How search accelerates your path to "AI first"

The combination of AI and search enables new levels of enterprise intelligence, with technologies such as natural language processing (NLP), machine learning (ML)-based relevancy, vector/semantic search, and large language models (LLMs) helping organizations finally unlock the value of unanalyzed data. Search and knowledge discovery technology is required for organizations to uncover, analyze, and utilize key data. However, a deluge of data means legacy search systems can struggle to help business users quickly find what they need. In response, modern search systems have made great leaps in the accuracy, relevancy, and usefulness of results by leveraging AI-based capabilities. Now, a new wave of AI — generative AI (GenAI) — is changing how forward-looking organizations approach search, knowledge management, and other forms of knowledge discovery.

Search is proving foundational to connect GenAI with business and real-world data by retrieving pertinent information from enterprise data sources, a process called retrieval augmented generation (RAG). By augmenting GenAI models with related information, search systems work to ensure accurate, relevant, useful answers and insights. Real-world questions and actionable insights are where the rubber of AI meets the road of real business context, and the importance of the retrieval step in RAG cannot be understated.

Before diving into RAG, let’s take a step back to understand how we got here. This is helpful for understanding where best to utilize different forms of AI and information retrieval to get the most out of technology investments and accelerate the path toward becoming an AI-first organization.

As the above diagram shows, there are a number of steps and technologies involved in RAG across the indexing/pre-processing and query pipelines. Depending on the type of data and the use case involved, either vector search or a combination of vector and keyword (a.k.a. hybrid) search may be needed. Companies should assess their resources to determine which of these pieces they want to build and maintain themselves. IDC recommends the following:

• Ensure the organization is leveraging strong search technology, with high levels of accuracy and relevancy, for the retrieval step of RAG. Aspects to look for include hybrid search (keyword and semantic/vector), automated reranking, and low-code/no-code tools that make testing and tuning easy for a wide variety of users. This step is crucial for ensuring that LLMs provide the most relevant, useful, and actionable summaries and answers.

• Assess the accuracy and freshness of data sources, and consider what tools will be used to connect, filter, or ingest data into the pipeline. Ensure that data governance and business rules, such as access permissions, are not lost in the process and that the system has strong security guardrails.

• Determine what types of AI are best suited to different use cases. GenAI should be applied strategically to ensure that its usage is feasible, valuable, and responsible. If needed, select a provider with the necessary experience to assist in prioritizing use cases and AI usage.

• Look for a partner that can assist with some or all of the steps required to connect enterprise data to LLMs, including parsing, chunking, embedding, storing, and using vector or hybrid search to retrieve key information to feed to the LLM.

Message from the Sponsor 

Elastic's complete, cloud-based solutions for search, observability, and security are built on the Elastic Search Al Platform, used by more than 50% of the Fortune 500. By taking advantage of all structured and unstructured data and securing and protecting private information, Elastic helps organizations use the precision of search to deliver on the promise of Al.

1. IDC’s North America Knowledge Discovery Survey, February 2023, n = 522
2. IDC’s GenAI ARC Survey, August 2023, n=1,363
3. IDC's Future Enterprise Resiliency and Spending Survey, Wave 1, January 2024, n = 881

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