AI agents, software that runs marketing campaigns and decisions on its own, can't tell good data from bad. If the starting data is inaccurate, the automation uses it anyway, and at scale. The result: budget spent on the wrong audience, faster and more systematically than before.
Mallory Gray of Skydeo, interviewed by Search Engine Journal, points to something many companies overlook: what matters isn't how often a brand is mentioned online, but the behavioral signals that show who really buys. Two different things, often confused.
If you run campaigns or are thinking about bringing AI automation into your marketing, the step before any agent is an audience data audit. Without it, the agent works, but works badly, and it does so faster than any human team.
The problem isn't the agent: it's what you feed it
In marketing, an AI agent is software that makes decisions on its own: it selects audiences, optimizes bids, shifts budget, personalizes messages. It doesn't wait for a human instruction at every step. It works well when the input data is reliable. When it isn't, it still executes, only it makes mistakes systematically and at scale.
Mallory Gray of Skydeo explains this clearly in an analysis published on Search Engine Journal: the AI model can't judge the quality of the data it receives. It takes it at face value and builds every later decision on top of it. A small error at the start becomes a big error at the bottom of the funnel.
Mention count vs. buying signals: the costliest mix-up
There's a common misconception among people who use social listening or brand monitoring tools: mistaking visibility for purchase intent. How often a brand is mentioned online tells you almost nothing about who has actually bought or is about to.
The signals that matter are behavioral: browsing, searches, interactions with specific content, transaction data. They're harder to collect and interpret than a mention count, but they're what an AI agent should be working with.
But if the agent is fed mention count as a proxy for real interest, it will optimize toward the wrong audience. And it will do so with a speed and consistency no human team could match, in the worst possible sense.
Whose routine changes this week
Not for everyone, and it's worth saying up front.
- If you're evaluating AI agents for campaigns or CRM: this is the time to stop one step earlier and audit the audience data you have.
- If you've already automated something: it's worth checking which signals the automation is optimizing for. Mention count? Generic demographics? Or real behavioral signals?
- If you don't use AI agents yet: the problem isn't urgent for now, but it becomes urgent the moment you introduce any form of automation into your campaigns.
The topic ties into what's happening more broadly in the agent ecosystem. If you're following how these tools evolve, the article on Google Data Manager and the data audit for AI agent targeting tackles the technical side of this problem from the perspective of one of the major players.
Four checks to run before you turn on any automation
You don't need a six-month project. Four practical questions are enough:
- Where does your audience data come from? Internal CRM, ad platforms, third-party data? Each has a different level of reliability.
- Does this data reflect who bought or who just looked? The difference is substantial. A user who visited a product page is not the same as one who completed a purchase.
- How often is it updated? Data that's six months old in a fast-moving market is often worse than no data at all.
- What is the agent optimizing for? If the main metric is CTR (click-through rate) rather than real conversion, the agent will find the audience most likely to click, which isn't necessarily the one most likely to buy.
On this front, the ChatGPT Work Data Agent for data analysis in SMEs is a tool that can help you explore your own datasets without advanced technical skills. Even there, though, the quality of the result depends on the quality of the data going in.
The error you only see later
The real risk isn't that the agent stops working. It's that it keeps working beautifully, producing rosy reports, while the budget is spent on the wrong audience. AI agents are great at optimizing toward the goal they're given. If that goal is poorly defined or the data is dirty, optimization leads you away from real conversion.
The same principle applies to voice agents, like those covered in the article on Ringg AI and automated customer service call handling: automation works when the context it operates in is defined correctly. Otherwise it scales errors, not performance.
It also applies if you're looking at OpenAI Sponsored Agents integrated with HubSpot and Shopify: the more data and operational levers an agent can access, the more the cost of a bad dataset multiplies.
Before any automation, a data audit isn't a bureaucratic step. It's what makes the investment worthwhile. To dive deeper into AI agents applied to marketing, start with the section on automation and AI agents, where we collect the most relevant analyses for people working in business.
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