However, customer service agents today spend most of their time on mundane, manual tasks, making it difficult to provide the level of service that customers demand. Generative AI-powered chatbots are revolutionizing customer service by quickly resolving issues through self-service. They deliver direct, conversational responses so customers get the answers they need, fast.

Imagine how much faster and easier basic issues could be resolved if customers could ask a generative AI-powered chatbot: “What steps should I take to resolve my internet issue?” The response returned is a summarization of the most relevant information pointing them to the optimal solution, instead of a response that requires the customer to sift through endless articles and guess which one would be most helpful.

If the chatbot can’t solve a customer’s issue, and they’re escalated to a live agent, that agent needs to understand the customer’s question and previous interactions. When the customer’s case is complex, which is often the case when dealing with complex digital services, multiple conversations often occur that all contain important information for an agent trying to solve the issue. This is where generative AI can help an agent better serve the customer.

Generative AI can summarize conversational exchanges so agents understand the context and can quickly propose resolutions. What's more, it can also recommend the next best action for the agent to take in resolve the customer’s issue.

After a case is complete, generative AI can summarize the case, a necessary yet time consuming and mundane task. Imagine an agent taking 10 minutes to manually summarize a case. That doesn’t sound like a lot of time but consider the fact that some agents manage more than 20 cases in a given day. That’s over 3 hours of time saved to work on that many more cases and other critical projects.

Stage 4: Service and network assurance

Last but certainly not least, CSPs need to be prepared to resolve critical issues fast. But when a network event happens, teams are inundated with technical details on what’s not working, why it isn’t working, and the steps required to solve the issue. Today, someone needs to read, comprehend, and summarize what broke, why it broke, and how to fix it. Generative AI can be used to summarize the incident, leading to faster issue resolution and improved customer experiences.

To start, GenAI can distill complex incident data into simple, actionable summaries for fast and efficient incident resolution. Take a fiber cut, for example. When a fiber cut occurs, incident management teams are hit with complex technical data generated by monitoring tools and systems. The vast amount of technical data from logs, alerts, and event details can quickly become unmanageable.

Imagine how much faster an agent could resolve the fiber cut incident if they could ask generative AI: “How do I summarize this incident and what is the next best action to take?”

Generative AI can provide teams with a clear account of the event, including notes with essential context, like the exact location of a fiber cut. As customers are notified about the case, generative AI ensures the message is easy to understand without any technical jargon, concise, and contains the information most relevant to each impacted customer.

Getting Started with Generative AI

In the era of generative AI, nothing is more important than responsible implementation. That includes ensuring data is accurate, consistent, relevant, and secure, and that the right governance is in place.

It’s also critical to prepare your workforce. Employees need to understand how generative AI will impact, augment, and improve their work. If employees don’t trust the tools, they won’t use them, and organizations will never realize their potential.

Roles will evolve and new skills will be required to maximize generative AI implementation. As we innovate, we must also educate. CSPs will need to train and up-skill their employees to ensure they have the necessary competencies and knowledge to effectively use AI.

Just as important is creating a culture that thrives on innovation and collaboration. Employee buy-in is table stakes for making the most of generative AI.

It’s important to remember that generative AI implementation can’t happen overnight. Start small. Pick an area that’s ripe for disruption, something that’s manual or time intensive, and build. Test and learn what works and expand from there.