Purpose
Systematically map where AI can create economic value across a business by decomposing its value chain and analyzing each activity through four value lenses — turning a vague "we need an AI strategy" mandate into a prioritized portfolio of value drivers, each tied to a specific AI application type and an expected impact rating.
When to Use
Invoke this skill when the user asks to:
- Identify where AI can create value in their business or operating model
- Build the opportunity map underpinning an AI strategy or transformation roadmap
- Prioritize which functions or processes to target for AI investment first
- Pressure-test whether proposed AI initiatives are tied to real revenue, cost, risk, or experience outcomes
- Find AI use cases beyond the obvious (chatbots, content generation) that map to their actual value chain
- Build the business case input for an AI investment proposal to the board or executive committee
Inputs Required
Before starting, collect:
- Business model: How the company makes money — products/services, revenue streams, customer segments
- Industry/sector: The market and competitive context
- Operating description: The major functions and processes (e.g., R&D, supply chain, sales, service, finance, operations)
- Cost structure: Largest cost categories or where margin pressure exists
- Strategic priorities: What the leadership most wants to improve in the next 12–24 months
- Data/tech maturity: Rough sense of data availability and current systems (high / medium / low)
- Risk/regulatory context: Any compliance, safety, or reputational constraints (optional)
If cost structure or data maturity is missing, infer reasonable defaults from the industry and note the assumption.
Instructions
Step 1: Decompose the Value Chain
Break the business into 6–9 discrete value chain activities. Use the description provided; where it's thin, apply a standard sector-appropriate value chain.
- Primary activities: The activities that directly create and deliver the offering (e.g., inbound logistics, operations/production, outbound logistics, marketing & sales, service)
- Support activities: Activities that enable the primary chain (e.g., procurement, technology development, HR, finance/admin)
- For each activity, write one line on what happens there and what "good" looks like operationally
Output a clean list of named activities before any analysis — this becomes the spine of the entire deliverable.
Step 2: Analyze Each Activity Through Four Value Lenses
For every value chain activity, scan for AI opportunity across all four lenses. Not every activity yields all four — only surface opportunities that are real.
- Revenue Growth: Where can AI increase volume, price, conversion, retention, or open new revenue? (e.g., dynamic pricing, propensity-to-buy scoring, personalized cross-sell, new AI-enabled products)
- Cost Reduction: Where can AI reduce labor, waste, error, or cycle time? (e.g., process automation, demand forecasting to cut inventory, predictive maintenance, document processing)
- Risk Mitigation: Where can AI reduce financial, operational, compliance, or safety risk? (e.g., fraud detection, anomaly detection, compliance monitoring, credit/underwriting risk)
- Customer Experience: Where can AI improve speed, relevance, accuracy, or satisfaction of the customer interaction? (e.g., intelligent routing, self-service resolution, real-time personalization, faster response)
Step 3: Map Each Opportunity to an AI Application Type
For every value driver identified, label it with the specific AI capability required — not a generic "use AI."
Use these application types:
- Predictive ML: Forecasting, scoring, classification (demand, churn, risk, propensity)
- Optimization/Prescriptive: Recommending the best action under constraints (pricing, routing, scheduling, allocation)
- Generative AI (LLM): Drafting, summarizing, extracting, conversing (content, knowledge retrieval, agents)
- Computer Vision: Image/video analysis (quality inspection, document capture, safety monitoring)
- Anomaly Detection: Spotting outliers (fraud, defects, security)
- Intelligent Process Automation: AI-augmented workflow automation (claims, onboarding, back-office)
Step 4: Rate Expected Impact and Feasibility
For each value driver, assign:
- Value lens: Which of the four (or multiple)
- Impact: High / Medium / Low — with a one-line rationale tied to the company's cost structure or revenue scale
- Feasibility: High / Medium / Low — factoring data maturity, complexity, and organizational readiness
- Time horizon: Quick win (0–6 mo) / Build (6–18 mo) / Strategic bet (18+ mo)
Step 5: Prioritize and Synthesize
- Plot the top drivers on an impact × feasibility logic and identify the 5–7 highest-priority value drivers
- Flag any "no-regret quick wins" (high feasibility, meaningful impact, fast)
- Flag any "strategic bets" worth funding despite lower near-term feasibility
- State one-line guidance on sequencing: what to fund first and why
Output Format
- Structure: Value chain spine → per-activity opportunity analysis (four lenses) → consolidated value driver table → prioritized shortlist
- Length: 1,000–1,400 words
- Format: Structured document with a master value driver table; the table columns are: Value Chain Activity | Value Driver | Value Lens | AI Application Type | Impact | Feasibility | Time Horizon
- Sections: Value chain decomposition → Opportunity analysis by activity → Master value driver table → Top 5–7 prioritized drivers → Sequencing recommendation
- Tone: Strategy advisor to the C-suite — analytical, value-anchored, decisive; ties every opportunity to a financial or strategic outcome, never to technology for its own sake
Quality Checklist
Before delivering output, verify:
- [ ] The value chain is decomposed into 6–9 named activities before analysis begins
- [ ] Every value driver is mapped to a specific AI application type — none labeled only "AI" or "automation"
- [ ] All four value lenses (revenue, cost, risk, CX) are represented across the full analysis
- [ ] Every driver in the master table has an Impact, Feasibility, and Time Horizon rating
- [ ] Impact ratings reference the company's actual cost structure, revenue scale, or strategic priorities — not generic claims
- [ ] The prioritized shortlist contains 5–7 drivers with explicit sequencing guidance
Notes
- The most common failure mode in AI strategy is starting from technology ("where can we use LLMs?") rather than value — this skill enforces the reverse: value chain first, application type second.
- Quick wins matter politically: surfacing one high-feasibility, fast-impact driver early funds credibility for the larger strategic bets.
- If data maturity is low, weight feasibility down accordingly — a high-impact driver that depends on data the company doesn't have is a strategic bet, not a quick win, and should be labeled honestly.
- Cross-cutting drivers (one AI capability serving multiple activities) are often the highest-leverage investments — call these out explicitly when they appear.
How to use this skill
This file is a Claude Code skill in Anthropic's standard format — markdown with YAML frontmatter at the top.
Installing in Claude Code:
~/.claude/skills/<folder-name>/SKILL.md— auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)<your-project>/.claude/skills/<folder-name>/SKILL.md— auto-loads only when working in that project (recommended for project-specific skills)
Restart Claude Code or open a new session to register.
Using in Claude.ai, ChatGPT, Gemini, or other AI tools:
- Drag this file into a new chat, or copy and paste its contents
- Send your task or request as your next message — the AI will apply the skill where it fits
For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills