| name | ai-opportunity-canvas |
| description | Systematically discover and rank AI use cases for any software project or module. Produces a prioritised AI Opportunity Register with business impact, implementation effort, and cost estimates. Invoke after any project description or module... |
| metadata | {"portable":true,"compatible_with":["Codex","codex"]} |
AI Opportunity Canvas
Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.
Use When
- Systematically discover and rank AI use cases for any software project or module. Produces a prioritised AI Opportunity Register with business impact, implementation effort, and cost estimates. Invoke after any project description or module...
- The task needs reusable judgment, domain constraints, or a proven workflow rather than ad hoc advice.
Do Not Use When
- The task is unrelated to
ai-opportunity-canvas or would be better handled by a more specific companion skill.
- The request only needs a trivial answer and none of this skill's constraints or references materially help.
Required Inputs
- Gather relevant project context, constraints, and the concrete problem to solve; load
references only as needed.
- Confirm the desired deliverable: design, code, review, migration plan, audit, or documentation.
Workflow
- Read this
SKILL.md first, then load only the referenced deep-dive files that are necessary for the task.
- Apply the ordered guidance, checklists, and decision rules in this skill instead of cherry-picking isolated snippets.
- Produce the deliverable with assumptions, risks, and follow-up work made explicit when they matter.
Quality Standards
- Keep outputs execution-oriented, concise, and aligned with the repository's baseline engineering standards.
- Preserve compatibility with existing project conventions unless the skill explicitly requires a stronger standard.
- Prefer deterministic, reviewable steps over vague advice or tool-specific magic.
Anti-Patterns
- Treating examples as copy-paste truth without checking fit, constraints, or failure modes.
- Loading every reference file by default instead of using progressive disclosure.
Outputs
- A concrete result that fits the task: implementation guidance, review findings, architecture decisions, templates, or generated artifacts.
- Clear assumptions, tradeoffs, or unresolved gaps when the task cannot be completed from available context alone.
- References used, companion skills, or follow-up actions when they materially improve execution.
Evidence Produced
| Category | Artifact | Format | Example |
|---|
| Release evidence | AI Opportunity Roadmap | Prioritised Markdown roadmap covering ranked AI use cases per project or module | docs/ai/opportunity-roadmap-2026-04-16.md |
References
- Use the
references/ directory for deep detail after reading the core workflow below.
Purpose
Identify every realistic place AI adds measurable value to a client's system. Output is an AI Opportunity Register — a ranked list of AI features with business case, effort estimate, and cost tier, ready for client presentation.
Invoke this skill: After project/module description, before HLD or feature planning.
The 10 Universal AI Opportunity Patterns
For each pattern, assess whether it applies to the current project module.
| # | Pattern | Business Value | Typical Token Cost Tier |
|---|
| 1 | Smart Summarisation | Compress reports, meeting notes, transactions into executive summaries | Low |
| 2 | Predictive Alerts | Forecast stock-outs, overdue payments, exam failures, crop risks | Medium |
| 3 | Intelligent Search | Semantic search across records (find "all unpaid invoices from March") | Medium |
| 4 | Auto-Classification | Categorise expenses, tickets, documents, leads automatically | Low |
| 5 | Decision Support | "Should I approve this loan?" with supporting evidence | Medium |
| 6 | Natural Language Reports | Generate narrative reports from raw data in plain English/Luganda | Medium |
| 7 | Anomaly Detection | Flag unusual transactions, attendance patterns, sensor readings | Low |
| 8 | Recommendation Engine | Suggest products, courses, treatments, suppliers based on history | Medium |
| 9 | Conversational Assistant | In-app chat bot for staff help, policy lookup, FAQs | Medium-High |
| 10 | Document Intelligence | Extract data from uploaded receipts, invoices, forms, ID cards | Medium |
Discovery Protocol
Run through these questions for every major module in the system:
Step 1 — Module Scan
For each module, ask:
- What decisions does a user make daily in this module?
- What data does this module accumulate over time?
- What manual work in this module is repetitive but requires judgement?
- What questions do users ask supervisors that could be answered by data?
- What early warnings would save this user money or time?
Step 2 — Pattern Matching
For each "yes" answer above, map it to one or more of the 10 patterns.
Step 3 — Score Each Opportunity
Score on three dimensions (1–5 each):
| Dimension | 1 | 3 | 5 |
|---|
| Business Impact | Nice-to-have | Saves hours/week | Core competitive advantage |
| Data Availability | No data exists | Partial data | Rich historical data |
| Implementation Effort | Custom ML needed | Standard LLM call | Single prompt |
Priority Score = Impact × Data × (6 − Effort)
Rank opportunities by priority score descending.
Step 4 — Gate Assessment
For each opportunity, state:
- Is this a candidate for the paid AI module (yes/no)?
- Recommended pricing tier: Starter / Growth / Enterprise AI add-on
AI Opportunity Register Template
## AI Opportunity Register — [Project Name] — [Date]
### Module: [Module Name]
| ID | Opportunity | Pattern | Impact | Data | Effort | Score | AI Module Tier |
|----|-------------|---------|--------|------|--------|-------|----------------|
| AI-001 | [name] | [pattern #] | /5 | /5 | /5 | [calc] | [Starter/Growth/Enterprise] |
**Business Case:** [One sentence: who benefits, what they save/gain]
**Data Required:** [What data the AI needs to function]
**Cost Tier:** [Low / Medium / High — detail in ai-cost-modeling]
**Dependencies:** [Any data quality or integration prerequisites]
**Gate Default:** OFF — activated per tenant when AI module is purchased
Domain Quick-Reference: Common Opportunities
School Management (Academia Pro, similar)
- Predict students at risk of failing before end-of-term → Decision Support
- Summarise teacher remarks into report card narrative → Summarisation
- Auto-classify fee payment exceptions for bursar review → Auto-Classification
- Answer parent queries via in-app assistant → Conversational Assistant
POS / Retail (Maduuka, Longhorn)
- Predict stock-outs 7 days in advance → Predictive Alert
- Flag transactions that deviate from user's normal patterns → Anomaly Detection
- Recommend reorder quantities by SKU → Recommendation Engine
- Generate daily sales narrative for owner → Natural Language Reports
Healthcare (Medic8)
- Flag patients overdue for follow-up → Predictive Alert
- Summarise patient history for attending clinician → Summarisation
- Extract structured data from uploaded lab reports → Document Intelligence
- Suggest drug interaction warnings → Decision Support
Farm Management (Kulima)
- Predict harvest yield from weather + soil data → Predictive Alert
- Classify crop disease from uploaded photo description → Auto-Classification
- Recommend fertiliser application by field zone → Recommendation Engine
- Generate farm performance report for cooperative → Natural Language Reports
ERP / Finance (Longhorn, BIRDC)
- Detect duplicate or anomalous payments → Anomaly Detection
- Classify GL accounts for uploaded receipts → Auto-Classification
- Summarise monthly P&L into board narrative → Summarisation
- Flag budget overruns before period close → Predictive Alert
Output Format
Deliver the AI Opportunity Register as a markdown table (above template) followed by:
- Top 3 Quick Wins — highest score, lowest effort, implement first
- Top 1 Strategic Bet — highest business impact, even if effort is high
- Cost Overview — reference
ai-cost-modeling for token estimates
- Recommended AI Module Tier — which opportunities bundle into Starter vs Growth vs Enterprise
What NOT to Include
- Do not propose building custom ML models — only LLM API integrations.
- Do not propose AI for features where a simple rule/filter suffices (e.g., "flag invoices > $10,000" does not need AI).
- Do not include opportunities where data does not yet exist and cannot be collected within 6 months.
See also:
references/analytics-patterns.md — Extended analytics opportunity patterns (A1–A10) with domain maps for school, healthcare, POS, farm, and ERP — use for analytics-heavy modulesai-feature-spec — Design any opportunity from this register into a full feature blueprintai-cost-modeling — Token cost estimates per opportunityai-metering-billing — How to gate and charge for AI featuresai-integration-section — Add AI section to SRS/PRD/HLD documentsai-analytics-strategy — Analytics maturity assessment before selecting opportunities
