Transitioning to an AI Contact Centre with CXCortex: Your step by step guide

An AI contact centre can understand customer enquiries and independently respond to them through several channels, such as email, chat and voice. Transitioning to an AI-driven contact centre can lead to significant benefits, including cost reduction, 24/7 service availability, enhanced automation, real-time analytics, increased sales, and improved service quality. This article outlines a detailed roadmap for transitioning to an AI contact centre, explores the benefits in depth, and examines future developments.

A modern AI contact centre streamlines the evaluation and transition process towards AI-assisted or fully autonomous customer service. This efficiency stems from the industry's focus on user-friendliness, a top priority in 2024's customer experience landscape. Always opt for a system that enables phased AI integration across communication channels, ensuring controlled and strategic deployment.

Roadmap for Transitioning to an AI Contact Centre

1. Email Automation

Your Outlook or Gmail email service is a good starting point because an AI agent can be added with minimal disruption and change from the customers perspective but with a significant improvement in response time. Responses can be automatic or human-supervised, where the responses are edited and saved to the AI agent's knowledge base, thereby improving the response accuracy and scope with every response.

Reduce email queues: Automate responses to common queries by creating a knowledge base of ideal answers which the AI agent can use to match a response to a customer enquiry.


  • - Quick response times: Automated systems can reply to emails within seconds, reducing wait times and improving customer satisfaction.

  • - Reduced workload for human agents: By handling repetitive inquiries, AI frees up human agents to focus on more complex issues that require human intervention.

  • - Improved customer satisfaction: Faster response times and accurate answers enhance the customer experience, leading to higher satisfaction rates.

Implementation Steps:

  • - Assess common queries: Identify frequently asked questions that can be automated. Analyse past email data to find patterns in common inquiries.

  • - Train the AI: Use historical data, PDFs, .csv files and APIs to train the AI on how to respond accurately to various types of emails and upload relevant documents which the AI agent can use. The AI agent can be trained and kept up to date by adding email responses to its knowledge base automatically.

  • - Monitor performance: Regularly review AI responses to ensure quality and make adjustments as needed. Use customer feedback to refine the AI's responses.
CXCortex AI contact centre
Pictured here is the Triage page for email, chat and voice calls where each message is given a status code and summary.

2. AI customer service agent for web sites

AI-powered customer service interfaces are integrated into websites through a simple code snippet insertion in the page headers. This implementation presents users with an intuitive chat widget, typically activated by clicking an icon. The process works as follows:

  1. Integration and Activation: Developers embed a small piece of JavaScript code into the website's HTML structure. This creates a chat icon or button, often placed in a corner for easy access without disrupting the main content.
  2. User Interaction and AI Engagement: When clicked, the icon expands into a full chat interface, mimicking familiar messaging apps. The AI chatbot then initiates conversation, ready to assist with queries, troubleshooting, or directing users to relevant information.
  3. Customisation and Scalability: Businesses can tailor the widget's appearance and behaviour to match their brand and specific customer service needs. This solution allows companies to handle multiple customer interactions simultaneously, improving response times and service efficiency.
  4. Data Collection and Insights: These interfaces gather valuable data on common customer issues and preferences, informing business decisions and service improvements. This continuous feedback loop helps in refining the AI's responses and overall customer service strategy.

This streamlined approach offers an efficient, user-friendly, and adaptable solution for integrating AI-powered customer service into websites.

Implement web agents: Deploy AI customer service agents on your website and social media channels to handle customer inquiries. These agents can resolve at least 70% of issues and escalate more complex problems to human agents.


  • - Instant customer service: AI customer service agents provide immediate responses, improving customer experience and reducing wait times.

  • - Reduced operational costs: Automating chats lowers the need for a large customer service team, leading to significant cost savings.

  • - Enhanced user experience: AI customer service agents can handle multiple languages and offer 24/7 service, catering to a global audience.

Implementation Steps:

  • - Design the AI agent: Create a conversational flow that covers common customer interactions. Include predefined responses for frequently asked questions and decision trees for handling more complex queries.

  • - Integrate with existing systems: Ensure the AI agent can access necessary data from your CRM, inventory management, document store and other relevant systems to provide accurate responses.

  • - Test extensively: Conduct thorough testing to identify and fix any issues before going live. Use beta testing with a small group of customers to gather feedback and make improvements.

3. Web Voice Integration

Voice interactions with AI customer service agents are now available through standard web browsers using WebRTC technology, enabling direct voice communication without additional software. Key points:

  1. Browser support and WebRTC: Modern browsers natively support Web Real-Time Communication, allowing real-time voice communication within browsers.
  2. Secure, plugin-free operation: Users can initiate voice calls without extra software. WebRTC includes built-in encryption for secure communication.
  3. Cross-platform and low latency: WebRTC works across devices, providing consistent, natural conversational experiences with minimal delay.
  4. Cost-effective and accessible: This approach reduces infrastructure costs and makes voice-based customer service more accessible to users preferring spoken communication.

Add voice chat: Integrate AI-driven voice chat systems to handle voice queries. Use high quality speech recognition and text to speech to ensure accurate and efficient communication using the shared knowledge base.

CXCortex AI contact centre - web voice
Pictured here is the web voice assistant shown from a PC and smartphone perspective.


  • - Enhanced accessibility: Voice chat makes it easier for customers who prefer speaking over typing, improving accessibility and user experience.

  • - Personalised customer interactions: Voice recognition allows for tailored responses based on customer history and preferences.

  • - Seamless escalation: Complex queries can be smoothly transferred to human agents when necessary, ensuring a seamless customer experience.

Implementation Steps:

  • - Customise: Select an AI voice agent with robust speech recognition capabilities, the target is 95% accuracy. Ensure it supports multiple languages and accents for global reach. Latency should be below 800ms.

  • - Train the system: The AI voice assistant can use the same shared knowledge base as the other AI contact centre communications channels but will need to have different instructions or prompts to suit a voice call.

  • - Integrate with CRM: Connect the voice chat system to your customer relationship management (CRM) software or database for context-aware responses. This allows the AI to access customer history and preferences.

  • - Monitor and refine: Continuously monitor the system’s performance and make improvements based on customer feedback and interaction data. This is made easy by providing real time or offline text transcripts of all voice conversations.

4. Phone call Integration

Integrate your AI customer service agent across both web and traditional phone channels, creating a unified, versatile support system. This approach replaces conventional Interactive Voice Response (IVR) systems with a more natural and robust solution.

Key points:

  1. Omni-channel consistency: The same AI agent handles interactions via web voice chat and phone lines, ensuring consistent responses and service quality across channels. This unified approach streamlines training and maintenance while providing a cohesive customer experience.
  2. Enhanced natural language processing: Unlike traditional IVR systems, AI agents employ advanced natural language processing, allowing them to understand and respond to a wider range of customer queries with greater accuracy. This reduces customer frustration and improves resolution rates.
  3. Dynamic adaptation: AI agents can learn from interactions across both channels, continuously improving their responses and understanding of customer needs. This adaptability allows the system to evolve with changing customer expectations and business requirements.
  4. Scalability and cost-efficiency: By using a single AI system for both web and phone interactions, businesses can more easily scale their customer service capabilities without proportional increases in cost. This approach also reduces the need for extensive human agent training on multiple systems.
  5. Enhanced analytics and insights: Unifying web and phone interactions under one AI system allows for comprehensive data collection and analysis. This provides valuable insights into customer behaviour, preferences, and common issues across channels, enabling data-driven improvements to products, services, and support processes.

This integrated approach modernises customer service infrastructure, offering a more flexible, efficient, and user-friendly experience compared to traditional IVR systems.

Integrate phone systems: AI contact centres will have in-built, low latency SIP-AI connectors . This allows AI to manage inbound and outbound calls, providing information or resolving issues as needed.


  • - 24/7 service: AI can handle calls at any time, offering constant availability and improving customer satisfaction.

  • - Reduced need for large call centre staff: AI can manage a significant portion of calls, reducing staffing requirements and operational costs.

  • - Consistent customer experience: AI ensures uniformity in responses and service quality, leading to a more consistent customer experience.

Implementation Steps:

  • - Configure call flows: Design call flows that AI can handle, with clear paths for escalation to human agents for complex issues. Define rules for when and how calls should be transferred.

  • - Train the AI: Use call recordings to teach the AI how to handle various scenarios. Include a diverse range of customer interactions to improve the AI's versatility.

  • - Monitor call quality: Regularly review call recordings to ensure the AI is performing correctly. Use customer feedback to identify areas for improvement.

5. Outbound Marketing and Surveys

AI voice agents have significantly improved outbound dialer campaigns through key technological advancements:

  1. Enhanced voice recognition and language understanding: AI agents now accurately handle diverse accents and dialects, comprehend context and intent in natural speech, and manage complex queries. They employ adaptive learning to continuously improve based on regional linguistic patterns.
  2. Reduced latency and improved real-time processing: Modern systems offer near-instantaneous responses, minimising conversational pauses. Enhanced processing power enables real-time speech analysis and response generation, supported by improved network infrastructure and optimised algorithms.
  3. Advanced conversational AI technology: Today's AI agents use natural-sounding voices with appropriate intonation, demonstrate improved turn-taking abilities, and handle interruptions effectively. They also exhibit enhanced emotional intelligence, responding appropriately to customer sentiment.
  4. Scalability and integration capabilities: These systems can manage high volumes of simultaneous calls and integrate seamlessly with existing CRM systems. They offer detailed analytics for campaign optimisation and allow easy script updates based on performance, ensuring continuous improvement.

AI for outbound marketing: Utilise AI voice agents to conduct outbound marketing campaigns and customer surveys by adding them as agent extensions to a predictive dialer.


  • - Increased efficiency: AI agents can handle multiple calls simultaneously, significantly increasing the number of successful connections compared to human agents. This maximises the effectiveness of the predictive dialer's call pacing algorithms.

  • - Consistent quality: AI voice agents deliver a consistent message and tone across all calls, eliminating human variability. They don't experience fatigue or mood swings, ensuring uniform quality regardless of call volume or time of day.

  • - Cost-effective scaling: Implementing AI voice agents allows businesses to scale their outbound operations without proportionally increasing staffing costs. This enables handling of larger campaigns or peak periods without the need for additional human resources.

Implementation Steps:

  • - Define goals: Clearly outline the objectives of your marketing campaigns and surveys. Determine what you want to achieve and how you will measure success.

  • - Segment audience: Use AI to identify and segment your target audience. Create customer personas based on demographic data, purchase history, and behaviour.

  • - Develop content: Create compelling messages and surveys tailored to each segment. Use AI to generate personalised content that resonates with your audience.

  • - Launch campaigns: Use AI tools to send out campaigns and surveys. Monitor performance in real time and make adjustments as needed.

  • - Analyse results: Leverage AI analytics to review responses and adjust strategies. Use insights to refine future campaigns and improve customer engagement.

6. Quality Assurance (QA) and AI

AI can significantly enhance Quality Assurance (QA) processes in contact centres. Using advanced speech recognition and natural language processing technologies, AI systems can automatically monitor and analyse 100% of customer interactions across various channels, including voice calls, chat sessions, and emails. This comprehensive coverage far exceeds traditional manual sampling methods. AI algorithms can assess these interactions for adherence to script guidelines, detect emotional cues in customer voices, identify potential compliance issues, and evaluate agent performance metrics in real-time. The system can flag interactions that require human review, allowing QA teams to focus their efforts on the most critical cases.

Furthermore, AI-driven analytics can uncover trends and patterns in customer interactions, providing valuable insights for training programs and process improvements. This data-driven approach not only ensures consistent service quality but also enables proactive measures to enhance overall customer experience and operational efficiency.

Real-Time Monitoring: AI can monitor interactions in real time, providing immediate feedback and ensuring compliance with company standards. This allows for prompt correction of issues and continuous improvement.

Data-Driven Insights: AI analyses large volumes of data to identify trends, areas for improvement, and training needs. This helps contact centres stay ahead of potential issues and improve overall performance.

Consistent Standards: AI applies consistent criteria across all interactions, eliminating human bias. This ensures that every customer receives the same level of service, regardless of the agent handling the interaction.

Implementation Steps:

  • - Define QA criteria: Set clear standards or objectives for evaluating interactions. Include metrics for response time, resolution rate, customer satisfaction, and adherence to company policies.
  • - Automate monitoring: Use AI to monitor all interactions and flag issues. This allows for real-time quality assurance and immediate corrective actions.

  • - Review and refine: Continuously review AI findings and make necessary adjustments. Use insights to update training programs and improve agent performance.

Future Developments in AI Contact Centres

The future of AI contact centres looks promising, with several advancements on the horizon:

Advanced AI Algorithms

More sophisticated AI algorithms will lead to better understanding and response capabilities. These algorithms will be able to handle more complex queries and provide more accurate responses.

Emotion Recognition

AI will be able to recognize and respond to customer emotions, providing empathetic and appropriate responses. This will enhance the quality of customer interactions and lead to higher satisfaction rates.

Predictive Analytics

AI will predict customer needs and proactively offer solutions or products, enhancing the overall customer experience. Predictive analytics will enable contact centres to anticipate customer issues and resolve them before they become problems.

Integration with IoT

AI contact centres will integrate with Internet of Things (IoT) devices, providing seamless support for connected products and services. This integration will allow contact centres to offer proactive support and maintenance for IoT devices, improving customer satisfaction and reducing downtime.

AI contact centre cost savings calculations

The following comparison uses time spent on tasks by AI and humans respectively, regardless of whether the task is complex or simple. Offloading routine customer service tasks to an AI contact centre saves time and hence cost:


  • - Average salary of a customer service agent: £25,000 per year

  • - Cost of AI contact center: £2.50 per hour

  • - Standard full-time hours: 40 hours per week, 52 weeks per year (2,080 hours annually)


  • - Additional employment costs (benefits, training, equipment) at 30% of salary

  • - Both human agents and AI system operate for the same 2,080 hours per year

  • - Human agents have 80% productivity (time actually handling calls)

  • - AI system has 100% productivity for the hours it's operating


  1. Annual cost per human agent: Salary: £25,000 Additional costs (30%): £7,500 Total: £32,500 per year
  2. Annual cost of AI system (2,080 hours operation): £2.50 * 2,080 hours = £5,200 per year
  3. Productive hours: Human agent: 2,080 hours * 80% = 1,664 productive hours per year AI system: 2,080 hours per year (assuming 100% productivity)
  4. Cost per productive hour: Human agent: £32,500 / 1,664 hours = £19.53 per hour AI system: £5,200 / 2,080 hours = £2.50 per hour

Statistics based on these calculations:

  1. Annual cost savings: £32,500 (human) - £5,200 (AI) = £27,300 per year per agent replaced
  2. Percentage cost reduction: (£32,500 - £5,200) / £32,500 * 100 = 84% cost reduction
  3. Return on Investment: The AI system breaks even compared to a human agent in about 2 months
  4. Productivity increase: AI system provides 416 more productive hours per year (25% increase)
  5. Cost per productive hour savings: £19.53 (human) - £2.50 (AI) = £17.03 saved per productive hour
  6. Efficiency ratio: For the cost of one human agent, you could operate 6.25 AI systems
  7. Potential 5-year savings: £27,300 * 5 = £136,500 per agent replaced over 5 years

These statistics show significant potential savings when comparing AI to human agents for the same number of working hours. The AI system offers substantial cost reductions while potentially increasing productive hours.

However, it's important to note that these figures don't account for initial setup costs of AI systems, ongoing maintenance, or potential limitations in handling complex or nuanced customer interactions. Also, the quality of customer service and ability to handle varied or complex queries are not factored into these purely financial comparisons.

Transitioning to an AI contact centre offers numerous benefits, including cost reduction, enhanced service quality, and increased sales. By following a structured roadmap, companies can effectively implement AI technologies and reap the rewards. As AI continues to evolve, the potential for further improvements in customer service and operational efficiency remains vast, promising a bright future for AI-driven contact centres.

For more information please refer to our AI contact centre web page

Contact Hostcomm for a free trial or demonstration: [email protected]

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