Knowledge Strategy for Contact Centers
Four steps to build accurate, governed knowledge your agents and AI can rely on. Reduce training time, improve response speed, and support compliance.
Step 1: What should your knowledge goal be?
Your goal should be: get the right answer, quickly.
This goal unlocks a knowledge strategy that scales for agents, contact centers, and AI tools.
When organizations reframe their knowledge goal away from agent memorization and toward quick access, new hire training time drops. One contact center cut new hire training by 50% by shifting from a program that required agents to memorize product details to one centered on teaching agents to quickly pull information from a single reliable source. Agents gained confidence, accuracy improved, and performance soared.
Once you reframe your goal, you can work backward and decide what tools, governance, and training you actually need.
Key takeaways:
- Reframing knowledge goals from memorization to "quick access" improves agent confidence and performance
- A clear goal defines what knowledge matters and how to organize z
- When agents rely on knowledge tools rather than memory, training time drops and first-contact resolution improves
- This goals works for human agents and AI tools alike
Step 2: How do you ensure knowledge accuracy?
Accurate knowledge is the prerequisite for an AI-ready strategy. If your knowledge base contains errors, both human agents and AI systems will deliver wrong answers.
According to webinar participants, 71% said human agents and AI are equally likely to give wrong answers when working from the same knowledge base. This points to a simple truth: knowledge accuracy is a shared problem, not an AI problem.
Two practices improve knowledge base accuracy. First, use one source of information whenever possible. Knowledge gaps get amplified when different systems contain conflicting or outdated information—one training team managed a three-month delay in updates simply because they maintained separate knowledge bases for training and agent use. Second, create a governance plan to assign clear ownership of accuracy. Without ownership, no one is accountable for keeping information current.
Key takeaways:
- A single source of truth eliminates conflicting information across systems
- When different teams maintain separate knowledge bases, accuracy breaks down and updates slow
- Governance is the foundation that keeps knowledge accurate over time
- Accuracy must be built before implementing AI systems
Step 3: Who should own knowledge management?
Knowledge governance requires a clear owner. Without one, accountability for accuracy disappears and gaps proliferate.
In our webinar, when asked who owns knowledge in their contact center, most participants answered "everyone"—a potential red flag. Distributed ownership makes it hard to instill accountability.
The best ownership model depends on your situation. Centralized ownership assigns one person or team to manage all knowledge; this works well for large volumes of constantly changing information. Role-based ownership assigns different knowledge domains to different teams—product updates product information, training updates procedures, managers update policies; this works when teams have established responsibilities. Hybrid ownership combines a central owner with role-based domain experts. For smaller teams, assign each agent a knowledge domain and make them responsible for keeping it current; this builds engagement and spreads the workload.
Key takeaways:
- Without a single owner, accountability for knowledge accuracy vanishes
- Centralized ownership works for high-change, high-volume knowledge
- Role-based ownership works when teams own distinct knowledge domains
- Hybrid models combine oversight with distributed expertise
- Small teams can engage agents by assigning them knowledge domains
Step 4: How do you unlock agent performance?
Once your knowledge is accurate and well-governed, train agents to use knowledge tools rather than rely on memory. This is where the "quickly" in "get the right answer, quickly" becomes measurable.
Start during new hire training. Design training around real scenarios agents will encounter in their work, ordered from simplest to most complex. In each scenario, challenge agents to use their knowledge tools to find the correct procedure, policy, or answer. They start slow, but agents rapidly gain speed and confidence as they become adept with the tools.
Embed knowledge tool usage into ongoing performance feedback. Quality assurance should check whether the correct answer was given quickly. One-on-ones should include knowledge management as a regular discussion point. When agents ask questions, share the source, not the answer—this reinforces tool usage. One contact center reduced new hire training by 50% using this approach; agents had higher confidence and performed better because they learned to rely on tools, not memory.
Key takeaways:
- Agents learn faster when trained on how to find answers, not what the answers are
- Knowledge tool proficiency drives faster resolution and higher first-contact resolution
- Training that emphasizes tool use scales better than classroom instruction
- Ongoing feedback that reinforces tool usage maintains performance gains
- Knowledge strategy is AI-ready once knowledge is accurate, governance is clear, and agents are trained
FAQ
Q: Can a contact center have an effective knowledge strategy without AI?
A: Yes. An effective knowledge strategy rests on four fundamentals: a clear goal, accurate information, clear ownership, and agent training. AI amplifies the value of a well-designed strategy, but it is not required to build one.
Q: How do you balance in-class training with on-the-job training?
A: Training is most effective when it resembles the actual work. Agents learn faster when they practice finding answers in their knowledge tools at their workstation, rather than in a classroom. One contact center eliminated virtually all classroom training for onsite agents and had them complete training from their desks; agents quickly learned to use tools to solve problems.
Q: How do you help agents feel empowered to make decisions rather than escalate?
A: Provide clear, well-documented empowerment procedures and decision guidelines. One company allowed agents to issue account credits up to $1,000 without supervisor approval, with clear guidelines for when and how much to credit. Agents were given clear boundaries but could not get in trouble for following them. An audit process identified inconsistencies and training opportunities without fear of penalty.
Q: How does knowledge governance support AI?
A: Knowledge governance ensures that AI systems draw answers only from approved, accurate, governed knowledge. Without governance, AI will amplify errors and compliance risks.
How USU Supports Knowledge Strategy for Contact Centers
Contact centers with 50 or more agents can use USU Customer Service Knowledge Management (CSKM) to build accurate, governed knowledge that agents and AI tools can rely on.
USU CSKM provides one reliable source of truth for agents, customers, and AI systems. The platform includes editorial workflows and approval processes to support the governance approaches described in this article, intelligent search, decision trees for complex procedures, and native integrations with Salesforce, Zendesk, and Intercom. Knowledge analytics let you measure performance and spot gaps before they become customer-facing problems. AI capabilities operate within a controlled architecture, meaning AI-generated answers draw only from your approved, governed knowledge.
Learn more at usu.com/en/knowledge-management
Get a Free Knowledge Audit
Not sure where your knowledge gaps are? USU offers a free knowledge audit to help contact center leaders assess the current state of their knowledge, identify opportunities for improvement, and build a roadmap toward an AI-ready strategy.
