AI and answer engines are only as good as the data behind them, and Master B2B’s research has repeatedly found B2B product data lagging what B2C data typically looks like. Manufacturers often supply a thin set of attributes on the assumption that a sales rep or spec sheet will fill the gap, which is exactly the problem CPG manufacturers worked through with retailers roughly a decade ago. Fixing it usually means capturing far more structured detail, weights, dimensions, compatibility, and full specifications, than a company has historically needed for a phone-and-fax sales process. Without that foundation, AI search, personalization, and agentic commerce all underperform regardless of how sophisticated the tools layered on top are.
Data quality problem
The widespread issue of B2B product and customer data being incomplete, inconsistent, or poorly structured, which limits how well AI tools, search, and personalization can perform.