AI Readiness

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

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

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:

• Strategy assessed: vision, governance, roadmap (all 1-5 scored)
• Data assessed: infrastructure, quality, governance (all scored)
• Tech assessed: MLOps, infrastructure, talent (all scored)
• Culture assessed: leadership, talent, adoption (all scored)
• ROI calculated: cost savings, revenue growth, ROI multiple
• Overall score: 1-5 with weighted breakdown
• Roadmap: 100-day plan with quick wins + foundation + scale
• Report generated: 3-5 pages, maturity score highlighted
• 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).

Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality