peterbamuhigire/ai-opportunity-canvas

AI Opportunity Canvas

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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.

References

• Use the references/ directory for deep detail after reading the core workflow below.

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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.

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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 |

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Discovery Protocol

Run through these questions for every major module in the system:

Step 1 — Module Scan

For each module, ask:

1. What decisions does a user make daily in this module?
2. What data does this module accumulate over time?
3. What manual work in this module is repetitive but requires judgement?
4. What questions do users ask supervisors that could be answered by data?
5. 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

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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

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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

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Output Format

Deliver the AI Opportunity Register as a markdown table (above template) followed by:

1. Top 3 Quick Wins — highest score, lowest effort, implement first
2. Top 1 Strategic Bet — highest business impact, even if effort is high
3. Cost Overview — reference ai-cost-modeling for token estimates
4. Recommended AI Module Tier — which opportunities bundle into Starter vs Growth vs Enterprise

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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.

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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 modules
• ai-feature-spec — Design any opportunity from this register into a full feature blueprint
• ai-cost-modeling — Token cost estimates per opportunity
• ai-metering-billing — How to gate and charge for AI features
• ai-integration-section — Add AI section to SRS/PRD/HLD documents
• ai-analytics-strategy — Analytics maturity assessment before selecting opportunities