The path to faster AI ROI realization? AI-powered contact center agents.

With any business investment, there’s pressure to demonstrate a positive return. So as more and more companies invest in artificial intelligence, there are definitely high expectations for positive ROI in the form of productivity gains, cost reductions, or revenue increases. 

Unfortunately, brands are often unable to demonstrate that positive ROI. According to a recent Gartner survey, while more than 700 IT leaders surveyed had adopted AI, nearly 50% struggled to show AI’s value.  

One of the main reasons that brands aren’t seeing strong returns on their AI investments is because they may have adopted the AI without first identifying use cases and a strategic plan for implementation. Fortunately, we’ve found that with contact center AI, when clients identify proven use cases up front and then match the technology to the problems to be solved, they see very positive ROI.

In this article, we’ll look at two examples where contact centers are successfully using AI to save money and increase productivity.

How AI and automation can empower agents

Contact center agents are one of your most valuable resources. They perform a truly impressive role in the customer journey, one in which they solve problems, relate to customers with empathy and understanding, and ultimately make the connections that only a human can make. With generative AI they can do even more. 

Eliminating language barriers

Language gaps and communications issues create friction in the customer experience – costing contact centers over $5B annually. To address this challenge, contact centers could put more effort into hiring agents for all the languages they need to serve – a strategy that’s neither cost-effective nor practical – or they could use technology to make any agent multilingual. 

The ROI benefits from this type of technology deployment are tangible almost immediately. Take Deliveroo for example. This food delivery service with locations across Europe, the Middle East, and Asia serves customers in dozens of languages, but during times of peak demand, customers often had to wait for an agent who spoke their language to become available. Staffing their contact centers with more multilingual agents was not a viable option. 

Instead, Deliveroo decided to deploy AI-enabled translation tools that allow agents and customers to select their preferred language, with the system automatically translating the message for both parties. For example, if an English-speaking agent receives a customer’s message in French, the web chat translation panel displays the customer’s original message next to the English translation. The agent can respond in English and the response will be automatically translated into French. Almost immediately, this implementation led to a 20% decrease in average handle time and an 83% first call resolution. 

The implications of real-time translation go well beyond productivity gains. Reducing language constraints allows contact center leaders to focus on hiring for the skillsets and traits that drive success. Eliminating language barriers can also reduce the need to operate costly low-occupancy language queues around the world. Finally, real-time translation opens the door to new geographies for service delivery that were previously impossible due to the availability of languages in the specific market. 

Reducing agent workload/increasing productivity

Post call wrap up or aftercall work (ACW) can cost contact centers an average of $30-$40 per day per agent, which translates to $8,000-$12,000 annually per agent. If a contact center employs 120 agents, this figure could add up to approximately $1.5 million annually. 

While expensive, this work is vital for contact centers to capture important customer insights and information while it’s still fresh in agents’ minds. Automation is already speeding up this process with simple topic extraction. Now generative AI can take these enhancements further, allowing agents to passively generate chat or call interactions and distill them down into core intents, key topics, and more across the contact center’s full contact volume. 

These automatically generated conversation insights can help inform macro-level trend monitoring to spot issues before they become even bigger problems. For example, a computer manufacturer using this type of AI in the contact center might spot a trend of customers using the term “overheating battery.” By flagging this issue early, the manufacturer can take proactive steps to remedy the problem. 

While it’s difficult to quantify the ROI of alleviating a hypothetical problem before it becomes worse, we can easily see the cost benefit of eliminating after-call work for contact centers altogether. Looking back at the average cost of after-call work in a 120-agent contact center, that’s a savings of about $1.5 million annually. 

What’s more, eliminating tedious after-call work not only frees agents up to handle more interactions, but it can also improve their job satisfaction by reducing the repetitive components of their jobs. 

Balancing AI with customer effort

Too often, brands use AI to automate part of the customer journey without accounting for how this change might introduce friction. For example, a brand could roll out a chatbot that automates the first 30 seconds of a customer engagement and then forwards the customer to a live agent once the topic has been identified. This automation may save on labor costs, but it creates friction by forcing the customer to switch channels to solve their problem. And if the customer must wait on hold during the hand off or reshare information with the live agent, imagine how frustrated the customer becomes. 

On the other hand, agent assist technology like real-time translation and conversation summarization are two effective ways to use AI for value realization without increasing customer effort or creating friction. Of course, these are just two examples of how advances in AI are changing the ROI equation. As brands look at ways to use AI to reduce costs, drive revenue, and increase productivity they must always keep customer effort reduction as their number one goal.