AI call routing uses natural language understanding to identify a caller’s intent and connect them with the right agent or resource. Unlike traditional IVR menus, AI routing enables conversational interactions, reduces transfers, improves first-call resolution, and shortens wait times.
AI call routing often comes at a premium price point, but it does more than just transfer calls to the appropriate human agent. In many cases, these systems function similarly to AI receptionists, answering questions, taking payments, scheduling appointments, and managing agent workloads.
Read on to find out what AI call routing is, how it works, what the benefits are, and whether it's the right choice for your call center.
What Is AI Call Routing?
AI call routing leverages natural language understanding and processing to determine each caller’s intent and route them to a suitable agent, department, or resource. While traditional IVR menu trees require customers to select from a list of options, often making two or three choices from submenus, AI call routing simplifies the process.
With AI routing, a customer states the reason for their call in natural language, and they are immediately routed to the appropriate place. Despite its name, AI call routing can also be used for customers reaching out on SMS/text, webchat, or email.
AI Call Routing vs Traditional Call Routing
While AI call routing and traditional call routing serve the same basic function, they work in very different ways. Here is a side-by-side look at how the two technologies measure up.
| AI Call Routing | Traditional Call Routing | |
| Caller input | Natural language | Keypad inputs |
| Routing rules | Interprets natural language in real time | Pre-programmed, static rules |
| Accuracy | High - based on actual intent | Dependent on caller correctly navigating menus |
| Misroute rate | Low - as long as knowledge base and CRM data accurate | Often high as callers tend to guess wrong options |
| First call resolution | 15-25% higher | Lower - more transfers needed |
| Customer experience | Calls are conversational and personalized | Long menu trees and repeated prompts are highly disliked |
| Setup costs | High (often require higher tier subscription or add-on feature) | Low (often included in basic CCaaS plans) |
| Handling complexity | Able to detect and factor in emotion, nuance, multiple intents, etc. | Struggle with anything outside the preset menus |
| Data and Insights | Log and classify every call, creating a rich picture of intent over time | Basic data only such as queue data, call volume, etc. |
How Does AI Call Routing Work?
AI call routing works by identifying callers, discerning their intent, pulling any history and then forwarding the call along with the context to the appropriate agent. Here’s a closer look at those steps.
Step 1: Caller Identification and Authentication
The AI agent first authenticates the caller by utilizing public phone number lookups as well as internal CRM matching. Spam calls are automatically filtered out before they ever reach an agent.
Some AI agents like Verint and NICE CXone also use voice biometrics to supplement caller ID and CRM data. Voice biometrics allow the AI agent to verify a person’s identity based on the unique pitch, rhythm, accent, cadence, and vocal tract shape of their voice.
Step 2: Intent and Sentiment Analysis
Once a caller has been identified and verified, the AI agent will interact with the person in natural language in order to determine their intent (the reason for their call). AI agents leverage natural language understanding to uncover the call’s purpose.
Sentiment analysis will also be conducted at this time in order to discern whether the call should be escalated to a supervisor or a more experienced agent fit to handle frustrated customers. This all happens in real time.
Step 3: Data Integration and Context Building
Once the AI agent has determined the caller’s intent such as billing, return, cancellation, etc., it will pull any context or relevant data in order to assist the human agent and ensure correct routing.
AI agents pull information, purchase history, past service calls, etc. from the company’s CRM such as Salesforce and HubSpot. Wherever the AI agent routes the call, it will also send this contextual information.
Step 4: Intelligent Agent Matching
AI agents are able to match calls with human agents with far more precision than traditional IVR. AI call routing takes the sentiment of the caller into consideration as well as their intent, matching more frustrated callers with more experienced agents, or even with agents that they have had a good experience with in the past.
AI call routing will also take agent skills into account, as well as other factors such as availability, longest idle time, etc. The transfer to a human agent is a warm transfer, meaning that the AI agent will provide the human agent with information about the caller and context via an AI-generated summary.
Core Technologies: NLP, Machine Learning, and Voice Biometrics
AI call routing relies on natural language understanding (NLU) and natural language processing (NLP) for intent, machine learning (ML) for pattern recognition, and voice biometrics for caller ID. Here’s a quick look at how those technologies work:
- NLP/NLU: Incoming text or transcribed speech is stripped down to “tokens”(words or characters), encoded as a “dense vector” (a mathematical representation that captures words and context), and then classified according to the most probable intent
- ML: Recognizes patterns that occur after being trained on hundreds or thousands of calls, determines the average (normal) behavior across thousands of variables, learns to classify emotions, and then reinforces learning outcomes over time
- Voice Biometrics: First creates a voiceprint by extracting acoustics features from the voice such as pitch contours and speaking rhythm and compresses them into a mathematical model stored as a template. Future calls are then compared to this template to find a match
Key Benefits of AI-Powered Call Routing
AI-powered call routing has a big impact on nearly every aspect of the call center from the customer experience, to productivity, turnover, and the bottom line. Here’s a closer look at the benefits of AI call routing.
Reduced Wait Times and Transfer Rates
Average benchmarks for call centers focused on customer service are 6 minute wait times for a first response and 33 minutes for a resolution. With AI call routing, first response happens in about 10 seconds, with resolutions taking only 2 minutes.[*] This means less transfers, and much less time waiting on hold.
Improved First-Call Resolution
AI call routing also has better accuracy than traditional call routing. AI triage systems have an 89% accurate rate, meaning there are less transfers, as callers are forwarded to the right place on the first try almost all of the time.[*]
Specifically, DoorDash has reported a reduction in escalation to human agents by thousands of calls per day since implementing AI call routing.[*]
Lower Operational Costs Through Automation
One of the biggest benefits of switching to AI call routing is a significant reduction in overhead cost. Because AI agents can handle hundreds, or even thousands of interactions each day, and spend less time on those interactions than human agents, contact centers often see a large decrease in labor costs and billable hours.
Companies that implemented AI call routing in customer service reported an average 68% reduction in the cost per interaction, as well as an ROI of 41% in the first year, 87% in the second year, and 124% in the third.[*]
Enhanced Customer Experience
Customers like AI call routing because it completely eliminates the need for long and tedious menu trees. Additionally, AI call routing allows customers to experience personalized interactions with intelligent AI agents that already know their history and context.
Customers don’t have to repeat themselves, and AI agents are available 24/7/365. Companies who have switched to AI call routing have reported CSAT score increases of up to 25%.[*]
AI Call Routing Use Cases by Industry
In addition to the general benefits listed above, AI call routing offers a myriad of specific benefits to different industries such as fast and informed triage for healthcare patients and fraud risk scoring for financial companies. Here’s a look at some specific use cases for AI call routing.
Healthcare: Patient Triage and Appointment Routing
One of the most important functions of a healthcare IVR system is to separate emergency calls from more routine queries. Instead of relying on callers to select the correct menu options, AI agents are able to use natural language understanding and sentiment analysis to detect urgency, while using voice biometrics to bring up patient history and ensure the caller is routed to the right specialist.
Automatic authentication via voice biometrics also means that callers will not have to provide sensitive information over the phone to identify themselves. And many AI call routing systems encrypt data in transit and at rest in compliance with HIPAA.
A large hospital network deploying AI routing across its main patient line can automatically separate mental health crisis calls, post-surgical complication calls, and prescription refill requests into distinct routing paths -- each with its own escalation logic and specialist pool -- within the first 15–20 seconds of the call.
Financial Services: Fraud Alerts and Account Support
In the financial sector, AI agents are not only discerning intent and routing calls accordingly, they are simultaneously screening for fraudulent calls by matching the voice pattern of the caller to the saved voiceprint, cross checking the phone number, and listening for specific word and phrase patterns that are consistent with an account takeover.
Similarly, AI agents use sentiment analysis to flag distressed customers who may have been victimized so that the call can be prioritized and transferred to a senior human agent knowledgeable in fraud and skilled in empathy and de-escalation.
Passive voice biometrics and behavioral analytics are used to identify callers instead of asking them for information which can be stolen such as social security numbers.
E-Commerce and Retail: Order Status and Returns
Self-service containment is the main goal with ecommerce and retail businesses which normally experience a high call volume with most calls falling into a handful categories such as returns, refunds, delivery status, etc.
Well integrated AI call routing systems will be able to handle most of these calls without any human intervention resulting in high self service containment rates of 65-80%.[*] For example, a large ecommerce company implements AI agents that authenticate the caller, retrieve the relevant order, respond to the customer in natural language, and take the necessary action such as updating the address, making a notation in the CRM, initiating a refund, etc.
AI agents can also quickly identify high-value, VIP customers before they even state their reason for calling, automatically transferring those calls to dedicated agents along with all of the relevant context that the human agent needs to personalize the call.
Contact Centers: High-Volume Call Management
For contact centers that experience a high volume of calls across multiple business lines, departments, and sectors, AI call routing keeps service consistent and efficient while reducing stress on human agents.
AI agents use predictive routing to determine when peak times will occur and take proactive action to redistribute routing logic, activate overflow queues, and adjust IVR deflection thresholds. Genesys has a robust predictive routing engine that also factors in past performance data, agent skills, tenure, certificates, availability, and timeout period to predict the agent with the best chance of solving the problem or bringing about the desired business goal such as making a sale or improving customer satisfaction.
AI call routing also reduces agent burnout by introducing variety into each agent’s workload, balancing simple and complex calls, and rotating high-stress interactions. While traditional IVR systems simply route calls to the next available agent, AI call routing tracks agent workload and recent interaction sentiment while engaging skill-to-call match tools.
Skill-based routing also becomes more precise with AI call routing, scoring each incoming call against a real-time matrix of agent attributes such as tenure, performance, and language skills, that is dynamically and automatically updated.
How to Implement AI Call Routing
AI call routing integrates with your VoIP or SIP platform and CRM to provide seamless automations and self service capabilities. Here is a step-by-step guide to implementing AI call routing.
Assess Your Current Call Routing Infrastructure
It is important to assess your current system as completely and honestly as possible before implementing AI call routing. Audit existing IVR menus and find out exactly what paths calls are following and which options and submenus are causing callers to abandon the call or hit 0 for a live agent.
Identify misdirected call rates, and evaluate baseline metrics so that you can accurately measure ROI. Update your CRM and agent skill lists, so that AI agents can properly match calls.
Integration with SIP, VoIP, and CRM Systems
AI systems sit at the intersection of telephony, customer data and workforce management, so it is important to properly integrate all of these components. Your AI routing platform must be fully SIP compatible to function in a modern IP call environment. You will also need to ensure that your VoIP system meets the latency and packet loss thresholds specified by the AI vendor.
Many AI platforms utilize REST APIs to integrate with CRMs, CPaaS, IVR, and workforce management platforms. Map out integration points early and involve your IT team to make sure that everything comes together smoothly.
Configure Sentiment Thresholds and Escalation Rules
Getting sentiment thresholds and escalation logic right is what allows AI call routing to have the biggest impact on customer service. Set frustration triggers by scoring caller sentiment and then running a sentiment model against historical call recordings to see if the model’s scores match the quality assurance team’s scores.
Set escalation thresholds by first dividing calls into separate categories (service calls, billing questions, etc.) and then creating separate thresholds for each category. There will likely be differences in the level of acceptable frustration depending on the context.
Specialized agent pools are groups of human agents that are adequately equipped to handle a specific type of call. For each escalation rule you define, you must also define a specialized agent pool that those calls will be transferred to.
Measure Success: Key Metrics to Track
Defining what success looks like before launching AI call routing is essential for meeting goals. Here are some key metrics you may want to track:
- First Contact Resolution (FCR): Refers to the percentage of interactions that are resolved during the first contact (don’t need to be transferred and call not abandoned)
- Average Handle Time (AHT): The average talk time should decrease as routing accuracy and problem identification improves
- Transfer rate: A decline in the mid-call transfer rate is indicative of an AI call routing system that has accurate intent classification and agent matching logic
- CSAT: Post-call customer satisfaction scores collected at both the agent and the routing path level will let you know what is causing an increase in CSAT
- Self-service containment rate: Percentage of calls handled completely within the automation layer (no human involvement)
- Queue abandonment rate: Track abandonment by time of day and call category to determine where queue pressure is most concentrated
