Knowledge management in customer service – how AI changes everything


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Put yourself in the shoes of a service agent: whenever a customer reaches out, you want to ensure you have everything you need to answer their query and the latest information at your fingertips. Not having this makes both your experience, and that of the customer, a frustrating one.

Achieving this is dependent on multiple factors, but a core component is knowledge management. Historically referred to as Knowledge-Centered Service (KCS) – or establishing a framework for collecting, structuring, reusing, and improving knowledge consistently to leverage it for maximising support outcomes – many brands have invested in better knowledge management to help juggle scalability and overall experience.

Despite this, knowledge management in customer service and beyond is often a mixed bag, with access to knowledge and knowledge sharing across organisations varying considerably. Indeed, some studies have found that the average large business loses millions in productivity each year as a direct result of poor knowledge management practices.

The gradual adoption of Artificial Intelligence (AI) in the service sector changes this in many ways – it reveals a possible new era for how knowledge is managed, but also exposes how far some brands still have to go to maximise the benefits. Let’s explore this further.

Knowledge management just got easier with AI

Knowledge management, if done correctly, is the cornerstone of good customer and agent experience. By having access to a demand-driven, self-improving knowledge base, brands will see numerous benefits ranging from time to knowledge, better turnaround times, and reduced agent onboarding times, to name just a few.

However, establishing and maintaining such a vast database has historically been a task only a few have done well. In short, it’s a time and resource-heavy exercise, requiring strong collective ownership, access to the right knowledge, and a culture of continuous improvement.

That’s until AI, and specifically Generative AI (Gen AI), have entered the frame and already started to alter possibilities when it comes to establishing, using, and maintaining a knowledge base. For example, there are new platforms that can harness Gen AI to catalogue written responses and even scrape websites and resources to create an AI-powered knowledge base in minutes. Rather than being manually updated, AI can help automate maintenance by parsing support chats, website updates, and company materials to ensure agents are using the latest information available from a single source.

This up-to-date, self-improving database can then be used to provide agents and customers with consistent and high-quality automated support. For example, as Gen AI can learn from materials and generate new content, once trained it can support agents live. With AI Assist features, AI can improve messages and even recommend tone alterations based on the brand voice.

For customers, the use of automated technology such as chatbots has often been a source of frustration, often down to engaging with bots that lack the requisite knowledge to provide the right answer quickly. In a similar way to how an AI-driven knowledge base can help reduce time to knowledge for agents, it can also improve the accuracy and speed of automation experiences for end customers.

But are businesses ready?

In the age of AI, your knowledge base underpins everything. But while the technology can go some way to help establish and then maintain your knowledge center, it is not as simple as ‘plug in this technology and you’ll be ready to go’. Before jumping in, all brands need to be asking themselves questions like: are we ready? What do I need to have in place to reap the full benefits? This is where a lot of brands may come unstuck.

For example, AI is only as useful as the knowledge and content it has access to. If you’re feeding it sparse information, incoherent articles, or badly out-of-date FAQs, then even if you’re time to knowledge can be improved, the quality of responses and service delivery will fall short. Importantly, these new models and up-and-coming Gen AI platforms aren’t able to understand customer needs, frequent issues, and use cases like your brand does – jumping in too soon, or relying on a model that isn’t fine-tuned to your specific business, can result in unreliable and low-quality experiences. You only have to look at the recent Air Canada example to see the impact this could have.

This is an important consideration in the context of wider challenges relating to data readiness amongst organisations. As the CEO of Accenture recently put it: “Most companies do not have mature data capabilities and if you can’t use your data, you can’t use AI.” Recent surveys reinforce this claim, with some finding that the majority of companies won’t be data-ready for Gen AI for some time.

Centring knowledge for human agents

This shouldn’t deter any brand looking to revamp and rethink its knowledge management capabilities. The impact of good knowledge management on service delivery has been known (and realised) for some time, and recent developments in AI open up new possibilities to empower agents and improve agent effectiveness, while also enhancing customer self-service experiences.

But it’s important to avoid jumping head-first before you’ve had a chance to take stock of your data-readiness. Doing so can be the difference between transforming the experience you’re offering, or potentially exacerbating existing issues.

Dvir Hoffman
Dvir is the Chief Executive Officer at CommBox; prior to this, Dvir was the Chief Product Officer for two years. In this time Dvir has overseen the integration of generative AI and developed a product that looks to the future of customer service trends. Now, with particular interest in innovation and automation, Dvir works to challenge traditional friction points and rethink how to approach customer relations.


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