The cost and quality of customer service are a constant balancing act. Some companies over-index on quality to make support a real differentiator. Others cut costs at the expense of quality. While companies may have different reasons for focusing on the cost-quality balance, it’s a critical goal for them all.

With the advent of support technology over the last 20 years, companies have invested in a new set of tools to get a little more quality for a little less cost. Today, most companies have at least an online help center and a ticketing system. Some have already gone further and deployed solutions to automate part of their support efforts with chatbots or interactive troubleshooting.

We are on the verge of a new evolution of Customer Service with AI presenting another promising avenue for customer service teams to elevate their standards of quality and efficiency. This happens at a time when companies are increasingly investing in proactively helping their customers to make them more successful and engaged.

With that in mind, I want to talk about three predictions that will shape Customer Service for years to come.

Prediction #1: Companies Will Shift More to Self-Service—and Customers Will Love it

Having customers be successful by themselves is the holy grail for companies. It makes customers happier and limits the need for support costs. Regrettably, today’s self-service support falls short of meeting customers' expectations. While 88% of customers want to self-serve, only 13% actually do so, highlighting a substantial gap between aspiration and reality in customer support efficacy (Source 1: Statista, Source 2: Gartner).

Let’s take a deeper look at this. The promise of Self-Service is that customers can get help instantly. It seems simple and fast. In reality, when customers have a question, searching for the answer by themselves seems cumbersome and risky: they might not find the right information, the content might not be up to date, and there might be options that are not publicly available. Therefore, it appears easier to delegate that responsibility and effort to support people: as a customer, I send my request and wait for someone to help me.

This will change soon. With AI-based experiences, people won’t have to choose between self-serve and contacting support: AI will analyze customer queries and guide them to precise answers or solutions tailored to their specific data and circumstances. All questions will either be dealt with via Self-Service—because the answer is in the documentation or a process has been defined—or the AI will direct the customer to human support, which will take the lead and help. There will be one simple, unique path to request help.

On top of self-service becoming the first stop for every customer request, there will also be fewer customer requests because help will become more proactive. Help will be pushed while customers are onboarding or struggling to do something. Imagine when filling out a form, instead of having a red error message, you get a guide to help you fill the form out properly, depending on your context. Solving most friction points will make the overall customer experience smoother and positively impact engagement and satisfaction.

Every company wants to reduce tickets and encourage customers to be more successful. AI and proactive support do exactly that.

Prediction #2: The Customer Service Agent Role Will Evolve

As described in the first prediction, improved AI-powered self-service experiences will transform level-one support. Automated handling of transactional queries and complex processes will become commonplace, reducing the need for human intervention. Yet, human support will still be needed for many reasons. Here are a few:

  • Unplanned issues that are not automated, such as bugs and new product launches. In these cases, manual handling of customer requests becomes necessary for external responses and internal information processing.
  • Requests that require human action. This includes verifying restricted feature access, compliance checks, situations requiring high empathy, or when automation isn't available yet.
  • Support-driven sales. Some questions lead to opportunities to upsell relevant products or services that require human interaction.
  • Customers who prefer live support. Even with excellent self-service, some customers prefer personal interaction. In these cases, support serves as the company's representative, handling and nurturing the customer relationship.

Support teams will likely be smaller, but support agents will continue to play a valuable role requiring deeper expertise compared to traditional level-one support. They will need to troubleshoot better, take care of more complex processes, communicate with the right teams to solve issues, and highlight where the customer experience needs to be improved.

Training, efficiency, and accuracy will continue to be key for agents, and they will rely on tools that help them autonomously navigate toward resolutions. As for finding the right knowledge, AI will play a pivotal role by helping agents identify the right resolution path for various scenarios and outlining the necessary procedures to follow.

Prediction #3: Great Knowledge Will Shape the Future of Customer Service

Companies already invest a lot in knowledge today, but the ROI is low because customers and agents underuse it. Not only do people not make an effort to find it, but even when pointed in the right direction, knowledge is difficult to navigate due to a bad user experience, such as one-size-fits-all articles. That’s why 72% of agents struggle to find the information needed to respond to customers (Source: Zendesk).

AI will be a game changer to improve knowledge usage, but the result will only be as good as its source. Here are a couple of things that can go wrong by mixing poor knowledge and AI:

  • If the answer isn't in the knowledge, AI won't locate it. AI is skilled at grasping the meaning behind questions and then searching through its vast knowledge to provide precise and tailored responses. AI won’t be able to help whenever a question is not documented.
  • If knowledge is ambiguous, there is a heightened risk of "hallucinations." A hallucination is when AI misinterprets content and generates a false answer. One of the main reasons for this is the lack of clarity or structure, making it challenging for AI to interpret and comprehend the intended meaning accurately.
  • Unstructured knowledge complicates verification, hindering the ability to confirm the answer source. If verifying what AI provides takes as long as searching a document, people will return to asking for help.
  • Some questions demand a process, not a simple answer. For instance, if someone says, "My Hoover isn't working," the ideal response should offer a troubleshooting guide to resolve the issue rather than a long generated text.

These are just a few examples of how the quality of the source knowledge will make or break AI-powered experiences.

Not all knowledge will be delivered through AI. To show customers and agents how to perform actions, companies will need to be able to push help where people need it proactively. Knowledge does not have to be passive. Why explain to customers how to turn on a setting if they can do it directly from the knowledge? Why show an agent how to navigate through the back office if an action can be automated from the knowledge?

Great knowledge will not only enable AI experiences but also provide the fastest route to resolution for any situation.

The Emergence of Knowledge Platforms for Customer Service

In the same way CRMs helped digitalize the Customer Experience, knowledge platforms will enable companies to service their customers with higher efficiency. With just one tool, companies can create, maintain, and distribute knowledge for agents and customers alike. No more siloed AI chatbots, help centers, internal documentation, and digital adoption tools—this functionality will be part of one consistent, reusable, adaptive solution.

We built Stonly to be the first knowledge platform dedicated to Customer Service. Stonly is different than other knowledge platforms in two important ways. The first is how content is structured—instead of linear articles, Stonly builds interactive content that adapts to every situation. The other is the ability to distribute content everywhere it's needed with no code. Stonly provides high flexibility to manage every situation, delivers AI experiences, makes content easy to maintain with reusable modules, and integrates with the rest of the Customer Service tools to automate actions and guide customers and agents to success. All this is tracked to build automated reports and dashboards to follow the ROI and monitor for obsolete content.

Siloed, one-size-fits-all, unstructured content will soon be an outdated way of organizing customer service knowledge. Stonly is the knowledge platform built for the future of customer service. It provides companies with everything they need for the customers to be satisfied and their agents to be effective. As companies embark on a new journey toward better and more efficient customer service, Stonly will be the solution their teams use.