Enterprises face a growing challenge: finding the right data quickly and efficiently. Most internal knowledge is hidden beneath the surface and scattered across many different applications. While traditional keyword-based search tools have long been the standard, they often fall short when it comes to understanding context, intent, and the nuances of human language.
Fortunately, that’s all changing with generative artificial intelligence (AI). Often synonymous with large language models (LLMs) and used on the open web, generative AI can also be tremendously useful in the enterprise. This article will explore the evolution of traditional search to generative AI-powered knowledge management strategies being used by data-driven organizations today.
The Limitations of Traditional Search
Conventional enterprise search engines rely heavily on keyword matching. Users type in a query, and the system returns results based on the presence of those keywords in documents. While this approach can be effective for simple queries, it has shortcomings when it comes to more complex, natural language-based requests.
Contextual blind spots are an example of where traditional search engines struggle. When each keyword is treated in isolation, the results are prone to irrelevance. Add the intricacies of language—synonyms, homonyms, or context-dependent meanings—and search engines become even more confused. To boot, domain-specific queries can add another layer of complexity when industry-specific jargon or technical terms come into play.
This can cause problems with reliability, safety, and security. While perfectly accurate, appropriately sourced information may be less important outside the enterprise, this is not an option for business users. In fact, even with the introduction of generative AI this is still a concern for users in the workplace and beyond. Lest not forget, generative AI can help us create quickly, but faster doesn’t equate to better. Enterprise search—assisted by AI or not—requires a level of scrutiny traditional search alone doesn’t provide.
Scalability is another issue. Modern enterprises generate vast amounts of structured and unstructured data, making it increasingly difficult to maintain tabs on all information across data sources. For instance, traditional search tools can struggle to capture information from a document, to a PDF, to a video clip, or a specific excerpt from a research paper.
User experience is another area where there’s much to be desired. Knowledge workers are busier than ever, and consumer technology has trained them to expect quick, accurate, Google-like search experiences. When enterprise search falls short, productivity and morale suffers, and ultimately, businesses are leaving time and money on the table.
Generative Enterprise Search for NextGen Knowledge Management
The ability of AI to understand, digest, summarize, and generate content at scale is unparalleled. Performing these tasks on millions, even billions of documents results in time- and money-savings that can’t be disputed. It streamlines human productivity and leads to better decision-making, and this is just the beginning.
According to Accenture, as much as 40% of working hours will be augmented or automated by generative AI. While this will vary by industry, writing, programming, and researching are all knowledge management areas that will benefit greatly from generative AI-driven search and discovery. But just as important as material savings, providing accurate and context-aware search results is where this technology shines.
Advances in neural networks have enabled users to efficiently implement semantic search to find relevant information needed to answer natural language queries, regardless of the keywords used. More relevant and transparent search results securely fed to an LLM like GPT demonstrate AI’s importance in the next generation of knowledge management.
However, it’s important to remember machine learning-powered search isn’t fail-safe, and decision-makers should prioritize AI solutions that are above all, safe. This falls into two categories: security and reliability. The first will focus on data privacy, access rights, and ID infrastructure. The second focuses more on appropriately citing materials, providing accurate results, and answers that are hallucination-free.
The next step is simply for organizations to get started. Leaders should focus on areas where productivity is most important and build from there. Next, explore prospective models and their hosting environments to ensure they will meet operational and regulatory needs. For example, in chemistry use cases, be sure your solution is finetuned on chemical terminology before deploying to answer user questions.
Challenges and Looking Ahead
While generative enterprise search will change the way we work, it’s not without challenges. Data privacy is an important consideration for organizations, ensuring the handling of sensitive data or personally identifiable information is secure and in compliance with industry regulations.
Monitoring and fine-tuning AI models, especially for domain-specific needs, can be resource-intensive. Lastly, like any new tech integration, introducing generative search into existing workflows and systems comes with hurdles. The process can be daunting, but it’s well worth the short-term effort.
As mentioned before, even with huge leaps forward in generative AI, we must look at solutions and their results with a critical eye. Still in its infancy, hallucinations are common and sometimes hard to spot, and taking appropriate security measures will always remain a moving target for enterprise users.
Generative enterprise search represents a paradigm shift in how organizations access and leverage their data. The power of generative AI allows enterprises to break free from the chains of traditional keyword-based search and unlock new insights and business opportunities. As AI continues to evolve, the possibilities for generative enterprise search are ripe for the taking, making it an important tool for future-thinking organizations.
Article by By Jakub Zavrel, Founder and CEO, Zeta Alpha