized example of a non-technical employee's AI growth profile
We scored people across six dimensions. Together they answer a question that seat counts and usage dashboards can't: is someone's AI capability actually producing organizational value, or just individual output?
These six dimensions aren't a maturity model in the traditional sense. There's no expectation that everyone reaches the top level on every dimension. The framework is diagnostic, not prescriptive — it shows where people are and where the organization's gaps cluster.
The findings challenged assumptions we didn't realize we were holding.
Going in, the team expected engineers and technical PMs to be furthest ahead. They weren't — at least not uniformly. Some of the highest AI fluency in the company came from people with no engineering experience at all. One was a former personal trainer who'd never written code. He got obsessed. Two weeks after starting: 14 custom skills and 7 MCP integrations.
“Technical background was a weak predictor of who was thriving. What mattered more was whether someone could see their own work clearly enough to know what to hand off to AI.”
Operational instinct — the habit of noticing when a process has been repeated three times and asking whether it could be better. The people who thrived could see their own work clearly enough to separate the judgment from the execution — and automate the execution while staying close to the judgment.
Willingness to sit with discomfort — patience with the early friction of learning something new, long enough for it to click. The people who gave up after one frustrating session stayed at the starting line. The people who carved out time and sat with it for a week broke through.
“Five minutes of talking produces roughly 10x more useful context than three typed sentences.”
This was the finding the team didn't see coming. Tribe's people are prolific builders. They're also, overwhelmingly, solo builders. The most impressive work in the company was sitting on individual laptops, in personal Claude projects, in folders nobody else knew existed. The instinct was to call this a “reluctance to share” problem. It wasn't. When the team dug in, they found six distinct barriers — each requiring a different intervention:
The team started calling this “the 80% cliff” because of how consistently it appeared. A striking number of people had working prototypes and personal automations that had never been deployed or put in front of their intended audience. The abandonment point was remarkably consistent: right at the transition from “this works for me” to “this works for someone else.”
“The sharing problem and the shipping problem are the same problem. People who don't finish also won't share.”
Research that doesn't change behavior is just content. The findings shaped one of the team's critical Q2 priorities: closing three specific gaps — what they're calling the three cliffs. Each one blocks the next: you can't share what you haven't deployed, and you can't deploy what you haven't built.
The cold start cliff. Some people have never built a first artifact. The intervention: pair them with someone who builds regularly. The goal is a first win, not a masterpiece.
The deployment cliff. The most tractable problem in the entire research. The gap between “this runs locally” and “this lives somewhere others can use” is a concrete skill that transfers forever. One session. Thirty minutes.
The sharing cliff. The hardest one, because the barriers are diverse — paired sharing for imposter dynamics, workflow triggers for the habit gap, and building infrastructure so there's somewhere to discover what exists.
“Each level increase on a single dimension adds roughly 12% to a person's effective capacity. The dimensions compound.”
If you want to run a version of this at your own company, start with the self-assessment. Have each person drop the prompt below into whatever AI tool they use most — wherever they have the deepest conversation history.
Given everything you know about me from our conversation history, assess my current AI capability level using the framework below. Be honest and evidence-based. Where there is no evidence, say “No evidence observed in our chat history” instead of guessing. Prefer specifics over vibes.
For each of the six capabilities — Product Thinking, Workflow Awareness, Tool Mastery, Quality Judgment, Rapid Building, and Knowledge Sharing — provide:
Then provide 2–4 strengths backed by specific evidence, growth areas with a suggestion for each, and a short summary of where I'm strongest and where I can grow.
Follow it with one-on-one conversations. The self-assessment captures what shows up in chat history. It misses everything else. The questions that produced the most useful signal for us: What have you built that nobody else knows about? What's sitting unfinished? What would you build if you had protected time?
Score against the dimensions, but don't publicly rank. When people feel ranked, they optimize for the metric instead of actually growing. When they feel diagnosed, they engage. The distinction matters.
The question most companies are asking is “are people using AI?” That's the wrong question. Usage is a given at this point. The better question is: is the knowledge compounding? Is what one person learns available to the next person who needs it? Is individual capability becoming organizational capability — or is it trapped in personal folders, on individual laptops, in projects nobody else can see?
When we ran this process internally, the honest answer to most of those questions was no. Not because people weren't capable — because nothing in the organization was designed to make individual skill compound. That's what this research was designed to surface. And once you can see where the gaps are, you can design specific interventions for each one.
