Every retailer wants AI agents, and very few have data an agent can use. Abhi Sachdeva, co-founder and CTO of EKYAM, has spent 18 of his 24 years in technology inside retail, running tech at 1-800-Flowers, Tory Burch and QVC. He joins Rick to explain what data readiness requires and where retailers keep getting it wrong.
A data lake stores rows. It doesn't tell an agent what those rows mean or which system to trust, so the agent picks up the first answer it finds and presents it with full confidence. Ask a retailer for its customer count or gross margin and different teams will often give different numbers. People know which dashboard to believe. Agents don't. Abhi walks through what he thinks a semantic layer needs to include, and the piece he says most companies leave out, which is the history of how the data changed over time.
Rick and Abhi also get into buy versus build. Abhi's view is that retailers should never build the control layer (governance, gateways, LLM monitoring, cost tracking) because it changes every time a new model ships. What retailers should own are their definitions and workflows. He thinks the big consulting firms are right to push master data cleanup and operating model work, and wrong when they spend 12 to 18 months building a bespoke agent data layer the client then has to maintain alone.
On ownership, Abhi puts meaning with the business (merchandising, finance, supply chain), enforcement with technology, and a small product data team between them. By his estimate, about 5 in 100 retailers he talks to have that team in place. He also explains what happens when a marketing manager starts uploading customer CSVs to a chatbot on their own, and why the answer is governance rather than a ban.
Abhi's company is built on the bet that data readiness is never finished. Weigh that accordingly. The last decade of retailers building and rebuilding their own checkouts suggests he has a point.
This episode is sponsored by EKYAM.
#AIAgents #Retail #Ecommerce
