I start my day by asking ChatGPT questions about an upcoming trip I am planning, including where I am heading, with whom I am traveling, and the expected weather. ChatGPT provides an itinerary of where to visit, places to eat, and what to bring on my trip.

Five minutes later, I visit several ecommerce websites to find and buy the recommended items. The search box barely fits two keywords, and after several tries, I give up and visit a second site and then a third.

I run out of time and VPN into my client’s network to begin my work. Today, I am reviewing how the customer support teams respond to product information inquiries.

It turns out that only 30 percent of the questions can be answered by reps when they search the CRM. When they don’t find the answer, they scavenger hunt through Sharepoint sites, Slack feeds, and multiple content management systems to find answers.

ChatGPT is a game-changer for search

Compare ChatGPT’s natural language capabilities and the verbose responses against the difficulties customers and employees face when querying for information. Even before ChatGPT skyrocketed to fame, I advised product, technology, and innovation leaders on how AI search is a digital transformation force multiplier and why CIOs should invest in AI search.

But ChatGPT raises the stakes with its capabilities and media attention, and I believe  ChatGPT will energize the consumerization of natural language search.

We may not be able to offer ChatGPT-like experiences to customers and employees today, but you should review search platforms built with large language models and relevance generative answering capabilities to stay ahead of people’s expectations.

Here’s how I unpack people’s ChatGPT-driven expectations and the five AI search capabilities customers and employees will expect in search experiences.

1. Natural language search

Customer and employee journeys start with how they browse results using faceted search capabilities and what they enter into keyword search boxes. I first implemented faceted search back in 1997 while CTO for a SaaS company serving the newspaper industry. While this capability has advanced significantly since then, most websites only support basic keyword searching capabilities.

Customers will expect natural language search capabilities with ecommerce customers asking questions like, “What are the hot t-shirts teenagers are buying for the summer,” or banking customers entering, “What low-risk small-cap funds should I consider if I plan to retire in ten years?”

Natural language search can also improve customer support and employee experiences. SaaS companies should target more customer self-service capabilities through natural language search to reduce customer service costs and improve customer experience. Enterprises can reduce tribal knowledge and improve productivity by enabling employees to ask questions and find the information they need to do their jobs faster.

2. Case classification

Let’s consider the use case where a field or remote worker needs technical help and opens a support ticket to the IT service management desk. Service requests are a broad use case, and enterprises often have HR, legal, facilities, and other support services. Additionally, online forms are the primary way customers ask questions or escalate issues to B2C and B2B businesses.

Such forms often require comprehensive information to help prioritize, route, and service tickets efficiently. But having too many fields on the form is an end-user nightmare, and many will abandon their quest for help when the process appears too daunting. This is a worst-case scenario for service desks because they lose the opportunity to address the customer’s or employee’s needs.

Case classification uses machine learning and natural language processing to bridge the gap. Forms use short open text fields instead of dozens of dropdowns, and the AI displays matching categories and terms for the end-user to validate.

Case classification and natural language search are two ways to reduce engagement friction and increase the likelihood of people asking questions.

3. Respond to search with smart snippets

After people ask questions, what do they want? Answers!

Customers and employees will expect generative AI capabilities and long-form answers in the near future. Today, companies can provide smart snippets that match a resulting document’s content with the end-user’s question.

When an employee asks, “What mental health options does my insurance cover,” they prefer seeing a snippet with the answers and avoiding reading through a long document. Snippets can be incredibly useful for answering policy questions or finding the right people to contact when a field or remote worker has a technical question.

4. Click-worthy recommendations

After a question is answered, most end-users want suggestions on where to go next to learn more, transact, contact an expert, or take another step in their journey.

Commodity search indexes address these needs by ranking search results based on the categories and rules defined by subject matter experts and implemented by software developers. It’s a brute force and inefficient development methodology that’s expensive to maintain and often provides underwhelming results. According to one study, 85 percent report manual tuning is required to improve search result relevancy.

Most users don’t want a stream of results that can be overwhelming and time-consuming to review. They seek click-worthy recommendations using machine learning to predict likely courses of action. These recommendation engines leverage search analytics and context on who the user is, when they are searching, and what they queried to provide their recommendations.

Returning to my example, an employee asking about mental health coverage wants their question answered through a snippet. But they should also be shown links on where to get help and whom to call if they face an emergency.

5. Simple to implement integrations

The first step for enterprise leaders in product, technology, innovation, and data is to recognize that developing natural language search will require centralizing access to content and data resources. Today, the CRMs, CMSs, and other SaaS tools search their siloed data repositories —  but natural language search will need training and connectivity to these primary data sources. Furthermore, the native keyword search boxes built into these platforms will need to be replaced with natural language search interfaces.

Top search platforms make this easy to implement with out-of-the-box data integrations to common platforms so that search snippets and recommendations tap into all of the enterprise’s knowledge. These platforms also include APIs, dev tools, and low-code dev options for easily embedding natural language search interfaces across SaaS and applications.

And enterprises need simplicity, especially when customer and employee expectations increase rapidly, and there’s an opportunity to leapfrog the competition.

This post is brought to you by Coveo.

The views and opinions expressed herein are those of the author and do not necessarily represent the views and opinions of Coveo.