Skill: afrexai-cto/afrexai-ai-adoption-readiness (version 1.0.1)

--- name: ai-adoption-readiness description: > Assess organizational readiness for AI adoption across 6 dimensions: culture, data maturity, tech stack, leadership buy-in, skills/talent, and process maturity. Generates a scored readiness report with gap analysis and a prioritized action plan. Use before building a change management plan to understand where an organization actually stands. Built by AfrexAI. metadata: version: 1.0.0 author: AfrexAI tags: [ai-adoption, readiness-assessment, digital-transformation, enterprise, strategy] --- # 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 | Score | Description | |-------|-------------| | 1 | Strong resistance to change. "We've always done it this way." Fear-based culture. | | 2 | Passive resistance. Leadership wants change but teams don't. No experimentation culture. | | 3 | Mixed — some teams innovate, others resist. No consistent change approach. | | 4 | Generally open to change. Past tech adoptions went OK. Some experimentation happening. | | 5 | Innovation culture. Teams actively seek better tools. Failure is treated as learning. | ### 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 | Score | Description | |-------|-------------| | 1 | Data lives in spreadsheets and email. No standards. No governance. | | 2 | Some databases exist but siloed. Manual data entry. No quality checks. | | 3 | Central data store exists. Some governance. Quality is inconsistent. | | 4 | Clean, accessible data. Governance in place. Teams use data for decisions. | | 5 | Data platform with automated quality checks. Real-time access. Strong governance. | ### 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 | Score | Description | |-------|-------------| | 1 | Legacy systems, no APIs, manual deployments. On-prem only. | | 2 | Mix of legacy and modern. Some APIs. Basic cloud usage. | | 3 | Mostly modern stack. APIs for major systems. Cloud infrastructure. | | 4 | Cloud-native. API-first architecture. CI/CD. Security controls in place. | | 5 | Modern platform with integration layer. Infrastructure as code. Zero-trust security. | ### 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 | Score | Description | |-------|-------------| | 1 | No executive sponsor. AI is a curiosity, not a strategy. | | 2 | Interested executive but no budget or authority allocated. | | 3 | Sponsor exists with some budget. AI tied to vague "efficiency" goals. | | 4 | Strong sponsor. Clear business case. Budget allocated. Willing to iterate. | | 5 | C-suite aligned. AI is strategic priority. Multi-year commitment. Success metrics defined. | ### 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 | Score | Description | |-------|-------------| | 1 | No technical talent. Team can barely use current tools. | | 2 | Some tech-savvy individuals but no AI knowledge. No training plan. | | 3 | General technical competence. 1-2 people with AI awareness. Training possible. | | 4 | Technical team capable of managing integrations. AI training underway. | | 5 | In-house AI expertise. Team can evaluate, customize, and maintain AI tools. | ### 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 | Score | Description | |-------|-------------| | 1 | No documentation. Tribal knowledge. Inconsistent execution. | | 2 | Some processes documented but outdated. Inconsistent across teams. | | 3 | Key processes documented. Some KPIs tracked. Mostly consistent. | | 4 | Well-documented processes with metrics. Clear candidates for AI. | | 5 | Process excellence. Documented, measured, optimized. Ready for intelligent automation. | ### 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.


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