Over the past 10+ years, IDC has periodically surveyed organizations about the challenges and benefits of enterprise search and knowledge discovery. Survey questions focus on some of the untapped value represented by “hidden” or unanalyzed data. We poll knowledge workers on how much time they lose on a weekly basis to search-related activities like looking for information that they never actually find, searching across multiple data sources for a single piece of information, or connecting the dots between multiple pieces of information to arrive at an insight or answer.
In aggregate across 2013, 2015, 2019, and 2023, the data from these questions shows that legacy search engines that have not significantly advanced in the last five years have struggled to keep up with the increasing volume and variety of organizational data. These legacy engines typically use traditional keyword search and brittle, rules-based systems instead of adaptive, intelligent, semantic, and hybrid search. As a result, organizations using these tools must cope with poor relevancy ranking, outdated or broken query understanding, and basic findability challenges.
On the other hand, the research shows that search systems that kept up with AI innovations have advanced markedly in the past five years. AI has brought significantly better capabilities for searching, translating, and combining information. These capabilities include:
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Increasingly sophisticated natural language understanding, allowing more users to ask questions in more natural language
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ML-based relevancy ranking, improving the order in which results are displayed and enabling personalization as well as popularity-based reranking
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Semantic/vector search, further enhancing natural language search capabilities by expanding the semantic understanding of search systems beyond exact keyword matching. The combination of keyword and vector search (a.k.a. hybrid search) is especially popular for ecommerce search use cases due to the ability to find both exact SKUs/product names as well as recommended or similar products, improving conversion, cross-sell, and upsell
Using modern search meant that employees spent 12 fewer hours a week in time lost to search-related activities in 2023 compared to 2019 — a significant productivity improvement.1 Meanwhile, customers are also more satisfied and more willing to spend. Retail organizations that adopted modern AI-powered search reported benefits such as increase in cost savings (39%), profits (35%), and customer satisfaction and engagement (34%), as well as the ability to direct resources to higher-value and/or revenue-generating tasks (25%).
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