Ninety percent of AI marketing vendors claim their platform is “proven at scale.” Fewer than one in five can point to a documented use case that matches your industry, budget, or team size. That gap is exactly what the AI Squared Insights 200 Use Case Map was built to close, and it is quickly becoming the reference document procurement teams pull out before signing anything with an AI logo on it.
If you have sat through a vendor demo that promised “40% efficiency gains” without a shred of methodology behind it, you already know why this matters.
What the 200 Use Case Map Actually Is
AI Squared Insights built the map as a categorized library of roughly 200 documented AI deployments across marketing, sales, customer service, and operations functions. Each entry logs the vendor, the specific use case, the reported outcome, the industry vertical, and, critically, whether the result was independently verified or self-reported by the vendor.
That last distinction is the whole point. Most AI vendor claims in the wild are self-reported. A platform says it “reduced content production time by 60%” and that number lives on a landing page with no case study, no client name, and no third-party audit attached. The Use Case Map forces a side-by-side comparison: here is what the vendor says, here is what a comparable deployment actually delivered, and here is the delta.
Of the roughly 200 use cases catalogued, AI Squared Insights found that fewer than a third included any form of independent verification, meaning the majority of AI marketing claims still rest on vendor-supplied numbers alone.
For brand and agency teams, that is a sobering baseline. It means the burden of proof sits with the buyer, not the seller. If you are not asking for verification, you are probably not getting it.
Why Vendor Claims Keep Outrunning Reality
This isn’t a new problem, but AI has made it worse. Marketing software has always had a credibility gap between the pitch deck and the production environment. What changed is speed. New AI tools launch monthly, case studies get written before campaigns finish running, and “results” often reflect a single best-performing pilot rather than a repeatable process.
Gartner’s own research backs this up. Our coverage of the firm’s recent scaling study found that only 30 percent of marketers feel ready to scale AI beyond pilot programs, which tells you something important: most of the “proven” claims circulating in vendor sales decks come from environments that never made it past phase one.
There’s also a structural incentive problem. Vendors are rewarded for closing deals, not for publishing failure rates. Nobody puts “this only worked in 12% of comparable deployments” on a homepage. That is precisely the information the 200 Use Case Map tries to surface by aggregating outcomes across vendors rather than trusting any single one’s marketing copy.
How to Actually Use the Map During Vendor Evaluation
Having access to a use case taxonomy doesn’t help if your team doesn’t build it into the procurement workflow. Here’s the practical sequence that’s worked for brand teams we’ve talked to:
- Match the use case, not the category. A vendor claiming success in “influencer marketing AI” is meaningless if their documented deployments are all in e-commerce product recommendations. Find the closest matching use case in the map, not the closest matching label.
- Check the verification flag. Self-reported outcomes should be treated as a starting hypothesis, not evidence. Ask the vendor directly whether their claimed results have ever been audited by a third party.
- Compare sample size. A single glowing case study is an anecdote. A use case documented across a dozen similar deployments is a pattern.
- Look for the failure cases. The map includes deployments that underperformed or were abandoned. That data is arguably more valuable than the success stories, because it tells you what conditions predict a bad fit.
This is essentially the same discipline outlined in our vetting scorecard for AI use case intelligence platforms, which treats vendor selection as a research exercise rather than a purchasing decision made on gut feel.
Red Flags the Framework Exposes Fast
Once you start running vendor pitches through a structured map, certain patterns jump out almost immediately.
The first is vague attribution. Vendors who say their tool “drove” a result without explaining the measurement methodology are usually hiding a weak signal. The second is outcome inflation, where a metric like “engagement” gets redefined mid-pitch to mean something more favorable than the original claim. The third, and probably the most common, is survivorship bias: showcasing only the clients who stuck around, while quietly dropping the ones who churned after six months.
This connects directly to a broader issue we’ve written about before. Dark data quietly wrecking AI marketing stacks often traces back to exactly this kind of unverified vendor input getting baked into dashboards and reports without anyone questioning the source.
A vendor that cannot name the denominator behind a percentage claim, how many total deployments, how many succeeded, is not giving you a metric. It is giving you a slogan.
If your team has ever approved a vendor based on a single case study PDF, this is the moment to build a better filter. It doesn’t need to be elaborate. It needs to be consistent.
Building This Into Your Procurement Process
The 200 Use Case Map is useful as a reference, but the real value comes from institutionalizing the habit of checking it. A few operational steps make that stick:
- Require every AI vendor proposal to cite at least one comparable, verifiable use case before it reaches budget approval.
- Assign someone on the marketing ops or procurement team to own vendor claim verification as a recurring task, not a one-time check.
- Build claim verification into your existing AI content governance committee workflow so it isn’t siloed from other AI risk review.
- Revisit vendor claims quarterly. AI tools update fast, and a claim that was accurate two quarters ago may no longer reflect the current product.
This kind of structured evaluation pairs well with the broader four pillar AI readiness framework many brands are already using to assess internal capability. Vendor claim verification is really just the external half of the same question: are we actually ready to trust this tool with budget and brand equity?
For teams managing multiple AI tools across the creator and media stack, the stakes compound. Our recent look at buyer scorecards for AI media orchestration agents makes a similar point: the more automated a system becomes, the more expensive an unverified claim gets when it fails in production rather than in a demo.
What This Means for Budget Owners
None of this is about being anti-AI. It’s about being pro-evidence. Marketing budgets are under enough scrutiny already, and our coverage of how AI budgets get treated as MarTech dollars in disguise shows exactly how quickly finance teams cut spend when ROI claims don’t hold up under review. A vendor’s unverified promise becomes your line item to defend in the next budget cycle. That’s a bad trade.
Industry benchmarking groups like eMarketer and analyst firms tracking martech adoption consistently find the same gap: reported AI performance and audited AI performance rarely match. Frameworks like the 200 Use Case Map exist precisely to narrow that gap before it becomes your problem instead of the vendor’s.
Trade groups such as the Federal Trade Commission have also signaled increased scrutiny of unsubstantiated AI performance claims in advertising, which adds regulatory weight to what was already good procurement hygiene. Vetting claims isn’t just smart, it’s increasingly a compliance expectation.
The next time a vendor opens a pitch with an eye-catching percentage, ask for the use case behind it, the sample size, and who verified it. If they can’t answer within thirty seconds, you already have your answer.
Frequently Asked Questions
What is the AI Squared Insights 200 Use Case Map?
It’s a categorized library of roughly 200 documented AI deployments across marketing and adjacent functions, built to help buyers compare vendor claims against real, comparable outcomes rather than relying on self-reported marketing copy.
How is this different from a vendor’s own case studies?
Vendor case studies are typically self-selected success stories. The Use Case Map aggregates outcomes across multiple vendors and flags whether each result was independently verified, giving buyers a broader and more honest comparison point.
How many AI vendor claims are actually independently verified?
According to the map’s own findings, fewer than a third of catalogued use cases had any form of third-party verification, meaning most vendor performance claims in the market are self-reported.
Who should own AI vendor vetting inside a marketing organization?
Most brands assign this to marketing operations or procurement, often folding it into the same governance committee that reviews AI content risk and compliance, so vendor claims get the same scrutiny as output quality.
How often should vendor claims be re-verified?
Quarterly at minimum. AI products update fast enough that a claim accurate two quarters ago may no longer describe the current version of the tool.
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