Knowledge Management: Digging into the Detail

Knowledge management continues to be a vital theme, with CCMA members actively engaged in discussions around best practices across several events and online platforms in recent weeks.

As contact centres navigate AI integration, evolving customer expectations and ever-changing knowledge requirements, leaders are grappling with practical questions about how to build and maintain effective KM systems. Here, we tackle some of the most common challenges that we’ve seen – based on responses collated from across our network.

How do you prioritise the advisor experience and ensure your Knowledge Management system caters to different learning styles?

Knowledge management has to be seen as a people strategy, not just a technology one. Different advisors absorb information differently – some prefer step-by-step guides, others visual aids or video content. Offering knowledge in multiple formats is important. Increasingly, AI-powered search is being trialled to surface the most relevant content based on individual advisor’s behaviour patterns. Regular feedback loops with frontline teams help identify what’s working and what needs adapting.

How do you encourage advisors to actively use the knowledge tool?

Making the business case visible helps. As one contact centre leader shared: “It’s been incredibly helpful for us to have association between the interaction and the knowledge that was used to solve it. We can then see all of the associated metrics with each interaction so can see the difference in AHT or NPS where knowledge was and wasn’t used.” When advisors see tangible proof that using knowledge improves their performance metrics, adoption naturally increases.

Creating knowledge champions within teams also drives engagement. As one member noted: “We’ve set up knowledge ambassadors – passionate staff from our contact centre who review new articles, meet monthly to keep them on the journey, and provide direct feedback.” This peer-led approach fosters ownership and continuous improvement.

How do you maintain accuracy without cumbersome review cycles?

This is where AI can prove valuable. Modern Knowledge Management platforms can proactively push alerts about duplicates, inconsistencies and content that hasn’t been updated recently. Research shows that 54% of organisations use more than five platforms to document information, creating unnecessary complexity.

Some organisations are also measuring ‘knowledge freshness’ as a key metric: tracking the percentage of content reviewed or updated within the recent timeframe to ensure accuracy without manual overhead.

When working with multilingual teams, how do you keep knowledge current across languages?

Automation can help. AI translation tools can flag when source content has changed, alerting teams that translated versions need updating. However, human validation remains essential for accuracy, compliance and tone of voice – especially in regulated environments where precision matters.

How can AI be used to enhance workflow?

Advanced tools now surface contextually relevant content in real-time, analyse sentiment to predict what advisors will need next, and generate draft responses for review. One member described their implementation experience: “I use it for our main Knowledge Base which our internal chatbot surfaces answers from. I add contents to longer articles and utilise dropdowns to separate areas.”

The most successful strategies use AI to handle scale and speed while maintaining human oversight for empathy and nuance.

How do you measure KM effectiveness?

Moving beyond basic metrics like article views is helpful. Some contact centres now track ‘time to knowledge’ (how long advisors take to find information), knowledge quality scores from user feedback, deflection rates showing how often self-service prevents contact, and correlations between knowledge improvements and Average Handle Time (AHT) or Customer Satisfaction (CSAT) changes.

These measures paint a broader picture of how knowledge is supporting performance and aiding continuous improvement.

The Bottom Line

You can’t future-proof what you can’t measure. Research suggests that people still spend up to 2.5 hours daily searching for information, and so accessible knowledge directly impacts productivity, consistency and customer trust.

Read the CCMA Good Practice Guide: Future-Proofing Your Knowledge Management Strategy