Imagine this. A super busy contact center with the phones ringing off the hook. Contact center agents engaged in conversations, doing their best to address customer concerns and troubleshoot problems. In this fast-paced environment, having prompt and accurate solutions to customer issues can be a game changer. In such instances, call notes and call summary aid the agent in accessing correct information. Customer issues are addressed relatively faster when call notes and call summaries are easily accessible to agents.

The names, call notes and call summary, might seem self-explanatory, but both these features in a real-time agent assist tool have additional aspects that make them indispensable to contact center agents.  

Call notes and call summary are generative AI-powered features in Gnani.ai’s real-time agent assist tool, Assist365. For more details on how they are a boon for agents, keep reading this blog.

Call Notes

Each contact center agent handles about 200 calls in a day on average. It is difficult to remember details of each of the calls handled in a single day. Remembering details of calls from weeks ago? Impossible!

So how are agents supposed to refer to relevant data from previous calls? What about in cases when the previous call was handled by another agent? Can the agents not access this information at all? This information is made accessible to agents only through call notes.   

How Call Notes Used to be Taken?  

Traditionally, contact center agents had to manually take down call notes after each call. Agents had to invest their time and effort into taking down these notes. The same resources could be used in handling more customer issues, but it was essential to keep records of the calls. So, agents had to write down call notes at the end of each call they handled. This was cumbersome for the agents and call notes were prone to more errors as agents were bound to be exhausted after handling a huge volume of calls daily. The after-call work (ACW) just added to the agents’ workload. But then came generative AI. Generative AI changed the way several tasks were performed including how call notes were taken in contact centers around the world.

One would wonder how generative AI models can help contact center agents take down call notes. The process is quite simple in reality. Read on to find out.

Using Generative AI for Notetaking 

After a call is handled by a contact center agent, a transcript of the call is generated using a speech to text technology. This transcript is then fed into an LLM which analyzes the transcript and extracts all the relevant information about the call from the transcript. This is then shared with the agent who handled the call.

Call Summary

A call summary is a condensed and organized representation of the key details of a customer interaction. It is a concise record of the information exchanged between a customer and a contact center agent during the course of a call.

How Call Summaries Used to be Written?  

Typically generated through automated processes, call summaries capture relevant customer inquiries, concerns, and resolutions discussed during the conversation. These summaries encompass factors such as the nature of the call, any products or services discussed, pertinent account information, and the resolution or the next steps agreed upon.

Using Generative AI for Summarization 

The process of generating call summaries is almost the same as that of generating call notes. A transcript of the call is generated using a speech to text technology. This transcript is fed into an LLM which draws up a point-wise summary of the interaction. This point-wise summary is shared with the agent and manager.

Benefits of Call Notes and Call Summary

Generative AI-powered call notes and call summary are helpful to agents because of the following reasons:

1. Enhanced accuracy and consistency: Generative AI guarantees that call notes/summary are accurately transcribed and consistent throughout various interactions, minimizing mistakes and misinterpretations that might arise when taking notes manually.
2. Time savings: By automating call notes and call summary production, agents can spend less time manually taking notes and summarizing after each call and instead concentrate more on attending to client needs and finding effective solutions.
3. Better customer experience: With more time available for engagement, agents can provide clients more one-on-one attention, which improves interactions and the customer experience on the whole.
4. Comprehensive information capture: AI-powered systems may gather a variety of data, such as crucial specifics, client sentiment, and pertinent context, resulting in more thorough call notes and call summaries that facilitate more efficient customer interactions in the future.
5. Quick information retrieval: Generative AI creates organized call notes and call summaries that are simple to search for and retrieve, allowing agents to rapidly find the information they need during follow-up encounters.
6. Possibilities for personalization: Call notes and call summaries generated by AI give agents insights into consumer preferences and history, allowing them to modify their talks in light of previous contacts and preferences.
7. Consolidated knowledge base: The AI-generated call notes and call summaries help to develop a strong knowledge base over time, which can be used to train new agents, enhance procedures, and share best practices throughout the contact center.
8. Shorter training period: Reviewing AI-generated call notes and call summaries can help new agents comprehend various circumstances and consumer enquiries. Thus, increasing their productivity.
9. Data-driven insights: By analyzing the structured call notes and call summaries generated by AI, it is possible to spot trends, recurring problems, and consumer behavior. This information is crucial for making strategic decisions and streamlining processes.

10. Scalability and cost efficiency: As call volumes rise, generative AI can effectively handle more call notes and call summaries without the need to add staff, leading to cost savings and improved scalability for contact center operations.

Notetaking and Summarizing with Assist365  

Assist365 is the agent assist module in Gnani.ai’s unified CX platform. This is a generative AI-powered tool that optimizes agent performance in real time. Generative AI-suggested hints, checklists, and guided workflows help agents handle calls faster and more efficiently. Assist365 also has an in-built knowledge search feature that provides agents with immediate access to relevant information and recommendations during live customer interactions from knowledge bases. This tool also supports easy integrations across various systems. Post-call notetaking and summarization is generative AI-powered in Assist365. So, post-call work is significantly reduced, and this time can be optimized to handle more calls. Overall, Assist365 helps in the efficient running of contact centers.

Conclusion

While call notes and call summaries were always a part of efficient running of contact centers, making them generative AI-powered has helped in ensuring more efficient customer service. Generative AI has transformed real-time agent assist tools to be more helpful in delivering seamless customer service. The difference that generative AI makes is clearly visible in call notes and call summaries generated through AI.

Check out Gnani.ai’s real-time agent assist tool, Assist365, powered by generative AI.