The Next Edge in Knowledge Management: KM for the Modern Workforce and the Era of AI
For KM practitioners and leaders, this is your moment to lead boldly.
The future of work has arrived, and it’s transforming the way organizations create, share, and use knowledge. For decades, KM has been rooted in a deeply human tradition of capturing the insights, experiences, and know-how that live “behind the eyes and between the ears” of people. Traditional KM has always recognized that the most valuable knowledge is not just stored in documents or databases, but exchanged through conversations, mentorship, and collaboration. It’s the wisdom that walks out the door when someone retires, the lessons learned from past successes and failures, and the expertise that enables teams to solve problems faster and innovate with confidence.
Today, however, the landscape has shifted. We have more hybrid teams, distributed expertise, and the continued rise of AI. The modern workforce expects seamless, personalized access to expertise in the flow of day-to-day work. Meanwhile, leaders ask for measurable outcomes and more agility. AI is already reshaping how knowledge is discovered, contextualized, and applied, which challenges KM practitioners to move from managing repositories to becoming designers of dynamic, human-centered knowledge ecosystems.
As we enter the “next” era of KM, the challenges and opportunities for KM leaders are clear. We need to honor the human-centric foundations of our discipline while embracing new technology, new mindsets, and new possibilities. The time to redefine KM for the modern workforce is now, and “AI is the technology KM has been waiting for,” according to APQC chairman Carla O’Dell.
Why Redefine KM Now?
There are multiple forces driving this transformation, but according to APQC’s research into KM priorities and trends (apqc.org/resource-library/resource-listing/ 2026-knowledge-management-priorities-and-trends-survey-report), these are three of the top priorities for today’s business leaders:
♦ Improving speed and scale: Work changes faster than knowledge can be documented. KM needs lightweight capture, rapid curation, and continuous reuse while prioritizing focus on the most critical knowledge to solve real business problems.
♦ Reskilling and preparing the workforce for what’s to come: Teams now span multiple functions, geographies, and time zones. Knowledge must be accessible and trustworthy across platforms and contexts. Employees must prepare for constant change such as shifts in technology and customer demands.
♦ Embedding AI capabilities into everyday workflows: Automation and large language models (LLMs) improve search capabilities, analyze patterns, and summarize content quickly. Without effective KM capabilities, AI learns from inconsistent or inaccurate content. With strong KM, AI can accelerate highquality decisions and productivity and provide innovation and learning at scale.
As these forces reshape the landscape, KM leaders are being called upon to rethink their strategies and practices to ensure knowledge is leveraged as a strategic asset and continues to drive real business value. In the sections that follow, we’ll explore how KM can deliver on this promise.
Improve Speed and Scale
KM practitioners are familiar with common pain points such as new hires spending weeks searching for information, teams duplicating efforts, and experts answering the same questions over and over. APQC research into the best practices in organizational productivity (apqc.org/resource-library/ resource-listing/best-practices-organizational-productivity-survey-report) confirms that the average knowledge worker spends about a third of their time on nonproductive tasks: searching for information, repeating updates, or tracking down the right person to ask. For the modern KM leader, the challenge is to create seamless knowledge embedded in everyday work to free up time for employees to perform higher-value work.
♦ Intentional knowledge transfer is at the heart of enhancing employee productivity. It’s not just about storing documents for later access; it’s about converting the know-how in people’s heads into content, tools, and processes that others can then use. Mature KM teams deploy a variety of approaches, ranging from facilitated interviews that capture tacit knowledge to repositories that house codified best practices and structured plans for transferring critical expertise.