Voice AI Customer Service for Contact Center Automation

Updated on May 15, 2025

Enterprises like Doordash receive over 40,000+ customer support calls per day. This volume is an operational challenge. Most businesses mitigate this by offshoring calls to a third-party contact center. 

However, contact centers and BPOs like this are expensive. And since CX leadership at enterprises is currently under high pressure to reduce costs and increase efficiency, the market for alternatives is getting bigger. 

Voice AI is the fastest and safest alternative for contact center automation. We’ve seen the result across enterprises:

1. Doordash currently automates 35000+ calls per day with a voice AI agent at a 94% success rate
2. Vodafone’s SuperTOBi assistant has increased their NPS score from 14 to 64
3. A global BFSI leader reported a 90% call containment rate with their voice AI integration

Voice AI improves BPO performance while reducing costs by 60-80%. This article will explore how voice AI achieves this and share real-life examples of how it is used across enterprises. We’ll cover:

1. How Does Contact Center Automation Reduce Costs for Your Business?
2. What is Voice AI, and How Does it help in Contact Center Automation?
3. How Does Voice AI Solve Your L1 and L2 Problems?
4. What is the ROI for a Voice AI Customer Service Solution?
5. How Can Voice AI Drive Operational Wins at Your Contact Center?
6. Voice AI Customer Service Case Studies
7. Our Recommended Migration Playbook for Voice AI
8. What are the Drawbacks of Contact Center Automation with Voice AI?
9. What are Some Future Trends and Technologies in Voice AI Customer Service?

How Does Contact Center Automation Reduce Costs for Your Business?

Whenever a contact center handles your inbound calls, it costs around $2.70 and $5.60. For context, this balloons your costs to over $2000/month even if you only receive 500 inbound calls. 

Contact Center automation can reduce these costs significantly. It can reduce your overhead at these call centers and help you handle increased customer demands without increasing costs. How much money can this save your business? Let’s look at the costs of third-party contact centers to confirm.

How Can Voice AI Reduce Contact Center Costs?

Most businesses pay their contact center costs in one of the following three ways:

1. Shared Inbound – Call center agents answer calls for multiple companies. Each call is priced at $0.35-$0.55 per minute. (Over 5x the price of voice AI). 
2. Dedicated Inbound – The call center employees are allocated to your business. Costs can range from $6/hour (for low-cost international providers) to $30+/hour (for U.S and Europe-based providers).
3. Monthly Bundles – Several agencies (mostly from international markets) bundle up their services and offer them for a monthly fee. This usually comes with fixed quotas and limits, for which you are charged extra. 

These pricing models are expensive (at least compared to contact center automation like voice AI). In comparison:

1. Shared inbound services cost 5x more than standard voice AI services.
2. Not only do you have to pay a standard fee for dedicated inbound services, but your contact center overheads aren’t reduced during times of low demand. On the other hand, you incur significantly higher costs during periods of high demand. 
3. Monthly bundles are unpredictable. You can’t estimate your charges at scale or during volume fluctuations. For example, your charges can increase your budgets during peak periods like the holidays. 

In our experience, voice AI can drive up to 10x more savings than traditional third-party solutions without affecting your workflows. Remember, the core problems and the ticketing processes familiar to your team will remain the same. Only the L1 and L2 level queries would be solved at scale with a voice AI integration. 

Increased operational efficiency is another cost-reduction method that is easier with contact center automation. 

How Does Voice AI Drive Operational Efficiency?

We have written about how your customer service operations at third-party contact centers can be painful. To recap, third-party call centers follow these processes:

1. Sampling Random Calls for QA – Most quality analyses at call centers involve sampling random calls. You miss key insights on improving customer service since you don’t have access to all call recordings and data.
2. Ever-Rotating Roster of Agents – The BPO industry records very high attrition rates. Agents have much less incentive to understand your products and services, which can affect the brand voice you want to convey to customers. 
3. Siloed Data Practices – Data from the BPO is stored on-premise and is often opaque to you. So, you lose out on key insights and indicators that would have helped you improve your quality of service. 
4. Delayed Script Changes – Once you’ve outsourced your front-facing communication outside, every change in your company policies will face a lag before being implemented. In time-sensitive industries like telecom, BFSI, and others, this lag can cause significant confusion for customers. 

What we’re trying to say here is relatively simple: When you employ a third-party contact center, you lose control over your costs and CX. 

With Voice AI, you get higher capacity limits, lower costs, and more control over your customer experience and service processes. Also, since voice AI is consistent and easily trainable, you can experiment and execute faster. 

Now that we understand how beneficial voice AI can be compared to traditional third-party contact centers, let’s explore the concept of Voice AI in detail.

What is Voice AI, and How Does it help in Contact Center Automation?

Voice AI refers to AI agents that can understand phone calls and hold conversations with your customers without any human intervention. Voice AI agents usually have three components:

1. Automation Speech Recognition (ASR) – This machine learning software understands what the customer says over the phone. 
2. Natural Language Processing and Generation – LLMs like ChatGPT and Claude handle this. This process understands the context behind the customer’s words and generates an appropriate response. 
3. Text-to-Speech (TTS) – Once the LLM generates an appropriate response for the customer, another machine learning tool called text-to-speech translates the text into a human-like voice. 

These systems can solve L1 and L2 queries without getting a human involved. The systems are connected to your backend documents in your CRM and other tech and can give customers the exact answers they want.

For more clarification, let’s look at some features that voice AI agents have. 

Features of Voice AI that Unlock Contact Center Automation

Voice-AI Feature	Automation Benefit
Natural, open-ended dialog	Handles L1/L2 calls end-to-end without menus
Round-the-Clock Availability	Immediately unlocks 24/7 customer service capabilities
Multilingual at launch	Serve global customers with one code base
Real-time analytics & QA	100 % call monitoring; automatic scoring & trending
Elastic cloud capacity	Scales instantly for product drops or outages
Continuous learning	Deploy script or policy updates in minutes
Integration Capabilities	Go live and start serving customers in hours.

These capabilities ensure that voice AI can solve contact center automation at scale. For CX leaders, this means they get access to a scalable infrastructure for their customer service operations at low ongoing costs without sacrificing quality. 

But, how do these tools maintain brand voice and quality across millions of customer interactions? Let’s explore that in the next section. 

How Does Voice AI Solve Your L1 and L2 Problems?

Currently, any enterprise that deploys AI for their business use-cases uses one of two methods to maintain response accuracy and brand compliance. These solutions are:

1. Fine-Tuning – You create a massive dataset (50000+) of customer service interactions and make the AI learn the cadence and interaction capabilities that are expected from it. 
2. Retrieval Augmented Generation (RAG) – You create an AI-readable collection of your documentation (including websites, SOPs, and standard help documents) and give AI the precise information it needs to answer customer questions.

While some enterprises are creating customized voice AI agents with option 1, most of these agents work with option 2, RAG. It works like this –

1. Your customer asks a question.
2. The AI agent understands the question, goes to the database, and searches for the appropriate answer.
3. Once it gets the appropriate response, it transforms that answer and tailors it to the customer’s unique context.
4. The AI agent gives your customers the answer.

Since the AI gets the exact information it needs for the customer’s question, its accuracy is on point, and thus, the L1 and L2 queries are resolved easily. We at Kommunicate are a leader in this area, scoring above marquee competitors like Intercom regarding accuracy and resolution rates.

Now that we know about voice AI, and the cost and operational efficiency you get from deploying it, let’s talk about the real business ROI of the product. 

What is the ROI for a Voice AI Customer Service Solution?

Why are CFOs Green-Lighting Voice AI Solutions?

1. Cost Savings – The average cost of handling a call in an American call center is around $7.68. Voice AI costs at Kommunicate start at $0.06/minute, saving you 10- 20x the cost per call. 
2. Increased Productivity – According to recent benchmarks, voice AI implementation reduces the average handling time (AHT) of inbound calls by 25%.
3. L1 & L2 Deflections – Customers report cost savings of up to 55% by implementing voice AI deflection that resolves L1 and L2-level queries. 
4. Attrition Avoidance – Voice AI takes over the repetitive customer queries and gives your agents more business-critical, complex tasks. This prevents burnout and reduces overall attrition levels. 

In our experience, customers break even on their voice AI costs within a year and get over  300% ROI over a 3-year horizon. 

We’ve also prepared an ROI model that you can directly use to pitch voice AI to your board. 

Board-Ready ROI Model for Voice AI Customer Service

Benefit Category	How to Quantify	Typical Range
Labor savings	(Human cost – AI cost) × minutes	60-80 % OPEX cut
AHT reduction	Minutes saved × agent cost	15-35 % more capacity
Containment/Deflection	% calls resolved by AI × cost per call	50-90 % for L1/L2
Attrition drop	Fewer exits × US $ 10–20k per agent	5-10 % fewer departures
CX lift	Higher NPS/CSAT → revenue retention	+5-15 pts NPS

These benefits translate to overall cost optimization across the board. Add in the fact that increased NPS and CSAT scores usually lead to higher profits and revenue, and you can see why enterprises are adopting these models at an accelerated pace. 

You can calculate your business’s ROI from voice AI on this page. 

Non-Cost Business Benefits

Other than cost optimization, voice AI also delivers on operational efficiencies. It can:

• Regulatory compliance: All calls are recorded, and you can monitor every call for PII compliance. 
• Instant global scaling: Most voice AI models come pre-loaded with multiple international languages. For example, Kommunicate supports 100+ global languages. 
• Data-rich continuous improvement: Since you will have data for each point, continuous improvement of the AI agent for clarity and accuracy is possible.

Let’s explore how these operational wins play out for voice AI and your business. 

How Can Voice AI Drive Operational Wins at Your Contact Center?

The core benefits of voice AI from an operational point of view are as follows:

Operational Lever	What Changes With Voice AI
Queue & Wait-Time Collapse	AI agents greet every caller instantly, deflecting 65 %+ of high-volume FAQs and routing complex cases with context.
Average-Handle-Time (AHT) & After-Call-Work (ACW)	Real-time transcription + auto-summary slashes wrap-up; agents spend < 30 s on notes.
First-Call Resolution (FCR)	LLM reasoning + dynamic knowledge resolves issues in one touch.
100 % Quality-Assurance Coverage	All calls are recorded and transcribed for quality assurance. Additionally, AI Insights gives you points that you can use to improve your team’s performance.
Elastic Peak-Season Capacity	Since AI agents are cloud-native, they can scale infinitely.

These performance improvements help your customer experience and customer service teams move forward. This results in robust improvements in KPIs and metrics across the board. 

Now that you understand these KPIs and how voice AI can improve them, we can get some hard proof about these improvements with some case studies. 

Voice AI Customer Service Case Studies

We will cover two success stories of Voice AI here: Doordash and Golden Nuggets.

Doordash: 94% Order Placement Success Rate in 6 Weeks

DoorDash, the U.S. market-leading food-delivery platform, had to telephone thousands of partner restaurants every hour to place “phone-only” orders. Since costs were directly proportional to the volume of calls, regular spikes in usage increased their costs to unsustainable levels. 

They faced the following challenges –

• Per-order costs kept increasing for offshore agents
• Regular increases in usage were tough to handle 
• Inconsistent call quality increased refund expenses

Voice AI integration solved this for Doordash at scale. Within a few weeks of integration, they saw –

1. 94% successful automated order placements (with 5 points of the human benchmark)
2. Handled 35000+ outbound calls per day
3. Overall costs were reduced

These improvements led to an operations process where Doordash could have error-free restaurant interactions at scale. 

Golden Nuggets – 34% of Reservation Calls Fully Automated

Golden Nugget operates resort hotels and casinos where 70 % of room reservations still happen by phone. Agents spent four to five minutes per booking, and staffing shortages increased guest hold times.

The core challenges they faced were:

• High call volume for reservations where different numbers (like loyalty points, dates, deposits) had to be juggled
• Increased wait times that harmed guest experiences and brand reputation
• Difficulty in optimizing staffing costs during peak seasons

With voice AI, this changed fundamentally. They saw the following improvements:

1. 34% of the reservation calls were automated
2. Freed up 3 days of agent time per week, giving them more time to focus on VIP bookings and other critical tasks
3. They were able to automate 300 conversations per week

With voice AI, Golden Nugget delivered a concierge-level experience to its customers at a much larger scale at lower costs.  This also led to higher revenue because agents could focus on improving upsell opportunities while catering to VIP and loyal clients. 

Thousands of case studies showcase the capabilities of voice AI and how it improves enterprise capabilities at scale. 

So, how can you migrate to voice AI? We have a recommended migration playbook that can help. 

Our Recommended Migration Playbook for Voice AI

We handle enterprise migrations every week. So, we have built a tried and tested methodology to migrate into Voice AI. We’ve summarized it into a table.

Phase	Timeline	Key Actions	Success Gates
0. Baseline & Business Case	Week 0-2	Map your calls and calculate L1/L2 volumes Track your current AHT, FCR, CSAT, and cost-per-call Quantify the ROI	CFO-approved savings target & KPI dashboard
1. Vendor & Use-Case Selection	Week 2-4	Short-list 2-3 voice AI vendors; run security & compliance checks Pick one high-volume, low-complexity queue to test during POC	Signed MSA/SOW; sandbox access; success metrics defined
2. 30-Day Pilot (Shadow Mode → Live)	Week 4-8	Deploy the bot in shadow mode (listens, doesn’t talk) to benchmark intent mapping. Flip to soft launch on ≤ 10 % traffic with human fallback	≥ 80 % intent accuracy, ≤ 3 % misroutes, zero PII leaks
3. Integration & Agent Enablement	Week 8-10	Connect CRM/ticketing APIs to get backend information Activate real-time summaries on the agent desktop. Train supervisors so they can train the AI agent and improve it continuously	Agents endorse UX; no SLA breaches during dual-run
4. Controlled Roll-Out	Week 10-14	Ramp traffic 10 % → 50 % → 100 % Monitor containment, AHT, CSAT daily; tune prompts & knowledge. Introduce multilingual flows	Containment ≥ 70 %, CSAT parity or better vs. baseline
5. Enterprise Scale & Optimisation	Month 4-6	Add new intents (password reset, balance inquiry, etc.) Deploy automated QA scoring across 100 % of calls. Embed VOC insights into product & policy loops	≥ 25 % cost-per-contact reduction, NPS +10 points
6. Continuous Improvement Loop	Ongoing	Quarterly prompt refresh & knowledge-base sync Bi-annual security/compliance audit Governance council reviews AI ethics, bias, and CX metrics	Year-one ROI > 200 %, payback < 12 months

Some of the highlights to focus on are:

1. Test First – Using the agent’s specific speech recognition capability across different call recordings to test for accuracy. If there are any problems, focus on improving agent performance first. 
2. Be Human-Aligned – Voice AI is made for automation, but you will need human agents for complex workflows. So, choose vendors that have agent-to-human handoff natively integrated into the platform. 
3. Focus on Real-Time Analytics – In enterprise use-cases, robust reporting and a real-time analytics dashboard are non-negotiable. This will provide you with the key analytics that you need to track performance and improve agent performance. 
4. Security and Compliance – HIPAA and GDPR should be baked into the platform to protect your customers’ PII data. 

You can start with this migration playbook immediately, but it’s essential to understand some drawbacks you might face during vendor selection. We will explore that in the next section. 

What are the Drawbacks of Contact Center Automation with Voice AI?

Voice AI is an emerging technology with some inherent risks associated with it. We’ve documented the risks and the mitigation strategies we employ to help our clients become more successful. 

Drawback	Why It Happens	Mitigation Playbook
Accent & Noise Sensitivity	Even best-in-class speech models stumble on heavy accents or high-noise environments, leading to misrecognition and caller frustration.	Choose vendors with native multilingual capabilities in their AI platform. Choose vendors that prioritize “human-in-the-loop” workflows
Hallucinations & Inaccurate Answers	Some generative AI models can give out wrong answers	Choose platforms that use retrieval-augmented generation (RAG) that cite authoritative sources If the AI isn’t able to answer, it should transfer the call to a human agent
Bias & Unfair Treatment	AI trained on skewed data can be biased against certain groups of customers.	Check your knowledge base data and remove possible biases.  Track fairness KPIs (FCR & CSAT by segment).
Security & Regulatory Exposure	Voice bots routinely capture PCI, HIPAA, or GDPR-sensitive data; mishandling can incur heavy fines.	Use PCI-compliant payment capture & PII redaction. Review vendor security certifications and plan to re-check the security measures every quarter
Autonomy & Legal Liability	AI agents can act outside policy, triggering legal or reputational fallout.	Choose vendors that have an AI agent-to-human handoff.

As with any new technology, the core component of success is governance. Voice AI needs to have proper processes built around it so that mistakes can be caught proactively and solutions can be deployed faster. 

You should also know the technology trends influencing voice AI customer service. Research-forward vendors should be able to integrate these future trends into their platform within the next few years. 

What are Some Future Trends and Technologies in Voice AI Customer Service?

Voice AI is on the cutting edge of AI research right now. Most of the research is on improving speech recognition and adding more human-like conversational capabilities to these tools. Some trends we’ve noticed are: 

1. More Multilingual Capabilities – Models like Google’s Universal Speech Model can now recognize more than 300 languages.
2. Real-Time Voice Cloning – Several voice AI customer service companies let you clone an agent’s voice when training an AI agent. This improves the human-like conversational capabilities of these bots. 
3. Emotion and Sentiment-Aware Conversations – New AI models are fine-tuning to understand your customers’ sentiments better.  
4. Predictive and Proactive CX – Modern AI platforms can provide better and proactive service to your customers by understanding their service expectations and current status. We cover this in more detail in our customer service trends article.

These capabilities should improve your overall customer service processes and customer experience. 

Conclusion

We have detailed how Voice AI presents a transformative approach to contact center automation. We’ve explored the significant cost pressures and operational hurdles businesses face with high call volumes and traditional BPO models. As highlighted, Voice AI customer service offers a compelling alternative, delivering substantial benefits:

• Drastic Cost Reduction: Slashing operational expenses by 60-80% compared to traditional methods.
• Enhanced Operational Efficiency: Automating L1/L2 queries 24/7, reducing AHT, improving FCR, and enabling instant scalability.
• Improved CX & Control: Ensuring consistent brand voice, providing 100% QA coverage, and enabling faster adaptation to policy changes.
• Proven ROI: Demonstrating significant returns, often exceeding 300% over three years, with real-world examples like Doordash achieving a 94% success rate and Golden Nuggets automating 34% of reservation calls.

You’ve also seen a clear migration path, understood potential challenges like accent sensitivity and accuracy (and how to mitigate them), and glimpsed future advancements in the field.

Now, considering the pressures on your contact center operations, how much could you save and what efficiency gains could you unlock by automating your high-volume L1/L2 interactions with Voice AI?

If you’re ready to explore escaping traditional contact centers’ high costs and limitations, we encourage you to quantify the potential impact for your specific situation.

• Calculate your potential savings: Use our Voice AI ROI calculator.

Ready to see how a tailored Voice AI solution can revolutionize your contact center automation strategy? Get in touch with our experts!