While there’s been a rush of excitement about AI’s potential to improve contact center efficiency, let’s bust one myth upfront. AI isn’t going to replace the contact center agent anytime soon.
Most customers still prefer speaking to a human customer service agent, especially when attempting to resolve complex issues. Live phone calls make up around 65% of all inbound contact center interactions, and that number is expected to remain relatively steady over the next several years, according to our research with ContactBabel.
A 2024 study of US consumers found that live phone calls are still the preferred channel for customer service (selected by 70% of respondents). That preference isn’t as surprising as it might seem on the surface. Human agents have contextual knowledge, can experience and express empathy, and can approach problem-solving in ways that even the most advanced AI solutions can’t.
So where can AI support customer service operations?
What AI can do is augment human capabilities. In ContactBabel’s survey of over 200 businesses, all respondents either agreed or strongly agreed that AI will be used to support agents, while only 26% agreed or strongly agreed that AI would replace agents.
AI is often used to automate repetitive activities, which frees up more time for contact center agents (and their managers) to solve complex problems and strengthen customer relationships. It can also analyze vast data sets and make predictions based on patterns, enabling contact centers to extract meaningful insights from their customer data and improve service delivery.
Here are six examples of AI in customer service operations:
Call summarization
Generative AI, which is trained on large language models (LLMs) to generate human-like responses to prompts, is a useful tool for summarizing contact center conversations. This helps managers quickly review what happened in any conversation and reduces the time agents spend on after-call work.
Real-time agent guidance
AI agent assistance software can analyze conversations in real time and dynamically guide agents with tools like intent-based checklists, coaching prompts, and knowledge base content recommendations. This reduces the time agents spend searching for information and helps them resolve issues without transferring the call or putting customers on a long hold, leading to a better customer experience.
Data analysis & insights mining
Machine learning, another type of AI, can analyze conversation data for meaningful patterns and trends. This helps contact centers identify call reasons, measure how specific agent behaviors impact the customer experience, uncover trending product or service issues, and more. Contact center leaders can use this conversation intelligence to improve agent training and real-time guidance or, in some cases, solve upstream issues to deflect future calls.
Quality assurance automation
Machine learning also helps contact centers automate the most time-consuming part of the quality assurance (QA) process—completing agent scorecards. While most contact centers are only able to manually QA 1-2% of all conversations, QA automation makes it possible to score 100%. This gives managers a better view of agent performance and trends, helping them deliver targeted coaching and improve their real-time scripts, prompts, and workflows.
Chatbot support
Chatbots aren’t new, but they’re getting more advanced (and better at delivering human-like responses) thanks to generative AI. Contact centers are increasingly using chatbots to deflect calls and help customers resolve relatively straightforward issues, like password resets or account information lookups. However, contact centers using generative AI chatbots still need human oversight to make sure their chatbots don’t share inaccurate or outdated information.
Predictive analytics
AI models can analyze data from past interactions to predict customer behavior and preferences. By analyzing conversation data, AI models can even predict how customers would have rated their satisfaction, effort, or overall sentiment, eliminating the need for contact centers to rely on post-interaction surveys. Predictive analytics help contact centers anticipate customer needs, personalize interactions, and offer proactive support, ultimately leading to higher customer satisfaction and loyalty.
AI is here to help, but agents are still stressed
While AI technology is becoming increasingly embedded in the contact center, much of the early focus has been on improving customer self-service and increasing overall operational efficiency. There’s been less focus on using AI to augment contact center agent skills, which is a missed opportunity to improve human-centric customer service.
Agents don’t have it easy. Contact center processes are complex, with agents frequently juggling multiple applications to access the data they need. Agents are expected to handle a large volume of calls and often deal with complaints and emotionally charged customer issues. And, as self-service channels pick off straightforward customer issues, agents are often left with the most challenging problems to solve.
Stressful work environments and lack of upward mobility take a toll on contact center agents, leading to average attrition rates of close to 40%. In turn, high attrition rates increase the time managers must spend hiring and training new agents. Additionally, it’s difficult to find experienced agents in the job market, and hiring inexperienced agents may lead to longer onboarding times and a higher risk of errors in customer interactions.
A negative agent experience impacts customers and employees at every level of the contact center. While there’s a lot of excitement around customer-facing AI solutions, more contact centers need to think about the AI solutions that eliminate complexity behind the scenes and help agents succeed.
Choose AI solutions that help your agents so they can help your customers
It’s clear that customers still want the option to speak to a human agent, especially when they have a complex problem to solve. Rather than attempting to fully automate customer service with AI, contact center leaders need to use AI in ways that help their agents deliver great customer experiences.
We take an in-depth look at the importance of supporting your agents and the role of AI in our eBook, “Simplify your agent experience to deliver for your customers.” You can access it now to get tactics for improving the agent experience and examples of real businesses that are successfully balancing human-centered support and AI technology.
