AI Implementation Playbook for Retail & E-Commerce

Map every customer touchpoint: pre-sale inquiries, order tracking, returns, reviews, complaints. Document volume per channel (email, chat, phone, social). Calculate cost-per-interaction and average resolution time.

From your top 20 inquiries, identify which can be fully automated (order status, shipping tracking, return policy), which need partial automation (returns processing, exchange requests), and which require human handling (complex complaints, custom orders).

Choose infrastructure: Supabase for database + vector storage, Vercel for hosting, Claude or GPT-4o for language understanding. For most retail businesses, the entire AI stack costs $300–$1,500/month.

Ingest your FAQ, return policy, shipping information, product catalog, and past ticket resolutions into a vector database. This becomes the AI's memory — it can only answer questions it has context for.

Build the first agent: an intake classifier that reads every incoming inquiry, determines category (order status, returns, product question, complaint), urgency level, and routes to the appropriate handler — automated or human.

Connect to Shopify/WooCommerce API. Agent pulls real-time order data, tracking info, and delivery estimates. Handles 'Where's my order?' queries autonomously — typically 25–40% of all support tickets.

Automate returns processing: verify eligibility, generate return labels, track return status, process refunds. Enforce your return policy consistently — no exceptions without manager approval.

Analyze historical sales data, seasonal patterns, and current stock levels. AI predicts reorder points, flags slow-moving inventory, and suggests markdown timing.

Track browsing behavior, purchase history, and engagement patterns. Generate personalized product recommendations, email content, and on-site messaging.

AI agents that monitor customer behavior and trigger proactive engagement: abandoned cart recovery, post-purchase follow-up, review requests, restock notifications, win-back campaigns for lapsed customers.

Connect all agents into a unified system where they share context. The customer service agent knows what the personalization engine recommended. The inventory agent knows what promotions are running.

Track key metrics: cost per interaction, resolution time, CSAT score, automation rate, revenue per customer. A/B test agent responses. Tune escalation thresholds. Optimize for both efficiency and customer satisfaction.

Hand off operations to your team. Train them on monitoring the dashboard, handling escalations, updating the knowledge base, and extending the system for new use cases.