Enterprise Search Market Size and Trends

The Global Enterprise Search Market is estimated to be valued at US$ 6.27 bn in 2024 and is expected to reach US$ 12.91 bn by 2031, exhibiting a compound annual growth rate (CAGR) of 10.9% from 2024 to 2031.

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The global enterprise search market growth is attributed to factors such as the increasing adoption of cloud-based technologies, AI, and analytics by enterprises. Additionally, rising demand for gaining useful insights from voluminous organizational data is further driving the adoption of enterprise search solutions. Key players are also innovating their product offerings by integrating advanced capabilities such as natural language processing and machine learning to offer personalized and contextualized search experiences. The industry is experiencing high demand for intuitive and easy-to-use solutions that can cater to the evolving information needs of users.

Rising demand for enhanced and powerful search solutions

The volume of data that organizations are dealing with has exploded in recent years. As digital transformation initiatives have become the norm, most businesses now generate huge amounts of data from a variety of internal and external sources on a daily basis. This includes data housed in different departments, systems, databases, files shares, and cloud repositories. With dispersed data silos spread across the enterprise, finding accurate information in a timely manner has become quite challenging for employees. Outdated legacy search tools are simply not equipped to efficiently search across such vast amounts of structured and unstructured data. This has led to reduced productivity, longer processing times, and inaccurate decision making.

Moreover, as remote and hybrid work models gain more popularity in the post-pandemic era, enabling anytime-anywhere access to organizational data is critical. Legacy search solutions that are dependent on location and device are becoming obsolete. There is a clear need for advanced enterprise search capabilities that can intelligently crawl internal and external sources, understand semantics and context, and deliver highly relevant search results in a centralized location. This is driving many organizations to invest in powerful next-generation enterprise search platforms that leverage latest technologies like artificial intelligence, machine learning and natural language processing. Such solutions make it much easier for employees to search through petabytes of data, discover hidden insights and take actions accordingly.

With business environments becoming more dynamic, the ability to quickly search vast amounts of enterprise data and extract useful information has emerged as a key imperative. Legacy tools are unable to meet rising user expectations while next-gen solutions are providing clear business benefits. This is a major factor fueling rapid growth of the global enterprise search market.

For instance, in April 2021, WalkMe, a leading digital adoption platform, acquired Zest, a company specializing in AI-driven search technology. This acquisition integrates Zest's advanced search capabilities, leveraging natural language processing (NLP) to create an on-demand graph database. This integration enhances the predictability of user search, providing faster and more relevant results for users.

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Demand for better knowledge management and discovery

Organizations are placing stronger emphasis on leveraging their collective intelligence, expertise, and knowledge assets to gain competitive advantage. Knowledge management initiatives aim to systematically acquire, organize, and disseminate valuable knowledge among employees. However, effectively discovering and sharing implicit knowledge that resides in the minds of individuals or buried in disparate data silos continues to pose a challenge. Outdated intranet search or departmental search tools are not helping organizations optimize knowledge flow and discovery.

At the same time, ever-growing volumes of enterprise data hold a gold mine of previously unknown connections, patterns, and insights. But traditional search methods are inadequate for surfacing such hidden knowledge.