The need for fast access to relevant data or information is growing across industries and in different application areas. This drives the need for intelligent search that makes workers more productive, provides personalized help and recommendations to customers, and improves the employee or customer experience. Specifically, what’s needed is an intelligent search solution that uses artificial intelligence to prompt users during queries, anticipate needs, and proactively present pertinent information.

And while there has always been a need for intelligent search, the demand is even higher and more critical now due to general changes in the way businesses operate. Factors driving this new need for intelligent search include the explosive growth of digital assets, COVID-related impacts in the way people work, and higher customer expectation levels due to pervasive, Amazon-like search and recommendation capabilities in modern apps.

Additional factors driving the need for intelligent search include:

• Many workers are not in the office, making it harder to share information and ask their peers questions,
• Customer service agents are overworked, contact centers are understaffed, and customer satisfaction is at its lowest level in 15 years,
• eCommerce customers are taking fewer trips to brick-and-mortar stores to acquaint themselves with products before shopping online.

In all cases, intelligent search can improve the situation.

Why AI-based intelligent search is needed

A Coveo survey of more than 600 tech practitioners responsible for search architectures quantified the scope of the problem many businesses face today. More than eight out of 10 businesses (81%) say the importance of search has increased over the past year—85% increased their investment in search. Yet only 13% of companies believe they are excelling in search. What’s more, all organizations candidly admit they struggle (99%) to deliver truly relevant enterprise search results to their users and customers.

What’s needed is a search solution that uses AI to provide more personalized, predictive information. In particular, people are accustomed to things like Amazon’s recommendation engine and would benefit from similar capabilities in their work or website visits.

Traditional search methods that rely on keywords, metadata, indexing of siloed datasets, and other techniques fall short. Today’s enterprise search engines use the index as the starting point for surfacing insights. But beyond returning information based on the degree to which an entered term matches a predefined keyword stored in an index, modern search engines bring other data and technology to the party. They use text analytics and machine learning technology to predict what the user is trying to find. Unfortunately, lack of talent and technical challenges are inhibiting the use of intelligence search.

Almost all (95%) organizations can’t address bad search functionality because they can’t find people who know how to fix the problems. The talent shortages stretch across multiple disciplines. AI leads the way, as 59% of companies struggle to find AI talent. More than half are also on the hunt for data analysis and business intelligence expertise, 44% need search engine result ranking experts, and 40% hope to find talent with data ingestion knowledge.

In fact, the lack of AI talent is one reason most organizations are not leveraging AI as part of their search deployments. While 82% have AI as part of their search stack, only 15% have implemented it. This is even though almost everyone (93% of organizations) believes the future of search involves AI.

Another issue is complexity. Most companies have different search applications deployed to address specific search challenges. By operating in silos, these search applications do not benefit from the collective intelligence based on drawing from a unified search set. Nearly 30% reported dealing with more than 10 enterprise search applications within their enterprise.

Teaming with a technology partner

To address internal shortcomings, almost three out of four organizations rely on external partners to help them with search. That’s where Coveo can help.

It facilitates the collection and classification of both structured and unstructured data from across all data sources, also known as federated search, and returns it to users searching for it. With machine learning, all that information and more can be used to make data-driven predictions and decisions without manual intervention.

Users might not always know the best words to search, but the Coveo intelligent search platform understands language and behavior. As a result, Coveo’s AI-powered search engine is designed to detect intent so that user queries yield relevant results automatically.

The Coveo SaaS-native, multi-tenant platform injects search, recommendations, and personalization solutions into digital experiences. There are solutions for eCommerce, service, website, and workplace applications. The solutions are designed to provide tangible value by helping drive revenue growth, reduce customer support costs, increase customer satisfaction and digital engagement, and improve employee proficiency and satisfaction.

For example, Medallia, which provides customer experience management and employee experience management software to hospitality, retail, financial services, high-tech, and business-to-business companies, found that by using Coveo, the company’s customer service agents could handle 34% more calls on their own, leading to significant cost savings.

And Caleres, a portfolio footwear company that owns and operates various brands, such as Famous Footwear and Allen Edmonds, uses Coveo to provide better and smarter recommendations on its eCommerce sites.

As these and other cases demonstrate and the survey finds, AI has transformed enterprise search into intelligent search, and it’s having a significant cross-industry impact on customer experience, and subsequently, ROI. The Coveo intelligent search solution helps:

• Brands and retailers respond to a surge in online shopping behavior,
• Customer support organizations deal with escalating case loads, self-service requirements and chat bots.
• A suddenly-remote workforce access knowledge in the flow of work.

To learn more about AI-powered intelligent search, read Coveo’s new report here.