AI Doesn't Have To Invent Wrong Answers—You Already Published Them

Mark van Berkel is Co-Founder and CTO of Schema App, specializing in ontology and semantic data for search, AI and agentic experiences.

​For two decades, search results hid our internal mess. A page could rank while the product name on it differed from the name in the help center or on the regional site. Ranking rewarded popularity, so incoherence went unnoticed.

AI systems don’t rank your page. They read your facts and reassemble them into an answer. Every contradiction becomes a public statement about your brand.

Despite these risks, organizations are rapidly funding AI initiatives before validating the data those initiatives depend on. A Gartner survey found that 63% of data-management leaders either lack, or are unsure if they have, the data-management practices required for AI. Gartner researchers also predict that organizations will abandon 60% of AI projects through 2026 when those projects lack AI-ready data. The Stanford 2026 AI Index likewise found that the share of respondents identifying AI inaccuracy as a “relevant risk rose from 60% to 74%” in one year.​

Bad answers don’t throw errors. They simply get repeated.

From Ranking Pages To Synthesized Facts​​

You used to publish a page and a human read it. Now, a machine retrieves facts from your pages and other sources, synthesizes them and answers in a chat response that you’ll never see. That changes what controls brand representation: Increasingly, the synthesized answer, rather than the source page, shapes how users understand a brand. Pew Research Center found that users clicked a traditional result on 8% of visits when an AI summary appeared, compared with 15% without one. They clicked a source within the summary just 1% of the time.

Placement is still relevant, but it isn’t the whole game anymore. If a machine can’t form a consistent picture of who you are, what you offer and how those things relate, you don’t have only a ranking problem. You have an understanding problem.