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The Eight Levels of AI Adoption

A guide to understanding your state of AI adoption, plus example prompts and how to know when you’re ready to move up

Mike Taylor Mike Taylor Laura Entis Laura Entis Claude Claude

All it takes is one viral post to make you feel like you’re using AI all wrong. Someone is running 12 Claude Code sessions in parallel. Someone else’s agent is answering emails while they sleep. Meanwhile, you’re still arguing with ChatGPT.

But here’s the thing: Keeping up with every power user isn’t the point. The best way to find value in AI is to use it in a way that fits your work—and to regularly check in to see if you could be getting more from it than you already are. (I was using Steve Yegge’s “Gas Town” post about directing dozens of coding agents to illustrate this in client presentations, but it didn’t quite match with my experience, and I needed to modify it.)

This guide maps eight levels of AI adoption, from basic chatbot use to full agent orchestration. With each new level, you delegate more of your work to—and place more trust in—the AI. The following sections explain how each level works in practice, complete with sample prompts, so you can figure out which levels match your current needs and workflows, what’s possible at each stage, and when it’s time to move to the next one.

Level Description
1—Chatbot You give it a task, it provides a response. (ChatGPT, Claude, Gemini)
2—Copilot The AI exists inside your files and completes work alongside you. (Cursor, Claude in Excel, Gemini in Google Docs)
3—Agent You describe a task, and the agent executes it step by step, asking for your approval before moving on. (Cowork, Codex)
4—Autopilot You skip approvals and let an agent complete a task on its own, then review the results. (Lovable, Codex, Claude Code)
5—Workflows You build a system that professionalizes the agent’s output. (Compound engineering, Claude Workflows, Copilot AI Studio)
6—Assistant The agent works proactively in the background without being prompted. (OpenClaw, Hermes Agent, Claude Managed Agents)
7—Multi-agent You’re managing multiple long-running agents at the same time. (Claude Managed Agents, OpenClaw, or Codex Goals)
8—Orchestrator A manager agent runs a team of sub-agents on your behalf. (Gas Town, Paperclip, Symphony)

A higher level isn’t necessarily better. The most sophisticated AI users I know operate at several levels at once, identifying the best level to work within based on the specific challenge in front of them. The right level for a task is generally determined by how much you trust the AI to do a good job without intervention—and how big a deal it’ll be if it does mess up. For high-stakes tasks, you should either stay at a lower level so you can supervise the AI, or be prepared to invest the time, engineering resources, and tokens necessary to get that same quality at a higher level with less human oversight.

Most people I talk to who are struggling to adopt AI have good reasons: The output quality is either too low for the work they do or it’s too expensive to achieve. Safely moving up to the next level requires effort and experimentation—or a jump in model capability.

The right level match for most of your tasks may also depend on your role. Broadly speaking, the sweet spot for knowledge workers right now falls somewhere between Levels 1 and 4. Engineers are more often in Levels 5 through 8, partly because they can build the scaffolding that makes newer, less stable systems usable before they’re ready for everyone else.

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The levels

Level 1—Chatbot

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What it is: You ask, it answers. This is the classic chatbot experience: ChatGPT, Claude, Gemini, or any other model that’s not embedded in your files or your systems. You give it a task, and it returns a response.

What changes at this level: You move from doing everything yourself to drafting and synthesizing with an always-available AI generalist.

What you can use it for: Writing from rough notes, summarizing documents, or answering questions about uploaded files

Try it:

Prompt

I need to send a post-meeting follow-up email to a client. Here are my rough notes, the decisions we made, and two risks we need to flag. Draft the email in a calm, confident tone and end with three clear next steps. Tell me if anything sounds unclear or unsupported before you start writing.

Input: Meeting notes

Output: A polished email draft that identifies if there’s any missing information that still needs to be filled in

Human judgment: Confirm that the tone and facts are right, and the email’s content is something you stand behind.

Prompt

I am uploading a 20-page PDF on our new benefits policy. Summarize the five changes employees will care about the most, and then answer these three questions: Who is affected, what specific policies does the new timeline impact, and what would likely confuse someone who is reading this quickly?

Input: A PDF or set of documents

Output: A summary and direct answers to your questions grounded in the source material

Human judgment: Verify the summary is factual, and that the model recognizes when the material is ambiguous.

When to move up: Chatbots can assist with a wide variety of tasks, but each session requires manual setup: You have to explain what you want, provide the necessary context, and transfer the chatbot’s response to wherever you’re getting work done. Consider moving to the next level if you get a lot of value from chatbot exchanges but are tired of copy and pasting.

Level 2—Copilot

Guide workflow

Level 3—Agent

Guide workflow

Level 4—Autopilot

Guide workflow

Level 5—Workflows

Guide workflow

Level 6—Assistant

Guide workflow

Level 7—Multi-agent

Guide workflow

Level 8—Orchestrator

Guide workflow

What the levels measure

Guide workflow

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