How AI Knowledge Assistants Are Disrupting Enterprise Search

It's so obvious that it's almost not worth saying: Informed employees are more productive employees. And yet, organizations spend almost no time trying to ensure that their employees are as well-informed as possible.

The volume of information generated in companies is increasing rapidly. According to Okta's research, companies in 2023 use an average of 89 apps at work, with this number increasing to 211 for companies with over 2000 employees.

According to a report from Gartner, one third of knowledge workers admit to making a wrong decision at work due to a lack of awareness of key information. Other research aiming to quantify the impact of enterprise search challenges found employees lose almost a full work day each week trying to track down information.

This is a puzzling issue, especially since Google has long been assisting us in finding all sorts of non-enterprise information.

Many companies have attempted to address the problem of inaccessible information in the enterprise, with varying degrees of success. Yet, the solutions remain costly, time-consuming, and challenging to use.

Why enterprise search solutions don’t solve the problem

Consider your own hard drive, likely filled with a variety of notes, documents, screenshots, scans, and more. Identifying the content of each file often requires opening them individually.

Similarly, enterprise data on a larger scale is just as, if not more, disorganized. It's stored across various formats and applications, including Slack messages, support message queues, call transcripts, and more.

Up to 80% of enterprise data is unstructured, scattered across different locations and formats. For this data to be included in enterprise search, it must first be located, read by a human or machine, categorized, tagged with keywords, or otherwise processed. This makes setup and maintenance a significant undertaking.

The quality of a company’s search results is dependent on its metadata tagging system. Inconsistent tag usage or inaccurate keywords can make information hard or even impossible to find.

Google search users have seen how search engines have improved in showing relevant search results on the first page. This progress is driven by the massive amounts of clicks on search results that they process from hundreds of millions of users daily, providing them with sophisticated insights into search relevance.

However, unlike Google, enterprise search solutions don't have access to this volume of data. As a result, they rely on basic algorithms that index and retrieve data based on keywords. This approach often yields an array of results, many irrelevant, forcing employees to manually filter through them.

Traditional enterprise search systems also lack understanding of the context in which a query is made. The same keyword can have different meanings in different departments or projects. Without this contextual understanding, search results can miss the mark. Moreover, unlike AI-driven systems, traditional search engines do not learn from user behavior, limiting their ability to refine results based on past searches or understand the evolving needs of an organization.

Generative AI powered by internal knowledge is the way forward

‍At Dashworks, we believe that generative AI offers a powerful alternative to enterprise search. It’s why we created Dash AI -- an AI knowledge assistant that can answer questions, find files, write code, create content and more – all based on a company’s internal knowledge. ‍

AI offers significant improvements over traditional keyword searches, including understanding the context and parsing the meaning of questions. This ability allows AI assistants to understand data more deeply, categorizing content with greater accuracy than keyword-based crawlers. For example, if an employee asks about the revenue from the latest marketing campaign, the assistant can gather data from both Salesforce and Google Drive to provide a comprehensive response.

This feature eliminates the effort required to implement enterprise search and maintain a company's knowledge management system. There's no need for tagging, creating knowledge cards, or extensive data processing.

The use of AI Knowledge Assistants introduces a more intuitive and user-friendly experience through natural language conversations. These assistants can learn from every interaction, improving their ability to predict the user's needs and streamline the search process.

In the future, these AI assistants could enable multimodal search capabilities. Employees could sketch a design and ask the AI to find similar concepts or hum a tune to find related marketing jingles.

However, the security of such a solution raises concerns. Many enterprises worry about potential data leaks from employee use of ChatGPT. Typically, the simplest way to provide data to an LLM company is to index and store it, but this approach can lead to security vulnerabilities and longer rollout times. An alternative - hosting the enterprise AI solution on the company's own cloud - can increase costs.

Dash AI takes a different approach. Instead of indexing data, it connects to applications via APIs, performs real-time searches, ranks the received data, and passes it through an LLM for a natural-language response. This method, which combines advanced API technology with sophisticated LLMs, significantly reduces the risk of a security breach as all data is stored in first-party apps. It also boosts company productivity by providing real-time natural language access to all company data at an affordable price.

What's next?

With growing burnout rates, and workforce shortages, helping employees seamlessly access internal information is more important than ever. But a traditional enterprise search or knowledge management platform is clearly not the answer. The electrifying rise of generative AI tools offer a new approach that have the potential to make company knowledge as ubiquitous and easy-to-access as a quick Google search. 

The ultimate impact will not only be hours saved from frustrated searching but better decision-making, stronger collaboration, and more fulfilling work-days.