---
name: decision-intelligence-opportunity-identifier
description: "Maps a business process or decision domain into a ranked portfolio of decision intelligence opportunities — classifying each decision as structured or unstructured, specifying data requirements and readiness, estimating impact and feasibility, and recommending an augment-vs-automate approach. Use when identifying where AI-driven decision intelligence could replace or augment human judgment across a function, process, or decision domain."
argument-hint: "[process-domain] [decision-context]"
allowed-tools: ["Read"]
---

# Decision Intelligence Opportunity Identifier

## Purpose
Take a described business process or decision domain and surface the specific decisions where AI-driven decision intelligence could replace or augment human judgment — classifying each by decision type, mapping data requirements and readiness, and ranking by expected impact and feasibility. Solves the executive problem of knowing *where* decision automation actually pays off versus where it adds risk or noise.

## When to Use
Invoke this skill when the user asks to:
- Find where AI or decision intelligence could improve a specific business process
- Identify decisions in a function that could be automated or augmented with data
- Build a decision intelligence roadmap or prioritization for a department
- Assess whether a recurring judgment call should stay human, be augmented, or be automated
- Map data requirements for replacing manual decisions in a workflow
- Justify or scope a decision intelligence investment to leadership

## Inputs Required
Before starting, collect:
- **Process or decision domain**: The function, workflow, or decision area in scope (e.g., credit approvals, inventory replenishment, churn intervention)
- **Key decisions made today**: The recurring decisions within that domain and who makes them
- **Decision frequency and volume**: How often each decision occurs (daily/weekly/per-transaction) and at what scale
- **Current decision basis**: How decisions are made now — gut, rules, spreadsheets, partial models
- **Available data**: What data exists today, where it lives, and its known quality issues
- **Business stakes**: The cost of a wrong decision and the value of a better one (revenue, risk, cost, speed)
- **Constraints**: Regulatory, ethical, or human-in-the-loop requirements that bound automation

If decision frequency or data inventory is missing, infer reasonable values from the domain and flag the assumption in the output.

## Instructions

### Step 1: Decompose the Domain into Discrete Decisions
Break the described process into its component decisions — not tasks, but choices where an outcome is selected. For each, capture:
- The decision statement (e.g., "Which leads get routed to sales vs. nurture")
- The decision owner / role
- Frequency and volume
- Current decision basis (intuition / static rules / analyst judgment / existing model)

Aim for 5–10 discrete decisions. If the domain yields more, group the long tail.

### Step 2: Classify Each Decision Type
Classify every decision along two axes:

**Structure axis:**
- **Structured** — clear inputs, known options, definable success metric, repeatable logic (e.g., reorder quantity, fraud flag). Strong candidates for automation.
- **Semi-structured** — patterned but with contextual exceptions requiring judgment (e.g., pricing exceptions, escalation routing). Candidates for augmentation.
- **Unstructured** — novel, ambiguous, high-context, or values-laden (e.g., entering a new market, M&A go/no-go). Decision support at best; rarely automatable.

**Repeatability axis:**
- High-frequency / repeatable → automation ROI compounds
- Low-frequency / one-off → augmentation only; automation rarely justified

### Step 3: Map Data Requirements and Readiness
For each decision, specify:
- **Required signals**: The features/inputs a model or system would need
- **Data availability**: Available now / partially available / must be built
- **Data quality risk**: Low / Medium / High — with the specific gap (completeness, latency, label availability, bias)
- **Outcome label**: Whether the historical "right answer" is captured to train or validate against — critical for structured automation

Flag any decision where the outcome is not measurable today; these cannot be automated reliably until instrumented.

### Step 4: Determine the Intervention Mode
For each decision, recommend one of three modes with rationale:
- **Automate** — system decides; human reviews exceptions only. Reserve for structured, high-volume, low-stakes-per-instance, well-labeled decisions.
- **Augment** — system recommends; human decides. Default for semi-structured and high-stakes decisions.
- **Inform** — system surfaces analysis; human reasons from scratch. For unstructured, low-frequency strategic decisions.

State the human-in-the-loop requirement and any regulatory or ethical constraint that forces a mode.

### Step 5: Estimate Impact and Feasibility
Score each opportunity:
- **Impact (1–5)**: Combine value-per-better-decision × frequency × current error/inefficiency rate
- **Feasibility (1–5)**: Combine data readiness, decision structure, and constraint burden (inverse)
- **Expected value statement**: One sentence quantifying or directionally sizing the upside (cost saved, risk reduced, speed gained)

### Step 6: Rank and Sequence the Portfolio
Produce a prioritized list. Order by a quadrant logic:
- **Quick wins** (high impact, high feasibility) → do first
- **Strategic bets** (high impact, lower feasibility) → invest in data foundations
- **Incremental** (lower impact, high feasibility) → batch when convenient
- **Deprioritize** (low impact, low feasibility) → explicitly park

Name the top 3 with a one-line reason each, and identify the single most important data or instrumentation gap to close first.

## Output Format
- **Structure**: Opportunity summary at top, then a decision-by-decision table, then ranked portfolio and recommendations
- **Length**: 900–1,300 words
- **Format**: Prose summary + a structured table for the decision inventory (Decision | Type | Mode | Data Readiness | Impact | Feasibility) + ranked list
- **Sections**: Domain summary → Decision inventory table → Classification & data notes → Intervention modes → Ranked opportunity portfolio (quadrants) → Top 3 + first data gap to close
- **Tone**: Strategy executive — decisive, quantified where possible, honest about feasibility limits; no hype about AI capability

## Quality Checklist
Before delivering output, verify:
- [ ] 5–10 discrete decisions are identified (count them) — decisions, not tasks
- [ ] Every decision has a structure classification (structured / semi-structured / unstructured)
- [ ] Every decision has a data readiness rating with a specific named gap, not just "Medium"
- [ ] Every decision has an intervention mode (automate / augment / inform) with rationale
- [ ] Each opportunity has both an impact and feasibility score, and the ranking follows the quadrant logic
- [ ] The single most important data/instrumentation gap to close first is named explicitly

## Notes
- The most common error is recommending automation for decisions whose outcomes aren't measured — automation requires a feedback label, so instrumentation often precedes intelligence.
- Unstructured strategic decisions almost never justify automation; positioning them as "inform" prevents overreach and protects credibility with the board.
- High-frequency, low-stakes-per-instance decisions are where decision intelligence compounds fastest — prioritize these even when each individual decision feels minor.
- When constraints (regulatory, ethical) force human-in-the-loop, default to augment and make that explicit rather than burying it as a caveat.

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## How to use this skill

This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — 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:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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