Nearly every enterprise is experimenting with AI, but an overwhelming 90% of AI projects never scale beyond the proof-of-concept stage, and more than 97% of organizations experience difficulties demonstrating the business value of generative AI (genAI), according to an Informatica survey.[i]
A major reason is that many cautious business leaders treat AI as a source of incremental improvements to existing processes rather than a tool to reshape core business functions. Too often, business leaders underestimate the people, behavior, and organizational changes entailed by strategically using AI.
In particular, the transformative potential of AI-powered search is overlooked. That’s despite the fact that search is a function knowledge workers use every day. As data volumes continue to grow, employees and customers are increasingly challenged to find the information they want.[ii] Various studies have found that employees spend between 20% and 30% of their time looking for information.[iii] They have become accustomed to instant gratification on the web, but the lack of investment many organizations make in relevance and content curation makes searching inside the corporate firewall maddeningly unproductive.
AI search not only incrementally improves productivity but can radically reshape core business capabilities. It replaces simple keyword searches with advanced semantic techniques that understand the intent and context behind a query. Semantic search combines technologies including natural language processing, vector data stores, and machine learning to deliver results that more closely match what users need than keywords without requiring major investments in content curation.
“We can now understand context better than was possible with keyword search alone,” says Steve Mayzak, global managing director of Search AI at Elastic. “With semantic search, you can search across an entire book instead of relying on the index alone.”
By leveraging genAI assistants and large language models, AI search can interpret a user request and deliver results in a business context.
When considering an AI search platform, look for these features.
Flexible integration with multiple data types and sources – Enterprise data is spread across a multitude of databases, internal applications, and software-as-a-service. An AI search engine should connect seamlessly to the data sources you need and deliver integrated results regardless of the location or type of data.
Near real-time data ingestion and indexing – The pace of business is too fast to permit most organizations the luxury of waiting hours or days for critical data. AI search should make data available seconds after it’s ingested.
APIs – These make the onboarding of new applications and data sources easier. Look for an open ecosystem that integrates with all the major AI foundation models and supports your own models so existing investments aren’t wasted.
A serverless architecture that scales up and down on demand to deliver maximum efficiency at the lowest cost.
Multimodal capabilities that support searching of images, video, and audio.
AI search makes it possible for organizations to consolidate a multitude of application-specific search engines into a single utility that works across all the organization’s data.
Elastic’s powerful, scalable, and AI-driven search solution delivers fast, relevant, and secure search experiences across structured and unstructured data in batch and real time. It offers advanced full-text search, vector search, and AI-powered relevance tuning, making it ideal for diverse use cases.
To learn more about Elasticsearch click here.
[i] “CDO Insights 2025 – global data leaders racing ahead, despite headwinds to being AI ready, latest survey finds,” March 14, 2025, Informatica.com.
[ii] “Amount of Data Created Daily (2025),” April 24, 2025, ExplodingTopics.com.
[iii] “Various survey statistics: Workers spend too much time searching for information,” Cottrill Research.