Discovering Workflows

Discover concrete opportunities where AI can improve your workflows. Produces a categorized opportunity report with a summary table, detailed opportunity cards, and a structured workflow candidate list.

Workflow

Work through four steps in order:

Step 1 — Memory & History Scan

Before asking any questions, review everything you already know about the user from conversation history, memory, project files, or any other available context.

Identify and list:

• •Their role, responsibilities, and domain
• •Recurring tasks or requests they perform
• •Pain points, frustrations, or bottlenecks they've mentioned
• •Workflows or processes they've described or demonstrated
• •Tools and platforms they use regularly
• •Any goals or priorities they've shared

Present your findings as a brief summary so the user can confirm or correct them before continuing. If you have no prior context, say so and move directly to Step 2.

Step 2 — Targeted Discovery Interview

Based on gaps in your understanding (or starting from scratch), ask focused questions to build a complete picture. Cover these areas:

1. •Role & responsibilities — What is your role? What are you accountable for?
2. •Repetitive tasks — What tasks do you perform daily or weekly that feel repetitive, tedious, or low-value?
3. •Information synthesis — Where do you spend time gathering, combining, or making sense of information from multiple sources?
4. •Multi-step processes — What workflows involve multiple handoffs, approvals, or sequential steps?
5. •Quality & consistency — Where do errors, inconsistencies, or quality issues tend to creep in?
6. •Communication overhead — What recurring communications (status updates, reports, summaries) take more time than they should?
7. •Decision-making — What decisions require you to weigh multiple factors or reference past precedents?

Ask these questions one at a time — not as a list. Use the user's answers to ask smart follow-up questions. Probe for concrete examples: "I spend 30 minutes every Monday formatting a status report from three Jira boards" is far more useful than "I do reporting." Continue until you can identify at least 3 concrete opportunities — typically 5-10 questions, fewer if the memory scan provided strong context.

Step 3 — Opportunity Analysis & Report

Once you can identify at least 3 concrete, specific opportunities with enough detail to fill the card format below, produce the structured report.

Step 4 — Workflow Candidate Summary

After presenting the full report, ask the user to pick their top workflow candidates — the ones they want to build. Once they've chosen, produce a Workflow Candidate Summary with structured metadata for each candidate:

For each candidate:

Append this summary to the output file under a ## Workflow Candidate Summary heading. Recommend which candidate to deconstruct first, with reasoning.

Output

Write the report to outputs/ai-opportunity-report.md. Create the outputs/ directory if it doesn't exist.

The report must include:

Summary Table

Detailed Opportunity Cards

Group cards by category. Within each category, order from highest to lowest impact.

For each opportunity:

[#] [Opportunity Name]

Category: Deterministic Workflow | Collaborative AI | Autonomous Agent

Why it's a good candidate: [What characteristics make this well-suited for AI — repetitive, pattern-based, language-heavy, clear inputs/outputs, etc.]

Current pain point: [What's slow, error-prone, inconsistent, or draining about how this is done today]

How AI helps: [Specific, concrete description — what AI takes as input, what it produces, how it fits into the workflow]

Getting started: [A practical, low-effort first step achievable this week]

Category Definitions

Use these definitions when categorizing:

• •Deterministic Workflow: A repeatable process with clear inputs, rules, and outputs that AI can execute reliably with minimal supervision. Examples: formatting reports, processing forms, generating routine communications, data transformation.
• •Collaborative AI: Human and AI work together in real time. The human drives the process; AI contributes suggestions, drafts, analysis, or feedback. Examples: co-writing, brainstorming, code review, data analysis.
• •Autonomous Agent: A goal-driven workflow where AI plans and executes steps autonomously. The agent reasons about what to do, calls tools as needed, and adapts its approach. Ranges from a single agent handling a complex task to multi-agent systems where specialized agents coordinate across steps. Examples: competitor monitoring and alerting, research → analysis → report pipelines, intake → triage → routing systems.

Workflow Candidate Summary

(Appended after user selects candidates — see Step 4 format above)

Guidelines

• •Ask one question at a time — never present a wall of questions
• •Use a conversational flow — let answers guide follow-up questions naturally
• •Push for concrete examples over vague descriptions
• •Be specific in recommendations: "AI could draft the weekly status email from your Jira board data" beats "AI could help with reporting"
• •After writing the report, ask the user to pick their candidates for Step 4. Once they've chosen, append the Workflow Candidate Summary and tell the user: "Opportunity report and workflow candidates saved to outputs/ai-opportunity-report.md. Pick a candidate and start the Deconstruct step to break it down."