When we talk about artificial intelligence and automation in the customer service context, the first thing that often comes to mind is chatbots, for both good and bad reasons. However, if your AI strategy is focused on customer-facing interactions like chatbots, you're missing some high-value opportunities to drive greater efficiency, agent productivity, and customer satisfaction.
In Valoir's recent study on generative artificial intelligence (AI), we found that customer service was ripe for automation driven by generative AI and other AI tools and technologies, but that customer service organizations lagged behind IT, finance, marketing, and other areas in the adoption of these technologies. On average, workers in customer service roles have automated 14 percent of their work in the past two years, while their peers average more than 20 percent.
We also found that customer service is one of the areas with the greatest potential for automation with emerging technologies like generative AI. An average of 39 percent of the current agent workday could be automated.
In our analysis of job-related activities for customer service representatives, we found that reading and responding to emails and entering data in applications—two areas that have high potential to be automated by AI—made up more than a third of the average customer service representative's day. Although that's not true for all customer service agents, there are a number of areas that should be high on your agenda for automation and AI. They include the following:
- Knowledge bases. Keeping knowledge bases up to date and delivering relevant information is a high-value application of AI, delivering faster case resolution for both customers pursuing self-service and agents looking to respond to customer issues. AI can be used to recommend answers, propose new knowledge articles that fill gaps, or update existing knowledge based on changing products or services. AI can also be used to automate agent and customer feedback on which articles are relevant or helpful, improving results for future similar cases.
- Case classification and summary. Applying natural language processing to case data and automating the case summary process is another high-value application, reducing the time agents spend closing out cases while increasing the detail of data that is being recorded for future analysis.
- Quality management. Putting AI in the hands of your quality team can help them improve quality review processes, quickly understanding trends and issues, identifying quality issues based on all cases rather than just a few, and pinpointing where training or reassignment can address a quality issue.
- Real-time agent coaching and guidance. AI can scale the capabilities of your best supervisors and deliver real-time guidance to all agents based on the specific details of the interaction they're having, AI can also identify coaching and training opportunities. And suggesting responses, for example, can flatten the learning curve for agents, accelerate case response times, and reduce escalations.
With these potential benefits and applications in mind, here are a few best practices for making the most of AI in your customer service organization:
- Deploy early and often. As the technology is rapidly evolving, you'll want to focus on pilots and projects that are incremental in nature and deliver rapid returns so you can afford to take advantage of what's coming next that delivers greater benefit.
- Bring all your channels and departments together. A coherent automation strategy means looking at all the ways customers interact with you, even those that might be outside your purview on the organizational chart.
- Demand flexibility and agility. Moving to a composable cloud architecture will enable you to best manage ongoing total cost of ownership, deliver new capabilities with less disruption, and be best prepared for whatever comes next.
Finally, as you build the business case for AI and automation in customer service, focus on opportunity, not job elimination. Savvy service leaders are taking advantage of labor savings driven by automation to deliver new service models that shift the contact center from a cost center to a growth one, driving greater agent satisfaction and retention and a better customer experience.
Rebecca Wettemann is founder and CEO of Valoir.