Train your AI like you’d train an employee

When we deploy AI into a company, we start with a question nobody expects.

We don’t ask about the tech stack or the data infrastructure.

We ask: if you hired someone for this role tomorrow, how would you train them?

The answer to that question is the entire blueprint.

The hiring model

Most companies think about AI as software. You install it, configure it, and it does things.

A better model: think of AI as a new hire.

You wouldn’t bring someone into the company, hand them a laptop, and say “figure it out.” You’d give them a job description. You’d introduce them to the team. You’d share how things work—not the org chart version, the real version. You’d teach them the edge cases. You’d give them time to absorb how the business actually operates before expecting them to perform.

That’s exactly how you deploy AI that works.

Each agent gets a job description—we call it an Instructions file—and domain expertise through Knowledge files that capture how the business actually works, in the language the team actually uses. It has tools it can access, outputs it’s responsible for, and rules that govern its behavior.

Skip any of those, and you get the equivalent of a confused new hire. Technically capable. Operationally useless.

Sit next to them

A few weeks ago we were deploying the system at a construction company. The first thing we did was sit next to the coordinators. Not to audit their process. To learn it.

We told one of their best performers: train us like we’re brand new. Teach me the job. What do you do first thing in the morning? How do you prioritize when you have a hundred open files? When do you escalate? What are the gotchas that nobody tells new people about?

She looked at me sideways. Nobody had ever asked her that.

Two hours of that conversation produced more useful knowledge than any SOP document we’d ever seen. She described things the company manual didn’t cover—the timing of insurance provider responses, which adjusters are responsive and which need three follow-ups, which job types need immediate scheduling versus ones that can wait a day.

That is the knowledge the AI needs. Not the org chart. Not the mission statement. The operational reality of how someone good at this job actually does it.

The depth problem

AI is only as good as the knowledge you give it. And most companies underestimate the depth required.

They give the AI a two-paragraph job description and a link to the company website. Then they’re surprised when the output is generic.

A good employee after six months knows the formal processes, sure. But they also know which clients need handholding, which internal systems are unreliable, which reports everyone ignores, and which shortcuts actually work. That accumulated knowledge is what makes them effective.

The AI needs the same depth. When we build a knowledge file, we’re capturing the version of reality that exists inside your best performer’s head. The stuff they’d tell a friend who just got hired. The stuff that never makes it into the handbook.

That’s why we literally say: train us like a new employee. Because the depth of a real training conversation is the depth the AI needs to perform.

Knowledge compounds

The first agent is the hardest. You’re building the core curriculum from scratch—the company, the product, the industry, the way things work. That’s the foundation. It takes time.

The second agent is faster. And the third is faster than that.

Because 60% to 80% of what an agent needs to know is shared. How the company operates. What the products are. Who the key clients are. What the tone of communication should be. That’s all core knowledge—built once, reused everywhere.

By the fifth agent, you’re adding two or three specialized files on top of a deep foundation. What took weeks for the first takes days for the fifth.

This is why “start with one” isn’t a limitation. It’s a strategy. That first deployment builds the foundation every subsequent one accelerates from.

The onboarding reversal

Something happens that surprises every company we work with.

When AI has 100% of the operational knowledge—the same knowledge a six-month employee would have—it changes how new hires ramp up.

Instead of the traditional approach where someone shadows a busy colleague for weeks, hoping to absorb enough to be useful, the new hire starts by talking to the AI. They ask questions. They learn the systems. They understand the edge cases. By the time they sit down with their manager, they’re asking probing, specific questions that only come from already having the foundation.

The busy manager who used to spend two weeks hand-holding? Now they spend one focused hour on the stuff that requires judgment and context. Everything else, the new employee already knows.

It’s onboarding in reverse: arrive proficient, then go deep.

Leaky knowledge

There’s a harder problem this solves, one that most companies don’t talk about.

People leave. And when they leave, their knowledge leaves with them.

The coordinator who knows which adjusters respond on Tuesdays. The salesperson who knows exactly how to position against Competitor X. The operations manager who has the workaround for that one system glitch that’s been there for three years.

All of that walks out the door when they give notice.

With the AI trained as a knowledge repository, that problem shrinks dramatically. The knowledge is captured, structured, and available the day someone new starts. Not because someone wrote an exit memo. Because the knowledge was captured continuously, as part of the work, every day.

The personalization layer

The core knowledge—80% of what an agent needs—is shared across the team. But the last 20% is personal.

Different people work differently. One coordinator prefers bullet points. Another prefers full paragraphs. One salesperson opens with small talk. Another gets straight to the point.

The AI adapts. Same core knowledge, same core rules, personalized to how each person communicates and works. Their proxy learns their style, their tone, their shortcuts.

This is the opposite of rigid software that forces everyone into the same box. The framework is consistent. The experience is personal.

The correction loop

What makes the system self-improving is simple: mistakes are data.

When the AI produces something that’s off—the tone is wrong, it missed an edge case, it prioritized the wrong thing—that’s a training opportunity. You correct it. The correction gets captured. The knowledge file gets updated. Next time, it gets it right.

This works because of the framework. Without structured knowledge files and clear behavioral rules, there’s nowhere for the correction to go. With them, every mistake makes the system smarter.

Over time, the gap between a generic AI tool and your knowledge-trained system becomes a canyon. The generic tool stays flat. Yours compounds.

What this means practically

If you’re deploying AI, think about it as hiring. Write the job description before you build anything. Sit next to your best performers and ask them to train you like a new hire—capture everything, especially the stuff that’s obvious to them but would take a new person months to learn.

Start with one role. Build the core knowledge. Deploy the agent. Let it make mistakes and learn from corrections. Then watch what happens: the second agent deploys faster, the third even faster, and the knowledge base gets richer every week.

The companies treating AI like software will keep getting software results. The companies treating AI like a workforce will build something that grows.