Presented by SalesForce
In the rapidly evolving landscape of artificial intelligence, the very definition of “search” is undergoing a profound transformation. No longer confined to simple keyword matching, enterprise search is shifting toward understanding and reasoning over data in a conversational interface, and ultimately, enabling autonomous AI agents to reshape how work gets done in an organization. This evolution — driven by innovations like vector search, knowledge graphs, and agentic reasoning — is reshaping how businesses access, understand, and act upon their vast troves of information.
Data challenge: Enabling AI agents to access enterprise-wide data
Today, organizations struggle to navigate their vast and fragmented data landscape. The data your organization gathers generally takes three forms — structured, semi-structured and unstructured. Organizations produce enormous volumes of unstructured content — call transcripts, formal documents, Slack messages, and emails that hold immense value but often go underutilized. Leveraging this content is challenging due to inconsistent formats, poor data quality, and growing requirements around privacy and security.
These challenges will only increase with the advent of interoperable AI agents who must not only identify accurate information but also act on this data autonomously and securely while maintaining critical trust, privacy and compliance guardrails.
