4th November 2024, Kathmandu
As businesses move towards knowledge automation, enterprise search is emerging as a vital tool for internal productivity. Mainstream adoption of Large Language Models (LLMs) has transformed workplace search, empowering businesses with productivity tools like Retrieval-Augmented Generation (RAG).
Why Enterprise Search is Challenging?
Despite substantial investments in enterprise search technologies, many businesses are slow to adopt them. This article explores the key technical challenges, data governance hurdles, and implementation complexities that make enterprise search so difficult—and what leaders can do to foster successful workplace search adoption.
1. The Promise of Enterprise Search Meets Reality
For years, enterprise search has been touted as “Google for business,” but creating a robust search tool involves more than just integrating a language model with databases and data connectors. Unlike public search engines that rely on structured web data, enterprise search systems must navigate vast quantities of poorly structured, internal data scattered across email servers, cloud drives, and communication platforms. Clean, structured data is essential for accurate search results, making “Garbage in, garbage out” a reality in workplace search.
Key takeaway: Clean data is the foundation for a good search. Enterprises need to prioritize data hygiene and limit the amount of unstructured or irrelevant information in their search indices.
2. Data Governance and Security Risks in Enterprise Search
One of the most significant barriers to enterprise search adoption is data governance. Integrating AI-based search with sensitive business data introduces new security risks. For instance, when AI chatbots generate responses, they may inadvertently access outdated or sensitive information. Data governance teams must mitigate risks by limiting knowledge sources and implementing strict access controls. A strong data governance framework helps ensure AI models provide accurate, secure results without exposing private information.
3. Technical Challenges of Enterprise Search Systems
Enterprise search tools face unique technical challenges due to data fragmentation across platforms like Google Drive, SharePoint, Slack, and email. Connecting these disparate systems requires third-party connectors, which add complexity and demand ongoing maintenance.
Additionally, search quality is subjective and varies widely between departments and organizations, making it difficult to meet the unique expectations of each team. Businesses must decide between dense vector searches, which focus on semantic meaning, and sparse vector searches, which rely on exact term matches.
4. High Costs and Complexity in Enterprise Search Deployment
High implementation costs and setup complexity are other barriers to widespread enterprise search adoption. Advanced workplace search platforms, like Coveo and Elastic, are expensive and technically demanding, creating a barrier for small and medium-sized businesses. For larger organizations, deploying an effective search system requires a high level of cross-departmental collaboration and may take months to complete.
5. Overcoming User Resistance to Change
Even when enterprise search tools perform well, user adoption can be slow. Employees may resist new search platforms, particularly if they’re accustomed to traditional methods for knowledge retrieval. Business leaders can encourage adoption by providing clear training and demonstrating how enterprise search enhances cross-departmental collaboration.
Key Areas Where Enterprise Search is Proving Valuable
Despite these challenges, enterprise search is a $12 billion market with rapid growth, especially in industries with strong knowledge-sharing needs. Leading use cases include:
Knowledge Management: Tools like Notion and Guru make internal knowledge easily accessible.
Questionnaire Automation: Sales and compliance teams automate responses through platforms like Vanta and Drata.
Document Analysis: Verticalized solutions like Harvey streamline legal document search and analysis.
Customer Support: AI-enhanced platforms like Intercom and Zendesk improve both customer and agent support.
Sales Enablement: Modern search solutions streamline knowledge retrieval, replacing legacy tools like Highspot.
Conclusion
While enterprise search holds significant promise, adopting it involves navigating complex data governance, and technical, and cultural challenges. By focusing on specific pain points, implementing robust governance, and encouraging user adoption, business leaders can unlock the true potential of workplace search.
In a fast-evolving digital landscape, enterprises that invest in secure, efficient search systems today will have a strategic advantage tomorrow.
Narrowing Focus to Maximize Adoption
Going “Narrow” on a problem set is a faster path to adoption and clear ROI.
The wider you’re trying to go, the harder it is to get value out of Enterprise Search. A broad set of use cases makes everything harder for the buyer and the vendor. For business leaders to unlock the true value of search, they should align it with a clear set of problems.
Examples of narrow search use cases include tools like Intercom for Customer Support, Harvey for Legal document analysis, or 1up for automating sales enablement. In all of these cases, success is compounded by urgency and pain within the specific problem space.
Narrowing the focus on specific use cases means limiting data sources and also meaningfully reducing risk. For infosec teams, this means worrying less about sensitive data leaking from one department to another. For users, this creates a better experience by reducing the risk of inadvertently connecting a sensitive data source to the Search index.
There are many reasons to be optimistic about the future of search within businesses. We think focusing will go a long way for businesses adopting this technology.
For more: Why Enterprise Search is Challenging
