The Persistent Challenge of Sharing What We Know
For years, knowledge management leaders have wrestled with a familiar and stubborn challenge: getting frontline support agents to consistently share what they know in the knowledge base. Whether it was known as “tribal knowledge,” “tacit knowledge,” or simply the knowledge trapped in the heads of experienced agents, the outcome was the same. Valuable insights, practical fixes, and real-world workarounds rarely made it into structured, reusable formats.
This friction has long been a barrier in the adoption of Knowledge-Centered Service (KCS) principles. In the early 2000s, many organizations tried incentivizing article writing, building workflow integrations, or mandating contribution as a metric. Still, resistance endured. Many agents viewed documentation as a distraction from case resolution. Others feared giving away their “edge” or lacked confidence in their writing skills. The knowledge base grew, but unevenly and often without sustained engagement.
AI: A New Lens on an Old Problem
Today, AI has reshaped knowledge sharing through Retrieval-Augmented Generation (RAG) models, enabling enterprises to deliver contextual, data-driven insights. Platforms like SearchUnify, Salesforce, ServiceNow, and Zendesk lead with enterprise-ready AI solutions. SearchUnify’s Knowbler, developed by Grazitti Interactive, uses a proprietary [F]RAG™ approach—likely federated RAG—to streamline knowledge curation and personalize support within CRMs like Salesforce. Salesforce’s Agentforce, powered by the Atlas Reasoning Engine, leverages RAG via the Data Cloud to boost agent productivity and customer experiences. ServiceNow’s Now Platform, with Now Assist and the Workflow Data Fabric from the Yokohama release, employs retrieval-augmented techniques to optimize IT and business workflows. Zendesk’s Resolution Platform, driven by Zendesk AI, uses a Knowledge Graph for Generative Search and AI Agents to automate support requests across channels.
As these systems gain traction, organizational knowledge has become more valuable than ever. They can surface answers, summarize case histories, and draft helpful articles—but only if the source material is strong. And therein lies the modern twist: if agents don’t contribute accurate, up-to-date knowledge, the AI cannot deliver meaningful outcomes.
We’re no longer just talking about internal process efficiency or knowledge reuse. The stakes are now front and center. Poor content means bad chatbot responses, irrelevant search results, and broken self-service experiences. AI tools magnify the quality of the content they consume. If garbage goes in, garbage comes out—at scale.
Redefining the Agent’s Role in the Knowledge Economy
This shift reframes the role of the support agent and the knowledge manager. We’re not asking agents to document for documentation’s sake. We’re inviting them to shape the AI-driven systems customers now interact with. Their observations, context, and real-world language feed directly into systems that power deflection, automation, and smarter support.
Fortunately, AI is not just a demanding consumer of knowledge—it can also be a generous partner. Leading tools now suggest articles based on ticket data, draft knowledge entries from case resolutions, and flag outdated content. Agents can shift from authors to editors. Knowledge managers can shift from chasing content to curating and coaching. The effort involved in contribution is shrinking, while the impact of each contribution is growing.
What Doesn’t Work: Incentives Without Insight
Gamification and traditional incentives have often led to a surge in article volume but not in quality. When contribution becomes a numbers game, content tends to be rushed, redundant, or too generic to be useful. Quality knowledge requires judgment, context, and editorial oversight—not just quotas. The solution is not to abandon motivation but to realign it with value and impact. Sustainable knowledge practices require thoughtful engagement, not point systems.
A Better Way Forward: Shifting Culture, Tools, and Roles
To create a sustainable future for knowledge sharing in support organizations, three shifts are necessary:
1. Reframe Contribution as Impact
Move beyond metrics like number of articles written. Showcase how a single contribution reduced case volume, improved chatbot accuracy, or helped a teammate. Highlighting tangible impact encourages meaningful engagement.
2. Make AI the Assistant, Not the Replacement
Use tools that draft, summarize, or flag content without removing the agent from the loop. Let agents focus on clarity, nuance, and practical language. Empower them to guide the AI, not fear it.
3. Treat Knowledge Work as a Career Path
Recognize and reward agents who excel at making knowledge usable. Build roles like Knowledge Coach or Content Curator. Make knowledge part of the performance conversation and an essential component of career growth.
The Future of Knowledge is Built on What We Share Today
Ultimately, the problem of knowledge hoarding hasn’t disappeared—but the landscape around it has changed. The incentive to share is now directly tied to the performance of the systems agents and customers rely on. The opportunity is to stop treating knowledge contribution as a separate task, and start treating it as the heartbeat of modern support.
The next generation of support experiences will be only as good as the knowledge they are built upon. With AI amplifying every insight, the agents who contribute what they know are not just solving today’s tickets—they’re actively shaping how tomorrow’s customers find answers.