Key Takeaways:
- AI tools give fast answers, but they don’t separate right answers from wrong ones. GWI found 72% of marketers say their organization made a significant business decision based on AI-generated insight that later turned out to be wrong or misleading, and only 37% verify AI insights most of the time.
- Wrong AI insights have real costs. 37% of marketers report financial losses, 35% customer complaints, and 32% reputational damage.
- For brands, a verification step protects budget, customer relationships, and credibility with leadership. For agencies, it also protects the agency’s reputation with clients.
- A five-step check makes verification repeatable: flag the claim type, trace each claim to a named source, check the data’s shelf life, cross-check against a second source before spending, and build a “source and date” field into the brief template.
- Not everything needs checking. Brainstorms, creative drafts, summaries of documents you provided, hypotheses you’ll test, and low-stakes decisions can skip the full check.
- Verification speeds work up. It reduces rework, and recommendations with a named source and date get sign-off faster.
Most marketing teams now use AI tools to perform a variety of tasks, from generating creative to determining strategy. Artificial intelligence answers audience questions, summarizes key research findings, and even generates segment ideas. One of the major advantages of AI is speed, but there’s also a risk in how embedded AI has become in marketing workflows. Namely, the tools don’t differentiate between right and wrong answers. It’s up to a human to figure out what’s right and what’s not.
This is already an issue for companies. GWI found that 72% of marketers say their organization has made a significant business decision based on AI-generated insight that later turned out to be wrong or misleading. Yet, only 37% say they verify AI-generated insights “most of the time.”
You can fix this problem by putting a verification step into your workflow that helps ensure an answer that needs checking gets flagged. In this blog, you’ll learn a simple, repeatable way to add that step.
Why Do Brands and Agencies Need an AI Verification Step?
The cost of a wrong AI answer isn’t theoretical. GWI reveals 37% of marketers say acting on inaccurate or misleading insights led to financial losses. Another 35% got customer complaints as a result of inaccuracies, and 32% said their company’s reputation was damaged.
For brands specifically, a verification step protects three things:
- Budget
- Customer relationships
- Credibility with leadership
Simply put, when you’re building campaigns, your audience insights need to be right, or every dollar you spend on the campaign is at risk. Having a verification step also makes it easier to back up your decision in front of management because you can show where you got the insights beyond simply referring to the AI tool you’re using.
For agencies, the stakes are even higher. It’s not just the campaign on the line: It’s also your agency’s reputation. Think about what would happen if you ran a client campaign based on an incorrect AI-generated insight. The client probably wouldn’t blame the AI tool you used. Instead, they’d question your expertise. A consistent verification step provides the speed to insight agencies need to stay flexible and competitive while ensuring they can comfortably stand behind the recommendations they’re making to clients.
How to Add an AI Verification Step to Your Marketing Workflow
Step 1: Flag the claim type
Start by keeping the workflow practical. Most of what marketers currently use AI for is generative, and auditing everything like an insight that needs checking would add more challenges rather than making things easier.
You can sort what the AI tool gives you into two buckets:
- Bucket 1: Verifiable claims: Stats, segment sizes, behavior rates, market shares, and anything about real people that’s framed as a fact goes in this bucket. All of these need sourcing.
- Bucket 2: Generative output: Creative concepts, messaging angles, hypotheses, and brainstorms can all go in this bucket. The key difference here is that you judge these things based on whether they’re useful, not whether they are true.
Step 2: Trace each claim to a named source
Now, go back through the claims you have in Bucket 1. You should be able to cite the source each one came from, including when the data was collected to ensure you aren’t making a decision based on outdated research.
AI tools can help you with this step. Just put each claim into the tool and ask it to produce a link to the source where it came from. Then, make sure you go and check that source out yourself. If the tool can’t tell you the source of something, treat it as unverified and take it out of the project until you can verify it yourself.
Step 3: Make sure the data’s shelf life hasn’t expired
It’s a reality of research: Some facts will hold for years, while others become outdated in a matter of months. And unfortunately, sometimes AI can make old data look new. The last thing you want to do is put together a campaign and then confidently repeat an outdated piece of information to leadership.
Here’s a quick guide to how slowly or quickly a claim decays:
- Slow to change: Basic demographics tend to change slowly
- Change at a moderate speed: Category purchase behavior, such as how often people buy or where they shop
- Change quickly: Motivations, sentiments, channel preferences, and anything tied to the economy or current events
Step 4: Before you spend, cross-check against a second source
Especially if you’re planning on putting budget behind a decision, and that decision is based on AI-generated insights, you should first confirm that claim against a second, independent source before you commit.
This might be your own or your client’s first-party data, a recent study with a published methodology, or a consumer data vendor who’s transparent about their sourcing and refresh dates.
Step 5: Build the check into your workflow
Verification needs to be something you and your team are doing regularly. Make it easy for yourself and others to remember by including fields like “source and date” in your brief template. Then, if a brief cites an AI-generated claim but doesn’t include a named source, make it a habit not to move forward with that insight until someone fills in the field.
Which Insights Don’t Require Increased Scrutiny?
The verification check is an extra step for you, so over-applying it would make it impractical. The following generally don’t need additional sourcing:
- Brainstorming, creative concepts, and messaging drafts
- Summaries of a document you provided, which you can check against the original
- Hypotheses you plan to test before acting on them
- Low-stakes decisions below your team’s spend threshold
Save the full check for claims that will drive budget, shape a client recommendation, or appear in public-facing content.
Why an Extra Verification Step Actually Speeds Things Up
A verification check reduces the amount of time you need to spend re-doing work because of mistakes. It also ensures leaders feel better about approving AI-assisted work. Why? Well, when a recommendation comes with a named source and date, leaders have less reason to hesitate, and sign-off happens faster
Need a Dependable Reference Point for Verifying Your Insights?
Resonate’s predictive consumer intelligence (PCI) is continuously updated and built on consistent, named data sources, which makes it a dependable reference point for checking claims about consumer behavior, values, and motivations. Ready to bring on a partner who will help you verify before you activate? Schedule a consultation with a data expert today.
Frequently Asked Questions
What counts as a good source for an AI-generated claim?
A named dataset with a known collection date and methodology. A confident sentence, or a reference to general trends, doesn’t count.
Which claims go out of date fastest?
Motivation, sentiment, and channel preference claims change fastest, especially when they’re tied to the economy or current events. Basic demographics change slowly.
When should a claim be cross-checked against a second source?
Whenever it will drive a spend decision above a threshold your team defines, shape a client recommendation, or appear in published content.
Does this mean AI tools aren’t reliable?
No. The point is narrower: verify before you activate. A consistent check lets teams use AI with more confidence, not less.
