Generative AI in Digital Commerce Strategies

McKinsey & Company: "𝗧𝗵𝗮𝘁'𝘀 𝗛𝗼𝘄 𝗖𝗜𝗢𝘀 𝗮𝗻𝗱 𝗖𝗧𝗢𝘀 𝗖𝗮𝗻 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗠𝗮𝘅𝗶𝗺𝘂𝗺 𝗜𝗺𝗽𝗮𝗰𝘁" This McKinsey & Co report highlights how #GenAI, when deeply integrated, can revolutionize business operations. I took a stab at CPG eCommerce use case below, and thriving with generative #AI isn’t about just deploying a model; it demands a deep integration into your enterprise stack. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 𝗠𝘂𝗹𝘁𝗶-𝗹𝗮𝘆𝗲𝗿𝗲𝗱 𝗚𝗲𝗻𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗖𝗣𝗚⬇️ 𝟭. 𝗖𝘂𝘁𝗼𝗺𝗲𝗿 𝗟𝗮𝘆𝗲𝗿: → The user logs in, browses personalized product recommendations, and either finalizes a purchase or escalates to a support agent—all seamlessly without grasping the backend processes. This layer prioritizes trust, rapid responses, and tailored suggestions like skincare routines based on user preferences. 📍Business Impact: Boosts customer satisfaction and loyalty, increasing conversion rates by up to 40% through hyper-personalized interactions that drive repeat purchases. 𝟮. 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 → Oversees user engagement: - Chatbot launches and steers the dialogue, suggesting complementary products - Escalation to a human agent activates if AI can't fully address complex queries, like ingredient allergies 📍Business Impact: Enhances efficiency in consumer support, reducing resolution times and operational costs while minimizing cart abandonment in #eCommerce flows. 𝟯. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗟𝗮𝘆𝗲𝗿: → Performs smart actions using context: - Retrieves user profile data - Validates promotions and inventory - Creates customized options, such as virtual try-ons - Advances the process, like adding to the cart 📍Business Impact: Accelerates innovation in product discovery, lifting marketing productivity by 10-40% and enabling dynamic pricing that optimizes revenue in competitive #FMCG markets. 𝟰. 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗔𝗽𝗽 𝗟𝗮𝘆𝗲𝗿 → Links AI to essential enterprise platforms: - User verification and access management - Promotion rules and order processing - Support agent routing algorithms 📍Business Impact: Streamlines supply chain and sales workflows, cutting technical debt by 20-40% and improving inventory accuracy to reduce stockouts and overstock costs. 𝟱. 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗲𝗿 → Delivers instant contextual details: - Consumer profiles - Purchase records - Promotion guidelines - Support team directories 📍Business Impact: Powers precise AI insights, enhancing demand forecasting and personalization to minimize waste in perishable goods while boosting overall data-driven decision-making. 𝟲. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗟𝗮𝘆𝗲𝗿 → Supports scalability, efficiency, and oversight: - Cloud or hybrid setups - AI model coordination - High-speed response handling - Privacy and compliance controls 📍Business Impact: Ensures robust, secure operations at scale, unlocking value by optimizing resource use, slashing IT ops costs.

From Varshika Prasanna at ARK Investment Management LLC: The proliferation of AI agents is likely to disrupt online commerce massively. On July 9, Perplexity launched Comet to browse and buy products on behalf of users, and last week Shopify began to power OpenAI’s in-app checkout. Now that traffic associated with generative AI is surging across retail sites, the era of agentic commerce is upon us. Model Context Protocol (MCP) is a new internet layer powering this shift. Much like https standardized secure communication between browsers and websites in the early days of the internet, MCP is creating a framework for AI agent interaction with APIs, databases, and user interfaces, equipping agents with the context necessary to understand, navigate, and transact across the web precisely and reliably. MCP’s impact on online commerce could be profound. Today, retailers manage a messy tangle of one-off integrations with sales channels like social media, ad networks, and marketplaces. MCP servers simplify those processes by allowing retailers to connect their back-end systems, providing a clearinghouse for commercial data like pricing, availability, and delivery windows. As a result, large language models (LLMs) can query and interpret real-time data and relay information to consumers through interfaces like voice assistants, search agents, smart TVs, and smart glasses, as depicted below. Importantly, this shift is creating a new paradigm of Agent-Oriented Optimization (AOO). In the internet era, search engine optimization (SEO) was necessary for relevance online. In the agentic era, MCP compatibility is key. AI agents will surface only products that they can access and understand. Retailers without MCP-compatible data could be out of luck.

Delving Deeper into Our Shopify App's AI In our product journey, I want to take a closer look at the #AI components included with our #Shopify App integration – no hype, just the facts. 🤖 Understanding AI Challenges: - AI isn't the end goal; it's a tool to enhance productivity. - We're all about making AI a practical solution, not just an add-on. - #GenerativeAI often lacks a vital feedback loop, which is puzzling. 💡 How We Addressed These Challenges: - Our AI composer is designed to create product titles and descriptions, informed by the collective wisdom of the crowd and the preferences of brand shoppers. - Our product page version creation process simplifies decision-making, helping users choose the right path, and closing the gap on actionable intelligence. - Analytics 📊 provide insights into the best-performing version, measured by revenue, add-to-cart rates, and bounce rates compared to the default page. No frills, no fluff. Experience it yourself – we're offering free versions and trials for Shopify merchants. #Prodport #AI #Shopify #ProductManagement #Ecommerce