Marketing AI Use Case Canvas
A structured one-page canvas to evaluate, scope, and justify a single AI use case before building. Designed for CMOs and marketing leaders to identify high-friction workflows where AI creates measurable ROI, avoid tool-first thinking, and present a clear business case to leadership with timeline and success metrics.
How to Use This Template
- 1.## Step 1: Identify the High-Friction Workflow
- 2.**Start with operational debt, not tools.** Before you name an AI solution, audit your team's calendar and task logs for the workflow that consumes the most time, creates the most rework, or blocks revenue. This is your target. In Section 1, describe the current manual process in plain language—how many people touch it, how many handoffs, how many approvals. Quantify the time leak: if your team spends 20 hours a week on email subject line testing, that's your friction point. Don't pick a use case because a vendor demo looked cool; pick it because your team is drowning in it.
- 3.## Step 2: Define What AI Actually Does
- 4.**Be specific about the AI action, not the tool.** In Section 2, write down the exact task the AI will perform. Instead of "use ChatGPT," write "generate 5 subject line variants ranked by predicted open rate based on historical email performance." This forces clarity. Ask: What input does the AI need? What output do we get? How does that output move into the next step of the workflow? If you can't answer these questions clearly, the use case isn't ready. This step prevents tool-first thinking and keeps you focused on the job to be done.
- 5.## Step 3: Map Data, Tools, and Governance
- 6.**Assess readiness honestly in Section 3.** Identify the data sources the AI needs (CRM, email platform, content library) and rate their readiness: clean and accessible, needs prep, or needs governance. Check whether you can use an existing tool (Salesforce Einstein, HubSpot AI, native platform features) or if you need a new vendor. Be candid about integration lift—plug-and-play is fast, but heavy engineering delays ROI. Flag brand and compliance risks early: if the AI generates customer-facing copy, you need tone guidelines and approval workflows. If it touches PII, you need legal review. This prevents silent failures and governance debt later.
- 7.## Step 4: Build a Financial ROI Model
- 8.**Make the business case concrete in Section 4.** Identify one primary metric (email open rate, content production speed, lead scoring accuracy) and set a baseline and target. Then calculate the financial impact: time savings (hours freed × hourly cost), quality lift (conversion improvement × revenue per conversion), and tool costs. Use a 6-month window to show fast ROI—this is what leadership expects. If the math doesn't work, the use case isn't ready. If it does, you have a number to defend. This section is your proof point; don't skip it or estimate loosely.
- 9.## Step 5: Create a Lightweight Pilot Roadmap
- 10.**In Section 5, map a 12-week path from audit to scale.** Week 1 is audit (data inventory, tool selection). Weeks 2–4 are pilot (test runs, manual QA). Week 4 is governance (brand guidelines, approval workflow). Week 5 is launch (live in one channel or workflow). Weeks 6–8 are measurement (baseline metrics, team feedback). Weeks 9–12 are scale (expand to the next workflow). This prevents pilots from living in silos. Each phase has a clear owner, deliverable, and success criterion. Share this timeline with leadership so they see fast, staged progress—not a six-month black box.
- 11.## Step 6: Identify and Mitigate Risks
- 12.**In Section 6, name the risks that could kill this use case.** Common ones: output quality below threshold (mitigate with manual QA on a sample), team resistance (mitigate with hands-on training and a champion user), data privacy or brand compliance (mitigate with legal review and tone guidelines), integration complexity (mitigate with a proof-of-concept in week 1). Rate each risk as low, medium, or high likelihood and impact. For high-impact risks, define a concrete mitigation action. This shows leadership you've thought through failure modes and have a plan. It also prevents surprises mid-pilot.
- 13.## Step 7: Present and Decide
- 14.**Use Section 7 to make a clear recommendation and next steps.** Should you proceed to pilot, refine scope, or defer? If you proceed, assign a sponsor, allocate budget, and schedule a kickoff and team briefing. If you defer, explain why and set a date to revisit (e.g., "once data is clean"). This forces a decision and prevents use cases from languishing in limbo. Share the completed canvas with your leadership team and cross-functional stakeholders. It's a one-page reference that keeps everyone aligned on the problem, the solution, the timeline, and the ROI.
Template
# Marketing AI Use Case Canvas
**Use Case Name:** [SPECIFIC AI APPLICATION, e.g., "AI-Powered Email Subject Line Optimization"]
**Owner:** [NAME & TITLE] | **Stakeholders:** [CROSS-FUNCTIONAL TEAMS] | **Date:** [MM/DD/YYYY]
---
## 1. The Problem: Where Is Time Leaking?
**Current Workflow:** [DESCRIBE THE MANUAL PROCESS IN 2-3 SENTENCES]
**Friction Points (Select all that apply):**
- [ ] Manual, repetitive tasks consuming [X] hours/week
- [ ] Coordination overhead between [TEAMS]
- [ ] Quality inconsistency causing rework or delays
- [ ] Approval bottlenecks slowing time-to-launch
- [ ] Data silos preventing insights or personalization
- [ ] Tool sprawl or manual data entry
**Business Impact of Status Quo:**
- Time cost: [X] FTE hours/month spent on [SPECIFIC TASK]
- Revenue impact: [DESCRIBE MISSED OPPORTUNITY, e.g., "Delayed campaigns cost ~$[X] in lost pipeline"]
- Team morale: [BRIEF NOTE ON BURNOUT OR FRUSTRATION]
---
## 2. The Opportunity: What Does AI Solve?
**AI Capability Required:** [e.g., "Generative text, predictive analytics, image generation, data classification"]
**Specific AI Action:** [DESCRIBE WHAT THE AI WILL DO, e.g., "Generate 5 subject line variants ranked by predicted open rate"]
**Why This Matters:**
- Removes [SPECIFIC BOTTLENECK] from the workflow
- Enables [TEAM] to focus on [HIGHER-VALUE WORK]
- Compounds with [OTHER WORKFLOWS/SYSTEMS] downstream
---
## 3. Scope & Constraints
| Dimension | Details |
|-----------|----------|
| **Data Required** | [SOURCES: CRM, email platform, content library, etc.] |
| **Data Readiness** | [ ] Clean & accessible [ ] Needs prep [ ] Needs governance |
| **Tool/Platform** | [EXISTING TOOL or NEW TOOL REQUIRED] |
| **Integration Lift** | [ ] Plug-and-play [ ] Light API work [ ] Heavy engineering |
| **Brand/Compliance Risk** | [DESCRIBE: tone, data privacy, regulatory concerns] |
| **Team Capability** | [ ] Ready to use [ ] Needs training [ ] Needs hiring |
---
## 4. Success Metrics & ROI
**Primary Metric:** [ONE CLEAR KPI, e.g., "Email open rate increase"]
- Current baseline: [X]%
- Target after AI: [X]%
- Measurement method: [HOW YOU'LL TRACK]
**Secondary Metrics:**
- [METRIC 2]: [BASELINE] → [TARGET]
- [METRIC 3]: [BASELINE] → [TARGET]
**Financial ROI (6-month window):**
| Category | Calculation | Amount |
|----------|-------------|--------|
| **Time Savings** | [X] hours/month × [HOURLY RATE] × 6 months | $[X] |
| **Quality Lift** | [X]% improvement × [REVENUE PER CONVERSION] × [VOLUME] | $[X] |
| **Tool Cost** | [SUBSCRIPTION + INTEGRATION] × 6 months | ($[X]) |
| **Training/Setup** | [HOURS] × [RATE] | ($[X]) |
| **Net ROI** | | **$[X]** |
---
## 5. Implementation Roadmap
| Phase | Timeline | Deliverable | Owner | Success Criteria |
|-------|----------|-------------|-------|------------------|
| **Audit** | Week 1 | Data inventory, tool evaluation | [NAME] | Data mapped, tool selected |
| **Pilot** | Weeks 2–4 | [X] test runs, manual QA | [NAME] | [X]% accuracy, team feedback |
| **Governance** | Week 4 | Brand guidelines, approval workflow | [NAME] | Documented, signed off |
| **Launch** | Week 5 | Live in [CHANNEL/WORKFLOW] | [NAME] | [X] outputs processed |
| **Measure** | Weeks 6–8 | Baseline metrics, team feedback | [NAME] | Data collected, report ready |
| **Scale** | Weeks 9–12 | Expand to [NEXT WORKFLOW] | [NAME] | ROI validated, roadmap updated |
---
## 6. Risks & Mitigation
| Risk | Likelihood | Impact | Mitigation |
|------|------------|--------|------------|
| [RISK 1: e.g., "Output quality below threshold"] | [ ] Low [ ] Med [ ] High | [ ] Low [ ] Med [ ] High | [ACTION: e.g., "Manual QA on 10% of outputs for first month"] |
| [RISK 2: e.g., "Team resistance or skill gap"] | [ ] Low [ ] Med [ ] High | [ ] Low [ ] Med [ ] High | [ACTION: e.g., "Hands-on training + champion user program"] |
| [RISK 3: e.g., "Data privacy or brand compliance"] | [ ] Low [ ] Med [ ] High | [ ] Low [ ] Med [ ] High | [ACTION: e.g., "Legal review + tone guidelines"] |
| [RISK 4: e.g., "Integration complexity"] | [ ] Low [ ] Med [ ] High | [ ] Low [ ] Med [ ] High | [ACTION: e.g., "Proof-of-concept with IT in week 1"] |
---
## 7. Decision & Next Steps
**Recommendation:** [ ] Proceed to Pilot | [ ] Refine Scope | [ ] Defer
**If Approved:**
- [ ] Budget allocated: $[X]
- [ ] Sponsor assigned: [NAME]
- [ ] Kickoff scheduled: [DATE]
- [ ] Team briefing: [DATE]
**If Deferred:**
- Reason: [BRIEF EXPLANATION]
- Revisit date: [DATE]
- Conditions to reconsider: [e.g., "Once data is clean," "After tool evaluation"]
---
**Notes & Assumptions:**
[SPACE FOR ADDITIONAL CONTEXT, DEPENDENCIES, OR CONSTRAINTS]
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Get the Full AI Marketing Learning Path
Courses, workshops, frameworks, daily intelligence, and 6 proprietary tools — built for marketing leaders adopting AI.
Trusted by 10,000+ Directors and CMOs.