AI shopping assistants are software tools that help retailers guide discovery, answer product questions, personalize offers, and shorten the path to purchase across web, mobile and conversational channels. By 2026 retailers are adopting a mix of capabilities — conversational commerce and embedded payments (e.g., Klarna integrated into ChatGPT), enterprise retail AI platforms (e.g., AWS Retail AI Assistant), visual commerce and virtual try‑on, and review‑driven decision support — to meet rising consumer expectations for relevance and convenience. Key categories and representative tools: LookBook AI provides avatar‑based virtual styling and try‑on for personalized outfit discovery; Veesual generates on‑model visuals and mix‑and‑match “shop the look” experiences for fashion commerce; ShopGuru analyzes Amazon product pages and reviews to surface concise, product‑specific answers; LandLyze applies AI to landing pages to identify conversion blockers; Katalis AI combines autonomous agents with human strategists to automate marketing tactics; Brevo offers integrated multichannel marketing, CRM and automation to orchestrate messages. Retailers evaluate assistants by accuracy, latency, integration complexity, privacy/compliance, and measurable lift in conversion or AOV. Common implementation patterns pair conversational interfaces and embedded payments for faster checkout, visual try‑on for lower returns in apparel, and review‑analysis or landing‑page optimization for higher trust and conversion. Key tradeoffs include cost, data governance, and model reliability. Selecting tools typically means combining specialized vendors (visual commerce, review summarizers) with platform-level assistants and marketing stacks to deliver consistent, measurable omnichannel experiences.