AI And Bad Data: Why Businesses Need To Go Back To Basics

Generative and agentic AI are changing how consumers discover and interact with brands, reducing the effectiveness of traditional marketing playbooks like SEO and click-based measurement. Marketing leaders feel pressure to adopt AI to keep pace, but many companies focus on AI capabilities while lacking the underlying data needed to use them effectively. Without data integrity and maturity, AI can only produce faster, higher-volume bad outcomes. Persistent data silos prevent collaboration, create inconsistent or questionable data quality, and make it difficult to build strategies aligned with reality. Fundamental data strategy and data management must be addressed first so AI can deliver scalable, meaningful results rather than applying new tools to flawed data.

"AI is the new competitive edge for marketing and communications leaders, and the race is on to implement the technology in the savviest, most effective way possible. Thanks to generative and agentic AI, consumers are discovering and interacting with brands in unprecedented ways, and traditional playbooks like SEO and counting clicks aren't as effective as they once were."

"But in trying to keep up with competitors, I'm seeing many companies put the cart before the horse. They're eager to take advantage of AI's ability to analyze data, predict trends and make informed recommendationsbut behind the scenes, they're still struggling to capture and operationalize data in the first place. Without data integrity and maturity, AI won't help anything. It will just deliver a bad result faster."

"One of the most common challenges I see is in fundamental data strategy. Many companies are still operating with persistent data silos. Teams can't collaborate to the extent they'd like to. Data integrity is inconsistent or even questionable. As a result, businesses have trouble building strategies that align with reality and generate the desired outcome."

"And based on my experience, it must be addressed before AI will actually be able to take root and start making a difference at scale. Otherwise, we'll just be pointing a different tool at the same set of bad data. In my experience, the first step to seeing meaningful outcomes from AI investments is getting your data management in order."

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