Steps of AI Adoption
From gated access to AI-native teams — where you are on the ladder, what blocks the next climb, and which products and guardrails help.
| Step & your role | Agents | What it looks like | What's the bottleneck | Products that help | Guardrails |
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| 0: Gated | 0 | Only older or lighter/faster models are approved, latency compounds through AI gateways and custom auth, no MCP governance, internal access to AI tools is gated or process-heavy. No IT infra or approval path for hosting Claude-created code or artifacts; outputs only exist locally. | Legacy security and approval processes, focuses on cost-per-token containment vs. outcomes, lack of true technical voices in decisionmaking. |
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| How to get from step 0 to 1: Executive/buyer alignment and escalation of blockers; frameworks for launching Claude securely. | |||||
| 1: AssistedYou + an agent (a pair) | ~1 | One engineer, one agent, mostly supervised—a fast pair programmer. You run one session at a time and review almost every change before it merges. Unlock: A change that used to fill an afternoon becomes something you finish between meetings. | Your attention and the need to inspect each response and code edit. Due to low trust for the model’s output and lack of self-verification, you feel you must read everything, so you never look away. Work is synchronous: you sit and watch while Claude works, rather than moving on to the next task. |
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| How to get from step 1 to 2: Run more than one agent at a time; a self-verification loop you trust (tests + build + lint + e2e with a real dev environment); auto mode, to avoid blocking permission prompts; automate code review. | |||||
| 2: ParallelOrchestrator | ~10 | One engineer orchestrates 5–10 agents at once, each on its own worktree or git checkout, jumping between them. Claude checks its own work—tests, build, lint, security scan—before you see it. Auto mode is always on. Automated code review and security review are on by default. Output multiplies, you review final diffs rather than keystrokes, and your backlog of maintenance work starts shrinking. Claude writes most of the code. Unlock: A backlog that used to take the team weeks becomes one engineer’s afternoon of orchestration. | Reviewing output. You’re hand-writing less code and instead checking six streams of it, and this takes up more of your time. Prompting and steering the model as you juggle sessions. |
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| How to get from step 2 to 3: Give Claude a way to pull in context (let Claude read code, wikis, discussions); agency and code review speed (agents may touch code owned by other teams); break up your work into loops and routines; let Claude kick off Claude. | |||||
| 3: Supervised autonomyManager of managers (an org tree) | ~100 | Claude writes all or nearly all of the code. “Did you read the code?” becomes “what context was the model missing and how do we solve it for next time?” Unlock: Claude proactively does work that you would have had to kick off manually before. Maintenance and cleanup that used to wait for someone to find the time now runs continuously in the background. | Trust in the loop and your team’s decision throughput. The agent tree is too deep to babysit and your trap is scaling agent count before the loop has earned widespread trust. Ensuring tokens are used efficiently as usage increases. Requires monitoring (via OTel or Analytics) and a culture that encourages experimentation while controlling costs once internal use cases find PMF. Ask yourself: is this something an engineer would have done? |
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| How to get from step 3 to 4: Scaled automation of domain-specific use cases (eg. code migration, fuzzing, feature-building, feedback remediation). | |||||
| 4: AI-nativeVP steering by intent | ~1,000+ | The loop is fully closed and most agents are kicked off by Claude. Hundreds to thousands of agents run; you steer by intent and monitor by exception. Unlock: The quarter-long migration becomes a workflow you kick off and check on. | Identifying and automating work at scale, and enforcing the right guardrails for each type of work. |
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Adapted from Anthropic's Steps of AI Adoption. Browse the shelf →