AI Commerce Consulting for E-Commerce

Agentic AI refers to a broad class of autonomous AI systems that can perceive their environment, reason about goals, plan multi-step actions, and execute tasks independently across diverse domains—such as software development, research, or personal assistance—often using tools like APIs or code interpreters.

Agentic commerce is a specific application of agentic AI to retail and e-commerce, where these agents specialize in shopping-related workflows: interpreting purchase intent, researching products across retailers, comparing options against user constraints (price, delivery, specs), negotiating deals, and completing transactions on behalf of the user.

The Agentic Commerce Protocol (ACP) is an open technical standard that enables secure, standardized communication between AI shopping agents, buyers, and merchants to complete purchases programmatically—without users leaving the AI interface (e.g., ChatGPT) for traditional website checkouts.

Developed by Stripe and OpenAI, ACP defines API endpoints and data formats for key steps: product discovery via feeds, creating/updating checkout sessions, handling tokenized payments, and confirming orders, while keeping the merchant as the official "merchant of record."

A real-world example is Stripe-powered shopping in Microsoft Copilot: Users in the US can ask Copilot for products from retailers like Urban Outfitters, Anthropologie, or Etsy, and the AI agent searches catalogs, assembles bundles, handles checkout and payments via Stripe—all within the chat interface, without redirecting to store websites.

Agentic commerce is powered by large language models (LLMs) that enable AI agents to interpret natural language intent and plan complex shopping workflows; multi-agent orchestration frameworks that coordinate specialized agents for tasks such as product search, review analysis, and price negotiation; standardized protocols including the Agentic Commerce Protocol (ACP), Model Context Protocol (MCP), and Agent Payments Protocol (AP2) that facilitate secure agent-to-merchant communication; and commerce backend APIs providing real-time access to product catalogs, inventory, pricing, CRM data, payment gateways, and logistics systems.

Implementing AI in an e-commerce store requires structured product data (descriptions, categories, images, pricing, inventory levels), behavioral data tracking customer interactions (clicks, searches, views, cart abandonment, session times), transaction history (orders, payments, returns, customer lifetime value), and contextual data (referral sources, device types, seasonality, demographics).

Real-time data pipelines for ongoing model updates and historical datasets for initial training are essential, alongside clean, consistent labeling to ensure accurate predictions for recommendations, personalization, dynamic pricing, and fraud detection.

No, GEO (Generative Engine Optimization) will not replace SEO. GEO optimizes content for inclusion and citation in AI-generated answers from tools like ChatGPT, Perplexity, or Google AI Overviews, focusing on semantic clarity, structured data (schema), authoritative sources, and conversational relevance—often resulting in zero-click experiences where users get answers without visiting sites.

SEO, by contrast, targets traditional search engines (Google, Bing) to rank pages high in link-based results, driving clicks through keywords, backlinks, technical optimization, and on-page elements.

SEO (Search Engine Optimization) optimizes content to rank highly in traditional search engine results pages (SERPs) like Google or Bing, driving clicks through keyword targeting, backlinks, technical signals, and on-page elements to attract users to websites.

GEO (Generative Engine Optimization), by contrast, optimizes for AI-driven platforms like ChatGPT, Perplexity, or Google AI Overviews, aiming for direct inclusion and citation in synthesized, conversational answers—often without requiring site visits—via structured data (schema), authoritative sources, clear direct answers, and conversational phrasing.

Diconium, a digital business transformation partner with more than 30 years of experience in e-commerce, helps you either prepare your infrastructure for agentic commerce (enabling compatibility with AI agents like ChatGPT or Gemini) or implement your own agent interface for a dialogue-based shopping experience.

We assess relevant use cases against business priorities, expected value, implementation effort, and strategic relevance. Our approach combines readiness assessment, use-case prioritization, business case evaluation, and roadmap development to guide investment decisions. 

An AI operating model defines the roles, processes, and governance needed to connect AI strategy with execution. In commerce, it provides an organizational foundation for integrating AI into customer-facing experiences and operational workflows.