In the contact center industry, it's safe to say that the initial buzz around AI has quickly given way to a constant hum. Today, organizations everywhere are shifting their position, strategy and investment decisions away from viewing AI as a shiny new toy to an area where they want to see tangible benefits.
But how is this playing out in practice? For many contact centers, AI's big impact is currently behind the scenes across a range of functions, from boosting productivity and improving the accuracy of scheduling and forecasting to monitoring customer performance or predicting customer behavior. Whatever the specific priorities are, by implementing AI to enhance the capabilities and working lives of agents and leaders, customers also see the benefits through improved interactions and outcomes.
Balancing out this widespread sense of enthusiasm, however, are some concerns. Firstly, it's essential to recognize that managers are apprehensive about AI's influence on agents' mental health and training needs. In addition, while AI's current role is primarily one of a supportive companion rather than a job thief, there is a great deal of uncertainty over what the future might hold. Over the medium to long term, the successful integration of AI into the contact center landscape will require organizations to formulate a strong game plan for addressing these challenges and ensuring success.
As anyone in the contact center industry knows, there is currently a machine-generated elephant in the room: how will AI impact the role of agents in contact centers across the world?
According to Gartner, for example, AI is "currently at the Peak of Inflated Expectations", but by 2025, "80% of customer service and support organizations will be applying generative AI technology in some form to improve agent productivity and customer experience." They have also predicted that by 2026, AI will have reduced contact center labor costs by $80 billion.
Elsewhere, industry research has shed more light on the emerging trends. When asked how AI will help the future contact center workforce, a quarter of respondents said increasing agent and manager productivity were among their top three responses. This was closely followed by optimizing forecasting and scheduling, measuring and understanding contact center productivity, predicting customer actions and behaviors and providing a chatbot service to customers.
Speaking directly to contact professionals, however, quickly uncovers a common thread, in that many believe AI will either replace jobs or, at the very least, play a major role in supporting agents and management so they can focus on more complex tasks.
This seems like a reasonable perspective to take. Examples of using AI in a contact center augmentation role are already common. For instance, the technology can be used to handle simple queries via self-service channels, allowing organizations to refocus agent resources on more complex tasks that require human expertise and experience.
The problem is AI is currently nowhere near becoming a 'plug and play' technology. Many agents will be used to spending their time dealing with errors made by the current generation of bots or assuming control of customer interactions when they reach their limits. Without a significant improvement in performance, it's likely that AI implementation will be piecemeal, particularly if performance problems damage leadership confidence in the technology.
The obvious question to ask at this point is what can organizations do to ensure their AI strategy delivers on the clear potential for improvement?
An ideal starting point is to ensure agents have full visibility of the entire customer journey, including all interactions with bots. This will help ensure they can analyze all the relevant information when they assume control over a conversation. While this may seem like an obvious requirement, many contact centers don't currently have this capability and agents have to work using an incomplete picture of the customer journey. In some situations, agents may need to ask customers to repeat information they have already provided, which is less than ideal for both parties in the conversation.
With full conversation visibility, however, contact centers are then in a much better position to harness the benefits of technologies such as conversational analytics to monitor and improve the performance and quality of AI bots. It also ensures that teams who manage the AI bot experience can replace guesswork with data-led insights to make better decisions when reviewing conversation performance and responsiveness.
With these foundations laid, organizations then have a range of options about where to apply AI technologies. At present, many are finding that the obvious place to start is to use it to better handle routine customer interactions that often happen at scale. In this context, only more complex enquiries are escalated to human agents in a process that not only improves contact center productivity but also reduces customer call waiting times.
Crucially, this isn't just about bolting on an AI tool. Embracing this new technology works best when users carefully consider how AI can be integrated with existing processes. The objective should be to enhance efficiency without disrupting workflow and also include mechanisms so improvements can be made in the light of real-world experience and customer feedback.
Despite the potential for AI to act as a catalyst for change in contact center environments, human skills, experiences and their capacity for empathy remain at the heart of any successful strategy. With this front of mind, leaders should recognize that their teams will need training and support to understand how AI technologies work, their operational role and how they - as contact center professionals - can influence their development within the organization.
As the pace of AI innovation continues to accelerate, it's increasingly clear that ignoring the changes the technology is already bringing is not a viable option. With so many organizations using their customer service capabilities as part of their messaging, contact canters that don't examine how AI can improve their processes are at real risk of being left behind. But, guided by these ideas, organizations stand a good chance of delivering a win-win situation in which contact center efficiency and customer outcomes improve simultaneously.
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