The hype is over: Generative AI is driving the evolution of search within enterprises

2025 is the year that generative AI goes from proof of concept to production in the enterprise. With the right data foundations, firms can unlock greater value from their knowledge base, paving the way for long-term transformation and competitive advantage. Whether in sales, customer service, or marketing, the number of viable use cases is ramping up, while early movers are already achieving efficiency gains. 

With so many opportunities, where do you make the first move? The answer can be found in the evolution of search, says Derek Rodriguez, managing director, Search and Content Analytics Group at Accenture. “For many years we’ve relied on traditional keyword-based retrieval where users enter search terms and use their judgment to filter results,” he says. “More recently, real-time indexing, distributed architectures, and semantic search have improved accuracy by understanding user intent and context.” 

Today’s AI-powered search platforms go further, unifying structured and unstructured data to summarize content and generate insights. By understanding intent, context, and relationships within data, these systems enable faster, accurate decision-making and reduce the need for manual research. “Automating search workflows dramatically lowers the cost of knowledge management, transforming the value equation and increasing the return on corporate data,” says Rodriguez. 

This innovative approach requires an equally solid data foundation, one that’s vectorized and optimized with advanced retrieval and ranking. With these fundamentals in place, businesses can combine first-party data with large language models (LLMs) to retrieve insights that are trustworthy and compliant.  

Steve Mayzak, managing director of Search at Elastic, says, “A well-vectorized, searchable knowledge base is a flexible starting point for integration with business systems to deliver long-term value in business-critical scenarios.”

This is where Accenture’s partnership with Elastic plays a vital role, combining Elastic’s scalable, AI-driven search capabilities with Accenture’s deep industry expertise to help enterprises maximize data value and drive real business impact.  

According to Rodriguez, Elastic’s appeal lies not only in the accuracy and relevance of its AI search technology, but also in advanced monitoring features and integration with a range of AI ecosystem partners. In return, Accenture brings unparalleled industry knowledge, strategic consulting expertise, and a global network of skilled professionals capable of implementing and integrating complex technology solutions. Accenture continues to hire and train its employees in Elastic technologies, highlighting the significance of the alliance.

Accenture and Elastic can also help businesses answer one of the toughest questions of all: where to start. Mayzak says that organizations should look for internal use cases where data is abundant and accurate. “Pick an initiative best suited to today’s LLM capabilities with a high chance of success. By proving value, you can unlock budget for more projects and build real momentum.”

Both organizations bring a wealth of experience managing unstructured data in disparate environments. “In the real world, organizational data is highly complex, spanning hierarchical structures, networks, and multidimensional relationships,” says Rodriguez. 

Many large organizations — especially in industries like pharmaceuticals, retail, automotive, and ecommerce — store hundreds of terabytes or even petabytes of data. But a large proportion of this digital wealth goes untapped. On average, businesses put only 32% of their data to work, leaving more than two-thirds untapped.¹

Accenture helps them bridge the gap, making intricate connections across nested records, business identifiers, and diverse ranking signals. These indicators include relevance scores, popularity, sales volume, and taxonomy classifications.

In such complex environments, using a basic vector database is like trying to illuminate a stadium with a single match — technically light, but hopelessly inadequate for the scale of the challenge.

With a solid foundation, organizations can architect their businesses for an AI future that’s racing toward us faster than most executives realize. This includes agentic frameworks that deliver both automation and autonomous decision-making alongside human supervisors. 

Rodriguez assigns agents to one of three categories:  

1. At the most basic level, organizations can create their own internal search and question-answering systems. Such agents are capable of performing tasks like natural language interrogation of structured and unstructured data, scheduling conference rooms, finding contact information, or providing directions.  

2. A second type of agentic behavior relates to robotic process automation (RPA) and business automation. Consider invoice processing: Incoming invoices trigger a series of checks and data registrations within financial systems. Generative AI automates manual steps in this process, potentially increasing accuracy and reducing costs.

3. Further down the line, agents can function as a team of collaborators, working together to solve problems. A supervisor agent might define a task, such as creating a marketing brief, and then delegate sub-tasks to other specialized agents. These sub-agents gather information and assemble it into the final product.

In all of these cases, a vectorized searchable knowledge store is imperative. “This is the investment that organizations should make to ta

Rodriguez also has a clear message for businesses that are unsure whether to act now or sit out the first wave of generative AI. “You need to think big. Look beyond chatbots that save a few seconds when drafting an email,” he says. “The good news is that platforms such as Elasticsearch offer sophisticated data modelling and search for enterprise-level challenges.”