There's a pattern in AI adoption that doesn't get discussed much, maybe because it takes a few months to see it clearly.
It goes like this:
- Company deploys AI tool
- 20% of the team uses it enthusiastically
- 80% of the team uses it occasionally or not at all
- Everyone reports "mixed results"
- AI initiative quietly stalls
This isn't a technology problem. It's a usage-curve problem — and the fix isn't more training. It's a different kind of deployment.
The early adopter trap
When a company "evaluates" an AI tool, they're almost always evaluating it with their most enthusiastic early adopters. These are the people who figure out novel tools independently, who stay curious about prompting techniques, who find clever use cases that weren't in the demo.
The early adopters love it. The company ships the tool broadly. Then the early adopter results don't replicate.
The rest of the team uses the tool the way they use every other tool: at the moment when they need something, without extensive setup, without context-building. They ask a question, get a generic answer, and go back to whatever they were doing.
The problem isn't that the tool is bad. It's that good AI results require good context, and the early adopters had it (they built it, consciously or not) while the broader team doesn't.
What actually sticks
The deployments that work have one thing in common: the AI is doing something useful before the user types their first message.
This sounds obvious but it's the opposite of how most tools work. Most AI tools are blank slates — extremely capable blank slates, but blank. The value comes from what you put in.
The deployments that stick are the ones where the organization's structure, priorities, and history are already in the system. When a team member asks "what should I be working on?" and the AI answers with specific reference to their Q2 priorities and last week's metrics — by name, accurately — that's the moment the tool stops being a novelty and starts being a habit.
The practical implication
If you're evaluating AI tools for your team, the right question isn't "is this AI good?" It's "how does this AI become good for us specifically?"
Look for:
- Can the tool ingest your existing documentation, org structure, and priorities?
- Does the quality of answers improve significantly between week 1 and week 6?
- Is there a setup cost that gets amortized over time, or is every conversation starting from zero?
The tools that compound in value are the ones worth deploying. The ones that are equally useful on day 1 and day 180 are probably useful shallowly — they're good at generic, and you need specific.
The broader point
AI adoption isn't failing because the technology isn't good enough. It's failing because most deployments treat AI like a search engine — always on, always ready, context-free.
The teams getting the most from AI treat it more like a well-briefed colleague: someone who knows the company, knows the priorities, knows what actually matters. That colleague doesn't exist on day one. They exist because someone invested in getting them up to speed.
The AI is the same way.
Freddy is Braingem's AI coaching system — six weeks of context-building in your Slack, then answers grounded in your actual priorities. braingem.ai
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