Contact centers are under constant pressure to deliver faster, more personalized support while keeping costs under control. Traditional IVR systems fall short, which is why many businesses are turning to the best AI agent for call centers – the modern AI voicebot. These voice agents combine speech recognition, language understanding, and real-time responses to resolve customer issues without long menus or wait times. But beyond deploying the technology, leaders need to answer a harder question – how do you measure its return on investment?
This blog will guide founders, product managers, and engineering leads through a practical ROI framework, showing how infrastructure like FreJun Teler can help build scalable, measurable, and profitable AI-driven inbound call handling systems.
What Are AI Voice Agents in Contact Centers?
Contact centers have always been a balancing act between delivering good customer service and controlling operating costs. For years, IVR menus and human agents were the only options. IVR reduced cost but was rigid and frustrating, while human agents were flexible but expensive.
AI voice agents, often called AI voicebots, are changing this equation. Unlike old systems that worked on fixed scripts, these agents rely on a full stack of technologies:
- Speech to text (STT) that converts spoken words into text.
- A language model or rules engine that interprets intent and decides what to do next.
- Retrieval augmented generation (RAG) that brings in context from company databases, CRMs, or knowledge bases.
- Tool calling, which executes actions such as updating an account, booking a slot, or resetting a password.
- Text to speech (TTS) that generates a natural voice reply back to the caller.
The combination of these elements allows an AI agent to carry out fluid conversations in real time. For inbound customer support, this means a caller can explain their problem naturally and receive an immediate, useful response without navigating confusing menus.
This is why many leaders now call voice AI the best AI agent for call centers. It is not a marginal improvement over IVR, but a shift toward dynamic conversations that scale without proportional cost.
How Do AI Voice Agents Handle Inbound Calls?
Inbound call handling is where most customer frustration is concentrated. A good AI agent must not just answer, but guide the conversation to resolution. The technical sequence is straightforward but must be executed with very low delay.
When a customer calls, the system first captures the audio stream. A speech to text engine transcribes the audio as the customer speaks, sending partial results in milliseconds. This transcript is handed to the AI logic, which could be a large language model or a domain-specific engine. If extra information is needed, the AI retrieves context using a RAG connector. For example, it may pull order details from the CRM.
The AI then decides what to do. If an action is required, such as issuing a refund or updating an address, it triggers a tool call through the backend system. Once the response is ready, a text to speech engine generates a spoken reply and streams it back to the caller. Importantly, the system must support barge-in, meaning the customer can interrupt and the agent can adapt immediately.
All of this happens in under half a second per turn if the system is well built. That level of speed is what makes conversations feel natural. Any longer, and the caller perceives the agent as robotic and unhelpful.
Why Should Businesses Measure ROI of AI Voicebots?
Building a voice AI agent involves costs. These include model usage, telephony minutes, infrastructure, and integration with existing systems. Without measuring ROI, these costs look like an additional expense rather than an investment.
For business leaders, ROI answers the simple question: are these agents paying for themselves and creating value? Measuring ROI in contact centers is not only about cutting costs. It is about showing how automation improves customer outcomes and even creates new revenue opportunities. In early deployments, organizations using contact center AI report 15–22 % improvement in customer experience and over 20 % reduction in average handle time, underscoring how AI voicebots can deliver measurable operational and CX gains.
For example, a company may save money by reducing the number of human agents required for repetitive queries. At the same time, they may increase revenue because customers who receive faster service are more likely to remain loyal or buy more. A clear ROI model helps founders, product managers, and engineering leads defend investments in AI voicebot projects.
What Metrics Define ROI in Contact Centers?
ROI is not a single number but a combination of metrics that show both efficiency and experience. These metrics fall into four groups.
- Operational metrics track efficiency. Examples include containment rate, which shows how many calls are fully handled by the AI, average handle time, and first call resolution. A reduction in abandonment rate is another important sign that customers are staying engaged with the system.
- Customer metrics measure experience. CSAT and NPS scores indicate satisfaction and loyalty. Sentiment analysis, which tracks how a caller’s tone changes during a call, gives another layer of insight.
- AI-specific metrics are unique to these systems. Word error rate in transcription, naturalness and latency in TTS, and the quality of escalation to humans determine how well the technical stack is performing.
- Financial metrics connect everything back to business results. Cost per contact, labor savings, and revenue uplift from retention or upsell opportunities show the real financial impact.
When all of these are tracked together, businesses can connect technical performance to customer outcomes and financial results.
How to Build a Framework for Measuring ROI?
Measuring ROI requires discipline. The first step is to set a baseline. Contact centers should track their key metrics for a period of time before AI deployment, usually four to six weeks. This creates a reference point.
The second step is to run a parallel test. Some inbound traffic continues to be handled by humans while the rest is handled by the AI. This makes it possible to compare performance across similar types of calls.
The third step is attribution. Each call should be tagged as AI-contained, AI-assisted, or human-only. This prevents confusion about whether the AI or the human resolved the issue.
Finally, leaders should use a calculator approach for ROI. This means defining assumptions like the cost of a human agent per hour, the cost of a telephony minute, and the usage cost of the AI model. Then, they calculate savings and gains across scenarios, from conservative to optimistic.
A simple table illustrates the potential improvement:
| Metric | Human Only | With AI Voicebot | Improvement |
| Containment | 0% | 60% | +60% |
| Average Handle Time | 8 min | 4 min | -50% |
| CSAT Score | 70 | 74 | +4 points |
| Cost per Call | $5.20 | $1.80 | -65% |
This type of structured reporting convinces decision makers far more than anecdotal success stories.
Want a step-by-step guide to designing an AI voicebot? Explore our blog on building inbound voice AI for contact centers.
What Technical Architecture Powers ROI Tracking?
ROI tracking depends on how the system is architected. A reference design for a voice AI looks like this:
- A telephony transport layer that captures inbound audio and streams it with low delay.
- A speech to text engine that produces transcripts fast enough for natural turn-taking.
- An AI decision layer, often a language model or orchestrator, that determines intent.
- A RAG connector that retrieves relevant knowledge from backend systems.
- A tool call interface that executes business actions in real time.
- A text to speech engine that replies in a natural voice.
- An analytics layer that records latency, error rates, and outcomes.
Every layer must produce logs and metrics. For example, STT must report word error rates, TTS must report round trip delay, and the orchestrator must record intent classification accuracy. These numbers are the building blocks of ROI calculations.
How Do Latency and Accuracy Impact ROI?
Two technical factors stand out when linking system performance to ROI: latency and accuracy.
High latency makes conversations feel broken. Each additional second of delay increases the chance a customer hangs up. That translates directly into lost opportunities and higher cost per call. A system that keeps latency under 500 milliseconds per exchange has a measurable advantage.
Accuracy has the same effect. If speech to text produces incorrect transcripts, the AI logic makes wrong decisions, forcing escalations or repeat calls. Even a small increase in word error rate can reduce containment significantly.
The link is clear. Better latency leads to lower abandonment and higher customer satisfaction. Better accuracy leads to higher containment and lower cost per contact. Both factors directly influence ROI.
The table below shows an illustrative relationship between latency and cost:
| Round Trip Latency | Abandonment Rate | Avg Cost per Call |
| 400 ms | 5% | $1.80 |
| 800 ms | 12% | $2.30 |
| 1500 ms | 20% | $3.00 |
This shows why measuring technical performance is not optional but central to ROI analysis.
Curious how transcription accuracy and latency impact ROI? Read our blog on achieving real-time transcription for AI voice agents.
Where Do Common ROI Pitfalls Occur?
Many deployments fail to demonstrate ROI, not because the technology is weak, but because measurement is incomplete.
- One common mistake is focusing only on cost savings. While automation reduces costs, it also improves retention and loyalty, which drive revenue. Ignoring this leaves half the ROI story untold.
- Another mistake is not setting baselines. Without a clear before-and-after comparison, improvements cannot be attributed confidently to the AI.
- Some teams fail to track audio quality, latency, or transcription accuracy, so they cannot prove why performance improved or declined. Others count all escalations as AI failures when in reality some escalations are policy-driven, such as fraud checks or compliance requirements.
- Lastly, vanity metrics such as total call volume handled do not convince decision makers. Resolution-driven metrics like first call resolution and containment are what matter.
Adopters have reported a 15 % drop in operating cost and 16 % improvement in call deflection – underlining that even modest gains in system accuracy or routing can yield large financial benefits.
How to Attribute ROI Correctly
One of the hardest challenges in proving ROI is attribution. If a customer issue is resolved, who gets the credit: the AI voicebot, the human agent, or the combination of both? Without clear attribution, ROI reporting can be misleading.
The practical approach is to classify each call into three categories:
- AI-contained: The issue was fully resolved by the voice agent without human involvement.
- AI-assisted: The AI handled part of the conversation, gathered context, and then transferred to a human with all information passed along.
- Human-only: The AI was bypassed or unavailable, and the human agent resolved the issue entirely.
This classification makes it possible to measure differences in handle time, cost, and customer experience across groups. For example, if AI-assisted calls show lower handle time than human-only calls, the ROI is still positive even if the AI did not contain the entire call.
How to Calculate ROI With Real Examples?
Consider a mid-sized contact center handling 100,000 inbound calls per month. The cost structure and performance before AI deployment looks like this:
- Average handle time: 8 minutes.
- Human agent cost: $20 per hour.
- Cost per call: $5.20 (including wages, telephony, and overhead).
- CSAT score: 70.
After deploying an AI voicebot, results after three months show:
- Containment rate: 60%.
- Average handle time (across all calls): 4 minutes.
- Cost per call: $1.80.
- CSAT score: 74.
Monthly Financial Impact
- Human-only monthly cost: 100,000 x $5.20 = $520,000.
- AI-enabled monthly cost: 100,000 x $1.80 = $180,000.
- Direct savings: $340,000 per month.
Additional Revenue Impact
- Higher CSAT improves retention by even 2%, equating to thousands of customers staying with the brand.
- Faster resolution allows agents to focus on high-value interactions, creating upsell opportunities.
The total ROI is not just the $340,000 saved but also the future value created by improved retention and additional sales. This example shows how combining operational and financial metrics builds a defensible ROI case.
What’s the Step-by-Step Process to Build a Voice AI for ROI?
Many leaders ask not just how to measure ROI, but how to actually build a system that can deliver it. The process can be broken down into five practical steps.
Step 1: Define Call Intents
Start by identifying the types of inbound calls most suitable for automation. Common candidates are password resets, order status queries, bill payments, and appointment scheduling. Each intent should be documented clearly.
Step 2: Select STT, LLM, and TTS Engines
The system is model-agnostic, meaning teams can choose the speech to text, large language model, and text to speech engines that best suit their needs. The focus should be on accuracy, latency, and language coverage.
Step 3: Connect the Telephony Layer
Inbound calls need to be streamed into the AI pipeline in real time. This requires a reliable voice infrastructure that can manage call signaling, audio capture, and media streaming with very low delay.
Step 4: Integrate with CRM and Backend Systems
For the AI to resolve real issues, it must have access to the right data. Retrieval augmented generation connects the AI to knowledge bases, while tool calling executes actions in ticketing, payment, or scheduling systems.
Step 5: Instrument Analytics for ROI Tracking
Every component must log its performance. Speech to text should track error rates, the AI orchestrator should log intents and outcomes, and text to speech should log latency. These metrics feed into dashboards that connect technical performance to business ROI.
This structured build process ensures the system is not only functional but measurable from day one.
How FreJun Teler Enables ROI at Scale
Building a voice AI agent is not just about selecting the right models; the real challenge lies in managing the telephony and infrastructure that carry every conversation. FreJun Teler solves this by acting as the dedicated transport layer for real-time voice. It streams inbound and outbound audio with ultra-low latency, supports any AI stack without vendor lock-in, and preserves conversational context across turns. Its infrastructure is designed for high availability, so agents remain online even at peak loads, while enterprise-grade security ensures data integrity and compliance.
For founders and engineering leads, this means faster deployments and reduced complexity. Instead of spending months building integrations with telephony systems, teams can focus on AI logic while Teler guarantees speed, reliability, and clarity. The result is improved ROI through quicker time to market, lower engineering effort, and consistent customer experiences that meet demanding latency and satisfaction thresholds.
Future Outlook: How Will AI Voice Agents Redefine Contact Centers?
The ROI of AI voice agents will not remain static. As models improve and integrations deepen, the impact will expand in three ways.
- Proactive engagement: Instead of only answering calls, AI agents will initiate outreach such as payment reminders or renewal notifications. These activities drive measurable revenue.
- Multilingual support: Expanding into new markets becomes easier when AI agents can converse in multiple languages without training separate teams. ROI improves by opening new revenue streams at low cost.
- Compliance and governance: Contact centers face growing regulatory demands. AI agents can provide audit logs, risk monitoring, and policy enforcement at scale, reducing compliance costs.
In each case, ROI becomes stronger not only from cost savings but from new forms of value creation.
Conclusion
Measuring the ROI of AI voice agents is not optional; it is the foundation for proving that automation drives both efficiency and growth. By linking operational metrics, customer experience outcomes, and financial impact, leaders can build a business case that is defensible and scalable. The roadmap is straightforward: identify high-value inbound intents, deploy AI agents with clear measurement in mind, and rely on robust infrastructure to manage voice transport without latency or complexity.
This is where FreJun Teler becomes critical – it gives you the enterprise-grade voice backbone to connect any AI stack, accelerate deployment, and deliver consistent ROI.
Ready to explore how?
Schedule a demo with FreJun Teler and start measuring results from day one.
Key Takeaways
- ROI is a combination of cost savings and revenue impact, not just one or the other.
- Metrics like containment, first call resolution, and latency directly influence financial results.
- Attribution must separate AI-contained, AI-assisted, and human-only calls.
- Worked examples show how AI can reduce cost per call by more than 60 percent.
- FreJun Teler provides the real-time voice infrastructure needed to achieve ROI at scale.
FAQs –
Q1: How can I measure ROI of AI voicebots in call centers?
A1: Track containment, handle time, CSAT, and cost savings while comparing AI-handled calls with human-only baselines.
Q2: What makes AI voicebots better than traditional IVR?
A2: AI voicebots use real-time speech, context, and actions, delivering faster, more natural conversations than rigid menu-based IVRs.
Q3: Why does latency matter for AI voice agent ROI?
A3: Higher latency increases call abandonment and reduces resolution rates, directly raising per-call cost and lowering ROI.
Q4: Can FreJun Teler work with any AI model?
A4: Yes, FreJun Teler is model-agnostic, supporting any LLM, STT, and TTS while ensuring reliable, low-latency voice infrastructure.