AI Adoption Readiness Assessment

Score how prepared an organization is to adopt AI agents and automation. Identifies gaps before they become failed implementations. Pairs with the change-management-plan skill — run this first, then feed results into the change plan.

When to Use

• Before deploying AI agents or automation tools
• Evaluating whether a team or department is ready for AI
• Building a business case for AI investment
• Identifying blockers that will kill an AI initiative
• Vendor evaluation — can this org actually USE the tool they're buying?
• Pre-sale qualification for AI services (are they ready to be a customer?)

How to Use

The user describes their organization. The agent conducts the assessment.

Input Format

Organization: [Company name, size, industry]
AI Initiative: [What they want to do with AI]
Department/Scope: [Which teams are involved]
Current Tools: [Existing tech stack, any AI tools already in use]
Budget Range: [Approximate budget for AI initiatives]
Timeline Pressure: [When do they need this working?]
Known Blockers: [Anything they already know is a problem]

If the user provides partial info, ask for missing critical fields (Organization, AI Initiative, and Scope at minimum). Infer reasonable defaults for the rest.

Assessment Framework

Scoring System

Each dimension scores 1-5:

• 1 — Not Ready: Major gaps, significant work needed before AI adoption
• 2 — Early Stage: Some awareness but no foundation in place
• 3 — Developing: Building blocks exist but inconsistent
• 4 — Ready: Solid foundation, minor gaps to address
• 5 — Advanced: Strong position, ready to accelerate

Overall Readiness = weighted average of all 6 dimensions.

Readiness Thresholds

• 4.0+ Overall: Green light — proceed with AI deployment
• 3.0–3.9: Yellow — address gaps in parallel with pilot deployment
• 2.0–2.9: Orange — foundational work needed before scaling
• Below 2.0: Red — not ready. Fix fundamentals first.

Dimension 1: Culture & Mindset (Weight: 20%)

Assess openness to change, experimentation, and technology adoption.

Questions to Evaluate

• How does the organization handle failed experiments? Blame or learning?
• Is there appetite for automation, or fear of job displacement?
• Do teams proactively adopt new tools, or resist until forced?
• Has the organization successfully adopted major tech changes before?
• Is there a culture of data-driven decision making?