From Keywords to Conversations: How eCommerce Will Be Reshaped by Conversational Agentic Commerce
Part I of the Agentic Commerce Series
The way buyers find, evaluate, and buy products is undergoing the most significant transformation since eCommerce started. The familiar pattern of someone typing keywords into a search bar, then using filters and facets to page through results is giving way to something fundamentally different: conversational, context-aware and agent driven commerce.
As buyers evolve, so too must eCommerce websites. But this transition is far less daunting than it appears. The path to agent-driven commerce begins with conversational AI search: a convergence of large language models, intelligent agents, and robust search infrastructure that together form the foundation of next-generation eCommerce experiences.
- Large language models are capable of understanding complex context aware requirements, which can be used to provide answers to intent-based queries, like questions about a product or its application. This is a ‘ChatGPT’ like experience.
- Enterprise-grade agents have the proper guardrails and rules, such as merchandising rules and entitlements, to drive fluid conversations; as well as the ‘agency’ to perform actions like putting products in your cart, checking out or even reaching out to other systems of record, like order history, to tell customers what they’ve purchased in the past.
- Traditional RAG (retrieval augmented generation) search infrastructure understands your product catalog, product attributes and specifications, so that the right products are showing up in the first place
The path forward is less disruptive than it sounds. A shopping assistant isn't a platform replacement or a new feature. It’s a new paradigm shift.
The convergence of these three things that work together: large language models, enterprise-grade AI agents and a search infrastructure create a new buying journey.
The shift to this new buying journey isn’t a future roadmap item anymore. It’s now.
The Shift from Keywords to Intent-Based Search
For two decades, search in commerce meant one thing: keywords. A consumer who wanted a white tshirt, or a buyer who needed a stainless steel compression fitting would type fragments of that requirement, like ‘white t’ or “compression fitting” and hope the catalog surfaced something useful. The burden of translation from human intent to machine-readable query fell entirely on the buyer and the number of keywords they typed into the search bar.
Natural Language, Intent-Based Search, Changed That
That model no longer works as buyers use websites that understand natural language and user intent. Buyers have grown accustomed to these conversational interfaces and asking complex questions, expecting answers. They are bringing those expectations to work. In a 2024 survey of enterprise procurement professionals, 72% reported that they now prefer to describe their requirements in natural language and expect the system to return precise, relevant results without requiring keyword refinement.
Modern AI powered search engines, powered by LLMs and multi-model models, can understand a sentence like “need a non-sparking muffler for a YZ250” or “a recommendation for a power saw to help me build a shed in my backyard” and return a curated, ranked list of products.
In this new paradigm, that is not a complex question when the system has guardrails in place to use all of the product specs, user buying history and has an LLM to provide real world context.
Why This Matters to You
B2C, or Consumer eCommerce, benefited from natural language search earlier because product catalogs are simpler and queries are shorter. While B2B is slower to adopt because products are more technical and decision processes take longer. However, in both cases, a shopping assistant has access to more technology to handle:
- Catalogs that contain thousands of SKUs with overlapping attributes
- Specifications that can be involved, even technical and context-dependent
- A wrong product recommendation that could mean upset customers, returns or even a compliance failure
These complexities mean that a companies that adopts intent-based search creates an enormous advantage, even an unfair advantage over competitors. Buyers will return to brands that understand them without friction, and they will recommend it to friends and colleagues.
The Path to Conversational Agentic Commerce
The window for proactive investment is now. Organizations that push this down the priority list risk not having the infrastructure compete effectively. The following details the pathway towards a website that has all of these elements, and ultimately offers agentic commerce.
Step 1: Develop An Agentic Commerce Roadmap
Define your organization’s target state for AI-ready commerce infrastructure and build a phased roadmap to reach it. This roadmap should be board-level visibility - the organizations leading in this space are treating it as infrastructure investment equivalent to their ERP or e-commerce platform decisions.
Step 2: Add Structured Product Data
Implement a plan to add data from other platforms, such as your ERP or PIM. This will give an AI powered search platform the ability to access structured data that can then be used in an agentic ecommerce experience, such as order history.
Step 3: Invest in Catalog Data Quality
Data quality is the foundation that all other capabilities depend on. An agent that queries a catalog with incomplete specifications, missing certifications, or inaccurate inventory data will return poor results and will not return to that supplier. Catalog data quality is now a revenue protection imperative.
Step 4: Upgrade to Intent-Aware Search
Replace or augment legacy keyword search with a semantic search engine capable of understanding natural language queries, domain-specific vocabulary, and attribute-based filtering. This investment pays for itself in improved human-buyer experience while simultaneously preparing the infrastructure for agent-driven traffic.
- Evaluate AI-powered, vector search platform and perform test searches to see how well they do against your product catalog
- Access your product data against new search platforms to determine if they can handle product data gaps and inconsistencies in naming, descriptions and attributes
- Implement analytics for your search with query-level analytics to identify where buyers are failing to find what they need
Step 5: Implement a Conversational Search Experience
Plan to launch a conversational interface that can handle natural language queries, remember context within a session, and surface relevant products from complex product specifications. Consider launching this internally, for your own staff, or externally for customers as well.
This interface will serve two functions simultaneously: improving human buyer experience and providing the foundation for full agentic commerce.
Conclusion
The shift from keyword search to intent-based, agent-intermediated commerce is not a distant scenario. It’s happening now. The organizations that will define the market of 2027 and beyond are making infrastructure investments today.
The buyers driving that spending will disproportionately favor companies that meet them where they are: in natural language, with accurate data, at scale.
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