2024 Reports

Trends, Patterns, and Insights on the Use of Artificial Intelligence-Based Knowledge Management Tools in Private Enterprises

Liker, Benjamin; Meinrenken, Christoph

Despite the widespread adoption of new artificial intelligence (AI) technologies, there is a notable lack of information on how they’re being used to manage knowledge within organizations. This study aims to fill this gap by examining how various companies are leveraging knowledge management software tools foundationally built upon new AI technologies (AI-KM tools) to enhance their businesses. The purpose of this is to identify any trends in the adoption of AI-KM tools, particularly ones that elucidate the nature of why certain companies may adopt different types of AI-KM tools across different teams, as well as find any trends that may predict future developments for how knowledge management is used in the private sector.

This study was carried out in three parts: A literature review, interviews with executives, and a thorough analysis of AI-KM tools and the companies who use them. The literature review yielded elucidations about the potential role of AI in the field of knowledge management, including an overview of the potential use, potential causes of slow adoption, and possible reasons for future widespread use. The interviews yielded the insight that the best way to examine AI-KM tool use and collect meaningful data is to follow/use a software-customer research taxonomy. After 5 software companies were identified as ideal candidates for examination, their customers’ usage data was analyzed across several metrics. The metrics fell into two categories: Tool use data describes what tools were used by which teams for each company; Company qualities include employee count (size), customer base, sector, industry, and location. The study demonstrates several distinct correlatory effects of most of these qualities on AI-KM tool usage:
1. Small companies tend to use AI-KM tools on a needs-based basis, medium companies tend to use AI-KM tools that can boost sales as much as possible, and large and giant companies spread utilization across teams and tool types.
2. Different sectors and industries show varied patterns in adopting AI-KM tools, with the technology sector leading the adoption of learning tools while healthcare and professional services led utilization of analysis and automation tools.
3. With respect to team utilization, service-based organizations tend to shift resources to external-facing teams such as sales and customer support, while internal teams like engineering and HR use AI-KM tools more in product-centric companies.
4. The tools that external teams use tend to be more complex than the ones internal teams use, but internal teams’ tools have a significantly stronger correlation between complexity and usage than external teams’ tools.

Broadly, this study’s findings confirm previous findings of other researchers included in the literature review. All indications point towards further adoption of AI-KM tools over the next several years, particularly at larger companies where cost savings meets improved worker performance, which has yielded higher net productivity in the case studies examined. While this has potentially negative effects for current knowledge managers’ future employment prospects, it confirms predictions that large enterprises are not seeking to replace knowledge workers with AI, but instead supplant them by automating away busywork, strengthening existing human-based support systems, and freeing up time for completing complex tasks.