- Efficient information retrieval is crucial for organizational productivity, as employees spend significant time searching for information, leading to productivity loss.
- Challenges Addressed by AI-Powered Search: AI-powered search solutions address challenges such as complex data structures, diverse content repositories, enhanced user experience, unstructured data complexity, and advanced data analysis.
- Integration with Enterprise Platforms: AI search can be integrated with enterprise conversation platforms like Slack, Microsoft Teams, and Cisco Webex, providing a seamless and efficient search experience directly within the conversation interface.
In today's fast-paced business world, quickly locating information is essential for ensuring smooth and efficient organizational functioning. With vast amounts of data in corporate systems, like intranets and databases, it's important to have advanced tools that make it easy for people to find and discover what they need. Fast information retrieval saves time and helps organizations make better decisions, encouraging innovation and quick responses. Let's explore why speedy access to information is so important for businesses, backed up by some important facts.
Importance of faster information retrieval
Enterprises generate and manage vast datasets, encompassing everything from HR records and financial transactions to customer interactions and project details. Efficient information retrieval ensures that employees can access the right data precisely when needed, eliminating the delays associated with navigating complex menu structures or manually searching through extensive repositories.
Data Points:
- According to a survey conducted by McKinsey, employees spend a significant portion of their work hours searching for information, leading to a considerable loss in productivity.
- In a study by KMInstitute, organizations with streamlined information retrieval processes experienced a notable improvement in decision-making speed, resulting in a competitive advantage.
Why organizations need an AI-powered search solution
Traditional search methods fall short when dealing with the intricacies of modern enterprise data landscapes. The adoption of AI-powered workplace search solutions becomes imperative to address the following key challenges:
Complex data structures: Enterprises often have multifaceted intranets, intricate websites, varying data models, and diverse applications, making manual navigation time-consuming and prone to errors. AI-driven search solutions simplify the process, providing a unified interface to swiftly retrieve relevant information and efficiently process unstructured data.
Diverse content repositories: Organizations store data across various platforms, from databases and data lakes to knowledge management systems. AI-powered search can seamlessly integrate and query these disparate repositories, ensuring comprehensive access to information.
Enhanced user experience: Employees expect a search experience similar to popular online search engines in their workplace. AI-driven search solutions not only meet these expectations but also offer intelligent suggestions, natural language processing, and personalized results, enhancing overall user satisfaction.
Unstructured data complexity: Managing and deriving insights from unstructured data, such as emails, documents, and multimedia content, presents a significant challenge. Traditional search methods struggle to organize and extract meaningful information from these diverse formats. AI-powered search solutions like Glean excel in handling unstructured data by employing natural language processing and machine learning, providing a structured and searchable format that enhances overall data management within the organization.
Data analysis and insights: Beyond retrieval, AI-powered search can facilitate advanced data analysis by identifying patterns, trends, and relationships within the enterprise data. This goes beyond traditional search capabilities, unlocking valuable insights for strategic decision-making.
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AI search in action: examples of AI enterprise search in action
Effective search functionality becomes indispensable in navigating the complexities of large datasets and intricate systems within enterprises. Consider corporate intranets, websites, and applications, each characterized by intricate hierarchical structures, nested menus, and various features. In response to this complexity, companies are increasingly adopting a streamlined approach – a singular search box that fetches content and addresses user queries. This shift simplifies the user experience by placing everything within easy reach through a unified search interface.
Below, we explore examples of AI enterprise search applications, shedding light on the types of content involved and the queries they aim to address:
1. Corporate portal/intranet:
Content examples:
- Corporate functions (timecards, expenses, payroll, IT, CRM, etc.)
- Policies, procedures, FAQs, support documentation, ethics, and forms
- Organizational details, personnel information, office locations
- Products, parts, projects, customer details
- HR benefits
Search queries in this context are designed to answer practical employee questions, such as "Who is John Doe?" or "How do I complete my timecard?" The objective is to make relevant information easily accessible with a simple search, eliminating the need for users to navigate through intricate menu structures.
2. Data catalog:
Content examples:
- Data sources from data lakes and data warehouses
- Business system APIs, MasterData, etc.
Search queries for data catalogs aim to facilitate the identification of datasets for analysis. Queries like "Maryland franchises" or "Financial customers with over $1m in sales" should efficiently pinpoint relevant tables and subsets within the enterprise data infrastructure.
3. Knowledge management:
Content examples:
- Proposals, pitch books, RFPs, RFIs, contracts, legal documents
- Internal and external research, sales, and marketing materials
- Documentation, guides, training resources
Search queries in the realm of knowledge management are geared towards helping users access specific insights within the vast sea of enterprise content. Beyond retrieving relevant documents, AI-enhanced search can leverage automated information extraction, providing precise answers to user queries without requiring manual search result exploration.
How does AI search integrate with enterprise conversation platforms?
AI search can be integrated with enterprise conversation platforms such as Slack, Microsoft Teams, and Cisco Webex to provide a more seamless and efficient search experience. Glean offers integrations with these platforms, allowing users to search for information directly from within the conversation interface. This integration saves time and increases productivity by eliminating the need to switch between different applications to find the information needed.
Overall, AI-powered enterprise search solutions are valuable for organizations looking to improve their search capabilities. By leveraging the latest advancements in AI technology, Glean provides more accurate and relevant search results, saving workers time and increasing their productivity.
