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AI Workflow Maturity Model

Where does your engineering team stand?

Most engineering teams are at Level 1 or 2 — individual engineers experimenting with AI tools, but no shared practices or way to measure impact. This framework helps you understand where you are and what it takes to move up.

1
Where most teams are

Individual Experimentation

Individual engineers try AI tools on their own. No shared practices, no visibility into what works. Usage varies wildly across the team.

What this looks like:

  • ✓Some engineers use Copilot or ChatGPT, others don't
  • ✓No shared prompting practices or tool standards
  • ✓Leadership has no visibility into AI tool usage or impact
2
Common next step

Shared Tools, No Shared Practices

The team has standardized on a few AI tools, but everyone uses them differently. There are no documented workflows or quality standards for AI-assisted work.

What this looks like:

  • ✓Company licenses for AI coding tools
  • ✓No documented best practices or prompting guides
  • ✓Inconsistent output quality from AI-assisted work
3

Documented Workflows

The team has shared, documented practices for how AI tools are used in common workflows. Code review includes AI output review. Prompting standards exist.

What this looks like:

  • ✓Written guides for AI-assisted code review, testing, documentation
  • ✓Prompt libraries or templates for common tasks
  • ✓Code review process accounts for AI-generated code
4

Measured Productivity Impact

The team measures the actual impact of AI tools on velocity, quality, and developer satisfaction. Data drives decisions about which tools and workflows to invest in.

What this looks like:

  • ✓Tracked metrics: cycle time, PR throughput, defect rates
  • ✓Before/after data on AI workflow adoption
  • ✓Regular reviews of which AI practices deliver value
5

AI-Native Development Culture

AI is embedded in the development culture, not bolted on. The team continuously experiments with new AI capabilities, shares learnings, and treats AI fluency as a core engineering skill.

What this looks like:

  • ✓AI workflows are part of onboarding for new engineers
  • ✓Team regularly evaluates and adopts new AI capabilities
  • ✓AI fluency is a hiring and development priority

Get a practitioner-led audit

The AI Workflow Audit includes 1:1 interviews with your engineers, a review of tooling and practices, and a specific roadmap for improvement — from someone who built and runs a 100+ agent system every day. $3,497, fully remote, 2-4 weeks.

30-minute call. No prep needed. No obligation.