Automation and cost savings are key drivers for contact centers investing in AI, but these ideas are too high-level to build a business case around. Automation holds universal appeal, especially for contact centers, which are still primarily rooted in legacy technology and have limited automation capabilities. As customer demand continues to change, the need for more and better automation becomes greater.

There are two sides to the automation coin to consider–customer-facing and internal. For customer-facing needs, self-service is a prime use case for automation, as legacy IVR falls short of meeting expectations. For this post, we’ll focus on the flip side of how automation brings value to internal operations.

Contact centers can stand to improve efficiencies in many areas, mainly due to the constraints of legacy technologies. Many tasks and processes in these environments have major manual components that cannot keep up with today’s fast-paced digital customer expectations. This is where AI-driven automation comes into play, not just for what customers demand but for the operational efficiencies that keep things running behind the scenes. To illustrate, here are three ways contact center AI can improve operational efficiency.

1. Automating Workflows

Seamless customer experiences (CX) may be transparent to the customer, but this requires a high degree of orchestration internally. Consider the range of workflows and processes needed to pull all the correct information together at the right time for every agent and interaction. Aside from agents sharing information among themselves, supervisors have direct and real-time workflows to manage with each agent.

Another layer down, there are processes between agents and various operational functions to manage things like address changes, order processing, payments, refunds/credits, approvals, scheduling, and workforce management. All of this is required for agents to be effective in their roles, and given the real-time nature of customer service interactions, internal operations must be efficient.

To varying degrees, these workflows run in isolation and require numerous human touchpoints. When call volumes are steady, operations can be fairly efficient, but these workflows break down when they rise or spike suddenly. Legacy contact center technologies may perform well for their intended purpose, but they don’t scale well when conditions change and are not adaptable when adding new forms of automation.

The starting point for AI-powered automation is that most of these workflows require manual inputs for highly structured processes. The ideal conditions for contact center AI are mapped-out workflows that can be handled in less time and with greater accuracy. With machine learning (ML), AI will keep improving, and analytics will help contact centers identify patterns across workflows that can lead to new efficiencies.

2. Automating Self-Service & Routing

This area directly impacts customers. Automating self-service and responses to inquiries improves customer satisfaction and reduces the volume of interactions agents need to handle, optimizing workflows and improving efficiency.

Contact center AI, such as conversational AI and chatbots, has improved its ability to understand context and intent, which improves its ability to route conversations when needed. For self-service, this means having customer-facing chatbots that can handle more complex inquiries, reducing the need for agent handoffs.

On the other hand, AI-based routing can assess the skill set of all available agents in real time and determine which agent is the best fit for each inquiry.

3. Automating Conversation Summaries

In most contact centers, agents must manually summarize each call, which is time-consuming and puts pressure on busy agents. Not surprisingly, these notes are often incomplete and inaccurate.

However, this information is critical for improving operations and understanding the customer, which makes this a leading use case for contact center AI. Since calls are recorded, the conversations become digital, making it easy to produce transcriptions. This is a common capability, and with AI, the transcripts can provide a much more significant impact on operational efficiency.

What AI brings is the ability to extract action items from those transcripts. AI can automate the entire post-call process. Even with a small amount of training, AI can tie in a customer’s entire journey with a contact center and contextually understand what needs to be done after the interaction. Then, those action items can be passed on to the correct people. Aside from making every agent’s job easier, the payoff becomes even more significant when considering the collective impact on overall operations.