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afrexai-ai-readiness

AI Readiness Assessment

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15
Source
dvcrn/openclaw-skills-marketplace
Updated
2026-05-29
Slug
dvcrn--openclaw-skills-marketplace--afrexai-ai-readiness
View on GitHubRaw SKILL.md

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Drops the SKILL.md into .claude/skills/afrexai-ai-readiness.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

AI Readiness Assessment

Run a structured AI readiness audit for any organization. Scores 8 dimensions, identifies gaps, produces a prioritized 90-day action plan with budget ranges.

When to Use

  • Before investing in AI/automation tools
  • Board or leadership requesting AI strategy
  • Evaluating build vs buy decisions
  • Annual technology planning

How It Works

Score each dimension 1-5 (1=not started, 5=optimized):

1. Data Infrastructure (Weight: 3x)

  • Centralized data warehouse or lakehouse operational
  • Data quality monitoring automated (freshness, completeness, accuracy)
  • API-first architecture for core systems
  • Data governance policy documented and enforced
  • PII/PHI classification and access controls active

Score 1: Spreadsheets and siloed databases Score 3: Warehouse exists, some pipelines automated Score 5: Real-time streaming, quality >99%, full lineage

2. Process Documentation (Weight: 2x)

  • Top 20 revenue-impacting processes mapped end-to-end
  • Decision trees documented for each process
  • Exception handling paths defined
  • Time-per-task benchmarks established
  • Process owners assigned

Score 1: Tribal knowledge, nothing written down Score 3: Major processes documented, some outdated Score 5: Living documentation, updated quarterly, covers 80%+ of operations

3. Technical Talent (Weight: 2x)

  • At least 1 person understands ML/AI concepts at implementation level
  • Engineering team comfortable with APIs and integrations
  • DevOps/infrastructure person can deploy and monitor services
  • Data analyst can query and interpret model outputs
  • Security team understands AI-specific attack surfaces

Score 1: No technical staff beyond basic IT Score 3: Good engineering team, AI knowledge is theoretical Score 5: Dedicated AI/ML engineer, cross-functional AI literacy program

4. Budget & ROI Framework (Weight: 2x)

  • AI budget allocated (not pulled from "innovation" slush fund)
  • ROI measurement criteria defined before project starts
  • Kill criteria established (when to stop a failing project)
  • Total cost of ownership model includes maintenance, retraining, monitoring
  • Benchmarks set against current manual process costs

Budget Reality by Company Size:

Company Size Year 1 Investment Expected ROI Timeline
15-50 employees $24K-$80K 4-8 months
50-200 employees $80K-$300K 3-6 months
200-1000 employees $300K-$1.2M 6-12 months
1000+ employees $1.2M-$5M+ 8-18 months

5. Change Management (Weight: 1.5x)

  • Executive sponsor identified and actively involved
  • Communication plan for affected teams drafted
  • Training budget allocated
  • Pilot team identified (volunteers, not voluntolds)
  • Success metrics shared openly with organization

Score 1: Leadership says "just do AI" with no plan Score 3: Exec sponsor exists, some team buy-in Score 5: Change management playbook active, regular town halls, feedback loops

6. Security & Compliance (Weight: 2.5x)

  • AI-specific data handling policy written
  • Vendor security assessment process includes AI criteria
  • Model output logging and audit trail planned
  • Regulatory requirements mapped (GDPR, HIPAA, SOX, SOC 2, EU AI Act)
  • Incident response plan covers AI failures

Score 1: No AI-specific security considerations Score 3: General security strong, AI gaps identified Score 5: AI governance framework active, regular audits, compliance automated

7. Integration Readiness (Weight: 1.5x)

  • Core systems have APIs (CRM, ERP, HRIS, etc.)
  • Authentication/authorization supports service accounts
  • Webhook or event-driven architecture available
  • Test/staging environment mirrors production
  • Rollback procedures documented

Score 1: Legacy systems, no APIs, manual data entry Score 3: Major systems have APIs, some manual bridges Score 5: API-first architecture, event-driven, CI/CD for integrations

8. Strategic Alignment (Weight: 1x)

  • AI initiatives map to specific business objectives (not "innovation")
  • 3-year technology roadmap includes AI milestones
  • Competitive landscape analysis includes AI adoption by rivals
  • Board/leadership educated on AI capabilities and limitations
  • Failure tolerance defined (acceptable experiment failure rate)

Score 1: AI is a buzzword, no concrete strategy Score 3: Strategy exists, loosely connected to business goals Score 5: AI embedded in strategic plan, quarterly reviews, competitive moat building

Scoring

Weighted Total = Sum of (Score × Weight) / Max Possible × 100

Range Rating Recommendation
0-25 🔴 Not Ready Fix foundations first. 6-12 months of groundwork before AI projects.
26-50 🟡 Early Stage Pick ONE high-impact, low-risk pilot. Build muscle.
51-75 🟢 Ready Deploy 2-3 agents in validated use cases. Scale what works.
76-100 🔵 Advanced Multi-agent deployment, autonomous operations, competitive moat.

90-Day Action Plan Template

Days 1-30: Foundation

  • Complete this assessment with honest scores
  • Document top 5 processes by time spent × error rate
  • Audit data infrastructure gaps
  • Set budget and kill criteria

Days 31-60: Pilot

  • Select highest-scoring use case (high data readiness + clear ROI)
  • Deploy single agent or automation
  • Measure daily: time saved, error rate, cost
  • Weekly review with stakeholders

Days 61-90: Scale or Kill

  • If pilot ROI > 2x: plan 2 more deployments
  • If pilot ROI < 1x: diagnose root cause, pivot or kill
  • Document learnings regardless of outcome
  • Update 3-year roadmap based on reality

7 Assessment Mistakes

  1. Scoring yourself too high — External validation beats internal optimism
  2. Ignoring data quality — AI on bad data = faster wrong answers
  3. Skipping change management — Technical success + team rejection = failure
  4. No kill criteria — Zombie projects drain budget and credibility
  5. Buying before understanding — Tool purchases before process documentation = shelfware
  6. Ignoring security until audit — Retrofitting AI security costs 3-5x more than building it in
  7. Comparing to tech companies — Your readiness bar is YOUR industry, not Silicon Valley

Industry Benchmarks (2026)

Industry Avg Score Top Quartile First AI Win
Fintech 62 78+ Fraud detection, KYC
Healthcare 41 58+ Clinical documentation, scheduling
Legal 38 52+ Contract review, research
Construction 29 44+ Safety monitoring, estimation
Ecommerce 58 74+ Personalization, inventory
SaaS 65 82+ Support, onboarding, churn prediction
Real Estate 35 48+ Lead scoring, valuation
Recruitment 45 62+ Screening, outreach
Manufacturing 42 56+ QC, predictive maintenance
Professional Services 48 64+ Proposal generation, time tracking

Get your industry-specific context pack ($47) → https://afrexai-cto.github.io/context-packs/

Calculate your AI revenue leak → https://afrexai-cto.github.io/ai-revenue-calculator/

Set up your first AI agent → https://afrexai-cto.github.io/agent-setup/

Bundles: Pick 3 for $97 | All 10 for $197 | Everything Pack $247