peterbamuhigire AI Growth Systems Design
Use When
- Use this skill when the requested outcome is specifically a AI growth systems design deliverable and the supplied brief falls within ai growth systems design.
Do Not Use When
- Use
ai-readiness-diagnosticwhen 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
| Artefact | Source/provider | Required? | If absent |
|---|---|---|---|
| AI marketing use-case brief, intended human control point and success measure | Requester or approved brief | Yes | Stop and request the missing decision context. |
| Brand voice, offer facts, constraints and approvals | Client source pack or authorised owner | Conditional | State assumptions; do not invent names, prices, results or approvals. |
| Performance, platform or research evidence used for claims | Traceable export, URL, document or named source | Conditional | Draft the narrowest reviewable version and flag the missing evidence. |
Capability and Permission Boundaries
Drafting is permitted within the supplied brief. Publishing, sending, spending, changing live accounts, or claiming certification 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 growth systems design deliverable; mark unavailable checks not assessed and never convert them into a pass.
Decision Rules
| Choice | Action | Failure or risk avoided |
|---|---|---|
| Data readiness, AI maturity and risk support the proposed operating level | Choose the lowest viable automation level and define its human approval gate. | Automating an unsafe or unevaluable marketing process. |
| A required fact or approval is missing | Stop that claim or action; request it or use an explicit placeholder. | Fabricated facts, implied consent or unauthorised publication. |
| Evidence is partial but a useful draft is possible | Deliver a qualified draft with gaps and the next verification step. | Treating an unassessed requirement as passed. |
Workflow
- Confirm the exact AI growth systems design deliverable, consumer, market, channel and approval boundary; route to
ai-readiness-diagnosticif it is the closer match. - Inventory supplied facts, source provenance, constraints and missing inputs; stop if the objective, audience or authority is unknowable.
- Select the domain method and record the material decision behind it before drafting.
- Produce the smallest complete AI growth systems design deliverable; keep facts traceable and placeholders visibly unresolved.
- Test the result against the decision table, domain quality criteria and anti-slop gate; recover by narrowing or qualifying unsupported portions.
- Deliver the artefact with evidence, assumptions, unassessed checks and the next approval or verification step.
Outputs
| Artefact | Consumer | Observable acceptance condition |
|---|---|---|
| Ai growth systems design deliverable | Requester, client reviewer or delivery team | The AI growth systems design deliverable addresses the named audience and objective, records assumptions, and passes the skill's domain checks without invented facts. |
| Decision and gap note | Approver or next workflow | Names the chosen route, evidence used, unresolved inputs and any action requiring authority. |
Evidence Produced
| Evidence | Format | Acceptance condition |
|---|---|---|
| Source/assumption register and completed release checklist | Inline table, checklist or linked source note | Every material claim, decision and unavailable check is traceable. |
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-slopship 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 growth systems design 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 assessedand 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.
Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.
Use When
- A client wants AI for social media, marketing automation, content production, personalization, lead scoring, analytics, chatbots, or sales enablement.
- You need to connect AI activity to revenue, retention, conversion, trust, service quality, or lower operating cost.
Growth Principle
AI should improve the growth system, not just produce more content. Tie every AI workflow to a funnel stage, customer decision, business metric, and feedback loop.
Workflow
- Map the growth system: Audience, channels, offers, content, conversion path, sales handoff, retention, and reporting.
- Identify the useful AI role: Research, ideation, creative testing, sentiment analysis, social listening, personalisation, lead scoring, chatbot support, reporting, or forecasting.
- Define metrics: Reach quality, engagement quality, lead quality, conversion, CAC, retention, response time, cost per asset, and revenue influence.
- Design data foundation: Brand knowledge base, customer segments, campaign history, content performance, CRM, web analytics, UTM discipline, and consent/privacy constraints.
- Select AI pattern: Prompted assistant, RAG brand brain, deterministic content workflow, predictive model, or bounded agentic workflow.
- Add governance: Brand voice, fact-checking, approvals, IP/copyright review, cultural bias review, crisis escalation, and platform policy compliance.
- Measure and improve: Experiments, dashboards, feedback, prompt/version changes, and monthly optimization.
AI Growth System Patterns
- Brand knowledge RAG: Produces on-brand outputs from approved voice, products, offers, FAQs, and proof points.
- Content operating system: Turns campaign goals into briefs, drafts, variants, approvals, publishing assets, and performance learning.
- Social listening intelligence: Classifies sentiment, topics, objections, competitor signals, and emerging opportunities.
- Lead intelligence engine: Scores and routes leads using engagement, intent, CRM, and sales feedback.
- WhatsApp/service copilot: Answers common questions, qualifies prospects, escalates sensitive cases, and records outcomes.
- Experiment engine: Generates hypotheses, variants, test plans, and post-test learning tied to funnel metrics.
Deliverables
- AI growth opportunity map.
- Data readiness and brand knowledge base plan.
- AI-enabled content/marketing workflow with approval gates.
- Measurement framework and dashboard specification.
- Governance checklist for brand, privacy, copyright, bias, and crisis risk.
- 30/60/90-day AI growth roadmap.
Hard Rules
- Do not optimize for vanity metrics alone.
- Do not publish AI-generated claims without source verification.
- Do not automate sensitive replies, regulated advice, or crisis communication without human approval.
- Do not train or personalize using customer data without consent, lawful basis, and retention rules.