AI in Contact Centers: Revolutionizing Customer Service with Virtual Agents and Agent Assist

The customer service landscape has transformed due to advancements in artificial intelligence (AI) technologies. Generative AI, natural language processing (NLP), and conversational AI are converging to create virtual agents that rival human interactions and empower frontline employees with assistance tools.

The rapid evolution of AI technology in customer service has led to widespread acceptance, driven by significant improvements in customer experience. Kore.ai’s Customer Experience Benchmark Report 2024 observed the following trends:

• Virtual agents are widely accepted by customers due to increased effectiveness, accuracy and efficiency in service interactions.
• The gap in capabilities between virtual agents and frontline employees is decreasing. As a result, customer satisfaction is similar whether consumers interact with virtual agents or frontline employees.

In this article, we describe the differences between virtual agent and agent assist solutions, explain the technology behind them, and discuss considerations for successful implementation.

The rise of virtual agents

Virtual agents, also known as chatbots or conversational AI, have advanced significantly from their rule-based predecessors. Today’s virtual agents leverage AI and NLP to provide a more natural, context-aware, and efficient customer experience across various channels, including phone, chat, email, and social media. Some of the features of a virtual agent in a contact center are:

1. Automated interactions: Handling routine inquiries, common customer service requests, and simple transactions.
2. 24/7 availability: Offering round-the-clock customer support without human agents.
3. Natural language understanding: Understanding and processing natural language inputs that provide accurate and relevant responses.
4. Integration with systems: Connecting with backend systems, databases, and CRM tools to retrieve information and perform actions on behalf of the customer.
5. Personalization: Customizing interactions based on customer data and previous interactions to enhance the customer experience.
6. Escalation: Seamlessly transferring complex or unresolved issues to human agents when necessary, often with context and interaction history for a smooth handover.
7. Learning and improvement: Continuously improving performance through machine learning, adapting to new queries, and enhancing response accuracy over time.

Agent assist: Empowering human agents

While virtual agents handle an increasing number of customer interactions, human agents play a crucial role in complex problem-solving and relationship-building. Agent assist leverages the same AI capabilities to support human agents during customer interactions to provide real-time assistance, insights, and automation to enhance the efficiency and effectiveness of frontline employees and improve the customer experience. Features of agent assist are:

1. Real-time information retrieval: Enabling agents to quickly access relevant information from organizational systems, articles, and FAQs based on the customer’s query in real time.

2. Real-time guidance: Providing agents with suggested responses based on the conversation as well as advising agents on the next best actions.

1. Automated workflows: Automating routine tasks such as data entry, call summaries, filling forms, and pulling up recent customer interactions to allow agents to focus on the customer interaction.
2. Sentiment analysis: Providing real-time analysis of customer sentiment to help agents adjust their tone and approach accordingly, as well as notifying supervisors of interactions that might require immediate intervention.
3. Speech and text analytics: Converting speech to text during calls for easier reference and analysis, including identifying key topics and customer concerns.

The technology behind virtual agent and agent assist solutions

Recent advancements in AI and automation have resulted in improvements in the capabilities of virtual agents and agent assist.

Natural language processing: The foundation for virtual agents

NLP serves as the backbone of virtual agents, enabling machines to understand, interpret, and naturally respond to human language. This includes:

• Accurately interpreting customer intent
• Extracting information from customer queries
• Handling multiple languages and dialects
• Recognizing and responding to sentiment and emotion

Generative AI: The game-changer

Generative AI, exemplified by large language models (LLMs) like GPT-3 and its successors, has revolutionized the capabilities of virtual agents. These models can:

• Understand complex queries and generate human-like responses
• Maintain context over long conversations
• Adapt tone and style to match customer preferences
• Handle a wide range of topics without explicit programming

Conversational AI: Bringing it all together

Conversational AI combines generative AI and NLP with other technologies to create a seamless, natural interaction. Features include:

• Turn-taking and dialogue management
• Multi-turn context retention
• Personalization based on customer history and preferences
• Seamless handoff to human agents when necessary

Integration with backend systems and RPA

In order to replicate the capabilities of human agents, AI-powered solutions must integrate seamlessly with enterprise systems to retrieve relevant customer information and generate tailored responses. These systems can integrate with enterprise systems using available connectors or traditional RPA to automate repetitive tasks and data retrieval, enabling virtual agents and human agents to:

1. Quickly pull relevant customer data from multiple systems
2. Initiate and track backend processes, such as refunds and order modification
3. Update customer records across various databases
4. Generate and send custom reports or documents

Key considerations for AI in contact centers

Implementing AI in contact centers comes with its own set of challenges and considerations. Organizations must navigate these to ensure a successful and responsible AI implementation. Data privacy, ethical AI development, and regulatory compliance are paramount. Organizations must also ensure a smooth transition by setting clear expectations for customers and providing comprehensive training for agents.

1. Safeguarding data privacy and security

With the increasing frequency of data breaches and stringent privacy regulations, protecting customer information is essential. Contact centers must implement robust security measures, including:

• Strict access controls for both AI systems and human agents
• End-to-end encryption for data transmission and storage
• Regular security audits and updates

It is crucial to restrict access to personally identifiable information (PII) only during active customer interactions to prevent unauthorized queries or access outside of legitimate service contexts.

2. Ensuring ethical AI and mitigating bias

AI systems are only as good as the data they’re trained on. Unchecked, they can amplify existing biases. To address this:

• Regularly audit AI systems for potential biases
• Ensure diversity in AI development teams
• Be transparent with customers about AI interactions

3. Navigating the regulatory landscape

The regulatory environment surrounding AI and data privacy is complex and evolving. Contact centers must stay abreast of relevant regulations such as GDPR, CCPA, or industry-specific rules. This includes:

• Obtaining proper consent for data usage and AI interactions
• Maintaining detailed audit trails of AI decisions
• Ensuring compliance with data localization requirements

4. Training and implementing appropriate fallback mechanisms

While AI can handle many tasks efficiently, it has limitations. Implementing robust fallback mechanisms for frontline employees is crucial. Additionally, organizations should focus on:

• Setting clear customer expectations about AI capabilities
• Training human agents to work effectively alongside AI
• Continuously refining the balance between automation and human interaction

5. Implementing appropriate change management strategies

The introduction of AI can raise concerns about job security among frontline employees. To mitigate concerns:

• Communicate clearly about the role of AI as a tool to enhance, not replace, human agents
• Provide comprehensive training and upskilling opportunities
• Adjust performance metrics to account for AI assistance

AI is a powerful tool shaping the way businesses interact with their customers. Virtual agents are evolving beyond basic tasks, offering human-like interactions, while agent assist empowers human agents with real-time insights and automation. The future of customer service is a collaboration between AI and human intelligence. Virtual agents will continue to handle routine inquiries, freeing up human agents to focus on complex issues and relationship building. Agent assist will provide real-time support, enabling agents to deliver a more personalized and efficient customer service. By adopting this technology strategically, businesses can create a seamless and positive customer experience, fostering loyalty and brand trust.