Customer service

10 examples of AI automation to help agents work faster

Enreach 09/04/2024
Clock icon 6 min
Level: Intermediate

Artificial intelligence (AI) developed for customer service is not intended to replace agents, but to complement them. The emotional intelligence of agents combined with the automation provided by AI is the perfect combination to deliver a flawless customer experience (CX).

But its power does not end there. This technology also has a positive impact on the agent experience (AX), freeing them from managing simple enquiries and performing repetitive tasks where they do not add value.

Here are ten use cases that demonstrate the benefits of integrating AI to help agents be faster, more efficient and work on their continuous improvement.


Bots are ready to ask an open-ended question like “How can I help you?”, interpret the customer’s response in the moment, and decidewhether to offer self-service (if within their capabilities) or transfer the conversation to the most qualified agent to handle the request.

As with IVR menus, you can use them to identify the user’s intention and intent and create transfer rules that take into account agent skills.

Unlike IVR, conversational and/or generative AI bots do not require you to configure the conversation diagram per channel and provide a better customer experience by understanding the customer’s query first.


Another task a bot can do before transferring the conversation to an agent is to identify the person it is talking to.

By querying the CRM, ERP or ticketing tool, it can search the database for the ID card, phone number or any other identifying information the user has provided.

This way, when the agent receives the call or chat, they can address the customer by name and quickly check their call/message history and customer record.


When the customer indicates they want to speak to an agent, or the bot has determined it’s best to transfer the conversation to an operator, the agent receives the call or chat along with a summary of the previous conversation.

In addition to a transcript of the conversation and a record of all actions taken by the bot, the summary of the conversation identifies keywords and the customer’s sentiment.

With this information in advance, the agent can resolve the customer’s query more quickly and efficiently, because they know exactly what the customer needs, what the bot wasn’t able to provide, and what tone to use depending on the customer’s emotional state.

For example, if the bot has detected that a customer is angry because they have a problem with their phone line, the agent will know all this information before answering the call and will be much more effective in their verbal communication and problem solving.


According to a recent study we conducted, bots can handle up to 80% of simple queries in a contact centre.

Whether answering FAQs about a product or service, resolving a technical issue found in a user manual, resetting a password or recommending a credit line, bots free agents from these types of calls or messages, giving them more time to handle inquiries that require emotional intelligence or to assist those customers with special needs.


In addition to resolving simple queries, bots can also provide self-service by sending documents to the customer, such as invoices, contracts, forms, etc.

Whether verbal or written, the bot can instantly deliver the document the customer needs through any platform.

For example, if a customer calls the call centre and asks for their latest electricity bill, the bot can send it via the customer’s preferred channel: WhatsApp, SMS or email.


As part of self-service, bots can also generate a ticket detailing the customer’s problem. By gathering all the relevant information from the conversation, they can create a new ticket, fill it in, and send the customer the incident tracking number.

Especially for queries that cannot be resolved in the first instance, bots can handle this part of the process, reducing human error and freeing agents from repetitive tasks.


As well as being omnichannel, Artificial Intelligence is multilingual, meaning it speaks and understands all languages. This feature opens up a world of possibilities for contact centres, not only to better deal with requests that are not in our native language, but also to provide service in any language.

Whether it is a voice or chat conversation, we can ask the bot to transcribe the customer’s answers in the language we want, and also respond to the customer in their own language.

This functionality is ideal for when we have a spike in Spanish calls in the queue, as we can redirect these calls to agents with basic knowledge and have them assisted by the bot to help customers more smoothly and professionally.


In addition to assisting during a call as a translator, the bot can also help agents when they have a question about a product/service or a clause in a contract.

If we share all our documentation with the AI: manuals, legal documents, blog posts, use cases, etc., we will end up with an internal ChatGPT that is knowledgeable about all our processes and products/services.

This will be the great ally of the agents, because if they don’t know how to answer a customer’s question, they can ask the internal chatbot, and if it’s in the documentation we’ve provided, it can answer it.

This functionality not only avoids prolonging the conversation, but also allows any agent to provide support on a topic they are not an expert in.


The After Call Work (ACW), refers to all the tasks that agents must complete after each interaction before moving on to the next customer.

Artificial Intelligence can speed up this process by creating a real-time transcript of the conversation, analysing customer satisfaction by highlighting keywords that serve as typification, and linking this data to the details of the conversation.

The ability to automate AWC time as much as possible is essential to increasing agent efficiency.


AI also has quality monitoring capabilities, which means it can evaluate the performance of agents. This data helps contact centre managers to identify each agent’s areas for improvement and work on strengthening them.

We can train AI to generate a report after each interaction that includes: the ID number of the interaction, a title, a summary and, for example, an assessment of the agent’s presentation, friendliness and effectiveness.

This functionality contributes to the continuous improvement of agents and therefore to their speed.


As we have seen from these examples, AI provides automation across the entire customer service cycle: from the tasks that precede the interaction to the assistance it provides during and after the conversation.

Currently, there is no other technology that can optimise so many processes at once, taking into account both the customer and agent experience.

Want to meet the artificial intelligence that will revolutionise your customer service? Call us on +34 900 670 750 or fill in this form.

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