Supporting customers gets harder as your business grows. In a recent Connected Knowledge podcast, Upland’s knowledge management expert Karen Holliday shared her insights on how to scale support teams without adding friction for customers oragents.  

You can tune in to the full episode here, but here are the main takeaways you won’t want to miss! 

Knowledge Shouldn’t Be Hard to Find 

Karen kicked off with a familiar pain point: it’s not about how much knowledge you have, but how easy it is for your agents to find it.  

When organizational knowledge lives in a dozen different places—CRM systems, internal wikis, or even in a veteran support agent’s head—finding the right solution can seem impossible. This eats away at agent productivity and can leave customers growing more frustrated by the minute as they wait for help. Knowledge discovery should be painless.

The biggest friction point isn't what we don't know, it's how long it takes to find what we already know.

If agents spend too much time tracking down answers, it leads to frustration on both sides. Customers get impatient, and agents feel burnt out. Making knowledge easy to access is the first step toward creating a smoother support experience for everyone. 

How AI Is Changing Content Creation 

Gone are the days when only tech writers built your knowledge base. Karen explained that modern KM solutions include AI tools to unburden agents by making it simple for anyone on your team to create and update support articles. That means expert agents and technicians can add helpful steps and solutions right after solving an issue. No need to wait for someone else to write it up. 

AI democratizes content creation. Your best technicians can now contribute their expertise without waiting for writers to translate their knowledge into articles.

AI doesn’t just save time. There’s so much more. It helps capture valuable know-how as it happens. If an agent finds a better way to solve a problem, AI can turn that into a step-by-step guide for everyone to use in real time. Accelerating actual content creation ensures your knowledge base grows and answers are hyper-relevant to address changing policies and meet customers’ needs. 

See how Paychex made AI work for their agents

Smarter Search for Faster Answers 

We’ve all had moments where you know the answer exists, but you just can’t find the right words to search for it within your systems. Karen highlighted how new AI-powered search tools are better at understanding semantics – what you mean, not just what you type. For example, if you ask about a “connection timeout,” the search can suggest tips about networks, servers, or other related issues, even if you don’t use those terms. 

When an agent searches for 'connection timeout,' the system understands they might need information about network latency, server responsiveness, or firewall configurations. This semantic understanding dramatically improves search accuracy and reduces the time agents spend hunting for relevant information.

Knowledge management solutions with AI built in make it much easier for agents to find exactly what they need, fast. Intelligent retrieval learns from user behavior, identifying which articles successfully resolve specific types of issues. This creates a feedback loop that continuously improves search results. The system learns from what works, so if a certain article helps solve lots of tickets, it’ll rise to the top of search rankings in future searches. 

The Human-in-the-Loop Imperative 

AI is powerful, but Karen reminded us of a core truth: people are still at the heart of great support. While AI can write and suggest articles quickly, things work best when humans review that information. Expert eyes are needed to make sure advice is accurate, relevant, and fits your company’s standards. 

AI can create content faster than humans, but humans ensure it's accurate, appropriate, and aligned with company values.

The human touch is crucial to avoid mistakes and ensure your knowledge base remains trustworthy. Organizations need to make sure their AI systems don’t perpetuate biases, provide inappropriate recommendations, or compromise customer data privacy. Human reviewers act as essential gatekeepers. It’s a teamwork approach: AI does the heavy lifting, but your experts always make the final call. 

Empowering Both Agents and Customers 

One overarching theme from Karen’s talk is that great knowledge management benefits both agents and customers. When agents have fast access to the best knowledge, even newer team members can handle tougher questions. That means fewer delays or escalations and a more confident support team.  

Junior agents can handle complex technical issues typically reserved for senior team members, reducing escalation rates and improving first-contact resolution metrics. 

When Level 1 agents have Level 3 knowledge at their fingertips, the entire support structure becomes more efficient.

At the same time, customers get the information they need right from your self-service options. Rather than generic FAQ sections, modern self-service platforms can understand customer intent and provide personalized troubleshooting guidance. They’ll spend less time on hold and more time solving issues themselves, which is something everyone appreciates. The result is a win-win: agents are less overwhelmed, and customers are happier.  

How to Measure What Matters 

To take stock of what’s working, it’s essential to measure the metrics that have the greatest impact on both agent performance and customer satisfaction. KPIs such as First Response Time, Resolution Time, and Customer Satisfaction (CSAT) scores provide valuable insights into the efficiency and quality of your support processes. Tracking self-service adoption rates can also be helpful.  

But to truly optimize your support strategy, Karen emphasized looking beyond the usual stats like call length or ticket count. She suggested tracking things like: 

• How often your knowledge base gets used 

• Which articles are most effective 

• How support content affects customer satisfaction 

These insights can help you spot gaps, show what’s working, and guide your next improvements. Through consistent monitoring, you can make informed decisions that lead to better outcomes for both your agents and your customers. 

Putting New Tools Into Practice 

Rolling out new technology takes planning. To kick off the process, start with the most common or challenging support issues you face, so you can focus on building strong knowledge content for those areas first. Training is also crucial, there’s no question. Agents need to not only learn how to use new tools, but also how to balance AI support with their own expertise. 

Ready to Take the Next Steps? 

Dive into our AI Activation Report with Metric Sherpa to gauge your AI readiness.

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