Building your AI bench.

I've been thinking about AI as infrastructure for how I run a business.

The shift happened when I started giving AI defined responsibilities.

Each of these is relatively simple on its own. Together, they start to form something much more useful: a second brain for the business.

The technology to do a meaningful version of this already exists. The harder part is designing the system well.

Start with responsibilities

One of the biggest lessons I've learned is to make every AI agent's responsibility specific.

“Help with marketing” leaves too much room for interpretation.

“Every Friday, review our competitors' websites, announcements and public activity. Identify meaningful changes in positioning, products, partnerships and go-to-market strategy. Explain why each change may matter to us.”

That's a job.

The same approach works across the business.

The roles become more useful as their scope becomes clearer.

For each one, I define its responsibility, information sources, expected output, cadence and level of authority.

That creates an AI operating model.

Build around recurring work

Recurring work is one of the easiest places to find practical applications.

Every business has things someone needs to remember to check.

Pipeline. Cash. Customers. Competitors. Campaigns. Commitments. Market changes. Upcoming meetings. Open decisions.

AI can take responsibility for much of the preparation around those rhythms.

My Monday morning agent could review what happened last week, upcoming meetings, outstanding commitments and current priorities, then prepare a briefing on where my attention is most useful.

Before an important meeting, another workflow could pull together previous conversations, relevant documents, open questions and recent developments.

Afterward, AI can capture decisions, commitments and follow-ups and feed them back into the system.

At the end of the week, it can review what moved, what stalled and what deserves attention next.

This is where AI starts giving cognitive capacity back to the operator.

You no longer have to hold every thread in your head.

Give agents context

The quality of an agent is heavily influenced by what it knows about the business.

Generic AI produces generic recommendations.

I want an agent to understand our strategy, positioning, customers, products, competitors, priorities, previous decisions and constraints.

That context can come from strategy documents, customer research, meeting notes, CRM data, financial models, previous work, brand guidelines and other business systems.

Then its instructions can reference that knowledge.

Imagine asking:

“What should I focus on this week?”

The answer becomes much more valuable when the system can see your calendar, active opportunities, outstanding commitments, strategic priorities and recent conversations.

Or:

“Should we pursue this partnership?”

Now it can evaluate the opportunity against criteria you've already established and surface relevant history from previous partnerships.

Context is what makes AI an operating layer.

Use multiple perspectives for important decisions

I've also found AI useful for creating structured disagreement.

If I'm evaluating a new market, I can have one agent research the opportunity.

Another can look specifically for weaknesses in the thesis.

A third can evaluate both against defined criteria.

I still make the decision, but I arrive at it with more of the thinking already done.

The same approach works for positioning, investments, partnerships, product decisions and hiring.

This is one of the areas where AI creates significant leverage for small teams. A founder can create some of the analytical depth that previously required several people, as long as the underlying information and instructions are strong.

Create clear approval levels

Agents also need boundaries.

I think about authority in levels.

Some agents can operate independently because the consequences of an error are small. Researching a competitor, organizing information or preparing a briefing usually falls into this category.

Other activities deserve review. Drafting customer communications, interpreting financial information or recommending a strategic action may sit here.

A smaller group should require explicit approval before anything happens externally or creates a meaningful commitment. Agentic tools reaching production without anyone approving the reach is how this becomes someone else's problem.

The appropriate level depends on the business and the task.

The important part is deciding deliberately.

Connect the agents

This is where the second-brain idea becomes especially powerful.

The research agent discovers something.

The strategy agent evaluates its significance.

The content agent recognizes that it creates an opportunity for a point of view.

The sales agent identifies three accounts where the development may matter.

The chief-of-staff agent surfaces the issue in your Monday briefing.

Now AI is helping information move through the business.

A signal enters once and becomes useful in several places.

That is much closer to how a strong team operates.

Give the system memory

The longer-term advantage comes from accumulation.

An agent should know what happened last time.

Which recommendations did we follow. Which ones worked. What customers actually responded to. Which assumptions proved wrong. What patterns keep appearing. What decisions have already been made. What we have tried before.

This is where the concept of a second brain becomes literal.

Your AI system starts carrying some of the operational memory of the company.

Six months of structured context should make the system more useful than it was on day one.

A year should make it better again.

The business begins to accumulate intelligence as it goes.

Measure whether it actually works

AI makes it remarkably easy to build complicated systems that feel impressive.

The test is whether they improve the business.

I look for fairly ordinary outcomes.

Hours saved. Faster preparation. Better follow-up. Fewer dropped commitments. Earlier identification of important signals. More complete research. Better-informed decisions. Greater consistency. More time available for the work where I add the most value.

I also look at how much management the system requires.

An agent that creates constant checking, correction and maintenance has limited leverage.

The useful ones eventually become boring. They do their job reliably enough that you stop thinking much about the mechanics.

Build your AI org chart from last week's work

The easiest place to begin is your own calendar and task list.

Look at the last seven days.

Find the work that required information gathering, preparation, synthesis, monitoring, organization or follow-up.

Then ask:

Could an AI agent own 70% of this process?

If yes, write the job description.

Over time, you start building an unusual kind of organization around yourself.

You still have your team, partners and advisors. You also have a layer of persistent AI support carrying research, preparation, monitoring, synthesis and coordination across the business.

For founders and small teams, I think this will become one of the most practical applications of AI.

The question I'm increasingly asking:

What am I still carrying in my head that my AI bench should be carrying for me?