How AI agents improve First Contact Resolution in Contact Centers

Customer - I am facing the problem with my internet connection

Agent - I am sorry to know that but I will try my best to get it sorted

And then there is a series of verification, keeping the customer on hold and from time to time saying sorry for the long hold, I am checking with the back end team. And finally the agent says - I am creating a ticket and our team will get back to you.

Customer - Huh!  So the issue did not get resolved and triggered the start of the customer dissatisfaction.

“In fact, over 60% of customer support calls end without resolution in the first interaction, according to SQM Group.”

First Contact Resolution (FCR) is a benchmark for effective customer service. It is the ability to resolve the customer’s issue in the very first interaction.

FCR is often correlated directly with CSAT and customer loyalty — every 1% improvement in FCR can lead to a 1% rise in customer satisfaction and up to 5% cost savings for the contact center.

While brands and customers are desirous of a high level of FCR but still this remains elusive.

Content

• Can AI agents improve FCR
• How can AI Agents improve FCR
• Can AI agents replace the live agents
• How AI agents can further enhance FCR
• How to get started

Can AI agents improve FCR?

Lets not try to give a Yes or No answer. Let us analyze what hinders in getting a high level of FCR.

1. Availability: Most of the business don’t run 24/7 but the customers may be using the service even in the non business hours. Let's take the example of a retail store which may close after business hours but the customer has found an issue with a product purchased. Support agents may not be available and if off business hours the available agents may not be trained to resolve issues quickly.  
2. Speed: When a customer reaches out to the contact center, one thing is sure that he/she is looking for a quick response. Usually the speed at which an agent can respond is dependent on the skill of the agent. Many times, incorrect routing or unavailability of skilled agents is the cause of delayed response.
3. Verification process: Some process needs to verify the customer. This is part of the business policy which can’t be avoided. However the longer the verification process, the anxiety level of customers goes up and that can create more pressure on the service agents.
4. Training and attrition: Training process is quite expensive and with frequent attrition the effectiveness of training is not realised. With new agents barely experienced on the system is a great recipe to lower the FCR.Also consulting with back end team is not always possible.
5. Impatience and expectation gap: We are in the era of instant gratification. So customers expect quicker responses irrespective of the complexity of issues.This creates the expectation gap. Unfortunately this gap will be growing with time.
6. KPIs alignment: More focus on Average Handling Time (AHT) may hamper the FCR. Agents will be in pursuit of reducing the AHT to resolve the next customer in the queue. And in the process FCR takes a back seat. Also the accountability of unresolved tickets may not be directly associated with the agents.

Sure, there can be more reasons attributed to lower FCR and it can be different from business to business or in different sectors.

The irony is that most of these challenges stem from system limitations, not agent intent - making them perfect candidates for AI augmentation.

How can AI Agents improve FCR?

AI agents are automated systems which can respond to customer queries over phone calls, chats or even emails. These are powered by LLM and RAG (Knowledge base). With proper system prompts, and tooling (to get/post data from back end system) the AI agents can complete the tasks quite autonomously.

Lets review the same parameters and see how AI agents score against them.

1. Availability: This is by far the most obvious reason to implement AI agents. They are available 24x7 and can be very relevant for businesses whose customers avail the services round the clock or across different geographies.  
2. Speed: AI agents can respond instantaneously. There are no IVR menus, no waiting in the queue. It has all the skills required, be it multilingual or the different process knowledge.
3. Verification process: This can be quite smartly handled by AI agents. For example: send the OTP to the registered number or check the secret answer from the backend. It can definitely reduce the verification process. To add another level of security layer Voice biometrics can be implemented as well.
4. Training and attrition: While there is no attrition problem, it can be progressively trained on new skills, processes and how to handle more complex queries. Slowly it can learn how to deal with most of the customer concerns. With Reinforcement learning and regular testing, the AI agents become more intelligent and capable.
5. Impatience and expectation gap: With always available and quick to respond (no waiting time, no keep on hold), the new generation's expectation can well be addressed. 
6. KPIs alignment: Business may not need to worry for AHT as the number of AI agents can be running concurrently. There is no limit on headcount and there is no call waiting in the queue. So the focus swiftly moves towards improving FCR.

Apart from the above points, AI agents can be consistent, polite and have context awareness from past interactions and it can retrieve customer data in real time

For example, a leading e-commerce brand using AI chat agents saw a 32% improvement in first contact resolution within 3 months, primarily by automating refund and delivery-related queries.

Can AI agents replace the live agents?

This is an evolving discussion and it will become more clear with time. In customer service, the Pareto principle applies. This means that 80% of the calls are usually repetitive and not very complex whereas 20% of the calls or conversations are complex in nature. So the AI agents can be best used for the 80% of the calls - less complex and repetitive. For more complex calls the AI agent can do a hand off to live agents.

Instead of replacement, think of it as role evolution: AI handles the repetitive 80%, while human agents handle the nuanced 20% — with AI providing context, summaries, and recommendations.

Also the role of live agents can evolve to supervisor agents. Live agents can realtime monitor the quality of conversations and if it is not going as expected, they can jump into the call instead of waiting for it to be forwarded. This we call the Human agent in loop. This approach will further improve the FCR and provide customer delight.

This hybrid model not only ensures empathy where needed but also provides real-time quality control - where human agents can intervene before dissatisfaction escalates.

How AI agents can further enhance FCR

AI agents are becoming multi modal that means it can call, chat and email so that the customer engagement is enriched. Example - the AI voice agent can request the customer to send the invoice copy or photo of the damaged product delivered. Once the image is received it can analyze the image or extract the content and then decide the next steps. If the product is damaged and the invoice is genuine, it can initiate a refund or arrange the pickup of the damaged product.

Can you visualize how the FCR will be improving with time?

How to get started

The approach to use AI agents can be broken into some simple steps:

1. Identify simple, high volume use cases (FAQs, order tracking etc)
2. Ensure the AI agent is configured with backend system like CRM, ticketing systems
3. Setup clear escalation logic to human agents
4. Measure FCR, CSAT, conversation outcomes
5. Continuously train and enhance using real conversation data

Conclusion

The future of contact centers will have AI agents and live agents working in tandem and assisting each other. This will start improving FCR which inturn will lead to happier customers and a leaner team. In future, such smart conversational AI agents will ensure every first contact is the last contact needed.

FAQs

1. What is First Contact Resolution (FCR)?

FCR measures a contact center’s ability to resolve customer issues in the first interaction—without follow-ups or escalations.

2. Can AI agents replace live agents?

Not entirely. AI agents handle repetitive queries efficiently, while human agents focus on complex, empathy-driven interactions.

3. How can AI agents further enhance FCR?

By using multimodal capabilities like voice, chat, and image analysis to deliver instant, accurate resolutions and reduce the need for repeat contacts.