As a KCS Program Manager with over a decade of experience leading enterprise Knowledge Management initiatives at companies like Symantec and Broadcom, I’ve witnessed the chaos that ensues when KM systems falter in a large support organization. Picture this: A global support team handling tens of thousands of cases monthly, agents scrambling to resolve recurring issues without reliable resources, leading to skyrocketing resolution times, frustrated customers, and burned-out staff—all while self-service portals sit underutilized. It’s a scenario I’ve navigated through massive acquisitions, like the $10.7B Symantec and $69B VMware integrations, where unifying knowledge platforms was key to slashing support volume through effective case deflection.
I’m Jeremy Henricks, a Knowledge Management and KCS leader based in Springfield/Eugene, OR, specializing in blending strategic oversight with hands-on execution to drive adoption, reduce costs, and boost customer satisfaction. In my roles at Broadcom and Symantec, I’ve scaled KCS programs for 1,500+ publishers and 300+ coaches, optimized AI-powered chatbots, and delivered analytics that contributed to $30M in savings in 2024 alone.
For those new to the term, Knowledge Management (KM) in the context of Knowledge-Centered Service (KCS) is a methodology that captures and evolves knowledge directly from support interactions, making it reusable for agents, customers, and self-service tools. It’s about turning everyday problem-solving into a structured, scalable asset.
In this post, we’ll explore the three primary hurdles in KM—Capture (documenting knowledge effectively), Search (making it easily findable), and Coaching (ensuring sustained adoption)—and provide tools to help you identify and overcome your organization’s biggest bottleneck.
Understanding KM Hurdles in Support Operations
In companies with sufficient case volume, KM is the backbone that connects frontline agents to institutional wisdom. It reduces repeat work, empowers self-service for customers, and fosters continuous improvement. But hurdles emerge from the sheer scale: Thousands of tickets daily, diverse product lines, and remote teams spanning time zones.
These challenges cluster around three core KCS processes:
- Capture: Documenting knowledge as it’s created.
- Search: Finding that knowledge when it’s needed.
- Coaching: Ensuring everyone buys in and executes flawlessly.
Ignoring any one can cascade into broader issues, like agent burnout or dipping CSAT scores. The key is diagnosing which hurdle is tripping you up most. In my work at Broadcom, where I managed KM through the integrations of Symantec and VMware, these hurdles were amplified by the need to unify disparate systems and teams across global operations. We handled case volumes in the tens of thousands monthly, and pinpointing the right bottleneck—whether it was incomplete capture during high-velocity support or ineffective search in a bloated knowledge base—was crucial to achieving a 25% reduction in support volume.
Hurdle 1: Capture – The Challenge of Documenting Knowledge
Capture is the foundation of any effective KM system. It’s the process of turning the tacit knowledge gained from resolving customer issues—those real-time insights and fixes—into explicit, reusable articles within the KCS framework. Ideally, this happens seamlessly in the workflow, allowing agents to document solutions without disrupting their rhythm.
However, in enterprise environments like those I’ve led at Symantec and Broadcom, capture often becomes a significant hurdle. Agents are under constant pressure, juggling high ticket volumes across complex products like security software, virtualization, and cloud management. Common challenges include:
- Time constraints: With back-to-back interactions, writing detailed articles feels like an added burden rather than an integrated step.
- Inconsistent standards: Without clear guidelines, articles vary in quality, leading to vague or incomplete content that’s hard to reuse.
- Lack of incentives: If contributions aren’t recognized or rewarded, agents may default to quick fixes without documenting for the collective good.
The impact is profound: Knowledge gaps persist, leading to repeated escalations and “reinventing the wheel” for every similar issue, which inflates both time-to-resolution (TTR) and the often-overlooked time-to-context (TTC)—the delay in understanding and applying found knowledge.
Hurdle 2: Search – The Quest for Findable Knowledge
Once knowledge is captured, the next critical step is making it accessible. Search involves leveraging tools and algorithms to quickly surface the most relevant articles, whether for agents resolving tickets or customers using self-service portals. In KCS, effective search turns a vast repository into a precision tool, reducing handle times and boosting first-contact resolution.
Yet, search hurdles are all too common in large-scale operations. From my experience optimizing self-service ecosystems at Broadcom, issues often stem from:
- Overwhelming volume and duplicates: In high-scale environments, knowledge bases naturally accumulate redundant content over time due to continuous creation across teams and evolving products, making it hard to find the “gold standard” article. While mergers integrated diverse product lines like security and virtualization, volume challenges were always present, and duplicates seem to be an inherent issue regardless.
- Suboptimal tagging and metadata: Poor governance leads to irrelevant results, especially when dealing with multiple teams and diverse product lines.
- Ineffective or misconfigured technology: Without advanced features like semantic re-ranking or NLP, searches typically rely on basic keywords, frustrating users and leading to abandonment. Tools like SearchUnify, which we used at Broadcom, require initial setup and regular tuning to address common pitfalls such as searches without results (due to knowledge gaps or synonym mapping) or searches without clicks (from irrelevant rankings caused by unoptimized relevance algorithms, outdated boosting rules, or insufficient query understanding).
The consequences? Extended resolution times (TTR) and increased time-to-context (TTC), as agents grapple with irrelevant or hard-to-digest results, lowering CSAT and underutilizing KM investments. In one initiative, I tuned search using SearchUnify, incorporating analytics from Python/NLP reports and dashboards to expose gaps. This is just one example of several that my counterpart and I undertook to scale our self-service to 1.5M+ monthly pageviews and 1,500+ daily chatbot interactions at mid-90% CSAT in late 2024/early 2025. Without addressing search, even the best-captured knowledge gathers dust, as agents opt for peer consultations or trial-and-error instead.
Hurdle 3: Coaching – Building a Knowledge-Centric Culture
Coaching is the sustaining force in KCS, focusing on human elements to ensure long-term adoption. It’s about providing ongoing training, feedback, and reinforcement to help agents internalize KM practices, from efficient capture to savvy search techniques. As a leader guiding 300+ coaches through enterprise acquisitions, I’ve seen coaching as the multiplier that turns good processes into great outcomes.
But it’s frequently the most overlooked hurdle. Challenges include:
- Resistance and skill variance: Veteran agents may resist new workflows, while new hires struggle with basics, especially in remote, global teams.
- Resource limitations: Managers may lack time for consistent one-on-ones or peer reviews, leading to inconsistent application.
- Cultural misalignment: Without leadership buy-in or tied metrics, KM feels like an ‘add-on’ rather than core to success—though at Broadcom, we were fortunate that our leaders were on board with KCS, enabling seamless adoption.
When coaching falters, the entire system weakens—adoption drops, quality erodes, and benefits like reduced TTR and TTC vanish. By implementing structured programs with certification paths, iterative feedback loops, and data-driven insights, companies can foster a culture where KM becomes second nature, ultimately deflecting cases by 50-80% (as seen in mature KCS implementations) through better self-service.
Capture vs Search vs Coaching: Where’s Your Hurdle?
These hurdles aren’t isolated; they interplay, but one often predominates based on your organization’s maturity. To help diagnose, consider this comparison drawn from my program audits:
| Aspect | Capture Hurdle Symptoms | Search Hurdle Symptoms | Coaching Hurdle Symptoms |
|---|---|---|---|
| Key Signs | Sparse or low-quality articles; agents cite “no time to write.” | Frequent search complaints; high abandonment rates in analytics. | Uneven KM participation; metrics vary wildly by team or individual. |
| Root Causes | Workflow inefficiencies; missing automation or incentives. | Poor metadata, outdated tools, or content overload. | Inadequate training; lack of feedback or cultural emphasis. |
| Quick Fix Ideas | Embed auto-drafting in ticketing systems; provide real-time feedback and recognition aligned with KCS principles for contributions. | Deploy AI-enhanced search with regular audits; refine taxonomy. | Establish coaching cadences tied to KPIs; use peer reviews and certifications. |
Reflect on your metrics: If case deflection is low despite a robust KB, search might be the culprit. In my Broadcom projects, diagnostics like these revealed coaching as the root for 60% of teams, leading to targeted interventions that unified processes post-acquisition.
Note: Auto-drafting—whether powered by AI tools like LLMs with Retrieval-Augmented Generation (RAG) or simpler template-based systems—should always incorporate human review as a core step to ensure accuracy, security, and alignment with internal standards. For example, SearchUnify’s Knowbler allows editing of AI drafts before submission while flagging issues for review; ServiceNow employs real-time anonymization (e.g., synthetic PII masking) and RBAC to protect sensitive data; and Salesforce’s Einstein provides source citations for verification, grounding outputs in secure internal data via the Einstein Trust Layer. Always source exclusively from internal, approved data sources (e.g., case notes or knowledge bases) and apply PII redaction techniques to prevent risks like data leaks or compliance violations.
Strategies to Overcome Your KM Hurdles
Overcoming these hurdles requires a tailored yet integrated approach grounded in KCS best practices, focusing on straightforward strategies that can be implemented with minimal disruption while driving quick improvements. For capture, leverage tools like LLM-driven auto-summarization from ticket transcripts (with human review to ensure accuracy and security), as this aligns with capturing knowledge in the moment and in the requestor’s context per the latest KCS v6 guidelines.library.serviceinnovation.org For search, invest time in understanding your analytics dashboards—such as those in SearchUnify—to further tune relevance; I often used these reviews to surface friction points and drive improvements that enhanced click-through rates (CTR), among other analytics.
For coaching, build scalable programs that span both the Solve Loop (real-time capture and reuse) and Evolve Loop leadership, as in my global adoption efforts: weekly huddles, intent/entity definition for AI content, and prompt engineering to guide improvements, incorporating updated techniques like Process Alignment Review (PAR) for adherence and quality. Holistically, align all three through KCS best practices.
A real success story from my work at Broadcom post-VMware acquisition illustrates this synergy: Facing all three hurdles, we prioritized coaching to unify 300+ coaches through simple, consistent practices, enhanced capture with structured tools, and improved search via ongoing tuning. This approach cut TTR and TTC across divisions, scaled self-service dramatically, and contributed to $30M+ in savings in 2024 by reducing case volumes.
Bonus Insight: What Actually Reduces Time to Context
Time to Context (TTC) is the silent killer of efficiency in support, often overlooked alongside the more commonly tracked Time to Resolution (TTR). It’s the delay before someone can truly act on the knowledge they found. And if they don’t understand it, the information is outdated, or is too long, they may continue searching.
Here’s what helps drive it down:
- Structured Templates – Articles that follow a consistent, well-defined format—Issue → Environment → Resolution—help readers navigate and apply content more efficiently.
- Live, Context-Rich Capture – Capturing knowledge in real time retains the language, details, and decision points that are often lost when writing later from memory.
- Search Optimization & Findability – Even well-written content fails if no one can find it. Strong metadata, semantic tagging, and relevance tuning are essential for surfacing the right knowledge at the right moment.
- Plain Language & Active Voice – Use clear, direct language that supports comprehension without technical overhead. Write as if you’re guiding a capable teammate who’s encountering the issue for the first time.
- Content Health & Maintenance – Overly long or outdated articles increase cognitive load and introduce doubt. Reuse should always trigger a quick review to ensure content remains accurate, concise, and relevant.
Elevate Your KM: Turning Hurdles into Opportunities
Pinpointing your primary Knowledge Management hurdle—whether Capture, Search, or Coaching—marks the starting point for revolutionizing your support operations. Drawing from over a decade of enterprise KCS leadership at Symantec and Broadcom, I’ve seen how targeting the right bottleneck with data-driven strategies can unlock transformative efficiency and ROI. Begin by auditing your processes against KCS v6 principles, focusing on metrics like article reuse, search abandonment, and case deflection rates (aiming for 50-80% as seen in mature implementations). Experiment with the practical solutions outlined—such as AI-assisted capture with human oversight, search tuning via analytics, and holistic coaching across Solve and Evolve Loops—and track progress to refine your approach. By committing to these steps, you’ll not only boost team productivity and customer satisfaction but also position your organization as a leader in scalable, knowledge-centric support.
If this sparks thoughts on your org’s challenges, connect with me on LinkedIn—I’m passionate about strategizing KCS roadmaps. What’s holding your KM back? Let’s leap those hurdles.