Predictions: What will most likely affect your customer center in 2024

The hype around generative AI will continue

One of the most important predictions of 2024 concerns the developments within generative AI, which has rapidly transformed from being a mere nice-to-have technology to a necessity for companies of all sizes. 

If you want to be successful in your work with generative AI, you must choose your partners and use cases carefully and lean into the exciting possibilities without getting carried away by the hype. Because while AI has raised the bar for what is possible to successfully automate, there are still many pitfalls – and it is important to keep in mind that the gap between good and bad AI will widen.

One of the big mountains to climb with generative AI within customer service is the application of Artificial Agents for both voice and text channels. To avoid compromising conversation quality while still tapping into the many possibilities that generative AI offers, organizations should start by having artificial agents handle your low-complexity conversations. Then, you can gradually move on to automating more complex calls.

Based on data from 35 M customer conversations, Capturi’s analysis shows that between 5-15% of all calls can typically be categorized in a low-complexity group. These types of conversations require no system integrations, are simple, and are often shorter than regular ones. Therefore, they are easy for artificial agents to handle and will offer measurable results right away.

If you want to know more about artificial agents, their possibilities and pitfalls, we have created a guideline for how to successfully implement them without compromising quality.

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Organizations will experience difficulties with generative AI investment returns

Research from Boston Consulting Group shows that a whopping 81% of Danish leaders are confident that generative AI will positively affect their businesses. However, a mere 5% have moved beyond early pilot projects.

Even though customer service is considered one of the areas that is most likely to benefit from this new technology, many departments are still struggling to navigate the opportunities.

The main reasons for this include the high costs of generative AI features, the challenge of determining where to start, and, finally, figuring out how to calculate an accurate return on investment (ROI), which can be a difficult and time-consuming task.

To ensure measurable and fast results, you should focus your initial investments on quick wins and low-hanging fruit and expand investments as the technology matures. This way, you continuously keep track of costs and increase your chances of going beyond the proof of concept (POC) stage. 

Due to the excessive amount of money and time invested in AI, it will be naive to assume that you will not make mistakes or that you will get a positive return on your investment right away – or even within the first 6 months. But keep in mind that it is okay to fail where it does not matter, but critical to succeed where it matters the most for your business.

Employee experience is becoming increasingly important

We often discuss the significance of increasing customer satisfaction and creating better experiences. However, keeping employees motivated and satisfied is equally important.

Employee experience is particularly important because customers' and employees' experiences are interconnected and influence each other. For instance, our research shows that when employees use positive words during a conversation, it leads to a 15% increased customer sentiment score.

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How to work with these predictions

In general, our predictions for this year revolve around new technologies, increased efficiency, and employee experiences.

While a one-size-fits-all technological solution is yet to be developed to fully automate customer inquiries and free up time for your employees to improve, you can still benefit from AI. In close collaboration with Adrian Swinscoe, we have created an article that guides you through the potential pitfalls of developing voice and chatbots.