Retail & E-commerce AI: From Recommendations to Dynamic Pricing

Retail AI often feels low risk because many systems focus on conversion and efficiency. But certain use cases — especially those that shape access, pricing, or user treatment — can introduce compliance exposure quickly.

Recommendation engines and chatbots usually fall into lower-risk categories, but transparency still matters. Customers should be informed when they interact with AI systems or consume AI-generated content.

Dynamic pricing deserves closer review. If automated pricing logic creates discriminatory or opaque outcomes, legal and reputational risk increases. Teams should define guardrails, monitor for anomalies, and keep decision logs for critical pricing pathways.

Fraud controls, customer segmentation, and behavioral scoring can also cross into sensitive territory depending on how outputs are used. If AI decisions materially affect customer access or treatment, strengthen oversight and documentation.

SMEs should start with a practical inventory, classify each use case by impact, and implement proportional controls. Doing this early supports safer experimentation while preserving growth velocity.