AI Chat vs. Search: Why Businesses Need Both to Succeed
What makes AI Chat and search equally important for businesses to excel?
AI chat has given businesses an entirely new toolkit to impact customer experience. But despite its advantages, there are other options than an intuitive, intelligent site search. To succeed in this new world, businesses must leverage both shares, Max Davish, senior product manager of Yext.
The introduction of ChatGPT and other competing generative AI tools represents the dawn of a new era. Particularly for those that work in content, creative, coding, and eCommerce, dipping a toe into the capabilities of the technology has been met with equal parts astonishment and uneasiness.
While generative AI has transformational potential, we’re just beginning to scratch the surface of its applications. Some have even speculated that ChatGPT or similar models threaten Google and search writ. It’s hard to know whether that will happen, but it does raise interesting questions about the difference between chat and search. If you squint, they seem awfully similar; both involve a user typing queries into a box and getting answers in return. Google and Bing are increasingly blending the two user experiences — creating chatbots that can use search and search results that you can chat with — further blurs lines of distinction.
By understanding where generative AI excels and where it lacks and contrasting that with the inherent strengths of search, we can better chart a path forward for both. Businesses will have utility for both search and AI chat, and harnessing the power of each will create a better customer experience overall.
Analyzing the Role of AI Chat
The advancements made in large language models has produced some truly revolutionary chatbots. By processing queries based on natural language, tools like ChatGPT replicate the experience of talking to a person. And, like talking to a person, the user making the queries gives generative AI the benefit of time. Inputs can be longer, with more detailed queries soliciting specific information. As a result, answers are much longer and more detailed, tailored to the specificity of the query.
Perhaps most importantly, this human-like interaction means generative AI also considers history. A user can submit multiple follow-ups to dig deeper on a topic. In the long term, repeat visitors to a website that engage with the chat can pick up where they left off and have a continuous conversation that pulls from past interactions.
This is more of an ephemeral benefit, but chat is also new. For users who have lived on search for decades now, the ability to have a focused conversation with a seemingly infinite repository of knowledge is exciting and engaging.
Unfortunately, that infinite repository is, well, not as infinite as it could be. Generative AI doesn’t include information after a certain date, sometimes, it “hallucinates” to provide inaccurate information, and it doesn’t consider anything local or specific to a user. For example, you couldn’t ask ChatGPT for a local restaurant open near you. Or, as Sam Altman, CEO of OpenAI puts it, ChatGPT is a “reasoning engine” rather than a database. It isn’t intended to be perfect as a repository of information (yet).
Still, for all its pain points, anyone who has used generative AI can see how powerful it is, particularly for research and idea generation.
See More: How to Use Generative AI in Marketing Effectively
Contrasting AI Chat to Search
Search versus AI chat’s main strength and weakness is that it generates nothing. Search is focused on providing results based on what has been indexed in the search’s database. Because of this more limited scope, search is much better for browsing experiences. eCommerce sites need search to deliver results based on business logic, pulling from their specific database of information: product information, how-tos, internal CMS content, and more.
Search is also far less expensive than AI chat. The processing power needed to deploy LLMs can run far more than the comparatively light work needed to integrate a better search interface. In the eCommerce arena, more often than not, search is more than sufficient to give customers the answers they need and can be accomplished virtually for free. That said, not all search is created equal, and bridging the gap between the answers-focused nature of generative AI and the site-specific results-focused reality of site search can be a powerful combination.
AI Chat vs. Search: Where Should Businesses Focus?
The answer to this question is simple: both! As we’ve seen, both AI chat and search have their specific utility, and both work to enhance the customer experience.
AI chat enables customers to have natural language conversations with a virtual agent, which can get them to answers they need to questions surrounding order status, product-specific questions, and things of that nature. Moreover, conversational AI has the luxury of remembering past chats, which can enhance customer engagement and build stronger retention.
Search is still the engine that drives the customer journey and engagement, particularly for anonymous or first-time site visitors. With search, a business can insert calls to action, provide local results that can connect a customer to the in-store experience more efficiently, and much more.
The search experience can also be enhanced for customers with accounts with a site. By providing a login, customers enable a website to remember their search history and purchase habits, which can provide more personalized and enriched interactions with a brand. While not necessarily unique to search (AI chat can also have this capability), the level of personalization offered by logged-in search is an excellent value add for the relative investment made in a better search function.
See More: Search: The Last Pillar of Digital Transformation
How Search Can Borrow From AI Chat to Offer the Best of Both Worlds
At the end of the day, while conversational AI has utility for businesses (particularly for chat and customer support), most ecommerce sites will continue to rely on search for product discovery and findability. But search can and should be better, taking cues from what makes AI chat successful.
By integrating natural language processing into search, customers can pose more detailed questions in their search queries. From there, a more advanced search will work off of a search index — a more sophisticated way of organizing information that pulls from a wide range of real-world entities and relationships — to deliver customers more exact answers, not just results.
The key difference between AI chat and search is that AI is focused on answering queries, whereas earlier iterations of search dealt in results, typically a loosely organized list of links to products or articles relevant to keywords.
Using natural language processing and search indices — pulling from all sources of information at a company’s disposal (product pages, user manuals, learning content and more)—businesses can deliver answers to search queries in context without clicking links to find solutions.
It’s an exciting time for businesses looking to leverage AI, but more importantly, it’s an exciting time for businesses looking to build better search based on lessons learned from the AI revolution.
How are you leveraging AI chat to boost your business? Share with us on Facebook, X, and LinkedIn. We’d love to hear from you!
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