Essay · 2025 · 7 min read
Nobody Learns AI Alone
The acceptance literature settled the social question decades ago. Enablement programs still haven't noticed.
I learned to work with large language models through conversation. Someone told me what they were using one. They sent me a link and I tried it.
As I gained fluency in AI tools, I taught what I learned to others by demoing it, and understood it substantially better afterward than before. In between, most of what I learned came from talking to people positioned differently on the question than I was — further along, more skeptical, in a different function, or earlier in their use.
Reading helped. Video helped. Experimenting helped enormously; nothing substitutes for direct contact with a system. But when I look honestly at where the capability came from, most of it came from other people. From the ordinary social surface of working near others who were also figuring it out.
That's not a nice observation about workplace culture. It's the mechanism, and most organizations have built adoption programs that are structurally blind to it.
This is not a new finding
Worth establishing up front: the idea that technology adoption is socially determined has been formalized in the literature since 2000 and empirically supported ever since.
TAM2 extended the original Technology Acceptance Model by adding social influence constructs — subjective norm, image, and voluntariness — and accounted for roughly 60% of the variance in perceived usefulness across four longitudinal field studies. UTAUT went further, synthesizing eight prior acceptance models into four core determinants, of which social influence is one, and explained about 70% of the variance in intention to use — substantially above the 17–41% range of the models it consolidated.
So when I say adoption is social, I'm not making a contrarian claim. I'm restating something that has been one of the best-supported findings in information systems research for a quarter century.
Which raises the more interesting question: why do enterprise AI programs keep getting built as though it weren't true?
The information assumption
Enterprise AI adoption is almost universally designed as an information problem. People would use these tools if they knew how, so the intervention is to distribute the knowledge — training, documentation, prompt libraries, recorded sessions, an internal wiki.
That model produces the pattern everyone recognizes: strong training completion, weak sustained usage. This does not predict adoption of generative AI. Some studies have shown that potentially social influence can.
Four things the social layer does
The acceptance models treat social influence as a single construct. In practice it's several different mechanisms doing different work, and organizations tend to fund one and ignore the rest.
Casual conversation sets the range. Someone mentions offhand what they used it for that morning. It's a throwaway remark, and it does something no documentation does: it establishes that this is a thing a person like you does, for work like yours, without ceremony.
Passive observation supplies the model. Watching a peer work through something gives you the shape of the interaction — how they framed the request, where they pushed back, what they did when it went wrong. A peer establishes that completing a task is achievable from your position.
Being challenged builds calibration. Someone articulating exactly why they don't trust the tool for their work is handing you a failure mode you haven't hit yet.
Explaining consolidates. You don't fully know something until you've had to explain it to someone else. Explaining forces your reasoning into the open, and you find out mid-sentence which parts of your understanding were load-bearing and which were pattern-matching you'd never examined.
Where the models stop
Here is what the acceptance literature does not give you, and why I think this matters more for AI than for previous technologies.
The social layer gives you range — a sense of what this looks like from positions other than your own, which is what judgment is made of. Instruction gives you the general case.
Solo experimentation gives you your own case. Only exposure to other people gives you the distribution. It's happening in DMs, in hallway conversations, in the two minutes before a meeting starts, in someone screen-sharing what they built over the weekend.
It's invisible to measurement, which is why it gets no investment, which is why organizations keep funding the mechanism the evidence says works least well and wondering why the training didn't take.
And for these tools specifically, that distribution is the whole ballgame. Calibration — knowing where the system is reliable, where it isn't, and what to verify — is the difference between a workforce that can use AI and one that can be misled by it at scale.
They measure whether people use it, not whether they got good at it.