AI voice calls: a starter guide for CX teams

AI voice calls: A starter guide for CX teams

Customer expectations around customer service have shifted. While, at one time, consumers might have preferred human-to-human interaction when calling in for customer support, they are becoming more aware of the speed and utility of AI resolution tools. 69% of consumers prefer using AI-powered self-service tools for fast issue resolution.

85% of customer service leaders will explore or pilot conversational AI solutions in 2025. The technology has reached an inflection point where AI voice calls can handle complex customer interactions with natural conversation flow.

For CX teams evaluating AI voice solutions, the path forward isn't about understanding every technical detail. You need a clear framework to test, integrate, and measure success quickly. This guide walks through the practical steps to launch AI voice calls that integrate with your existing support stack.

Why voice AI matters now

The business case for AI voice calls has solidified. Companies using AI-powered customer service report up to a 30% reductionin operational costs, with some organizations seeing call handling time reduced by 35% and customer satisfaction rising by 30%.

Three technical advances have made this possible:

Lower latency: Modern voice AI systems achieve sub-200ms response times through colocated infrastructure, matching natural conversation pace.

Better language models: OpenAI reduced GPT-4o Realtime API pricing by 60% for input and 87.5% for output in December 2024, making real-time voice processing economically viable. 22% of the most recent Y Combinator class consists of companies building with voice.

Unified platforms: Instead of stitching together multiple vendors for telephony, speech-to-text, and AI inference, modern platforms combine these capabilities in one system.

Planning your voice AI implementation

Start with use case selection

Not every customer interaction needs AI voice handling. Voice becomes essential for complex, multi-step processes. Focus initial deployment on:

• High-volume routine inquiries (password resets, order status, appointment scheduling)
• After-hours coverage to provide 24/7 availability
• Tier 1 support triage before escalating to human agents
• Proactive outreach for appointment reminders or follow-ups

AI chatbots can handle 80% of routine customer inquiries, freeing your human agents for complex cases that require empathy and creative problem-solving.

Define success metrics upfront

Establish baseline measurements before deployment:

• Response time: Current average speed to answer and handle time
• Resolution rate: Percentage of issues resolved on first contact
• Customer satisfaction: CSAT scores for voice interactions
• Cost per interaction: Total cost including agent time and infrastructure

Companies see an average return of $3.50 for every $1 invested in AI customer service, with leading organizations achieving up to 8x ROI from AI investments.

Building your voice AI system

Choose the right infrastructure

The foundation of effective AI voice calls is infrastructure that eliminates latency at every step.

When evaluating platforms, prioritize:

Native telephony integration: Look for platforms with direct PSTN access as licensed carriers. This eliminates third-party handoffs that add latency and points of failure.

Colocated processing: Speech-to-text, AI inference, and text-to-speech should run in the same network points of presence as telephony infrastructure. Geographic distribution matters, processing calls in the same region as your customers reduces round-trip time.

Private network architecture: Public internet routing adds unpredictable latency. Platforms with private global networks minimize the physical distance data travels, reducing latency and ensuring uninterrupted conversations across the world.

Design conversation flows

Voice AI conversations need different design patterns than text chatbots. 59% of customers expect chatbot responses within 5 seconds, but voice interactions demand instant responses to feel natural.

Key design principles:

• Acknowledge immediately: Use filler phrases ("Let me check that for you") while processing
• Keep responses concise: Voice retention is lower than visual. Break complex information into chunks
• Build in clarification: Design explicit confirmation steps for critical actions
• Plan graceful handoffs: For issues that require human attention, ensure the service agent is given the full context of the issue and the prior exchange with voice AI

Integrate with existing systems

Your AI voice system needs to connect with your current tech stack. The average company uses 112 SaaS applications, so seamless integration is critical.

Essential integrations:

• CRM systems: Pull customer history and context in real-time
• Ticketing platforms: Create and update support tickets automatically
• Knowledge bases: Access current product information and policies
• Analytics tools: Stream conversation data for continuous improvement

CRM integration increases customer satisfaction by providing agents and AI with complete customer context.

Testing and quality assurance

Run controlled pilots

Start with limited deployment before full rollout. An estimated one-quarter of contact centers have already implemented AI for customer experience, and that number could double in 2025.

Pilot approach:

1. Select a narrow scope: Choose one specific use case or customer segment
2. Set duration limits: Run for 2-4 weeks with daily monitoring
3. Gather feedback actively: Survey both customers and agents
4. Iterate quickly: Adjust conversation flows based on real interactions

Monitor conversation quality

48% of customers find it harder to distinguish AI from humans, indicating the technology has reached human-like conversation quality. Still, continuous monitoring remains essential.

Quality checkpoints:

• Transcription accuracy: Review samples for speech recognition errors
• Intent recognition: Verify the system understands customer needs correctly
• Response relevance: Ensure answers match the questions asked
• Escalation patterns: Track when and why calls transfer to human agents

Launching and scaling

Prepare your team

Only 21% of agents express satisfaction with AI training, despite 72% of CX leaders claiming teams receive adequate preparation. Bridge this gap with comprehensive training.

Training priorities:

• How AI voice handles different call types
• When and how to take over from AI
• Reading AI-generated call summaries and context
• Using AI as a real-time assistant during calls

AI-powered tools in the healthcare industry have saved representatives 2-3 hours per day on administrative tasks, but only when agents understand how to leverage them effectively.

Scale gradually

Once your pilot succeeds, expand systematically:

1. Add use cases: Extend to additional call types with proven ROI
2. Increase volume: Route higher percentages of eligible calls to AI
3. Expand hours: Use AI for after-hours coverage before 24/7 deployment
4. Enable proactive calling: Launch outbound campaigns for appointments and follow-ups

By 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, cutting operational costs by 30%.

Measuring ROI and optimization

Track business impact

87.2% of consumers rate their chatbot interactions as positive or neutral. Quantify this satisfaction alongside operational metrics:

Efficiency gains:

• AI reduces average resolution times by 87%
• 45% time savings on calls with 44% faster issue resolution
• 13.8% increase in customer inquiries handled per hour

Cost reduction:

• Average cost per chatbot interaction is $0.10 versus $8 for human interaction
• Conversational AI will reduce contact center agent labor costs by $80 billion by 2026

Revenue impact:

• 83% of sales teams with AI agents saw revenue growth in the past year, versus 66% of teams without AI
• In some industries, AI chatbots can increase ecommerce conversion rates by up to 70%

Optimize continuously

Voice AI improves through iteration. 70% of consumers state there is a clear gap forming between companies that leverage AI effectively in customer service and those that don’t.

Optimization cycle:

• Review conversation transcripts weekly
• Identify common failure points or confusion patterns
• Update conversation flows and knowledge bases
• A/B test different response strategies
• Measure impact on key metrics

Getting started with Telnyx Voice AI Agents

The complexity of building AI voice systems has decreased dramatically. Platforms now exist that combine all necessary components, telephony, speech processing, and AI inference, in unified systems designed for low latency.

Telnyx Voice AI Agents exemplify this integrated approach. By colocating GPUs with telephony infrastructure across a private global network, the platform achieves consistent sub-200ms round-trip times. As a licensed carrier with native PSTN access, Telnyx eliminates the latency and reliability issues that plague multi-vendor solutions.

The platform includes:

• Global phone numbers and SIP trunking
• Built-in speech-to-text and text-to-speech
• AI inference running on the same network as telephony
• Crystal-clear audio quality through private network routing
• Direct integrations with existing contact center platforms

For CX teams ready to implement AI voice calls, the technology has matured beyond experimental phase. 95% of customer interactions are expected to be AI-powered by 2025. The organizations that move thoughtfully now, starting with clear use cases, measuring systematically, and choosing infrastructure built for voice, will capture the efficiency gains and customer satisfaction improvements that AI voice calls deliver.