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.
To be truly effective, AI agents need real-time access to comprehensive, accurate, and contextually rich information — especially about their customers. Often, they’re unable to identify insights needed to solve customer problems or take proactive action. For instance, data about a customer’s loyalty history or family status may be buried across systems — blocking even simple autonomous actions, like sending a personalized push notification for a family-friendly resort deal.
When data is siloed, fragmented, or noisy, AI agents are forced to guess, resulting in unreliable outputs. This leaves organizations stuck in the ‘garbage in-garbage out’ cycle that’s plagued many CIOs today. Simply put, bad data = bad AI.
The evolution of search: From keywords to meaning
Traditional search engines rely heavily on keywords. If a document doesn’t contain the exact phrase you’re looking for, you might miss crucial information. The first significant leap in AI-enabled search came with vector search. Where queries are often spoken or expressed in natural language, systems need to grasp the meaning behind the words. Vector search converts data and queries into numerical representations (vectors), allowing the system to match based on semantic similarity, not just literal word presence. This means a query like “customer sentiment about product XYZ” can find relevant documents even if they don’t explicitly use the word “sentiment,” but discuss customer opinions, reviews, or feelings.
However, the complexity of enterprise data demands more. While vector search is a powerful initial step, the sheer variety of content formats and the need for deeper contextual understanding led to the rise of enriched indexing. Here, AI goes a step further, first understanding the data and building a graph-like ontology. Think of this as organizing messy, unstructured data (fact: 80% of enterprise data today is unstructured in nature) — documents, emails, presentations — into a structured network of who, what, where, when, and why. This “knowledge graph” provides the critical context that enhances the quality of search responses, allowing for more insightful and accurate results.
Bridging the divide: Unstructured, structured, and deep search
The enterprise doesn’t just deal with unstructured documents; a vast amount of critical information resides in structured databases. To truly unify the search experience, natural language to SQL (NL2SQL) technology comes into play. This innovation allows users to pose questions about structured data in plain English (e.g., “Show me sales figures for Q1 in California for product A”), and the AI system automatically translates it into SQL code for data retrieval. This complements vector search, creating a holistic approach to querying both unstructured and structured information.
At Salesforce, we are heavily focused on optimizing Search and Retrieval Augmented Generation (RAG) in Data Cloud to enhance the performance and accuracy of gen AI applications, particularly for powering AI agents like Agentforce. Salesforce’s hybrid approach of combining vector search and keyword search addresses the limitations of either model alone — leading to more consistent and accurate results. Additionally, Salesforce is implementing methods to embed additional metadata into our documents and indexes. This allows AI models to access structured context before generating responses, helping to prevent the LLM from fabricating answers based on partial or ambiguous data.
Grounding LLMs to increase autonomous trustworthiness
Large Language Models (LLMs), the powerful engines driving generative AI, have revolutionized how we interact with technology. They can tackle complex questions, conjure original content, and even code with impressive fluency. Yet, businesses quickly hit a wall: LLMs alone are limited by their training data, which is often static and doesn’t include an organization’s specific, real-time, or proprietary information.
This is precisely where Retrieval-Augmented Generation (RAG) becomes indispensable. RAG acts as the critical bridge, allowing companies to securely connect their unique, internal data directly to LLMs. This connection transforms AI’s potential for businesses, making responses not only more trustworthy and relevant but also up-to-the-minute accurate.
Imagine this: with RAG seamlessly linking an LLM to your internal knowledge base, an autonomous AI agent can instantly provide customer service responses that factor in a client’s entire interaction history, or generate marketing briefs perfectly aligned with the very latest brand guidelines and campaign performance data. It’s the difference between generic AI and an intelligent system deeply informed by your business’s living data.
To unlock unparalleled efficiency and success across your entire organization, you’ll need to bring together the power of LLMs, a cloud-based data engine, your CRM, and conversational AI through RAG. This potent combination will enable you to deploy a fleet of powerful agents, each informed and precisely tailored to the unique demands of every department — deeply integrated into your workflows, and constantly refreshing information to drive business outcomes.
The road ahead: Enterprise intelligence and autonomous agents
The ultimate vision is nothing short of enterprise intelligence powered by autonomous AI agents. Imagine AI agents within a company that can independently access and search across all enterprise information to perform complex tasks. This could mean an AI agent researching market trends, another compiling competitive intelligence, or yet another resolving a customer service issue by autonomously gathering data from various internal systems. However, realizing this vision comes with its own set of significant implementation challenges. Key hurdles include:
- Data Integration: Connecting to and integrating diverse data systems while strictly honoring permissions
- Data Quality: Ensuring the input data is clean, consistent, and high-quality, as reasoning engines are only as good as the data they consume
- Ontology Building: The complex task of building enterprise-level knowledge graphs and ontologies that accurately reflect the nuances of a business’s information
- Performance: The critical need for speed and efficiency in search, especially when dealing with massive datasets and complex reasoning processes
Despite these challenges, the trajectory is clear. The future of enterprise search is intelligent, interconnected, and increasingly autonomous. By embracing innovations like vector search and knowledge graphs, businesses are poised to unlock unprecedented levels of insight and operational efficiency, transforming how information drives decisions across the organization.
Rohit Kapoor is VP Product, Search and AI at Salesforce.
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