Using Voice AI to Increase First Contact Resolution (FCR)

Updated on May 15, 2025

Frost and Sullivan classify First Contact Resolution (FCR) as the “home run” of call center metrics because “it affects every other meaningful statistic from the call center.” 

FCR captures the number of calls that are resolved on first contact. 

And since 80% of customers cite FCR as the reason for switching to a competitor, improving this metric is a high-priority goal for all customer service leaders. 

But, there’s a catch. Call centers usually have difficult-to-manage QA and change processes; your plan has to move through multiple hands before you can even track the results. That’s why monitoring performance and administering contact center fixes is a full-time job. 

One way to bypass this is through voice AI transformation. Many enterprises are adding phone AI agents to their call center workflows to increase the rate of L1 and L2 resolutions. This increases first contact resolution, reduces the average handling time, and helps human agents focus on business-critical problems that must be prioritized. 

This article will highlight the role of voice AI transformation in improving FCR. We’ll cover:

1. Why Traditional FCR Improvement Strategies Aren’t Enough in the Age of AI?
2. What is Voice AI and How Does it Improve First Contact Resolution?
3. What are the Benefits of Voice AI in FCR?
4. How can you use FCR to Boost Brand Loyalty and Reduce Churn?
5. What are the Best Practices for Voice AI Implementation?
6. What are Some Case Studies About Voice AI and FCR?

Why Traditional FCR Improvement Strategies Aren’t Enough in the Age of AI?

According to SQM, contact centers reduce their operational overheads by 1% every time they improve their FCR score by the same amount. This has meant that customer service leaders and contact center executives have spent decades trying to optimize this metric. 

However, established FCR strategies are less effective today than ever before. And there are a few reasons:

1. Complexity of Customer Problems – Most customers now expect personalized help when they get on a phone call. However, traditional call center processes armed with pre-made scripts and templates and restricted access to internal documentation severely limit a human agent’s ability to access information and provide resolutions during the first call. 

2. Manual Quality Assurance – One of the problems with third-party contact centers is that they randomly sample calls for QA. While the practice is standardized because it reduces the absolute time required for quality assurance, it can also hide crucial information during the process.
   You don’t get to look at all the information, and don’t have granular details about why a metric might not be improving. It’s also significantly harder to get to actionable insights when reviewing the raw call transcript. 

3. Siloed Information – Most information about customer problems already exists in internal documentation. However, customers must constantly repeat themselves because these data points are often siloed. For example, your sales and marketing CRM might have information about why a customer dislikes a newly launched feature. However, because no one at the contact center can access this information, your customers have to explain themselves repeatedly. 

4. Lengthy Agent Onboarding – The contact center industry is plagued by high attrition (with rates reaching over 30% in 2021). New agents also take 3 to 6 months to get fully onboarded. This means some of your agent workforce will always be under training and underperforming in FCR. 

Traditional systems were built around the needs of a different generation of customers. As workflows and customer needs have evolved, they’ve become less effective overall. 

However, that doesn’t erase the fact that 33% of customers are frustrated because they must constantly repeat themselves to customer service representatives. 

One way to solve this gap is through voice AI. The following section will discuss how this technology helps you solve your FCR performance.

What is Voice AI and How Does it Improve First Contact Resolution?

Voice AI agents for customer service are specialized software that can pick up customer calls and solve their L1 and L2 queries. This AI agent works in five steps:

1. Speech Recognition (Speech-to-Text) – Converts the spoken words from your customers into text. 
2. Natural Language Processing (NLP) and Natural Language Understanding (NLU) – After translating the customer’s voice into text, the AI agent understands the intent and the core issue the customer is discussing. 
3. Conversational AI – Using the context of the question, the AI agent answers.
4. Text-to-Speech (TTS) – The response is again translated into a human-like voice.
5. AI-Powered Analytics – The AI agent monitors the call to understand customer sentiment and behavior, and once the conversation ends, it collects metrics around CSAT, AHT, and FCR.

Since voice AI can solve simple queries faster, it improves your overall efficiency in answering calls. Let’s understand how this works.

How Does Voice AI Directly Elevate Your First Contact Resolution (FCR) Rates?

Voice AI has some mechanisms that directly address FCR performance –

1. Intelligent Self-Service

Voice AI agents drastically reduce the workload on your human agents by automating a large majority of L1 and L2 level queries at scale. These AI agents can query your databases, find relevant information, and give it to the customer, deflecting many incoming calls.

Most L1 and L2 queries are already resolved on first contact, but automating these processes gives your human agents time to review more critical issues in depth. With this increased time, they can also increase FCR rates for complex and nuanced customer queries. 

2. Precise Call Routing

One of the major bottlenecks in customer service lies with routing. If the customer’s call doesn’t reach the right agent, the resolution takes several more rounds of routing. 

Voice AI agents have specific ticketing algorithms that help you decide the right agent for different issues, making the overall process easier. 

Minimizing the number of times a call is routed to different agents helps agents solve relevant problems faster and improves FCR. 

3. Agent Assist for Complex Issues

Data silos are a significant problem for enterprise data. Your human agents must jump between windows and multiple tabs to find relevant information to help your customer. Voice AI agents can automate this process by performing a documentation search and finding the best possible answer to the customer’s question.

So, when the call is routed to a human agent, it also comes with proper documentation of the problem and the relevant documents that make the resolution process easier and faster. 

4. Deeper Understanding of Customer Intent

While most human agents do take the time to match the customer with their backend 

account to pull up relevant information, this is a lengthy process. With Voice AI, this contextual retrieval is built into the program. 

So, when the call is routed to an agent, they get the full view of the issue, the customer profile, and possible solutions. This increases the likelihood of a quick resolution. 

5. Post-Call Automation and Insights

The voice AI agent transcribes every customer call as part of its operations. This effectively improves your QA process so you can identify specific patterns in customer problems, isolate agent-specific performance, and implement solutions for FCR improvements from day one.

The basic principle here is simple. Voice AI is the more intelligent and intuitive cousin of IVR-based deflection, and it allows you to solve for FCR problems faster. It also offers more tools to your agents so that they can resolve the tougher queries on the first call, improving your overall performance at the contact center. 

Following up on this principle, let’s understand the key benefits voice AI adds to your contact center and how that connects to your FCR. 

What are the Benefits of Voice AI in FCR?

Now that we understand why and how voice AI improves FCR, it’s time to zoom out from the metric. Strategically, implementing these AI agents unlocks several improvements in your overall operations. 

Some of these improvements are:

1. Accelerated and Optimized Workflows

Customer journeys become much easier when their basic questions are answered immediately. For example, if a customer wants help setting up a router, it’s easier if the voice AI can guide them through the process because they don’t have to wait for a human agent to be available. Similarly, appointment bookings, order changes, and basic actions like changing delivery timings are much less painful with voice AI and zero hold time. 

This makes the entire workflow around basic queries streamlined and observable. You can monitor and make changes to these workflows whenever you want. And any small changes in company policies or scripts can be implemented instantly without waiting for contact center approvals. 

2. Slash Operational Costs

One of the first benefits we’ve seen with AI agents is that they reduce the overall operational costs of any customer service operation. In a voice AI context, you reduce your reliance on a call center and can focus on training in-house employees who can quickly solve complex problems. 

Solving L1 and L2 queries is also generally good for reducing per-call costs. Voice AI-mediated calls cost significantly less than call centers and save you on overage and direct operational costs, especially around times of increased usage. 

3. Scalability

Peak usage times are often hard to handle at call centers. Remember, you must onboard a new agent to handle the increased call volume and have everything prepared in advance. 

This can be harmful in one of two ways: You add another line in your balance sheet to account for potential overages, or you lose customer satisfaction if phone queues get longer due to larger call volumes. 

Voice AI is built explicitly to reduce this peak call volume and give human agents space to breathe. Even when your call lines face more calls, your human agents get more time per call and can deliver more first contact resolutions. 

4. Personalization

This is a benefit of AI in general. By implementing AI workflows into your customer service process, you add another layer of personalization that delights customers. 

This greatly benefits your business since 71% of customers want more workflow personalization. Voice AI can use the backend systems to find contextual data and use that to improve personalization for your customers.

This increases CSAT and NPS scores and helps you retain more customers. 

5. Proactive Customer Service

We discussed this in detail in our customer service trends article, but proactive customer service is now a baseline expectation for many customers. 

Voice AI can help you achieve some level of automation for proactive customer service. It can find out potential issues that a customer might face during a call. And general AI implementation gives you insights into usage and purchase patterns that reveal potential bugs and problems.

As we point out in this section, voice AI goes beyond FCR and improves brand loyalty and retention. These metrics directly affect your profitability and revenue metrics and can help you advocate for more budget in your customer service operations. 

But let’s dig deeper, how does improving FCR and voice AI increase brand loyalty and retention?

How can you use FCR to Boost Brand Loyalty and Reduce Churn?

First Contact Resolution is directly connected with brand loyalty. When customers contact support, they expect a quick solution. And your brand’s response to these calls shapes how the customer sees you.

By using voice AI to improve FCR, you:

1. Cut Customer Effort: Your customer has to put in much less effort if their problems are solved in one call. They don’t have to:
   1. Call back.
   2. Repeat their problems to different agents.
   3. Go through your IVR menus again.
   4. Wait for callbacks or further investigations.

Customers value their time and convenience. And when your brand respects that time, customers reward you with loyalty. Voice AI helps here by solving L1 and L2 queries faster.

2. Build Trust: Customers trust you more when you can solve their problems on the first try. You signal that the company understands your business and customer issues and can solve them quickly. Customers don’t feel understood and heard if you don’t do this. And when customers think that your brand doesn’t understand or solve their problems, they’re much more likely to switch to a provider. 
3. Create a Positive Experience: Every customer issue increases negative sentiments. So, when answering a support call, you’re already climbing an uphill battle.
   But if you resolve their queries faster, customers feel heard and understood. They remember these interactions and are more likely to refer you forward. Voice AI improves your routing and resolutions to make these positive experiences likelier.
4. Reduce Churn: Customers don’t like repeating themselves in support calls. And when they go through many of these experiences, they’re more likely to churn. With higher FCR rates, you increase customer confidence and prevent churn. 
5. Showcase Added Value: Customer service is a key differentiator in crowded markets. When a company provides excellent First Contact Resolution (FCR) through technologies like Voice AI that personalize and simplify interactions, customers see value beyond the product or service itself. This perceived added value builds stronger customer loyalty and reduces concern about pricing.
6. Proactively Solve Problems: Voice AI insights help identify why First Call Resolution (FCR) fails. By fixing these root causes, companies can improve FCR, proactively improve customer experiences by preventing issues from arising, and boost loyalty by making things easier for customers.

A high first call resolution (FCR) rate is vital for creating positive customer experiences. Voice AI’s intelligent routing, self-service, and agent-assist features help resolve issues quickly, leading to greater customer satisfaction and loyalty, and decreasing the chance of customers leaving. Customers who feel valued and receive efficient service are likelier to remain loyal to the brand.

Now that you understand voice AI can help improve your contact center operations, let’s know the best practices for voice AI implementation. 

What are the Best Practices for Voice AI Implementation?

Transforming operations into a new technology is hard. To help you with the process, we’ve readied a list of the best practices that enterprises practice during AI adoption:

1. Define Clear Objectives – Understand the current problem in your technological infrastructure and use voice AI to supplement these parts. For example, if your contact center has a lengthy QA process with manual sampling, the first product you need to implement is at-scale call transcriptions so you can understand the exact needs that voice AI will solve. 
2. Start Small – When choosing vendors, small POC exercises are often beneficial to understand scalability and potential issues. Talk with your customers, determine which segment is more open to technical changes, and deploy your new implementation one step at a time. 
3. Choose the Right Vendor – Most enterprises undergo months-long vendor vetting processes before zeroing in on an AI solution. Mostly, you should look out for the following features:
   1. NLP and NLU – Can the voice AI agent understand and answer customer questions?
   2. Database Access – Can the voice AI agent access your customer information database?
   3. Speed of Inference – How fast does the voice AI agent respond during calls?
   4. Routing Features – Can the voice AI agent successfully route your calls to the right agent?
   5. Analytics – Does the AI agent provide up-to-date and real-time statistics about your contact center performance?
4. Prioritize Knowledge Base Management – A sound knowledge base will help voice AI solve your problems faster. Focus on creating articles that help customers by looking at the call center transcripts from point 1.
5. Design Human Conversation Flows – Use clarifying languages and train your AI agent to approach calls in a structured manner. The AI agent should be able to listen to the customer’s problems, ask clarifying questions, and find insights about the issue during the call. 
6. Prioritize Human Handoff Features – When the query is complex, the voice AI agent should be able to hand it off to human customer service agents. This process should be seamless and natively integrated into your AI agent platform. 
7. Focus on Security and Compliance: Voice interactions often involve sensitive customer data. Ensure your chosen Voice AI solution adheres to relevant data privacy regulations (like GDPR, CCPA, HIPAA, if applicable) and industry security standards (like PCI DSS for payments). Implement features like PII redaction where necessary.
8. Monitor, Analyze, and Continuously Optimize: Deployment is just the beginning. Continuously monitor the Voice AI’s performance against your defined objectives. Use analytics to identify areas for improvement in conversational flows, knowledge base content, or NLU accuracy. Regularly gather feedback from both customers and agents to refine the solution.

These eight practices should help you deploy your AI agents at the contact center in a structured, standardized manner. It will also help you avoid potential customer fallout if something goes wrong. 

For more context about voice AI adoption for enterprises, let’s look at some successful implementations. 

What are Some Case Studies About Voice AI and FCR?

We’re going to take two case studies here. One is from Elisa, a major telecommunications company from Northern Europe, and the other is from a financial service provider from Texas.

Elisa – AI Agent 43% FCR Rate for Customer Service

Elisa is a major telecommunications and digital services company serving a large customer base primarily in Northern Europe. They handle a significant volume of customer service inquiries, around 100,000 monthly inbound requests across all channels.

Problems before Voice AI/Chatbot:

• Increasing demand for customer service and the need to meet evolving customer expectations for fast, relevant responses (often expected in minutes).
• Maintaining profitability with a 100% human customer service team for such high volumes is challenging.
• Ensuring customer service scalability and enabling collaboration between key departments like product management and customer service.

Solutions seen after Voice AI/Chatbot (Annika, an AI-based chatbot):

• The AI chatbot (“Annika”) began handling up to 70% of all inbound customer contacts/questions.
• The chatbot achieved a First Contact Resolution (FCR) rate of 42% for the conversations it handled, meaning it fully resolved 34% of all total inbound contacts automatically.
• Call routing accuracy to the correct human team improved to over 90% when escalation was needed, saving time on each call.
• In 2022, the chatbot successfully resolved 82,000 customer contacts across 150 different topics and 800 problems, saving the company nearly a year of net agent time.
• The chatbot implementation achieved a Net Promoter Score (NPS) above 30.

Elisa successfully leveraged an AI chatbot to manage many customer inquiries, significantly improving automated resolution rates, FCR for handled contacts, and operational efficiency while maintaining positive customer satisfaction.

Texas Bank sees its FCR Rates Increase by 16% with Agent Assist.

A Texas-based financial services provider with approximately $10 billion in total assets, which offers financial services, urgently needed high-quality call center responses to its customers.

Problems before Voice AI (Employee-Centric AI for Performance Enablement):

• The Quality Assurance (QA) team faced challenges in effectively monitoring and improving agent performance at scale.
• Suboptimal First Call Resolution (FCR) rates led to increased repeat calls and associated costs.
• There was an opportunity to improve agent coaching frequency and impact to enhance customer satisfaction.

Solutions seen after Voice AI (An employee-centric AI platform):

• First Call Resolution (FCR) rates increased by 16%, from 54.46% to 63.04%.
• This improvement in FCR led to a significant reduction in repeat calls (over 487,000 fewer calls for the year), translating to annual savings of over $1,462,800.
• Customer satisfaction with agents improved by 17%, increasing from 62.26 to 72.73.
• The frequency of coaching sessions per agent increased by 153% (from 1.9 to 4.8 per month) due to insights provided by the AI platform.

By implementing an employee-centric AI performance enablement platform, Prescott National Bank significantly boosted its FCR, leading to substantial cost savings and improved customer satisfaction through more effective agent coaching and performance.

Conclusion

Adopting voice AI to enhance First Contact Resolution (FCR) represents a strategic shift directly impacting customer satisfaction, operational efficiency, and profitability. Traditional approaches to improving FCR, limited by outdated processes and manual interventions, fail to meet today’s customer expectations for immediate, personalized support.

Voice AI accelerates resolutions by efficiently managing simple queries and enhances human agent performance through intelligent routing, automated information retrieval, and comprehensive call analytics. These capabilities collectively enable a significant boost in FCR rates, translating into tangible reductions in operational costs and customer churn.

As demonstrated by successful implementations like Elisa’s remarkable 42% automated FCR rate and the Texas-based financial services firm’s substantial FCR improvements and operational savings, voice AI solutions deliver measurable value.

Embracing best practices—such as clearly defining objectives, starting small with targeted implementations, selecting suitable technology partners, and continuously optimizing based on data insights—will further ensure the success of your voice AI initiatives.

Ultimately, integrating voice AI positions your contact center as a proactive, responsive, and customer-centric entity, empowering your brand to build stronger customer relationships, enhance loyalty, and sustain competitive advantage in an ever-evolving marketplace.

Talk to us if you need help implementing a voice AI solution for your contact center!