Contact Center AI | Use Cases & How to Get Started

1. Receive customer inquiries

Contact center AI agents begin by processing incoming customer requests from multiple channels, such as phone calls, live chat, email, or messaging apps. AI voice assistants and chatbots interpret the customer’s intent using natural language understanding (NLU) to recognize key details like urgency and specific requests.

For instance, if a customer asks, ‘Where is my order?’ the AI agent identifies it as a tracking request, retrieves relevant order details from the system, and provides an immediate response.

2. Understand context

Contact center AI considers context to provide more accurate responses. It analyzes previous interactions and purchase history to provide personalized assistance instead of generic replies.

A customer asking about a refund will receive relevant updates based on their past orders, preventing unnecessary back-and-forth. 

3. Retrieve and process information

Once intent and context is recognized, AI connects with backend systems like CRMs and knowledge bases to find accurate details.

If a customer requests an order modification, the AI locates account records and, if eligible, initiates the process. When troubleshooting, it identifies relevant guides or recommends appropriate next steps based on the issue described. 

In more complex cases, the AI may gather additional details before escalating the request to an agent.

4. Generate a response

The AI generates a response based on the data collected. It ensures replies are:

• Context-aware: The AI considers customer history based on past interactions and the specific request to personalize responses.
• Actionable: If the process requires customer input, such as confirming an order change, the AI provides clear, step-by-step instructions.
• Adaptive: Responses adjust dynamically based on real-time conditions like stock availability or policy updates.

For instance, if a customer inquires about an order status, the AI won’t just provide a tracking number. It will explain the expected delivery time and suggest options such as rerouting or expedited shipping.

5. Escalate to a human agent

When AI for contact centers cannot fully resolve an issue, it transfers the customer to an employee, while:

• Transferring conversation history, so customers don’t have to repeat themselves.
• Providing context, summarizing key details like intent and attempted solutions.

In cases where specialized assistance is required, AI routes the request based on expertise or urgency. A billing issue might be directed to a finance specialist, while a technical problem goes to IT support. 

6. Monitor performance 

After handling interactions, contact center AI analyzes its performance to optimize future responses and increase efficiency.

• Tracking containment rate.
• Analyzing customer sentiment.
• Detecting common pain points.

7. Continuously learn and improve

Contact center AI refines its accuracy and effectiveness over time by analyzing interactions and making adjustments based on real-world usage.

• Updating responses: AI refines its replies based on customer interactions, making them more accurate and relevant.
• Expanding knowledge: It incorporates new information, such as policy changes or product updates, to stay up to date.
• Improving efficiency: AI adapts to recurring issues by optimizing workflows and reducing unnecessary escalations.

Advanced call routing for efficient support

Using algorithms, AI can direct customers to the right agents or automated services based on caller details and service needs. Instead of relying on basic menu selections, AI analyzes multiple factors to determine the best course of action.

• Customer history and intent: AI reviews past interactions to determine if a customer has an open support ticket or ongoing issue, ensuring continuity in service.
• Issue type and complexity: Simple requests, such as password resets or order tracking, can be handled by automated systems, while more complex inquiries are routed to specialized agents.
• Urgency and sentiment analysis: AI detects frustration or urgency in a customer’s voice or message, prioritizing cases that require immediate attention.

Interactive voice response (IVR) AI

AI IVR systems improve traditional call handling by using natural language processing (NLP) to interpret and respond to spoken requests. 

Instead of forcing customers to navigate rigid button-based menus, AI IVR allows them to speak naturally, improving both efficiency and user experience.

Businesses can implement IVR systems that allow callers to request information through voice or keypad inputs. Traditionally, these systems relied on dual-tone multi-frequency (DTMF) signals to trigger prerecorded messages or text-to-speech responses. With AI and NLP, IVR has evolved to support more natural interactions

Chatbots for customer engagement

Reducing call center strain starts with AI contact center chatbots that handle routine customer inquiries efficiently. These virtual agents allow customers to get quick answers to common questions without waiting on hold.

By integrating with backend systems, AI chatbots for contact centers can provide real-time updates on orders and even assist with account management. 

Proactive customer outreach

AI systems notify customers about upcoming appointments or payment deadlines. Businesses can also use AI to send personalized promotions based on customer preferences, improving engagement while reducing missed opportunities.

Customer sentiment analysis

To understand customer sentiment, AI tools analyze interactions as they happen. 

If frustration is detected, the system can prioritize the case for escalation. Businesses can also use sentiment trends to improve agent training and refine customer service strategies. 

By continuously monitoring feedback across multiple channels, AI helps identify recurring issues before they impact customer satisfaction.

Real-time agent assist

Contact center AI listens to live customer interactions, pulling relevant information from knowledge bases and past interactions as it is needed. It improves agent efficiency by:

• Retrieving helpful resources, such as FAQs or troubleshooting guides, based on the customer’s issue.
• Recognizing previous interactions to suggest solutions that align with the customer's history.
• Identifying frustration or urgency, allowing agents to prioritize critical conversations.
• Automating call transcription and summarization, reducing the need for manual note-taking.

Multilingual support

AI translation tools remove language barriers, allowing businesses to assist customers in multiple languages. Whether through chat or voice, AI ensures smooth communication without requiring a large multilingual support team.

Fraud detection and security screening

AI analyzes voice patterns and authentication attempts to identify suspicious activity. If something seems unusual, the system can request additional verification or escalate the case to a security specialist.

1. Define your scope

The first step to create a contact center AI agent is simple – what’s it going to do? Start by clearly outlining the purpose of your agent.

Decide whether it will focus on:

• Customer inquiries
• Agent assistance
• Call routing
• Proactive engagement
• A mix of these services

Clearly defining the scope ensures the AI agent is designed to meet specific business needs and improve overall customer support.

2. Pick a platform

Select an AI platform that supports NLP and automation, while ensuring real-time data retrieval and integration.

There’s no shortage of AI agent platforms to choose from. If you’re looking for inspiration, our curated list of the top AI platforms is a great place to start.

When evaluating platforms for contact center AI, consider factors such as:

• Compatibility with your CRM and ticketing systems
• Support for both text and voice-based interactions
• Customization options to match your business’s needs
• Scalability to handle growing customer demands

For contact center AI, platforms like Botpress offer advanced tools, including Autonomous Nodes, which enable LLM agents to determine when to follow a structured workflow or generate responses dynamically. Developers can guide the AI using plain language prompts, making it easier to create adaptive, context-aware virtual agents that handle customer interactions in real time

3. Integrate tools and knowledge bases

After choosing your platform, the next big step is integrating any tools, platforms, or systems that will be relevant to your AI workflows.

A low-code platform will offer pre-built integrations. A flexible chatbot platform will offer custom integrations – a developer can integrate any internal system or platform you use that you want the chatbot to be able to work with.

You’ll also want to create Knowledge Bases for the chatbot to draw from – sources like official policies and procedures, updated wait lists, or legal requirements.

4. Test and refine

Even after deploying your contact center AI agent, there will constantly be opportunities for refinement. Real-world interactions will reveal how the AI can improve, from response accuracy to handling complex customer issues more effectively.

Ensure that your plan incorporates the ability to reiterate on your AI agent – this is by far the best way to maximize your ROI.

5. Deploy 

Once your contact center AI agent is ready, it’s time to deploy it and integrate it into your customer service operations. There are multiple ways to make it accessible:

• Implement it within your IVR system to assist with inbound calls.
• Deploy it as a live chat widget on your website.
• Integrate it with customer service platforms like Zendesk or Salesforce.
• Connect it to messaging channels such as WhatsApp, Facebook Messenger, or Slack for seamless customer interactions.

To ensure maximum impact, communicate its availability to customers and support teams. Proper onboarding and awareness will help users engage with the AI agent effectively, maximizing its value in streamlining contact center operations.

6. Monitor

A quality AI agent platform will offer ongoing analytics to track performance and identify areas for improvement. Monitoring key metrics ensures that the AI continues to enhance customer support rather than create new inefficiencies.

• Real-time analytics help measure response accuracy and containment rates. If the contact center AI agent frequently escalates cases that it should handle, adjustments can be made to improve its responses.
• Conversation logs provide insights into recurring issues, allowing businesses to refine workflows and enhance customer interactions. If certain topics lead to frustration, updating training data or refining automation can help.

If chatbot analytics are available, businesses can track engagement patterns and identify areas where the AI needs further optimization.

1. Is Contact Center AI only useful for large enterprises, or can small businesses benefit too?

Contact Center AI is not limited to large enterprises. Small businesses can also benefit by automating repetitive support tasks and improving customer experience without hiring a large support team. It enables lean teams to deliver 24/7 service and scale efficiently.

2. What’s the difference between Contact Center AI and a Virtual Assistant?

The difference between Contact Center AI and a Virtual Assistant lies in their scope and function: Contact Center AI is designed specifically for managing customer support at scale – handling ticket routing, call deflection, and CRM integration – while a Virtual Assistant focuses on helping individuals with personal tasks like scheduling or general queries.

3. How do you balance automation with the human touch in customer service?

To balance automation with the human touch in customer service, use AI to handle routine, high-volume inquiries and set clear rules for escalating complex or sensitive issues to a human agent. This ensures customers get quick responses without sacrificing empathy or personalized help when it matters most.

4. Can the AI be trained using our historical support tickets and call transcripts?

Yes, Contact Center AI can be trained using historical support tickets and call transcripts to better understand your tone and resolution patterns. This allows the AI to mirror your team’s style and improve accuracy over time.

5. How does the AI handle ambiguous or unclear customer requests?

When faced with ambiguous or unclear customer requests, Contact Center AI typically uses clarification questions to narrow down intent, or it falls back to escalation logic to transfer the conversation to a human agent.