Applied AI, B2B ecommerce, and data-driven processes.
In many companies with an active B2B ecommerce, something very concrete happens: the sales team looks at the digital channel with a certain degree of skepticism.
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Why does your ‘AI-powered’ chatbot fail on technical questions? Not all AI systems do the same things. A practical guide to AI architectures (LLMs, RAG, multi-agent systems, tool use): real capabilities, operational limits, and how to evaluate vendors for complex B2B ecommerce without falling for hype.
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Google and Shopify have launched UCP, an open protocol that allows AI assistants (Gemini, ChatGPT, Alexa) to query merchant inventories, prices, and policies in real time to complete transactions.
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In B2B technical catalogs, critical information already exists but is scattered across PDFs, manuals, and datasheets. When customers, support, and sales can’t access answers quickly, opportunities are lost due to poor accessibility, not lack of expertise.
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In B2B ecommerce with complex products, AI and configurators create value only when integrated with real data, rules and processes—not as a tech showcase, but as operational tools that guide correct choices, reduce errors and speed up purchasing decisions.
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Discover when AI in B2B ecommerce delivers real value and when it’s just marketing hype. In complex catalogs, AI must understand data, compatibility rules, and technical documentation to reduce friction, improve product search, and increase conversions — not just enhance the interface.
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