What are AI voice agents?

1. Speech input

The customer speaks into a device, such as their smartphone or a call center line. For example, they might ask, "What’s the balance on my account?" or "Can I reschedule my delivery?" Their words are turned into an audio signal and sent to the voice assistant for processing.

2. Speech recognition 

The audio signal is processed by an automatic speech recognition (ASR) system, which converts the sound into text. The ASR system ensures the transcription is accurate, even with different accents or speaking styles. So, the ASR system processes a voice saying, 'Check my order status,' and converts it into text.

3. Natural language understanding

The text from ASR is sent to a natural language understanding (NLU) system, a branch of NLP that allows machines to understand human language. 

Based on the customer’s input, 'How much is left in my account?', the NLU system determines the customer's intent, such as 'check my account balance', and identifies key details, like 'balance for account ending in 1234'. 

Similarly, for inputs like 'Reschedule my delivery,' it extracts the intent 'reschedule a delivery' and details such as 'delivery for this Friday.’

4. Processing and decision making

AI voice agents determine the appropriate action by analyzing user input and accessing relevant data. 

This step is enhanced by incorporating retrieval-augmented generation (RAG), which allows AI voice agents to access and use external knowledge sources in real-time. This leads to more accurate and contextually relevant results.

So, when a customer asks, 'How much is left on my balance?', the system, possibly using RAG, identifies the intent (check account balance), retrieves details (account ending in 1234), and queries the database. 

Likewise, for 'Can I reschedule my delivery to next Friday?', it accesses the scheduling platform, updates the delivery, and provides a real-time confirmation to the customer.

5. Response generation

Once the response is determined, the system uses an LLM to generate a reply. 

The LLM ensures the response is clear and professional, such as ‘Your account balance is $500’ or ‘Your delivery has been rescheduled to Saturday.’

6. Text-to-speech 

The text-based reply is converted into speech by a text-to-speech (TTS) system, ensuring the message sounds natural.  

7. Voice output

The synthesized speech is played back to the customer through the device’s speaker, completing the interaction.

So, a user might hear their phone reply, 'Your account balance is $500.75 as of 12:35 PM today.' 

Similarly, for a delivery rescheduling request, the phone might respond, 'Your delivery has been successfully rescheduled to Saturday, January 11th.

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Enhance customer experience

AI voice agents are available round-the-clock, and thus offer instant answers to customer inquiries without the frustration of long wait times. 

Using natural language and emotional cues, such as frustration, AI voice agents make interactions feel more genuine. They also adapt to accents, languages, and conversational styles.

And like any good customer support chatbot, AI voice agents are trained to escalate complex issues to human agents while retaining full context.

Streamline operations

AI voice agents take routine tasks off the plate,  such as appointment scheduling, order processing, status updates, so human agents can focus on nuanced, high-value interactions. They handle high call volumes without missing a beat, keeping service consistent even during peak hours. 

By integrating with backend systems to access real-time data, AI voice agents deliver accurate, instant responses and minimize errors.

Scale with ease and communicate globally

Designed to handle surges in call volume, AI voice agents help businesses experiencing growth or seasonal spikes. 

By integrating with backend systems to access real-time data, they deliver accurate, instant responses and minimize errors, an aspect that’s especially valuable for growing businesses.

Collect and analyze data

AI voice agents gather important customer data during interactions, uncovering patterns and insights that can refine strategies. 

If many customers call to complain about a new feature, the AI voice agent can immediately detect the spike in complaints and alert the business. 

By analyzing ongoing trends from phone calls and other voice-related interactions, AI voice agents help businesses make data-driven decisions.

Increase accessibility

By enabling voice-based interactions that require no physical input, AI voice agents provide inclusive support for a wide range of users.This makes them an essential tool for serving customers with disabilities.

Also, their multilingual capabilities break down language barriers to serve a diverse, global audience. 

Financial benefits

1. Cost savings
   • AI voice agents automate repetitive tasks, reducing the need for large customer service teams and saving significantly on labour costs.
   • Long-term ROI comes from reduced operational expenses and improved service efficiency.
2. Revenue growth
   • Proactive engagement, such as cross-selling or upselling during interactions, can increase average order value and overall revenue.
   • High containment rates show that AI systems effectively resolve routine issues without human intervention, enhancing operational efficiency and reducing the need for escalations. 

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