Global organizations must expose “decision knowledge” beyond simple product descriptions to enable artificial intelligence to make confident and relevant customer recommendations, industry experts reported.
The shift is necessary because AI recommendations often fail not from a lack of product data, but from an inability to access specific insights AI needs to qualify products for diverse customer purchasing decisions, experts said.
Customers typically base purchasing decisions on whether a product effectively solves their problems faster, better, or easier, rather than solely on technical specifications. AI systems, therefore, need to synthesize evidence to answer complex questions about when, for whom, and how a product compares to alternatives.
This “decision knowledge” frequently exists within organizations, embedded in sales conversations, customer support interactions, and the expertise of internal staff. However, it is rarely organized or made accessible for AI systems to process and reason over, according to recent analysis.
One business-to-business Software-as-a-Service (SaaS) company, for example, struggled to generate effective AI recommendations for small and medium-sized businesses. The company’s website lacked specific content explaining why its product was uniquely suited for that particular market segment, hindering AI’s ability to identify appropriate matches.
The challenge highlights a broader need for companies to structure their information in a way that provides AI with a deeper understanding of product value propositions tailored to specific customer needs and use cases.
Source: Search Engine Journal
