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Steps of AI Adoption

Author: Boris Cherny Date: Jul 16, 2026 Original: Steps of AI Adoption — PDF (single page, six-column table), supplied by Wim 2026-07-19 Captured via: https://claude.ai/code/artifact/bfdfaef9-bc62-4dfe-ba9e-c58a26c9accf — a Claude artifact rendering the same table as HTML Captured: 2026-07-19

Provenance note: this markdown was converted from the artifact, then checked cell-by-cell against the original PDF on the same day. The two are textually identical — every step, transition, product and guardrail bullet matches — so this file is a faithful record of the PDF, not of a re-rendering. The PDF's own layout adds nothing beyond the table (the printed page hyperlinks the same product names the artifact linked).

A five-step maturity model (0–4) for engineering-organisation adoption of AI coding agents, presented as a table with columns: Step & your role · Agents · What it looks like · What's the bottleneck · Products that help with each step · Guardrails. Transition rows between steps describe how to advance.

Step 0: Gated

Your role: — Agents: 0

What it looks like

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.

What's the bottleneck

Legacy security and approval processes, focuses on cost-per-token containment vs. outcomes, lack of true technical voices in decisionmaking.

Products that help

  • Claude.ai chat

Guardrails

  • SSO/SCIM plus role-based access
  • Org-level budget caps
  • Deploy inside existing approvals/IAM
  • Data governance package

How to get from step 0 to 1: Executive/buyer alignment and escalation of blockers; frameworks for launching Claude securely

Step 1: Assisted

Your role: You + an agent (a pair) Agents: ~1

What it looks like

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.

What's the bottleneck

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.

Products that help

  • Claude Code in the Desktop, CLI, or IDE
  • Claude Cowork, Claude Design
  • Usage via Anthropic API, Bedrock, Vertex, or Microsoft Foundry
  • Claude Code analytics dashboard + Analytics API
  • Compliance API for Claude Enterprise
  • Plan mode to review intent before edits

Guardrails

  • Per-seat spend caps
  • Centrally managed model/effort settings
  • Centrally managed policy
  • OpenTelemetry export into existing SIEM/observability stack

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 testing with a real dev environment); auto mode, to avoid blocking permission prompts; automate code review

Step 2: Parallel

Your role: Orchestrator Agents: ~10

What it looks like

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.

What's the bottleneck

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.

Products that help

Guardrails

  • Analytics to monitor team usage
  • Automatic code quality enforcement: lint, automated tests, typecheck
  • Claude powered end-to-end verification (eg. using the Claude Chrome extension or iOS/Android simulator MCP)
  • Manual code review, code merge, and security review. Hold the same quality bar for human and agent-generated code
  • Pre-approve common safe bash and MCP commands in settings.json

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

Step 3: Supervised autonomy

Your role: Manager of managers (an org tree) Agents: ~100

What it looks like

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.

What's the bottleneck

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?

Products that help

  • Subagents with worktree isolation (so parallel agents don't collide)
  • Routines, /loop, /batch, and /goal to fan out repetitive work
  • Dynamic workflows
  • Claude Tag (have it monitor a channel or data source and kick off tasks proactively)

Guardrails

  • Automatic code review
  • Automatic security review
  • Agent sandboxing
  • CLAUDE.md and Skills to encode standards
  • Tune Auto mode classifier based on your team's usage
  • Manage token use with model selection, advisors, LSPs, breaking up CLAUDE.md into lazy Skills

How to get from step 3 to 4: Scaled automation of domain-specific use cases (eg. code migration, fuzzing, feature-building, feedback remediation)

Step 4: AI-native

Your role: VP steering by intent Agents: ~1,000+

What it looks like

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.

What's the bottleneck

Identifying and automating work at scale, and enforcing the right guardrails for each type of work.

Products that help

  • Claude Agent SDK to programmatically build and schedule agents
  • Claude Tag (active in most Slack channels, auto-responding to posts)

Guardrails

  • Cost controls for automation
  • Model selection for automation