afrexai-ai-adoption-readiness（Afrexai AI 采用准备情况）

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?

Scoring Criteria

Red Flags

• Recent layoffs tied to automation (trust is broken)
• "AI will take our jobs" narrative unchallenged by leadership
• No history of successful technology adoption
• Middle management actively blocking change

Dimension 2: Data Maturity (Weight: 20%)

Assess data quality, accessibility, and governance — AI is only as good as its data.

Questions to Evaluate

• Is business data centralized or siloed across departments?
• Are there documented data quality standards?
• Can teams access the data they need without IT bottlenecks?
• Is sensitive data classified and governed?
• What percentage of key decisions are currently data-driven?

Scoring Criteria

Red Flags

• Critical business data only in one person's spreadsheet
• No data backup or disaster recovery
• Regulatory data (PII, financial) ungoverned
• "We don't really track that" for key metrics

Dimension 3: Technical Infrastructure (Weight: 15%)

Assess whether the tech stack can support AI tools and integrations.

Questions to Evaluate

• Is the tech stack modern or legacy-heavy?
• Are there APIs available for key systems?
• Can the infrastructure handle additional compute/storage?
• Is there CI/CD and version control?
• How is security managed (SSO, MFA, access controls)?

Scoring Criteria

Red Flags

• Core business runs on software that can't integrate (no API, no export)
• No IT team or all IT is outsourced with no AI expertise
• Security is an afterthought (no MFA, shared passwords)
• Systems are at capacity — no headroom for AI workloads

Dimension 4: Leadership & Sponsorship (Weight: 20%)

Assess executive commitment — AI adoption without leadership backing fails 90% of the time.

Questions to Evaluate

• Is there an executive sponsor with authority and budget?
• Does leadership understand what AI can and can't do?
• Is AI adoption tied to a business outcome (not just "innovation")?
• Will leadership shield the initiative from short-term ROI pressure?
• Is there board/investor alignment on AI investment?

Scoring Criteria

Red Flags

• "The CEO read an article about AI and wants us to do something"
• Budget allocated but no clear owner
• Expectation of immediate ROI from AI (unrealistic timeline)
• Leadership turnover expected (sponsor might leave)

Dimension 5: Skills & Talent (Weight: 15%)

Assess whether the team can use, manage, and maintain AI tools.

Questions to Evaluate

• Does anyone on the team have AI/ML experience?
• Is there a training budget for upskilling?
• How tech-savvy are the end users who'll interact with AI?
• Is there capacity to manage AI tools (or will it be outsourced)?
• Can they evaluate AI outputs for accuracy?

Scoring Criteria

Red Flags

• Plan to "hire an AI person" without knowing what that means
• End users have no say in the tools they'll use
• No training budget
• Outsourced IT with no AI capability

Dimension 6: Process Maturity (Weight: 10%)

Assess whether processes are documented and consistent enough for AI to augment.

Questions to Evaluate

• Are key business processes documented?
• Are workflows consistent or does everyone do it differently?
• Is there a way to measure process performance (KPIs, SLAs)?
• Which processes are candidates for AI augmentation?
• Are there compliance/regulatory requirements on process documentation?

Scoring Criteria

Red Flags

• "Only Janet knows how that works"
• No SOPs, runbooks, or process maps
• Processes change constantly without documentation
• Compliance requirements met through manual effort only

Output: Readiness Report

Generate the full report in this structure:

1. Executive Summary

• Overall readiness score (X.X / 5.0) with threshold label (Green/Yellow/Orange/Red)
• One-paragraph verdict: ready, conditionally ready, or not ready
• Top 3 strengths and top 3 gaps

2. Dimension Scorecard

For each of the 6 dimensions:

• Score (1-5) with brief justification
• Key evidence (what the assessment found)
• Red flags identified (if any)

3. Gap Analysis

• Prioritized list of gaps blocking AI adoption
• For each gap: severity (Critical/High/Medium/Low), effort to close, and timeline

4. Readiness Roadmap

Phased action plan based on overall score:

If Red (< 2.0): 6-month foundation phase

• Data governance basics
• Leadership education
• Process documentation sprint
• Target: reach 3.0 before any AI deployment

If Orange (2.0–2.9): 3-month preparation phase

• Address critical gaps
• Run small AI pilot in most-ready department
• Build internal champions
• Target: reach 3.5 within one quarter

If Yellow (3.0–3.9): Parallel track

• Deploy AI pilot while addressing gaps
• Focus on highest-weight dimensions
• Measure and iterate monthly
• Target: reach 4.0 within 2 months

If Green (4.0+): Accelerate

• Deploy AI across target scope
• Address minor gaps in parallel
• Focus on adoption metrics and value tracking
• Target: full deployment within 6 weeks

5. Quick Wins

3-5 actions that can start this week with no budget and minimal effort. These build momentum.

6. Risk Register

Top 5 risks to AI adoption success, each with:

• Likelihood (High/Medium/Low)
• Impact (High/Medium/Low)
• Mitigation strategy

7. Next Steps

• Recommended immediate actions (next 7 days)
• Who should own what
• When to reassess (typically 30/60/90 days)
• If applicable: "Feed this assessment into the change-management-plan skill for a full rollout plan"

Integration with Other Skills

This skill is designed to work in a pipeline:

1. AI Adoption Readiness (this skill) → Assess current state
2. Compliance Readiness → Check regulatory alignment
3. Change Management Plan → Build the rollout playbook
4. Vendor Risk Assessment → Evaluate AI vendor options
5. Incident Response Plan → Prepare for AI failures
6. SLA Monitor → Set up reliability guarantees

Recommend the next skill based on assessment results.

Tips for the Agent

• Be honest, not optimistic. A low score with a clear action plan is more valuable than an inflated score.
• Use the organization's own language and examples — don't be generic.
• If information is missing, flag it as a gap rather than assuming the best case.
• Always tie recommendations back to the specific AI initiative they described.
• If they score below 2.0, don't discourage them — frame it as "here's the clear path to get ready."
• For pre-sales: a readiness assessment positions AfrexAI as a consultative partner, not just a vendor.