Webinar, August 19, 1pm ET|10am PT
Every ecommerce team is being told that AI will take over search tuning. Agents will adjust relevance, generate synonyms, enrich the catalog, and catch degradation before customers do. That future is close. But agents optimize against an information model. When the taxonomy is inconsistent, attributes are incomplete, or behavioral signals never reach ranking, the automation underperforms and the gains do not hold. There is no AI without IA.
Most organizations already have search metrics. Dashboards exist, zero-result rates are tracked, conversion is reported. The harder questions come next. Are you measuring what predicts whether AI-driven retrieval will work? Do those measurements connect across catalog operations, search configuration, and personalization, or does each function optimize its own numbers? When the data calls for a change, who decides, and how does that change reach production?
Seth Earley and Sanjay Mehta walk through the Ecommerce AI Search Readiness framework: five domains, 18 factors, and a five-level progression from Reactive through Instrumented, Optimized, Autonomous, and Predictive. Using real B2B catalog examples, they cover which measurements matter at each level, the governance practices that turn measurement into action across functions that usually work separately, and how consulting methodology, instrumentation, and agentic tooling combine so that established practices run continuously rather than depending on who is available that month. Recent results include an 8% relevance lift, 30% fewer zero-result failures, and search conversion from 1.8% to over 3%.
Organizations that automate before the underlying model is sound automate their existing problems. Advancing one level at a time, in the right domain order, compounds the gains.
Attendees leave with the full framework and the sequencing logic for deciding which capability to build next. Anyone who wants their own placement against the model can request a readiness review with the EIS team.
For leaders in ecommerce, search, product information, personalization, and digital merchandising who are evaluating what AI-driven search requires.
