Proving the Value of Knowledge Management

Traditionally, the value of KM was viewed qualitatively. It often underpinned, yet did not directly translate into, revenue-generating activities. Its value was based on clarifying terminology that linked it to understandable business concepts, streamlined processes, and repeatable, single source of truth knowledge repositories.

ROI wasn’t necessarily connected to these activities, but to the applications for which they were employed, such as processing loans in the financial services industry or inputting patient information in healthcare. Today, the qualitative aspects of KM are beginning to translate into quantitative terms. The crux of this metamorphosis lies in rapid advancements in the technology ecosystem surrounding KM.

The newfound capabilities of modern process automation systems, content management repositories, and contemporary applications of AI are not just empowering end users to do more. They’re also allowing organizations to further exploit their KM practices— which is having a profound effect on the value, and the perception of value, within the enterprise.

“There are opportunities for organizations, especially now with some of the GenAI [generative AI] tools, to get pretty rapidly to a value built on KM,” reflected Don Schuerman, Pega CTO. “I think the value gets really fast for some of these deployments in ways that don’t require as much specialized skill.”

Quantifying KM

Quantifying its benefits proves the value of KM. In addition to clarifying terms and business concepts, it formalizes how best practices and “explicit knowhow, a common starting point, and project plans are easily tied into workflows and operational processes,” commented Alex Smith, iManage global product lead— knowledge, search, AI. The workflows and knowledgebase Smith mentioned are tangible examples of explicit knowledge. According to Laserfiche CIO Thomas Phelps, KM also includes “tacit knowledge that’s based on individuals’ personal experiences, intuition, and how you come to problem solving to make decisions.”

When explicit and tacit knowledge are codified by credible KM practices, organizations can quantify value. Phelps referenced a contract negotiation use case: “Based on how you negotiate an agreement, based on your structured process and leveraging your judgment, experience, and process of negotiation, can you get to a greater contract that provides you with larger discounts, incentives, and a cap on uplift? Absolutely. There’s cost savings there.” 

As Schuerman implied, other demonstrations of KM’s value can be quantified in terms of saving time and accelerating processes. For certain applications, these gains are amplified by advanced machine learning techniques. “Thanks to AI, the ROI of KM is no longer abstract,” commented Rohan Vaidyanathan, VP of product for content intelligence at Hyland. “Whether it’s reducing onboarding time through intelligent search, or enabling faster claim resolutions via contextual recommendations, the benefits are more immediate and trackable.”

AI Versus KM

The pervasive use of statistical AI, particularly generative machine learning models, has dramatically altered the concept of how to measure value. When search, one of the cornerstones of KM, is suddenly revamped into natural language question-answering and summarization on demand, the value of traditional taxonomies, tagging, and metadata curation is called into question. According to Schuerman, “There’s a little bit of a debate as to whether or not users still need a lot of that stuff.” Although the need for these and other foundational components of KM is debatable, there’s no mistaking the fact that they significantly enhance almost any deployment of GenAI.