AI in E-Commerce
AI in E-COMMERCE
From GEO to Agentic AI: Building the foundation for a new era of commerce
The rules of e-commerce are changing. With the announcement of the first open-source protocols, the idea of customers delegating shopping tasks to AI agents has become more tangible. User expectations are rising, demanding hyper-personalized experiences rather than being haunted with “holiday ads” weeks after booking. And economically challenging times driven by rising costs are forcing companies to seek new ways to enhance profitability.
For companies, these changes can be overwhelming.
Making it hard to prioritize what really matters from a neutral perspective, and bringing up questions like:
What does AI mean for e-commerce brands or retailers?
Will Agentic Commerce become a mass‑market phenomenon or remain a niche?
Which opportunities should my company actually pursue, and which are just buzz?
Our answer to current market changes
3 Pillars for AI in E-Commerce
Operational Efficiency
The era of "growth at any cost" has ended. You and many industry leaders might find yourself prioritizing profitability over revenue caused by economically challenging times, shrinking margins and rising costs. The following use cases demonstrate how AI can help you balance costs and revenue in the long term by improving the efficiency of your operations.
AGENTIC COMMERCE
As more searches are happening through AI rather than traditional search engines you may have noticed your SEO numbers dropping – bringing the need of AI visibility into your focus. And also Agentic Commerce doesn’t seem that far on the horizon: the first agentic protocols have been announced, making purchases through agents soon possible. The following use cases demonstrate how you can prepare for the Agentic Commerce era by optimizing your AI visibility and ensuring technical readiness.
AI Commerce Experience
Customer Experience may have been your “North Star” for decades, but AI has raised the standard: Users now expect hyper-personalized interactions based on real-time intent and are accustomed to communicating with AI agents that understand them instantly. At the same time, growing competitive pressure is making offerings increasingly interchangeable, shifting the focus towards loyalty.
The following use cases show how AI can lift your customer's experience up to their expectations and help you speak your user’s language.
WHAT OUR CLIENTS ARE SAYING
The collaboration with Diconium enables us to visualize the potential applications of AI on our commerce platform. The concept study impressively demonstrates how we can create personalized experiences for our customers in real time — an important building block in the future development of uCommerce.
Peter Weisbach
Senior Vice President uCommerce, CANCOM
Diconium's “Generative AI – Content Revolution Workshop” was a game-changer for our marketing community. The hands-on exercises and real-world examples were valuable, and the inclusive and interactive atmosphere made it a collaborative experience.
Stephan Karg
New Experience Marketing, Bosch Rexroth
Why us?
"We didn’t follow the e-commerce trend – we’ve shaped it since 1995. Now we’re building the intelligent commerce systems of tomorrow with AI. By linking data, sales channels, and business processes through AI, we increase operational efficiency, make organizations agent-ready, and deliver hyper-personalized experiences that build loyalty. As your trusted partner, we cut through the hype and deliver AI Commerce that creates value at every touchpoint.”
Jasmin Eichler, Group CEO, Diconium
Start into the new era of commerce with us
If you want a first-time consulting about where commerce is headed, choose our AI Commerce Consulting in the dropdown field. If you want to choose one of our specific use cases select the relevant pillar.
FAQ
What is agentic commerce and how does it differ from traditional e-commerce?
Agentic commerce is an approach to digital commerce in which autonomous AI agents act as proxy decision‑makers for consumers or businesses: they interpret user goals and constraints, continuously monitor options, and research, evaluate, and sometimes execute purchases across channels on the user’s behalf. In contrast, traditional e‑commerce treats shopping as a sequence of discrete, user‑initiated steps—searching, browsing, comparing, and checking out—where humans remain the primary economic actors and systems mainly support them via interfaces, search, and recommendations, rather than delegating the end‑to‑end process to software agents.
What is the difference between agentic commerce and agentic AI?
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.
What are the key components of agentic commerce?
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."
What is an example of agentic commerce?
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.
What are the underlying technologies for agentic commerce?
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.
What data is required to implement AI in an e-commerce store?
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.
Will GEO replace SEO?
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.
What’s the difference between SEO and GEO?
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.
Which companies specialize in AI-driven agentic commerce technology?
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.