AI Marketing Canvas Assessment

Use when

• Generates a completed AI Marketing Canvas for a client based on Venkatesan & Lecinski's five-step framework (The AI Marketing Canvas, 2nd ed., Stanford Business Books, 2026). Diagnoses the client's current step (1–5), maps AI readiness across the four customer moments (acquisition, retention, growth, advocacy), and produces a concrete 12-month roadmap. Invoke when a client wants to understand where they stand on AI adoption in marketing and what to do next — particularly useful at strategy kick-off, annual planning, or following an AI audit.
• Use this skill when it is the closest match to the requested deliverable or workflow.

Do not use when

• Do not use this skill for graphic design, video production, software development, or legal advice beyond the repository's stated scope.
• Do not use it when another skill in this repository is clearly more specific to the requested deliverable.

Workflow

1. Collect the required inputs or source material before drafting, unless this skill explicitly generates the intake itself.
2. Follow the section order and decision rules in this SKILL.md; do not skip mandatory steps or required fields.
3. Review the draft against the quality criteria, then deliver the final output in markdown unless the skill specifies another format.

Anti-Patterns

• Do not invent client facts, performance data, budgets, or approvals that were not provided or clearly inferred from evidence.
• Do not skip required inputs, mandatory sections, or quality checks just to make the output shorter.
• Do not drift into out-of-scope work such as code implementation, design production, or unsupported legal conclusions.

Outputs

• An AI-focused strategy, audit, system design, or prompt asset in markdown with human review and control points.

References

• Use the inline instructions in this skill now. If a references/ directory is added later, treat its files as the deeper source material and keep this SKILL.md execution-focused.

Purpose

Most businesses talk about AI in marketing without knowing where they actually stand or what "progress" looks like for their size and context. This skill produces a completed AI Marketing Canvas — a structured diagnostic and roadmap grounded in Venkatesan and Lecinski's (2026) five-step model — that tells a client exactly what step they are on, what they should do in each of the four customer moments, and what their next 12 months should look like.

For the full 41-item AI readiness diagnostic, use the sister skill ai-readiness-diagnostic. This skill focuses on canvas completion and roadmap output.

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

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Required Input

Before generating any output, ask for the following:

1. Client business name — the trading name used in all deliverables.
2. Industry — what sector is the client in (e.g., retail, hospitality, financial services, NGO)?
3. Country / city — where is the business based and where is its primary audience? (Default: Uganda / East Africa)
4. Primary marketing goal — what is the single most important marketing outcome this year?
5. Current tools in use — list any CRM, content tools, scheduling platforms, and analytics tools currently in use. If none, say so.
6. Team size — solo operator, 2–5, 6–20, or 20+ people in the marketing function.
7. Monthly marketing budget range (UGX) — approximate band: under 500k, 500k–2M, 2M–10M, 10M+. If a different currency applies, note it.

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Step 1 — Run the Canvas Diagnostic

Ask the client the following nine questions. Record yes/no answers.

#	Diagnostic Question	Step Indicator
1	Does the client have a centralised customer database (CRM or equivalent)?	Step 1
2	Is there a named person responsible for data and analytics?	Step 1
3	Has the client used any AI tools for content creation, scheduling, or analytics?	Step 2
4	Have any AI experiments shown measurable results (e.g., time saved, engagement lift)?	Step 3
5	Is AI integrated into more than two marketing functions?	Step 3
6	Does the client have a documented AI use policy?	Step 3
7	Is there real-time personalisation in any channel?	Step 4
8	Does AI inform budget decisions or campaign strategy?	Step 4
9	Are any revenue streams enabled or substantially transformed by AI?	Step 5

Diagnosis logic — count total yes answers:

Yes count	Current Step	Label
0	Step 1	Foundation
1–2	Step 2	Experimentation
3–5	Step 3	Expansion
6–7	Step 4	Transformation
8–9	Step 5	Reinvention

State the diagnosed step clearly at the top of the canvas output. Be honest: most East African SMEs will land at Step 1 or Step 2. Do not inflate the diagnosis.

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Step 2 — Complete the AI Marketing Canvas

The canvas has five steps (rows) and four customer moments (columns), producing 20 cells. Populate every cell for the client's current step and next step in full. Summarise the remaining steps in one sentence each.

The Five Steps

Step 1 — Foundation AI is not yet deployed. The focus is data infrastructure and organisational readiness.

Step 2 — Experimentation Small, low-risk AI experiments begin. Content tools, basic scheduling automation, simple chatbots.

Step 3 — Expansion Successful experiments are scaled. AI is integrated into three or more marketing functions. An AI use policy is in place.

Step 4 — Transformation AI is core to the marketing operating model. Personalisation at scale. AI informs strategy and budget allocation.

Step 5 — Reinvention AI enables new business models and entirely new customer value propositions.

The Four Customer Moments (Columns)

• Acquisition — attracting new customers: awareness, consideration, trial.
• Retention — keeping customers engaged: satisfaction, loyalty, repeat purchase.
• Growth — increasing customer value: upsell, cross-sell, referral.
• Advocacy — turning customers into brand promoters: UGC, referral, word-of-mouth.

Canvas Cell Content

For each cell at the client's current and next step, include:

• AI capability — what AI tool or technique is deployed here.
• Data required — what data the client needs to make this work.
• Expected result — what measurable outcome this produces.

Reference Cell Content by Step and Moment

Use the following as the basis for populating cells, adapted to the client's context.

Step 1 — Foundation

Moment	AI Capability	Data Required	Expected Result
Acquisition	None yet. Audit existing channels and content performance.	Website analytics, social reach data	Baseline understanding of what is working
Retention	None yet. Map the customer journey manually.	Purchase records, complaint logs	Identification of retention gaps
Growth	None yet. Document referral and upsell patterns.	Sales data, customer records	Understanding of natural growth triggers
Advocacy	None yet. Monitor brand mentions manually.	Social mentions, customer feedback	Baseline sentiment score

Step 2 — Experimentation

Moment	AI Capability	Data Required	Expected Result
Acquisition	AI caption writing (ChatGPT, Claude); AI image generation for social ads	Content calendar, audience demographics	Faster content production; 20–30% time saving
Retention	WhatsApp broadcast automation for follow-up messages	Customer contact list, purchase history	Improved response rate; reduced churn
Growth	AI-generated blog briefs and SEO content for organic discovery	Keyword data, topic clusters	Increased organic traffic over 3–6 months
Advocacy	Sentiment monitoring via free tools (e.g., Google Alerts, Mention free tier)	Brand name, product keywords	Early detection of negative sentiment

Step 3 — Expansion

Moment	AI Capability	Data Required	Expected Result
Acquisition	AI-assisted audience segmentation; lookalike targeting on Facebook	CRM export, pixel data	Reduced cost per acquisition
Retention	WhatsApp automation via Africa's Talking or similar; triggered re-engagement sequences	Customer segments, purchase recency	Measurable retention improvement; reduced churn rate
Growth	AI-powered cross-sell recommendations in email or WhatsApp	Purchase history, product catalogue	Increased average order value
Advocacy	Social listening with AI sentiment tagging; UGC identification and repurposing	Branded hashtag data, review platforms	Higher volume of curated UGC

Step 4 — Transformation

Moment	AI Capability	Data Required	Expected Result
Acquisition	Predictive lead scoring; AI-optimised ad creative testing	CRM data, ad performance data	Higher quality leads at lower CPL
Retention	Personalised mobile money promotions; SMS segmentation by behaviour	Transaction data, behavioural data	Improved retention rate and lifetime value
Growth	Dynamic pricing signals; AI-informed upsell timing	Purchase patterns, customer lifetime value scores	Measurable revenue lift from existing customers
Advocacy	Automated referral programme management; AI-generated personalised referral prompts	Advocate profiles, referral history	Scalable word-of-mouth growth

Step 5 — Reinvention

Moment	AI Capability	Data Required	Expected Result
Acquisition	AI-native product discovery (conversational commerce, AI search)	Full customer data graph	New acquisition channels; reduced dependency on paid media
Retention	Predictive churn prevention; hyper-personalised experience	Real-time behavioural data	Near-zero preventable churn
Growth	AI identifies and creates new revenue streams from existing customer relationships	Full data platform	New business model lines
Advocacy	Community AI — AI tools that advocates use to create and share content	Community platform data	Advocate-led growth at scale

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Step 3 — Produce the 12-Month Roadmap

Structure the roadmap by quarter. Every action must include a named tool or channel, a named metric, and a Q assignment.

Standard roadmap template — adapt to client's diagnosed step and context:

Q1 — Diagnose and Establish

• Confirm current step based on diagnostic results.
• Complete a data audit: identify where customer data lives, who owns it, and what is missing.
• Set up or clean the customer contact list (CRM, spreadsheet minimum standard).
• Launch 1–2 low-risk AI experiments appropriate to current step (e.g., AI caption writing in ChatGPT, scheduling via FeedHive or Buffer).
• Metric: baseline content output volume, time spent on content creation per week.

Q2 — Measure and Scale One Experiment

• Review Q1 experiment results against baseline.
• Select the single most successful experiment and scale it (increase frequency, apply to more channels, or expand to a second content type).
• Begin mapping the customer journey for the retention moment.
• Metric: time saving from AI tools, engagement rate change, WhatsApp open or reply rate.

Q3 — Integrate and Expand

• Integrate AI into one additional marketing function beyond content creation.
• For EA context: consider WhatsApp automation for retention or sentiment monitoring for advocacy.
• Draft and publish an internal AI use policy (see playbook-ai-content-workflow for guidance).
• Metric: functions using AI (target: 3+), policy in place (yes/no).

Q4 — Review Canvas Progress and Set Year 2 Targets

• Re-run the nine-question diagnostic to check step progression.
• Update the canvas: complete the next step's cells for any moment where Q1–Q3 work is confirmed.
• Set SMART objectives for Year 2 based on confirmed capabilities.
• Metric: step movement (did the client advance one step?), Year 2 AI investment recommendation in UGX.

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Step 4 — Write the Plain-Language Summary

After the canvas and roadmap, produce a section titled "What This Means for Your Business".

This section must:

• State the diagnosed step in plain language ("You are at Step 2 — Experimentation").
• Explain what that means in one paragraph without jargon.
• Name the single highest-impact action the client can take in the next 30 days.
• Be honest about the gap between current state and transformation — do not promise shortcuts.
• Be written as if speaking directly to a business owner, not a marketing professional.

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Step 5 — Monetisation

At this stage, AI capabilities become a source of competitive advantage and new revenue:

• AI-powered products or services sold to customers (e.g. a personalisation engine licensed to partners)
• Proprietary audience data monetised through partnerships
• AI-driven efficiency gains reinvested into market expansion
• Brand reputation as an AI-first organisation attracts premium clients and talent

Consultancy note: Most EA clients will not reach Step 5 within a 12-month engagement. Frame it as a 3–5 year horizon goal and use it to demonstrate the long-term value of starting the Canvas journey now.

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East Africa Context Notes

Apply these adaptations throughout the canvas and roadmap:

• Data scarcity — most EA SMEs have no CRM; customer data lives in WhatsApp groups, spreadsheets, or paper records. Step 1 must address this before any AI deployment.
• WhatsApp first — WhatsApp is the primary channel for retention and advocacy in Uganda and across East Africa. Prioritise WhatsApp automation at Step 2–3 over email sequences.
• Mobile Money — MTN Mobile Money and Airtel Money are transaction platforms; personalised promotions tied to Mobile Money behaviour are a Step 4 opportunity.
• Step 2 tools for EA context — ChatGPT or Claude for captions and blog briefs; FeedHive or Buffer for scheduling; Canva Magic Write for short-form copy; Google Alerts for brand monitoring.
• Step 3 tools for EA context — Africa's Talking for WhatsApp and SMS automation; Hootsuite Insights or Brandwatch (if budget allows) for sentiment monitoring.
• Most EA SMEs are at Step 1–2 — calibrate ambition accordingly. A Step 2 roadmap executed well is more valuable than a Step 4 roadmap that cannot be implemented.

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Agile Sprint Approach (Venkatesan and Lecinski, 2026)

AI marketing initiatives fail when treated as annual strategic plans. Recommend monthly sprint cycles:

1. Sprint planning (Day 1): Select one AI use case to test this month
2. Pilot execution (Days 2–20): Run the experiment with a defined audience segment
3. Measurement (Days 21–25): Compare AI-assisted results vs human-led baseline
4. Review (Days 26–28): Replicate, iterate, or abandon based on results
5. Next sprint (Day 30): Select next use case based on learnings

KPI for each sprint: measure lift vs human-led control. A 10% improvement justifies scaling.

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AI-to-AI Marketing Readiness (Venkatesan and Lecinski, 2026)

As consumers increasingly use AI agents (ChatGPT, Perplexity, Google Gemini) to research, compare, and purchase, brands must be readable by machines as well as humans. Assess:

• Are product pages structured with clear, machine-parseable pricing and features?
• Are brand values explicitly stated in factual, accessible language?
• Is content well-sourced and factually accurate (LLMs avoid citing inaccurate sources)?
• Does the website use structured data markup (Schema.org)?

This is a 2–5 year horizon for most EA markets but should inform content architecture decisions now. Brands that are not AI-readable risk becoming invisible as AI-native search and AI shopping agents become mainstream.

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Quality Criteria

Output meets standard when it:

• Diagnoses the client's step based on evidence from the nine questions, not aspiration or optimism — step inflation is a failure mode.
• Addresses all four customer moments for both the current step and the next step in full, with named AI capabilities, required data, and expected results.
• Produces a roadmap that is concrete: every action names a tool, a channel, and a metric.
• Reflects East African context throughout — WhatsApp, Mobile Money, and data scarcity are noted where they affect the recommended approach.
• Assigns every roadmap action to a specific quarter (Q1, Q2, Q3, or Q4).
• Includes a plain-language "What This Means for Your Business" summary written for a business owner, not a marketing specialist.
• Is honest about step progression — a realistic Step 1–2 plan is better than an inflated Step 4 plan the client cannot execute.
• Cites Venkatesan and Lecinski (2026) on first use of the framework.

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References

• Venkatesan, R. and Lecinski, J. (2026) The AI Marketing Canvas, 2nd ed. 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.

Related skills:

• ai-readiness-diagnostic — the full 41-item AI readiness diagnostic; run this before or alongside the canvas assessment for a more detailed organisational audit.
• playbook-ai-content-workflow — execution playbook for AI-assisted content production.
• playbook-ai-automation-workflow — automation and tool integration guidance.
• 05-social-media-strategy — full social media strategy; the canvas roadmap feeds into this.