Conversational shopping assistant
A use case in Retail and e-commerce, seen as a chain of named AI tasks.
What it is. Conversational shopping assistant is an AI use case in Retail and e-commerce. Its input is a mix of data types, and underneath it is a short chain of named tasks: it finds the most relevant material, then answers a question from that material, then recommends the best matches. That makes it an agent: several tasks with a generative step are usually built as an agent, which runs the steps in a loop, uses tools, and decides what to do next.
How you tell it is working. Each task is measured, and fails, differently. The weakest step sets the ceiling for the whole chain, so concentrate testing there.
- Retrieve / search (finding & recommending): whether the right item shows up near the top of the results. Watch: if the right item is not retrieved, everything after it is wrong.
- Answer a question (language): whether the answer is correct and backed by the source.
- Recommend (finding & recommending): whether the top suggestions are the ones people actually want. Watch: popularity bias and cold start.
How much it decides. As drawn, it sits at the agent level: plans, uses tools, and acts in a loop; it does things. The same use case can be built with more or less autonomy, and more autonomy means more reach and more to check.
Judging the materiality. This page does not assign a risk tier. How material it is depends on your deployment: who it affects, whether a person genuinely re-decides or only approves, whether the action can be reversed, and your regulatory and fairness exposure. Rate it on impact, complexity and reliance as the AIRG expects, and let the controls follow. See use-case risk and materiality.
Related: the interactive AI use-case explorer, AI use cases by industry, and the AI risks taxonomy.