AI agents — software that autonomously execute marketing campaigns and decisions — can't tell good data from bad. If the starting data is inaccurate, automation uses it anyway, and it uses it at scale. The result is budget spent on the wrong audience, faster and more systematically than before.
Mallory Gray of Skydeo, interviewed by Search Engine Journal, points out something many companies overlook: what matters isn't how many times a brand is mentioned online, but the behavioral signals that indicate who's actually buying. Two different things, often confused.
For those managing campaigns or considering introducing AI automation in marketing: the step that precedes any agent is an audience data audit. Without it, the agent works, but it works poorly — and it does so faster than any human team.
The problem isn't the agent: it's what you feed it
An AI agent in marketing is software that makes decisions autonomously: it selects audiences, optimizes offers, moves budgets, personalizes messages. It does this without waiting for human instruction for every step. It works well if the input data is reliable. If it's not, it still executes — it just makes mistakes systematically and at scale.
Mallory Gray of Skydeo explains it clearly in an analysis published on Search Engine Journal : the AI model doesn't know how to evaluate the quality of the data it receives. It takes them as good and builds every subsequent decision on them. A small error at the beginning becomes a big error at the end of the funnel.
Mention count vs. purchase intent signals: the most expensive confusion
There's a common misunderstanding among those who use social listening or brand monitoring tools: confusing visibility with purchase intent. How many times a brand is mentioned online says almost nothing about who has actually bought or is about to buy.
The signals that matter are behavioral: browsing, searches, interactions with specific content, transactional data. They are harder to collect and interpret than a mention count, but they are what an AI agent should work with.
If, instead, the agent is fed mention counts as a proxy for real interest, it will optimize for the wrong audience. And it will do so with a speed and consistency that no human team could match — in the worst possible way.
Who this changes things for this week
Not for everyone, and it's worth saying upfront.
- Those considering AI agents for campaigns or CRM: this is the moment to stop one step before and audit the available audience data.
- Those who have already automated something: it's worth checking what signals the automation is optimizing for. Mention count? Generic demographics? Or real behavioral signals?
- Those who don't use AI agents yet: for now the problem is not urgent, but it becomes so the moment any form of automation is introduced into campaigns.
The topic is connected to what is happening more generally in the agent ecosystem. If you are following the evolution of these tools, the article on Google Data Manager and data audit for targeting with AI agents addresses exactly the technical side of this problem from the perspective of one of the main players.
Four checks to perform before activating any automation
You don't need a six-month project. Four concrete questions are enough:
- Where does the audience data come from? Internal CRM, advertising platforms, third-party data? Each has a different level of reliability.
- Do these data reflect who bought or who looked? The difference is substantial. A user who visited a product page is not the same as one who completed a purchase.
- How often are they updated? Data that is six months old in a fast-moving market is often worse than no data.
- What does the agent optimize? If the main metric is CTR (click-through rate) instead of actual conversion, the agent will find the audience most inclined to click — which is not necessarily the one most inclined to buy.
On this front, the ChatGPT Work Data Agent for data analysis in SMEs is a tool that can help explore your datasets without advanced technical skills — but even there, the quality of the result depends on the quality of the input data.
The error you only see later
The real risk isn't that the agent stops working. It's that it keeps working perfectly, producing optimistic reports, while the budget is spent on the wrong audience. AI agents are good at optimizing towards the goal they are given. If that goal is poorly defined or the data is dirty, optimization leads away from real conversion.
It's the same principle that applies to voice agents — like those analyzed in the article on Ringg AI and automated customer service call management : automation works if the context in which it operates is correctly defined. Otherwise, it scales errors, not performance.
It also applies to those looking at OpenAI Sponsored Agents integrated with HubSpot and Shopify : the more data and operational levers the agent has access to, the more the cost of a wrong dataset multiplies.
Before any automation, data auditing isn't just a bureaucratic step. It's the condition that makes the investment worthwhile. To delve deeper into the entire topic of AI agents applied to marketing, the starting point is the section dedicated to automation and AI agents where we gather the most relevant insights for those working in the company.
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