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Assess portfolio company AI readiness, AI adoption maturity. Use when user says "AI readiness", "AI maturity", "assess AI adoption".
domain
financial
author
oyi77
license
Apache-2.0
subdomain
financial-analysis
tags
["analysis","finance","investment","readiness"]
version
1.0.0
AI Readiness!
Persona:!
AI Strategist — Inspired by the ai-readiness skill from anthropics/financial-services. Masters AI adoption frameworks, maturity assessment, and value creation through AI.
Core Philosophy: AI readiness isn't about having GPUs — it's about culture, data, and leadership. Assess the COMPANY, not the tech stack.
Overview
Assesses portfolio company AI readiness across 5 dimensions: Strategy, Data, Tech, Culture, ROI. Outputs maturity score (1-5) and roadmap.
Anti-Rationalization Table
Rationalization
Reality
"I'll figure it out as I go"
A structured approach saves time and reduces errors. Follow the workflow in this skill rather than improvising.
"I already know this topic"
Familiarity breeds shortcuts. Use the checklist to verify you haven't missed critical steps.
"This doesn't apply to my situation"
The patterns here generalize across contexts. Adapt, don't skip — the underlying principles hold.
"One more tool will fix it"
Adding complexity rarely solves process gaps. Master the core workflow first.
When to Use
Trigger phrases:
"ai readiness"
"Due diligence (pre-investment)"
"Board presentation (AI strategy update)"
"Portfolio company assessment (annual)"
Due diligence (pre-investment)
Board presentation (AI strategy update)
Portfolio company assessment (annual)
Value creation planning (AI-driven)
Strategic review (underperforming AI adoption)
Investment committee memo (AI thesis)
When NOT to Use:!
Earnings analysis (use financial/earnings-viewer)
Building DCF models (use financial/model-builder)
General portfolio monitoring (use financial/portfolio-monitor)
Pitch deck creation (use financial/pitch-deck)
Implementation:!
The implementation follows a phased approach: assess strategy, data, tech, culture, and ROI across the five AI readiness dimensions.
Phase 1: Strategy Assessment!
AI Strategy Scorecard:
strategy_assessment = {
"vision": {
"score": 4, # 1-5"evidence": "CEO committed, AI-first messaging",
"gap": "No AI KPIs linked to exec comp"
},
"governance": {
"score": 3,
"evidence": "AI steering committee exists",
"gap": "No AI risk framework documented"
},
"roadmap": {
"score": 5,
"evidence": "3 AI products launched 2024",
"gap": "No 2026-2027 AI roadmap yet"
}
}
Phase 2: Data Maturity!
Data Readiness:
data_assessment = {
"infrastructure": {
"score": 3, # 1-5"details": "Data lake exists, but siloed",
"gap": "No unified data warehouse"
},
"quality": {
"score": 4,
"details": "95% clean customer data",
"gap": "5% products missing category tags"
},
"governance": {
"score": 2,
"details": "No data lineage tracking",
"gap": "Implement data catalog + lineage"
}
}
Phase 3: Tech Stack!
AI/ML Capabilities:
tech_assessment = {
"mlops": {
"score": 3,
"tools": "Basic MLflow, limited automation",
"gap": "No model registry, no A/B testing"
},
"infrastructure": {
"score": 4,
"cloud": "AWS (SageMaker)",
"gap": "No GPU cluster for training"
},
"talent": {
"score": 3,
"headcount": "5 data scientists, 2 ML engineers",
"gap": "No Chief AI Officer"
}
}
Phase 4: ROI Analysis!
AI Value Creation:
roi_analysis = {
"cost_savings": {
"2023": 500000, # $500K"2024": 1200000, # $1.2M"2025_pct": 8000000, # $8M projected"roi": "3.5x"# Return on AI investment
},
"revenue_growth": {
"ai_products": "$12M (15% of revenue)",
"growth_rate": "80% YoY",
"target": "25% of revenue by 2027"
}
}
Phase 5: Maturity Score!
Overall AI Readiness (1-5):
maturity_score = {
"overall": 3.4, # Weighted average"strategy": 4.0,
"data": 3.0,
"tech": 3.5,
"culture": 3.0,
"roi": 4.0
}
# Interpretation:# 1.0-1.9 = AI Novice (avoid or heavy discount)# 2.0-2.9 = AI Explorer (monitor closely)# 3.0-3.9 = AI Adopter (standard weighting)# 4.0-4.9 = AI Leader (premium valuation)# 5.0 = AI Native (top-tier valuation)
Phase 6: Roadmap!
AI Roadmap (100-Day Plan):
# AI Roadmap: [Company Name]## Quick Wins (0-30 Days)1. ✅ Appoint Chief AI Officer
2. ✅ Launch data catalog (lineage tracking)
3. ✅ Set AI KPIs for exec team
## Foundation (30-60 Days)1. Build unified data warehouse (Snowflake/BigQuery)
2. Implement MLOps (model registry + A/B testing)
3. Hire 3 more ML engineers
## Scale (60-100 Days)1. Launch 3 AI products (generative AI features)
2. Achieve $20M AI revenue run-rate
3. Target AI Readiness: 4.0+ (up from 3.4)
Common Rationalizations
Rationalization
Reality
"They have GPUs, they're AI-ready"
GPUs ≠ strategy/culture/data — assess ALL 5 dimensions
"AI readiness is a tech checklist"
Culture = #1 predictor of AI success (people, not tools)
"Startup, skip assessment"
Early-stage = biggest AI upsie potential OR downfall
"5.0 is the goal always"
3.5-4.0 is plenty for most B2B companies
Red Flags
AI readiness < 2.0 (novice — heavy discount or pass)
No AI strategy (CEO doesn't mention AI in earnings)
Data quality < 80% (garbage in, garbage out)
No AI talent (0 data scientists/ML engineers)
ROI < 1.5x (AI investment losing money!)
No AI KPIs (not measured = not managed)
Culture score < 2.5 (leaders don't understand AI)
Verification
After completing AI readiness assessment, confirm:
Investment thesis: AI impact assessed (strengthens/weakens/neutral)
Integration Points:!
Cross-Skill References:
financial/model-builder — For AI-driven revenue in DCF
financial/meeting-prep — For board presentation
trading/black-edge — For AI competitive intelligence
references/trading-checklist.md — For AI investment risk!
MCP Server Integrations:
PitchBook MCP — For AI company comps
S&P Global MCP — For AI sector analysis
FactSet MCP — For AI adoption benchmarking!
Load references/trading-checklist.md for complete trading checklists (strategy, risk, execution, portfolio).
Cross-reference: For comprehensive multi-asset financial analysis, risk management, and institutional-grade frameworks, see financial/all-in-one-finance (16 modules) and financial/wolf-finance (22 modules).
