While the industry buzzes about sophisticated "agentic AI," the most common real-world applications in e-commerce are far more basic. Retailers are primarily using AI for task-oriented work like optimizing SKU description pages, highlighting a significant gap between current capabilities and future hype.
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Anticipating a shift to "agentic commerce," SharkNinja is actively re-optimizing its e-commerce site for Large Language Models. The company believes what drives human conversion today may not rank highest in AI-driven search and expects commerce via AI platforms to be meaningful by Christmas 2025.
The next frontier in e-commerce is inter-company AI collaboration. A brand's AI will detect an opportunity, like a needed digital shelf update, and generate a recommendation. After human approval, the request is sent directly to the retailer's AI agent for automatic execution.
Despite marketing hype, current AI agents are not fully autonomous and cannot replace an entire human job. They excel at executing a sequence of defined tasks to achieve a specific goal, like research, but lack the complex reasoning for broader job functions. True job replacement is likely still years away.
The future of AI in e-commerce isn't just better search results like Amazon's Rufus. The shift will be towards proactive, conversational agents that handle the entire purchasing process for routine items, mirroring the "one-click" convenience of the original Amazon Dash button but with greater intelligence.
Forward-thinking companies like Shark Ninja are not waiting for AI-driven "agentic commerce" to mature. They are actively optimizing their direct-to-consumer websites for Large Language Models (LLMs) like ChatGPT, anticipating that what drives conversion today may not rank well in future AI-powered searches.
Marketers observe a significant disconnect between the sophisticated AI workflows discussed online and the more basic applications happening inside companies, even at the CMO level. This highlights the need for practical, real-world examples over theoretical hype.
As AI agents automate day-to-day e-commerce optimization, the primary role for humans evolves. Core competencies will shift from data analysis and execution to high-level decision-making and managing the complex, collaborative joint business planning process with retail partners.
Early AI adoption focused on idea generation and copy help. The next wave involves autonomous AI agents that execute tasks like creating webpages, optimizing campaigns, and auto-building reports, moving AI from a thought-partner to an active tool that 'does' the work.
There's a significant gap where marketers leverage AI for brainstorming and copy help, but few use autonomous AI agents to execute tasks like creating webpages, optimizing campaigns, or building reports.
There is a significant gap between how companies talk about using AI and their actual implementation. While many leaders claim to be "AI-driven," real-world application is often limited to superficial tasks like social media content, not deep, transformative integration into core business processes.
