ai-readiness-diagnostic

name	ai-readiness-diagnostic
description	Use when AI Readiness Diagnostic is needed to produce a scored diagnostic for social-media or digital-marketing work; use `ai-use-case-mapping` when its narrower outcome is requested.
metadata	{"portable":true,"compatible_with":["claude-code","codex"]}

AI Readiness Diagnostic

Use When

• Use this skill when the requested outcome is specifically a scored diagnostic and the supplied brief falls within ai readiness diagnostic.

Do Not Use When

• Use ai-use-case-mapping 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

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	Issue a qualified finding and identify the evidence needed.

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 scored diagnostic; 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

1. Confirm the exact scored diagnostic, consumer, market, channel and approval boundary; route to ai-use-case-mapping 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 scored diagnostic; 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

Artefact	Consumer	Observable acceptance condition
Scored diagnostic	Requester, client reviewer or delivery team	The scored diagnostic 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
Finding-to-source register and unassessed-check list	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-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 scored diagnostic, 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-use-case-mapping is the nearest routing comparison for this skill.
• Repository agent guide defines the engine-wide market, safety and anti-slop gates.

Purpose

Assess a client's current AI marketing maturity across five domains, calculate their AI Marketing Canvas step, and produce a practical, prioritised action plan that reflects their actual starting point. Output is structured for a non-technical business owner to read and act on immediately.

After completing this diagnostic, refer the client to the sister skill ai-marketing-canvas-assessment for full canvas completion and strategic roadmap development.

Required Inputs

Ask for the following before beginning:

• Business name — trading name of the client organisation
• Industry — sector and nature of the business
• Country / city — primary operating location (default: Uganda/East Africa)
• Primary marketing goal — what the client most wants marketing to achieve
• Team size — approximate number of people in the marketing function

Then walk through all 41 diagnostic questions together with the client, recording Y (Yes) or N (No) for each item. Do not skip questions. If the client is genuinely uncertain, default to N and note it for follow-up.

The 41-Item Diagnostic

Domain 1: Data Foundation (8 items)

1. Do you have a single, centralised customer database?
2. Is customer data cleaned and deduplicated at least quarterly?
3. Do you track customer behaviour across more than one channel?
4. Do you have explicit customer consent for data collection and use?
5. Can you segment your customers by behaviour, not just demographics?
6. Do you have a named person responsible for data quality?
7. Do you have a documented data governance policy?
8. Can you link online and offline customer data?

Domain 2: Technology Stack (8 items)

9. Do you have a CRM system in active use?
10. Do you use a social media scheduling tool?
11. Do you use an email marketing platform?
12. Do you have website analytics in place (GA4 or similar)?
13. Do you use any AI-powered content creation tools?
14. Do you use any marketing automation (Zapier, Make, or similar)?
15. Do you use any AI analytics or reporting tools?
16. Do you have a documented tech stack with assigned owners?

Domain 3: Team Capability (8 items)

17. Has your marketing team used ChatGPT or a similar LLM in the last 30 days?
18. Does anyone on your team understand prompt engineering basics?
19. Has your team received any AI tools training in the last 12 months?
20. Do you have a documented AI use policy for staff?
21. Is there a senior leader who champions AI adoption?
22. Does your team feel comfortable experimenting with new tools?
23. Do you regularly review how AI tools are being used?
24. Does your team know the difference between AI-generated and human-created content?

Domain 4: AI Deployment (9 items)

25. Do you use AI for any content creation (captions, blogs, emails)?
26. Do you use AI for social media scheduling or posting?
27. Do you use AI for customer segmentation or targeting?
28. Do you use AI-powered chatbots or automated messaging?
29. Do you use AI for sentiment analysis or social listening?
30. Do you use AI for paid advertising optimisation?
31. Do you use AI for performance reporting or forecasting?
32. Do you have real-time personalisation in any marketing channel?
33. Has AI changed how you make budget or strategy decisions?

Domain 5: Business Impact (8 items)

34. Has AI reduced time spent on content creation by at least 25%?
35. Have AI experiments produced measurable ROI?
36. Has AI improved audience targeting or engagement rates?
37. Has AI enabled you to produce more content at the same cost?
38. Has AI created new revenue streams or business models?
39. Do you have AI-specific KPIs in your marketing plan?
40. Do senior stakeholders receive regular AI performance updates?
41. Has AI changed your competitive position in your market?

Scoring

Step 1: Calculate domain scores

Count the number of YES answers in each domain:

Domain	Items	Max Score
1. Data Foundation	1–8	8
2. Technology Stack	9–16	8
3. Team Capability	17–24	8
4. AI Deployment	25–33	9
5. Business Impact	34–41	8
Total	1–41	41

Step 2: Identify the AI Marketing Canvas step

Total Score	Canvas Step
0–8	Step 1 — Foundation
9–16	Step 2 — Experimentation
17–24	Step 3 — Expansion
25–32	Step 4 — Transformation
33–41	Step 5 — Reinvention

Step 3: Interpret domain scores

Apply these thresholds to identify specific gap areas:

Domain	Score	Interpretation
Data Foundation	0–3	Critical gap — no AI programme can succeed without this
Technology Stack	0–3	Tool gaps limit what AI can do
Team Capability	0–3	Training is the priority before any tool investment
AI Deployment	0–4	Experimentation is the immediate priority
Business Impact	0–3	Either early stage or AI is not yet being measured

Output Structure

Produce the following five sections in order.

Section 1: Score Summary Table

Present a clean table showing:

• Each domain name
• Domain score (e.g., 3/8)
• A one-word status: Strong, Developing, or Critical
• Total score and Canvas step

Example format:

Domain	Score	Status
Data Foundation	3/8	Critical
Technology Stack	5/8	Developing
Team Capability	4/8	Developing
AI Deployment	2/9	Critical
Business Impact	1/8	Critical
Total	15/41	Step 2 — Experimentation

Section 2: Canvas Step Diagnosis

Write one paragraph in plain English explaining:

• Which step the client is at and what that means in practice
• What characterises organisations at this step
• What is typically holding them back
• What the realistic next step looks like

Be honest. If the client is at Step 1, say so directly and explain what that means without softening the message into vagueness.

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

Section 3: Top 3 Priority Gaps

Identify the three most urgent gaps based on domain scores and individual question responses. Rank them most urgent first. For each gap:

• Gap title — name the specific weakness
• Why it matters — one sentence on the business consequence
• What is missing — the specific Y/N items that are N
• First action — the single most important thing to do about it

Section 4: 90-Day Action Plan

Structure three 30-day blocks. Include exactly three named, specific actions per block. Actions must be concrete (name a tool, name a process, name a person or role).

Days 1–30: Stabilise the Foundation

• Action 1
• Action 2
• Action 3

Days 31–60: Build Capability

• Action 1
• Action 2
• Action 3

Days 61–90: Begin Deployment

• Action 1
• Action 2
• Action 3

Calibrate ambition to the client's step. A Step 1 client's 90-day plan is about getting basic data and tools in place — not deploying AI-powered personalisation.

Section 5: Recommended Tools

Recommend tools appropriate to the client's current Canvas step and the East African market. Prioritise:

1. Free tiers before paid subscriptions
2. Tools accessible from Uganda without VPN or restricted payment
3. UGX pricing or USD pricing with local payment options where available

Step 1–2 tool recommendations to consider:

Need	Tool	Tier
CRM	HubSpot CRM	Free
Email marketing	Mailchimp	Free up to 500 contacts
Social scheduling	Buffer	Free (3 channels)
Website analytics	Google Analytics 4	Free
AI content creation	ChatGPT (GPT-4o)	Free / Plus at ~UGX 130,000/month
AI content creation	Gemini	Free
Automation	Zapier	Free (5 zaps)
WhatsApp automation	Africa's Talking	Pay-as-you-go, UGX-compatible
SMS automation	Africa's Talking	Pay-as-you-go

Step 3–4 tool recommendations to consider:

Need	Tool	Tier
CRM with automation	HubSpot Starter	From ~USD 20/month
AI writing at scale	Jasper or Copy.ai	From ~USD 39/month
Social listening	Brandwatch Lite or Mention	From ~USD 29/month
Reporting	Looker Studio	Free
AI analytics	Polymer or Akkio	From ~USD 20/month
Chatbot	Tidio or Landbot	Free tier available

Note the WhatsApp advantage: if the client is already using WhatsApp for customer communications, this counts toward the Technology Stack domain and can be formalised quickly via Africa's Talking or similar APIs.

East African Market Context

Most Ugandan and East African SMEs score between 4 and 12 on this diagnostic, placing them at Step 1 or early Step 2. This is not a failure — it reflects the market's current stage of AI adoption.

Common patterns to look for:

• No CRM: Customer contacts held in Excel spreadsheets or WhatsApp groups. Recommend HubSpot Free as the immediate priority.
• WhatsApp already in use: Formalise this into the tech stack. Africa's Talking enables automated WhatsApp and SMS flows at low cost.
• Team capability gap: Many teams have experimented with ChatGPT informally but have no policy, no training, and no structured prompting practice. The training-client-team skill provides a structured training programme.
• Budget sensitivity: Lead with free tiers. Demonstrate value before recommending any paid subscription.

AI Use Case Matrix (Venkatesan and Lecinski, 2026)

Once the diagnostic score is known, map the client's AI readiness to the 2×2 Use Case Matrix:

	Internal	External
Productivity	Automate internal workflows: reporting, content briefs, data analysis	Scale customer-facing functions: chatbots, FAQs, WhatsApp automation
Growth	Build proprietary data and algorithmic advantages	Deliver personalised customer experiences at scale

Recommendation by score: (Convert raw score to percentage: total ÷ 41 × 100)

• Score 0–25%: Start in Internal/Productivity quadrant only
• Score 26–50%: Internal/Productivity + begin External/Productivity pilot
• Score 51–75%: All four quadrants in scope; prioritise highest-ROI cell
• Score 76–100%: Full AI transformation; consider Monetisation (Canvas Step 5)

AI Maturity Wave Assessment (Nayebi, 2025)

Identify which wave the client currently operates in:

• Wave 1 — Automation: Rules-based workflows, scheduled posts, canned responses, basic chatbots. Most EA SMEs are here.
• Wave 2 — Predictive ML: Customer segmentation, engagement prediction, A/B test optimisation, sentiment analysis. Achievable for EA mid-market clients.
• Wave 3 — Agentic AI: Autonomous agents that perceive, reason, act, and learn without human prompts. Horizon planning for EA; immediate for multinational clients.

Ask the client: "Which wave best describes your current AI marketing activity?" Use their answer to calibrate the roadmap in the final section.

Quality Criteria

• All 41 items are addressed — no question is skipped or estimated without a reason recorded
• The total score is calculated accurately and the Canvas step is stated clearly
• Domain breakdown reveals specific gap areas and is not reduced to a single total score
• The 90-day plan contains specific, named actions — not generic advice such as "improve data quality"
• Tool recommendations match the client's step and are accessible in the East African market, with free tiers prioritised
• Language is honest: if the client is at Step 1, the diagnosis says so clearly and explains what that means in practical terms
• Output is structured so a non-technical business owner can read it without a consultant present and know exactly what to do next
• The score summary table is the first thing the client sees — context and explanation follow the data

References

Venkatesan, R. and Lecinski, J. (2026) The AI Marketing Canvas, 2nd edn. Stanford Business Books.

Chaffey, D. (2024) Digital Marketing: Strategy, Implementation and Practice. Pearson.

Bodnar, K. and Cohen, J. (2012) The B2B Social Media Book. Wiley.

Nayebi, F. (2025) Foundations of Agentic AI for Retail. Gradient Divergence.

For full canvas completion and strategic roadmap development, use the sister skill ai-marketing-canvas-assessment after completing this diagnostic.