Before we start the AI transformation, it’s helpful to clarify what Knowledge-centered Service (KCS) is and what it is not. KCS is more than just knowledge management. It is a methodology, developed and refined by the Consortium for Service Innovation, that integrates knowledge creation and maintenance directly into the process of resolving customer issues.

Unlike traditional knowledge management, which often depends on dedicated authors and post-hoc documentation, KCS considers knowledge a by-product of problem-solving. Articles are written and refined by the same support agents who are closest to the customer. It’s designed for dynamic environments where speed, accuracy, and shared learning are essential.

This agent-authored, reuse-first model helps organizations transition from static, centralized knowledge systems to dynamic ecosystems that adapt in real-time. However, even KCS has faced challenges: authoring can be time-consuming, and successful adoption often depends on changing frontline behaviors. That’s where AI is ready to make a transformative impact.

The Persistent Friction Problem

While KCS has long promised faster, more scalable support by empowering agents to create and reuse content in the flow of work, it has always faced a stubborn obstacle: it requires support professionals to step away from solving problems (the work they were hired to do) to document knowledge, often through cumbersome workflows.

This context-switching penalty has real costs. Agents lose momentum on active cases, struggle with blank-page syndrome when authoring, and often delay documentation until details fade from memory. The result? Inconsistent adoption and knowledge that’s either missing or lacks the depth needed for reuse.

AI as the Great Enabler

AI fundamentally transforms this dynamic. Oracle is embedding generative AI directly into the KCS process, enabling automatic content generation from service requests (SRs), smart compose tools to assist with knowledge authoring, and automated content recommendations to support the evolve loop. Here’s an example:

For example, instead of an agent needing to stop and manually create a knowledge article after resolving a complex issue, generative AI can summarize the entire SR, extract the resolution steps, and draft an article in seconds. Smart compose tools can enhance this further by rephrasing technical content into customer-friendly language, organizing information into structured formats, and even translating it into different tones or languages. This minimizes context switching, eases writing anxiety, and accelerates time to publish.

But the real breakthrough isn’t just speed – it’s removing psychological barriers. When agents see a well-structured draft instead of a blank page, they’re more likely to refine and contribute rather than abandon the task entirely.

The Multiplier Effect

This provides much more than just efficiency; it eliminates the main obstacle that has historically limited KCS adoption. With AI handling the heavy lifting of initial drafting and formatting, agents can focus on problem-solving while still building a growing and evolving knowledge base. The result? KCS becomes not only more scalable but also more intuitive, integrating smoothly into the agent’s workflow. AI transforms KCS into a proactive system for continuous learning and quicker resolutions.

Continue reading part 2 here.