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Scores 8 dimensions, identifies gaps, produces a prioritized 90-day action plan with budget ranges.\n\n## When to Use\n- Before investing in AI/automation tools\n- Board or leadership requesting AI strategy\n- Evaluating build vs buy decisions\n- Annual technology planning\n\n## How It Works\n\nScore each dimension 1-5 (1=not started, 5=optimized):\n\n### 1. Data Infrastructure (Weight: 3x)\n- [ ] Centralized data warehouse or lakehouse operational\n- [ ] Data quality monitoring automated (freshness, completeness, accuracy)\n- [ ] API-first architecture for core systems\n- [ ] Data governance policy documented and enforced\n- [ ] PII/PHI classification and access controls active\n\n**Score 1:** Spreadsheets and siloed databases\n**Score 3:** Warehouse exists, some pipelines automated\n**Score 5:** Real-time streaming, quality >99%, full lineage\n\n### 2. Process Documentation (Weight: 2x)\n- [ ] Top 20 revenue-impacting processes mapped end-to-end\n- [ ] Decision trees documented for each process\n- [ ] Exception handling paths defined\n- [ ] Time-per-task benchmarks established\n- [ ] Process owners assigned\n\n**Score 1:** Tribal knowledge, nothing written down\n**Score 3:** Major processes documented, some outdated\n**Score 5:** Living documentation, updated quarterly, covers 80%+ of operations\n\n### 3. Technical Talent (Weight: 2x)\n- [ ] At least 1 person understands ML/AI concepts at implementation level\n- [ ] Engineering team comfortable with APIs and integrations\n- [ ] DevOps/infrastructure person can deploy and monitor services\n- [ ] Data analyst can query and interpret model outputs\n- [ ] Security team understands AI-specific attack surfaces\n\n**Score 1:** No technical staff beyond basic IT\n**Score 3:** Good engineering team, AI knowledge is theoretical\n**Score 5:** Dedicated AI/ML engineer, cross-functional AI literacy program\n\n### 4. Budget & ROI Framework (Weight: 2x)\n- [ ] AI budget allocated (not pulled from \"innovation\" slush fund)\n- [ ] ROI measurement criteria defined before project starts\n- [ ] Kill criteria established (when to stop a failing project)\n- [ ] Total cost of ownership model includes maintenance, retraining, monitoring\n- [ ] Benchmarks set against current manual process costs\n\n**Budget Reality by Company Size:**\n| Company Size | Year 1 Investment | Expected ROI Timeline |\n|---|---|---|\n| 15-50 employees | $24K-$80K | 4-8 months |\n| 50-200 employees | $80K-$300K | 3-6 months |\n| 200-1000 employees | $300K-$1.2M | 6-12 months |\n| 1000+ employees | $1.2M-$5M+ | 8-18 months |\n\n### 5. Change Management (Weight: 1.5x)\n- [ ] Executive sponsor identified and actively involved\n- [ ] Communication plan for affected teams drafted\n- [ ] Training budget allocated\n- [ ] Pilot team identified (volunteers, not voluntolds)\n- [ ] Success metrics shared openly with organization\n\n**Score 1:** Leadership says \"just do AI\" with no plan\n**Score 3:** Exec sponsor exists, some team buy-in\n**Score 5:** Change management playbook active, regular town halls, feedback loops\n\n### 6. Security & Compliance (Weight: 2.5x)\n- [ ] AI-specific data handling policy written\n- [ ] Vendor security assessment process includes AI criteria\n- [ ] Model output logging and audit trail planned\n- [ ] Regulatory requirements mapped (GDPR, HIPAA, SOX, SOC 2, EU AI Act)\n- [ ] Incident response plan covers AI failures\n\n**Score 1:** No AI-specific security considerations\n**Score 3:** General security strong, AI gaps identified\n**Score 5:** AI governance framework active, regular audits, compliance automated\n\n### 7. Integration Readiness (Weight: 1.5x)\n- [ ] Core systems have APIs (CRM, ERP, HRIS, etc.)\n- [ ] Authentication/authorization supports service accounts\n- [ ] Webhook or event-driven architecture available\n- [ ] Test/staging environment mirrors production\n- [ ] Rollback procedures documented\n\n**Score 1:** Legacy systems, no APIs, manual data entry\n**Score 3:** Major systems have APIs, some manual bridges\n**Score 5:** API-first architecture, event-driven, CI/CD for integrations\n\n### 8. Strategic Alignment (Weight: 1x)\n- [ ] AI initiatives map to specific business objectives (not \"innovation\")\n- [ ] 3-year technology roadmap includes AI milestones\n- [ ] Competitive landscape analysis includes AI adoption by rivals\n- [ ] Board/leadership educated on AI capabilities and limitations\n- [ ] Failure tolerance defined (acceptable experiment failure rate)\n\n**Score 1:** AI is a buzzword, no concrete strategy\n**Score 3:** Strategy exists, loosely connected to business goals\n**Score 5:** AI embedded in strategic plan, quarterly reviews, competitive moat building\n\n## Scoring\n\n**Weighted Total = Sum of (Score × Weight) / Max Possible × 100**\n\n| Range | Rating | Recommendation |\n|---|---|---|\n| 0-25 | 🔴 Not Ready | Fix foundations first. 6-12 months of groundwork before AI projects. |\n| 26-50 | 🟡 Early Stage | Pick ONE high-impact, low-risk pilot. Build muscle. |\n| 51-75 | 🟢 Ready | Deploy 2-3 agents in validated use cases. Scale what works. |\n| 76-100 | 🔵 Advanced | Multi-agent deployment, autonomous operations, competitive moat. |\n\n## 90-Day Action Plan Template\n\n**Days 1-30: Foundation**\n- Complete this assessment with honest scores\n- Document top 5 processes by time spent × error rate\n- Audit data infrastructure gaps\n- Set budget and kill criteria\n\n**Days 31-60: Pilot**\n- Select highest-scoring use case (high data readiness + clear ROI)\n- Deploy single agent or automation\n- Measure daily: time saved, error rate, cost\n- Weekly review with stakeholders\n\n**Days 61-90: Scale or Kill**\n- If pilot ROI > 2x: plan 2 more deployments\n- If pilot ROI < 1x: diagnose root cause, pivot or kill\n- Document learnings regardless of outcome\n- Update 3-year roadmap based on reality\n\n## 7 Assessment Mistakes\n\n1. **Scoring yourself too high** — External validation beats internal optimism\n2. **Ignoring data quality** — AI on bad data = faster wrong answers\n3. **Skipping change management** — Technical success + team rejection = failure\n4. **No kill criteria** — Zombie projects drain budget and credibility\n5. **Buying before understanding** — Tool purchases before process documentation = shelfware\n6. **Ignoring security until audit** — Retrofitting AI security costs 3-5x more than building it in\n7. **Comparing to tech companies** — Your readiness bar is YOUR industry, not Silicon Valley\n\n## Industry Benchmarks (2026)\n\n| Industry | Avg Score | Top Quartile | First AI Win |\n|---|---|---|---|\n| Fintech | 62 | 78+ | Fraud detection, KYC |\n| Healthcare | 41 | 58+ | Clinical documentation, scheduling |\n| Legal | 38 | 52+ | Contract review, research |\n| Construction | 29 | 44+ | Safety monitoring, estimation |\n| Ecommerce | 58 | 74+ | Personalization, inventory |\n| SaaS | 65 | 82+ | Support, onboarding, churn prediction |\n| Real Estate | 35 | 48+ | Lead scoring, valuation |\n| Recruitment | 45 | 62+ | Screening, outreach |\n| Manufacturing | 42 | 56+ | QC, predictive maintenance |\n| Professional Services | 48 | 64+ | Proposal generation, time tracking |\n\n---\n\n**Get your industry-specific context pack ($47) →** https://afrexai-cto.github.io/context-packs/\n\n**Calculate your AI revenue leak →** https://afrexai-cto.github.io/ai-revenue-calculator/\n\n**Set up your first AI agent →** https://afrexai-cto.github.io/agent-setup/\n\n**Bundles:** Pick 3 for $97 | All 10 for $197 | Everything Pack $247\n","readmeExcerpt":"AI Readiness Assessment Run a structured AI readiness audit for any organization. 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