There’s a question flooding every Slack channel right now: “Which AI tool should we try next?”
Spoiler: most so-called “AI marketing platforms” are little more than fancy wrappers around the same open-source models you can access for pennies. Before you hand over the company credit card — or worse, your customer data — run through the nine questions below. They’ll save you budget, legal headaches, and endless migration pain.
Why bother vetting marketing AI tools?
Three reasons this matters more than people realise.
Hype vs. help. Roughly 80% of new “AI” launches are glorified macros. Proper diligence keeps you focused on tech that actually drives pipeline.
Data responsibility. Marketers touch PII, ad spend, and brand voice. A leaky API or hallucinating copywriter bot can torpedo trust overnight.
Workflow fit. A beautiful dashboard is worthless if it breaks your existing approval flow or forces the team into yet another interface.
Sound familiar? This is your 10-minute due-diligence crash course.
The 9-question vetting checklist
1. “What model powers this?” If the vendor can’t name the underlying LLM or proprietary algorithm, you’re looking at smoke and mirrors.
2. “Can I test it with my own data in under 30 minutes?” A real platform lets you upload or connect data quickly. No 30-day “white-glove onboarding” gatekeeping.
3. “How is my data secured and stored?” Look for SOC 2 Type II or ISO 27001 certification and encryption both in transit and at rest.
4. “Can I export prompts, outputs, and training data?” If you can’t take your data elsewhere, you’re one pricing change away from disaster.
5. “What’s the true cost per action at scale?” Beware hidden token or API fees that eat ROI as soon as your campaigns take off.
6. “How does the tool prevent hallucinations and bias?” Top vendors bake in confidence scores or human-review queues. The rest shrug and blame “the model.”
7. “What uptime SLA and support do you guarantee?” Marketing deadlines are brutal. Shoot for 99.9% uptime and sub-2-hour support during business hours.
8. “Does it integrate with my current stack out of the box?” Native connectors — or at least Zapier/Make modules — cut context-switch time to almost zero.
9. “Who else in my niche uses it, and what results did they see?” Named case studies with hard numbers trump anonymous “Fortune 500 client” claims every time.
Pro tip: Weight each question 1–5 based on your risk tolerance, add them up, and set a pass mark before the demo even begins.
Red flags to walk away from
- A lifetime deal that magically expires tonight with no public roadmap.
- “Proprietary AI” claims backed by zero technical whitepaper.
- No role-based permissions — meaning the intern could accidentally view all your ad spend data.
- Copy-pasted answers to your security questionnaire.
- Pricing hidden behind “contact sales” even for starter tiers.
If you spot two or more of these, close the tab and move on. There’s always another shiny tool.
Common pitfalls (and how to fix them)
Demo dazzle. You green-light a tool after a slick Loom demo. Fix: impose a 24-hour cooling-off period and run the checklist.
Stack sprawl. Three overlapping AI copy tools create chaos. Fix: map your workflow first, then assign one tool per stage — research, writing, optimisation, and so on.
Data leakage. Someone pastes PII into a public LLM. Fix: enforce a “no raw customer data” rule and use placeholders or masked datasets for prompts.
Audit before you automate
AI can 10x your marketing — or 10x your chaos. Spend an extra hour on vetting today and you’ll save weeks of migration pain tomorrow.
Got a marketing AI tool you love (or hate)? Drop a comment — I’m curating a crowd-sourced list of vetted winners and losers.
Same hoodie, smarter stack. ✌️
Dom O'Brien
CMO at MATE and author of The Startup Marketing Playbook. Fifteen years building lean marketing teams that punch above their weight.
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