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// Skill profile

AI Adoption Readiness Assessment

Name: AI Adoption Readiness Assessment
Author: afrexai-cto

name: ai-adoption-readiness

by afrexai-cto · published 2026-04-01

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2026-04

// Install command

$ claw add gh:afrexai-cto/afrexai-cto-afrexai-ai-adoption-readiness

View on GitHub

// Full documentation

---

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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