opportunity-scanner

AI Opportunity Scanner

Generates 3-5 testable AI opportunity hypotheses for your organization based on industry patterns, company size, and known challenges. This is Step 1 of the Discovery Sprint methodology — giving you a structured starting point instead of "where do we even begin?"

How It Works

When you run this skill, I'll ask you a few questions about your company, then generate a structured hypothesis document you can use to guide interviews, audits, and deeper investigation.

What I Need From You

Provide the following (I'll ask for anything missing):

1. Company name
2. Industry (e.g., Healthcare, Manufacturing, Professional Services, Retail, Finance, Technology, etc.)
3. Company size (approximate employee count)
4. 2-5 known challenges or pain points (what keeps leadership up at night?)
5. Key departments to focus on (optional — if you already know where the biggest problems are)

What You'll Get

A Hypothesis Document containing:

For Each Hypothesis (3-5 total):

HYPOTHESIS: We believe that [DEPARTMENT/FUNCTION] spends significant time on
[ACTIVITY] which could be [AUTOMATED/AUGMENTED] using [AI APPROACH],
resulting in [ESTIMATED IMPACT].

VALIDATION METHOD:
- Who to interview: [Roles/people]
- Questions to ask: [Specific questions]
- Data to collect: [Metrics to validate]

CONFIDENCE: [High/Medium/Low] based on industry pattern strength
POTENTIAL IMPACT: [Hours/year saved or $ value range]

Plus:

• Interview Priority List — Who to talk to first, organized by the U-shaped method (executives first, then frontline, then back to executives)
• Pattern Alerts — Common patterns your industry typically exhibits (so you know what to watch for)
• Quick Win Candidates — 1-2 hypotheses most likely to yield fast, visible results

Industry Pattern Library

I draw on validated patterns seen across multiple organizations:

Pattern	Description	Industries Where Common
Data Without Insights	Lots of data in tables/systems but no synthesis into actionable intelligence	All industries with data teams
Manual Scheduling Cascade	Schedule changes require manual updates across multiple people/systems	Operations-heavy, multi-department
Report Assembly Line	Same information reformatted for different audiences manually	Any org with reporting requirements
Institutional Knowledge in Heads	Critical processes depend on specific people's memory	Mature orgs, specialized domains
Communication Silos	Information flows through personal relationships, not systems	Multi-department, 50+ employees
Analytics Capacity Crunch	Analytics team is bottleneck; can't serve all departments	Any org with central analytics
Text/Email as System of Record	Critical info lives in messages, not structured systems	Orgs that outgrew their tools