In this episode of The AI-Native Attempt, we’re taking a big step — laying out the entire plan for what it really means to build an AI-native company from the ground up.

If you’ve ever wondered how to take an existing business and start layering in automation and AI in a practical, step-by-step way, this breakdown will show you exactly how. The goal isn’t to replace people. It’s to design a business that runs smarter — where AI handles the heavy lifting and humans stay focused on strategy, creativity, and decision-making.

What Does “AI-Native” Actually Mean?

The term AI-native comes from the book AI First by Adam Broman and Andy Sack — a must-read if you want to understand how AI is reshaping business.

An AI-native company is built on the foundation of artificial intelligence. AI isn’t just a tool; it’s baked into the company’s strategy, operations, and products.

In practice, this means:

• Humans set the strategy and direction.
• AI and automation execute 85% of the work.
• Humans step back in for the final 15%.

That last step — review, polish, add personality and insight — keeps the human touch while letting AI handle the repetitive, time-consuming parts.

Agents vs. Automations: What’s the Difference?

Before we can start building, we need to define two key players: agents and automations.

• Automations are the skills — self-contained processes that take an input, do something with it, and return an output. Think blog-post generation, lead enrichment, or social content repurposing.
• Agents are the brains — they understand context, know which skill to use, and when to use it.

For example:
Imagine a Slack channel connected to an AI agent. You type, “Write me a blog post for my editorial calendar.” The agent reads your request, identifies that “blog post” is a writing skill, grabs the right automation, and runs it. The output: a ready-to-edit draft — all triggered by a single message.

Agents are built on top of automations. So the first step in this process is to build a library of automations (skills) that agents can later tap into.

How AI Fits Into an Organizational Structure

To understand how AI agents and automations fit into a business, it helps to look at an organizational chart — or what the Entrepreneurial Operating System (EOS) calls an accountability chart.

At the top sits the Visionary (the CEO) and the Integrator (the COO). Beneath them are the main divisions:

• Sales & Marketing
• Operations
• Finance
• (and in this model) Data & Reporting

Each division is made up of departments, and each department contains a series of tasks or skills — these are your automations.

Once those are in place, you can build AI agents to manage them.
For instance:

• A Content Marketing Agent oversees automations like blog creation, case study drafting, and social repurposing.
• A Sales Agent handles automations for lead scoring, outreach, and proposal prep.
• A Support Agent monitors tickets, FAQs, and help resources.

Layer by layer, this structure allows AI to manage routine work while humans supervise performance and strategy.

The Backbone: Tracking and Reporting Agents

The most important layer in this AI-native org chart is tracking and reporting.

Every AI agent and automation depends on accurate data. That’s where reporting agents come in.

These agents pull from:

• Website analytics (traffic and engagement)
• Platform usage data
• Sales pipelines
• Customer feedback

They don’t just report — they inform decisions. If traffic drops or conversion rates fall, the data flows into the marketing agent, which knows to adjust output or trigger a campaign.

This reporting layer becomes the feedback loop that makes the entire AI system smarter and more effective over time.

Building the Roadmap: The AI Automation Prioritizer

Of course, you can’t build everything at once. That’s why I created a tool called the AI Automation Prioritizer — a simple framework to decide what to automate first based on two key factors:

1. Impact — How much time or revenue will this save or generate?
2. Readiness — How prepared are we to automate it? (Do we have the process, data, and access in place?)

Each task is scored across multiple factors like:

• Time spent per month
• Revenue impact
• Output value (how reusable the results are)
• Complexity
• Skill level required
• Frequency of human error or missed execution

Then we assess readiness based on:

• How well-documented the process is
• How much context and data already exist
• API or system accessibility
• Whether someone can manage the automation once it’s live

When you rate and weigh each factor, you end up with a prioritization matrix — a visual quadrant that shows:

• Automate Now: High impact + high readiness
• Start Prepping: High impact + low readiness
• Quick Wins: Lower impact + easy to build
• Revisit Later: Low impact + low readiness

Our Starting Point

After scoring 52 potential automations for PlayThru, three rose to the top:

1. Charity Outing Resource Content
2. Content Repurposing for Social & Email
3. Mini Golf Outreach

Each of these will become its own automation, and the next few episodes of The AI-Native Attempt will focus on building them. Once those are live, we’ll start layering agents on top — bringing the AI-native vision to life.

Wrapping Up

This entire process — defining what AI-native means, mapping automations, building agents, and prioritizing what to do first — is how you turn an ordinary business into a self-running one.

The key steps:

1. Build your org chart.
2. Define the skills (automations).
3. Prioritize based on impact and readiness.
4. Start building — one automation at a time.

Over time, your agents will manage more of the day-to-day work, freeing you to focus on growth, creativity, and leadership.

If you want to follow along, subscribe to the newsletter or YouTube channel — every episode includes highlights, takeaways, and the tools we build along the way.

The AI-Native Attempt isn’t theory. It’s a real-time experiment — and the best part is, you can build right alongside it.

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