AI Maturity Assessment

Enablement & Skill Development

Question 1 of 12

How do engineers in your organization learn to use AI tools?

They figure it out on their own. No formal training exists. Engineers share tips informally, but there's no structured program. We have formal AI training and established practices shared across teams. AI literacy is widespread. Most engineers confidently experiment with and refine their usage. Continuous AI skill development is embedded in our engineering culture.

Question 2 of 12

When a new AI tool or capability becomes available, what typically happens?

A few individuals try it on personal accounts. Most of the org doesn't hear about it. It spreads through word of mouth, but adoption is uncoordinated. There's a defined process for evaluating and rolling out new AI capabilities. Teams proactively evaluate tools against existing workflows and propose integration plans. New capabilities are continuously assessed and integrated as part of our platform strategy.

Policy & Governance

Question 3 of 12

Does your organization have policies for how AI is used in development?

No guidelines or expectations exist around AI usage. Basic or informal policies exist, but they're inconsistent across teams. Clear organizational expectations are in place for how AI is used in development. AI usage policy is aligned with company strategy and regularly reviewed. Policies are continuously updated as AI systems and capabilities evolve.

Question 4 of 12

How does your organization handle sensitive code, data, or IP when using AI tools?

No guardrails exist. Developers decide for themselves what to share with AI tools. Informal guidance exists, but it's not consistently enforced. Clear rules define what can be shared with AI tools, and teams follow them. Policies are enforced through tooling and access controls, not just documentation. AI systems operate within structured governance frameworks with automated compliance.

Validation & Testing

Question 5 of 12

How is AI-generated code reviewed before it ships?

Developers manually review AI output. There's no AI-specific process. Basic checklists or review prompts exist for AI-generated work, but adoption varies. Automated checks validate AI-generated code as part of the CI/CD pipeline. Dedicated AI validation systems review work before release and escalate edge cases. Validation systems continuously learn from production signals to improve AI output quality.

Question 6 of 12

When AI-generated code introduces a bug, how does your team typically find out?

The same way we find any bug. Usually in production or during manual testing. Sometimes in code review, but there's no systematic way to flag AI-related problems. Our CI/CD pipeline catches most issues before merge, regardless of origin. We have monitoring specifically designed to track AI-generated output quality. Systems automatically detect and feed back AI quality issues to improve future output.

Embedding in Workflows

Question 7 of 12

Where does AI fit in your development workflow?

AI is used outside our core engineering tools, mostly in a browser or standalone app. IDE copilots and chat assistants are widely used, but not deeply integrated into workflows. AI is consistently used across development workflows: coding, testing, documentation, and reviews. AI agents coordinate across multiple engineering systems like CI, issue trackers, and code review. AI orchestrates workflows across the full development lifecycle with minimal manual handoffs.

Question 8 of 12

How involved is AI in non-coding tasks like documentation, testing, and planning?

AI is mostly used for code generation. Not much else. Some engineers use AI for docs or tests, but it's ad hoc and inconsistent. AI is a standard part of multiple stages of development, not just coding. AI handles significant portions of testing, documentation, and other tasks with light oversight. AI manages end-to-end workflows across coding and non-coding tasks. Humans focus on decisions.

Workflow Automation

Question 9 of 12

How are AI-assisted tasks triggered in your workflow?

AI is not used to automate engineering tasks. Developers manually trigger AI to assist with tasks. Some AI tasks trigger automatically based on system events. Agents execute bounded engineering tasks automatically when triggered. AI orchestrates multi-step engineering workflows autonomously. Humans handle exceptions.

Question 10 of 12

What is the broadest scope of work AI completes independently?

AI only produces output when a developer explicitly asks for it. AI suggests code or content, but a developer acts on every suggestion. AI takes routine actions within guardrails, like auto-fixing lint errors. AI agents complete well-defined tasks independently and escalate when uncertain. AI manages complex, multi-step workflows with minimal human intervention.

Data Context & Access

Question 11 of 12

How much internal context do your AI tools have access to?

AI tools cannot access our codebase, docs, or internal systems. Developers manually provide internal context to AI tools as needed. AI tools can access parts of the codebase or internal documentation directly. AI automatically retrieves relevant internal context when performing tasks. AI operates on a structured knowledge system spanning code, docs, and internal processes.

Question 12 of 12

How well can AI tools answer questions about your internal systems and architecture?

They can't. AI has no access to internal systems. Only if an engineer pastes in the relevant docs or code first. AI can pull from some internal sources, but answers are partial. AI retrieves relevant internal context and gives grounded answers. AI draws from a comprehensive internal knowledge base for thorough, accurate responses.

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