ai-readiness

name	ai-readiness
description	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:

• 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