AI Use Case Mapping

AI Use Case Mapping

Use When

• Use this skill when the requested outcome is specifically a AI use case mapping deliverable and the supplied brief falls within ai use case mapping.

Do Not Use When

• Use ai-readiness-diagnostic when its narrower output is the real deliverable; do not use this skill as a generic substitute.
• Do not use it to publish, send, spend, alter a live account, or make unsupported legal, platform, performance, or certification claims.

Required Inputs

Capability and Permission Boundaries

Default to read-only: inspect supplied material and report findings. Editing, publishing, contacting people, spending, or changing live systems requires separate explicit authority. Minimum capabilities are read access to supplied files and search across the authorised evidence set. Use only the files, tools, accounts and evidence made available for the engagement, expose every unassessed check, and obtain explicit authority before any mutation.

Degraded Mode

Fallback: if files, network access, platform data, language review or production tools are unavailable, return the narrowest useful qualified AI use case mapping deliverable; mark unavailable checks not assessed and never convert them into a pass.

Decision Rules

Workflow

1. Confirm the exact AI use case mapping deliverable, consumer, market, channel and approval boundary; route to ai-readiness-diagnostic if it is the closer match.
2. Inventory supplied facts, source provenance, constraints and missing inputs; stop if the objective, audience or authority is unknowable.
3. Select the domain method and record the material decision behind it before drafting.
4. Produce the smallest complete AI use case mapping deliverable; keep facts traceable and placeholders visibly unresolved.
5. Test the result against the decision table, domain quality criteria and anti-slop gate; recover by narrowing or qualifying unsupported portions.
6. Deliver the artefact with evidence, assumptions, unassessed checks and the next approval or verification step.

Outputs

Evidence Produced

Quality Standards

• Preserve the domain guidance and East African market context below; replace it only when the requester names another market.
• Use British English unless the target language or market requires otherwise, and verify names, figures, quotations and platform rules before use.
• Make the key choice visible, cover failure and edge cases, and keep the result ready for its named consumer.
• Run the repository's anti-ai-slop ship gate; a blocking factual, cultural, safety or permission defect stops release.

Anti-Patterns

• Writing before the objective and audience are known. Fix: stop and obtain the missing brief fields.
• Reusing a neighbouring skill's template because the headings look similar. Fix: route by the requested AI use case mapping deliverable, not vocabulary overlap.
• Adding a price, result, quotation, platform limit or cultural claim without a traceable source. Fix: verify it or qualify/remove it.
• Treating missing access, evidence or native-language review as approval. Fix: mark the check not assessed and narrow the result.
• Publishing, sending, spending or changing a live account from drafting authority alone. Fix: obtain explicit action-specific authority and retain the approval record.

References

• ai-readiness-diagnostic is the nearest routing comparison for this skill.
• Repository agent guide defines the engine-wide market, safety and anti-slop gates.

Purpose

Produce a structured, evidence-based map of AI opportunities for a client's marketing activities using the 2×2 AI Use Case Framework. The output moves the client from a vague awareness that "AI could help" to a concrete, prioritised shortlist of use cases they can begin implementing within 90 days.

Source framework: Venkatesan, R. and Lecinski, J. (2026) The AI Marketing Canvas (2nd ed.). Stanford Business Books.

Required Input

Before generating any deliverable, ask the client for:

1. Business name — exact trading name of the organisation
2. Industry — sector and sub-sector (e.g. retail FMCG, professional services, NGO, hospitality)
3. Country / city — location and primary market (defaults to Uganda/Kampala)
4. Primary marketing goal — the single most important objective for the next 6 months (e.g. grow WhatsApp enquiries, improve content consistency, reduce reporting time)
5. Current marketing activities — list every recurring marketing task the team performs. If the client cannot provide a list, apply the standard 12-activity starter list in Step 1.
6. Current AI tools in use — list any AI tools already in use, even informally (e.g. ChatGPT for captions, Canva Magic Write). If none, record as zero.
7. Team size and technical comfort level — number of people managing marketing; rate their comfort with new software as Low / Medium / High.

The 2×2 AI Use Case Framework

Source: Venkatesan and Lecinski (2026).

The framework classifies every AI use case on two axes:

• Vertical axis — Value type:
  • Productivity — efficiency, cost reduction, speed, volume handling
  • Growth — revenue, engagement, new capability, competitive advantage
• Horizontal axis — Audience:
  • Internal — staff, operations, workflows, team processes
  • External — customers, prospects, public-facing communications

This produces four quadrants:

Quadrant 1 — Internal Productivity

AI used to make internal marketing operations faster and cheaper.

Examples:

• AI-generated first drafts (briefs, captions, email copy)
• Automated reporting and dashboard summaries
• AI-powered content repurposing (one post → multiple formats)
• Meeting notes and action-item extraction
• Competitor monitoring alerts
• Prompt libraries for team-wide use

Quadrant 2 — External Productivity

AI used to make customer interactions more efficient at scale.

Examples:

• AI chatbots for FAQs (Messenger, WhatsApp)
• Automated response templates for comments and DMs
• Personalised email sequences triggered by behaviour
• WhatsApp broadcast automation with audience segmentation
• SMS reminders and follow-ups via Africa's Talking

Quadrant 3 — Internal Growth

AI used to grow capability, insight, or competitive advantage internally.

Examples:

• AI-powered audience segmentation from CRM data
• Sentiment analysis to inform content strategy
• Predictive analytics for campaign planning
• AI-assisted A/B test design
• Competitive intelligence aggregation

Quadrant 4 — External Growth

AI used to drive revenue and engagement directly with customers.

Examples:

• Personalised content recommendations
• Dynamic ad creative testing
• AI influencer matching and brief generation
• Real-time personalisation on landing pages
• Loyalty programme personalisation

Step 1 — Establish the Activity List

If the client provides a full activity list, use it. If not, apply the standard 12-activity starter list:

1. Content creation (captions, blogs, emails)
2. Content scheduling and publishing
3. Community management and response
4. Customer service (comments, DMs, complaints)
5. Campaign planning and briefing
6. Audience research and persona development
7. Competitor monitoring
8. Performance reporting
9. Paid advertising management
10. Influencer identification and briefing
11. Email marketing
12. WhatsApp / SMS communications

Add any client-specific activities not covered by the list. Remove any activities the client does not perform.

Step 2 — Map Each Activity to a Quadrant

For each activity, assign it to the quadrant that best describes its primary AI opportunity. Note that a single activity may have opportunities in more than one quadrant — if so, create a separate row per quadrant opportunity.

Quadrant assignment rules:

• Ask: does this AI application help internal operations, or does it face the customer directly?
• Ask: does it save time/cost (Productivity), or does it generate new revenue or capability (Growth)?
• When an activity spans two quadrants, assign it to the quadrant where the highest-value AI opportunity sits.

Step 3 — Score Each Activity

Rate each activity on two dimensions:

Current AI Use (0–2):

• 0 = No AI in use for this activity
• 1 = Partial AI use (e.g. occasional ChatGPT, one automated step)
• 2 = Full AI integration (systematic, consistent AI throughout this activity)

Opportunity Score (1–5): Assess based on these four factors — sum them, then divide by four and round:

• Volume — how many times per week/month does this task occur? (1 = rare, 5 = daily or multiple times daily)
• Repetition — how standardised is the task? (1 = highly variable, 5 = near-identical every time)
• Data availability — does the client have data to feed an AI tool? (1 = no data, 5 = rich, structured data available)
• Time cost — how many hours per week does this task consume? (1 = under 30 minutes, 5 = over 5 hours)

Priority:

• High — Opportunity Score 4–5 AND Current AI Use 0
• Medium — Opportunity Score 3–4 OR Current AI Use 1
• Low — Opportunity Score 1–2 OR Current AI Use 2

Step 4 — Build the Priority Matrix

Output the completed matrix in this format:

Ensure all four quadrants are represented. If the client's activity list produces an empty quadrant, add at least one standard example activity from that quadrant and mark it as a suggested addition.

Step 5 — Quadrant Summary

After the matrix, produce a one-paragraph summary per quadrant:

• State how many activities fall in each quadrant.
• Identify which quadrant has the most High-priority opportunities.
• Note any quadrant that is notably underdeveloped (few or no High-priority items) — this may indicate a strategic blind spot.
• Recommend which quadrant to address first, with a one-line rationale.

Step 6 — Top 5 Priority Use Cases

Select the five activities rated High priority. Present each in this format:

Use Case [N]: [Activity Name] — [Quadrant]

• Why now: [One sentence on the business case — what is being lost by not doing this today]
• What to do: [Specific AI application — name the action, not just the category]
• Recommended tool: [Named tool available and accessible in East Africa — include free/paid tier and approximate cost in UGX or USD]
• Expected result: [Measurable outcome achievable within 4 weeks — state the metric]
• Effort to implement: Low / Medium / High

Quick wins must be achievable within 4 weeks using free or low-cost tools (under UGX 100,000/month). Flag any use case that requires a paid tool above this threshold clearly.

Step 7 — 3 Use Cases to Defer

Identify three activities that score High on Opportunity but are not suitable for immediate implementation. For each, provide:

• Activity: [Name]
• Reason for deferral: [One of: technical complexity, data not yet available, team skill gap, tool cost not yet justified, requires a prior workflow to be in place first]
• When to revisit: [Milestone or timeframe — e.g. "After 90-day quick wins are embedded" or "When CRM data reaches 1,000+ contacts"]

Step 8 — 90-Day Implementation Sequence

Sequence the Top 5 priority use cases into a realistic 90-day plan. Use the following structure:

Days 1–30 — Foundation (Quick Wins)

• Implement use cases that require no new tools or only free-tier tools.
• Focus on Q1 (Internal Productivity) first — these build team confidence and reduce daily friction without customer-facing risk.

Days 31–60 — External Activation

• Implement Q2 (External Productivity) use cases — chatbots, auto-responses, WhatsApp automation.
• Require that Q1 foundations are in place before customer-facing AI is activated.

Days 61–90 — Growth Layer

• Begin Q3 and Q4 use cases that require data, testing, or higher tool investment.
• Review Days 1–60 performance before committing budget to Growth-quadrant tools.

State clearly which team member owns each action and what the success metric is for each 30-day phase.

EA-Specific Opportunities

Apply these as default suggestions for East African clients unless the client's context makes them irrelevant:

• WhatsApp broadcast automation (Q2 — High priority for most EA clients): Most EA businesses manage broadcasts manually. Automation via ManyChat or Africa's Talking saves 3–5 hours per week and improves segmentation.
• AI caption writing (Q1 — virtually universal gap): Nearly every client writes captions manually. A structured prompt library (see: prompt-engineering-library) with a Brand Context Block reduces caption production time by 60–80%.
• Automated comment responses in Luganda / Swahili (Q2 — emerging opportunity): AI-assisted response templates in local languages improve response rates and audience trust. Flag the need for human review of all AI-generated local- language responses before publishing.
• Mobile Money payment confirmation messages (Q2): Automated SMS or WhatsApp confirmations via Africa's Talking reduce customer service volume and increase trust at point of payment.
• AI-generated market research summaries (Q3): Where local quantitative data is scarce, AI can synthesise qualitative signals — social listening, review aggregation, WhatsApp conversation patterns — into usable audience insight. Flag data scarcity explicitly; do not present AI-synthesised insight as equivalent to primary research.

Quality Criteria

Output from this skill meets the standard when:

1. All four quadrants are populated — no quadrant is left empty; if the client's activity list does not cover a quadrant, at least one suggested activity is added and marked as such.
2. Priority ranking is evidence-based — every High, Medium, and Low rating is derived from the Opportunity Score formula, not intuition or assumption.
3. Quick wins are genuinely quick — all Top 5 use cases are achievable within 4 weeks and use free or sub-UGX 100,000/month tools; any exception is flagged and justified.
4. EA context is reflected throughout — WhatsApp features as the primary customer channel; Africa's Talking is cited for SMS/WhatsApp API use; Mobile Money is acknowledged as a payment and communications touchpoint; data scarcity in local markets is not ignored.
5. Deferred items include a clear reason and a revisit trigger — no use case is dismissed without explanation; deferral is always time-bound or milestone-bound.
6. Tool recommendations are named and EA-accessible — every tool cited has a free tier or is payable via Visa, Mastercard, or MTN Mobile Money via Payoneer; no tool is recommended without a cost indication.
7. Output is actionable by a non-technical marketing manager — no assumption of developer skills, API access, or data science knowledge unless the client's team profile explicitly supports it.
8. The 90-day sequence is owned and measurable — every phase names a responsible person and a success metric; the plan is a working document, not a wish list.

Cross-References

• ai-marketing-canvas-assessment — use for full AI marketing maturity assessment and strategic canvas before or after this use case mapping exercise.
• prompt-engineering-library — use for ready-made, client-specific prompts to activate Q1 (Internal Productivity) quick wins immediately.
• playbook-ai-automation-workflow — use for detailed workflow automation planning once Q2 (External Productivity) use cases are approved.
• playbook-ai-content-workflow — use for content production automation planning, particularly for Q1 caption and blog use cases.

References

• Venkatesan, R. and Lecinski, J. (2026) The AI Marketing Canvas (2nd ed.). Stanford Business Books. [2×2 AI Use Case Framework — cited in Steps 2–8]
• Chaffey, D. (2024) Digital Marketing: Strategy, Implementation and Practice. Pearson. [RACE framework; channel strategy context]
• Bodnar, K. and Cohen, J. (2012) The B2B Social Media Book. Wiley. [ROI formula: (TLV − COCA) ÷ COCA — apply when calculating expected return on AI tool investment]