AI-Assisted Search is Changing How Companies Find Vendors

Anyone who has recently performed an online search for a specific service—say, “marketing fulfillment”—can’t help but notice that AI has fundamentally changed the experience.

Rather than just getting a list of names and links, users are being presented with a comprehensive analysis of the request, including a thoughtful evaluation of various qualified vendors that meet their unique requirements.

It’s quite a change from the old methods of finding information online.  The industry is rapidly moving away from simple search engines to more sophisticated answer engines, and it’s all thanks to artificial intelligence.

In this blog, we’ll examine AI-assisted search, describe what makes it unique, and where it may fall short.  Next week we’ll explore best practices for using answer engines to find the service vendor you’re looking for. 

From Keywords to Intentional Search

In the recent past, traditional search using a browser such as Google Chrome relied heavily, if not exclusively, on keywords—for instance, “marketing fulfillment vendors.”  The success of the search depended on users’ ability to enter the right phrasing; if they are off a bit, or if the phrase is somewhat vague or broad, the results aren’t nearly as valuable.

Searches powered by AI agents like ChatGPT, Microsoft Copilot, or Perplexity AI, on the other hand, understand intent and context.  This means that, rather than just entering keywords, you can ask complex or conversational questions such as “which marketing fulfillment vendors offer print, shipping, storage, event management, and storage services?”  AI then interprets what you mean, not just what you type, providing robust answers that offer a deep dive into the topic, including expert analysis.

To refine the response, follow-up questions (such as, “which of these vendors have an international presence”) can be asked, narrowing the results.  It effectively does exhaustive preliminary research, saving you considerable time and providing more actionable answers.

Rather than getting pages and pages of links that you must click, scan, and compare manually, the AI answer engine model finds and aggregates dozens of sources, summarizes the findings, and provides a direct answer or recommendations.  

Of course, there are risks—for instance, using the answer engine approach increases reliance on AI’s judgment.  However, the dramatic time savings provide sufficient opportunities to conduct a more focused evaluation of a short list of prospects.

Adapting to the New Model

Search engine optimization (SEO) hasn’t simply transitioned to answer engine optimization (AEO) organically. Companies have had to adapt, pivoting from developing keyword lists to restructuring their content to facilitate better AI machine learning.

While this has improved the ability of searchers to find vendors, especially in the early stages of discovery and evaluation, it isn’t perfect. However, the benefits far outweigh the downsides, and as AI technology improves, any disadvantages should fade into the background.

The immediate improvements include the following:

Faster Market Scanning Provides More Actionable Data

Rather than searching for, say, the “top print fulfillment companies in California,” which would simply produce a list of dozens of sites, AI allows you to pose the query as a focused question: “Compare the top marketing print fulfillment vendors in California.” The answer optimization engine will quickly identify the relevant vendors, summarize their respective capabilities, and highlight the pros and cons of each. You can further refine the answer by asking about budgets or specific locations such as Southern California vs. the Bay Area. A search that would normally take you hours or even days is reduced to minutes.

Discovery Produces Results That Exceed SEO

Traditional keyword searches tend to favor bigger and better-known brands, as well as companies that have developed a strong SEO presence. AI searches can cut through that clutter, revealing lesser-known niche providers, regionally strong vendors, and other specialists that offer potentially greater value but do not rank very high in SEO results. Not only does this create a more level playing field for all vendors, both small and large, it helps you find those diamonds in the rough that can deliver just the right level of services for your business.

Natural Language Searches Yield Better Results

Rather than conduct multiple searches on vague or restrictive keywords, companies can now search for vendors using natural language that reflects their real needs. For instance, instead of conducting three separate searches—one on “kitting and assembly,” a second on “warehousing,” and a third on “same-day shipping”—you can now submit a single query requesting “vendors that can handle kitting and assembly, warehousing, and same-day shipping.” The AI answer engine will interpret the complex requests you’ve laid out and provide a comprehensive answer without requiring precise targeted keywords. Adding in other requirements, such as “sustainable packaging” or “API integration” or “west coast distribution” will result in equally impressive and detailed results.

Searches Provide Early Due Diligence Support

As discussed, AI-assisted searches don’t just provide lists of links. They assemble, aggregate, and assess available reviews, case studies, certifications, articles, and anything else that impacts industry reputation, providing an objective overview of the vendor and their capabilities. While it may not necessarily be a replacement for formal vetting, these search results certainly accelerate and inform the RFP research process.