AI Agents In Customer Service: How To Build And Implement Them Effectively

AI is revolutionising customer service and Gartner was not mistaken saying 80% of customer service tasks will be handled by AI agents by 2025. Now, many businesses use AI agents in customer service to answer FAQs, process orders and troubleshoot technical issues, while only escalating complex cases to humans when necessary.

Hence, 38% of leaders see improving customer experience and retention, while also cutting support costs.

The arrival of ChatGPT in 2022 contributed to the proliferation of AI agents, making them affordable for businesses worldwide. This, in turn, launched an unprecedented surge of AI adoption in customer service, with business owners expecting to build their own “pocket ChatGPT.”

While integrating AI assistants into customer service is often a smart move, yet thoughtful planning is essential to ensure a worthwhile investment:

• First, an AI agent is not necessarily just another version of ChatGPT
• Second, an AI agent is not almighty
• Third, building an effective AI agent takes time and success depends on clearly defining its purpose, capabilities and limitations from the start

 

Understanding AI Agents and How They Work

 

AI agents: chatbots, voice AI and more are virtual assistants that help businesses manage customer interactions. These agents use knowledge bases to understand, respond to and resolve customer enquiries. AI agents differ from traditional customer support solutions by providing 24/7 automated assistance, the ability to handle multiple interactions simultaneously and reducing the possibility of human error.

The key building blocks of an effective AI agent include perception, decision-making and action. Perception involves gathering data through sensors or user inputs, decision-making uses algorithms and models to process information and determine responses and action executes tasks based on the agent’s objectives.

 

Steps to Build and Implement an AI Agent in Customer Service

 

As mentioned earlier, success in building an AI agent depends on close collaboration. Setting objectives, understanding the business and regulatory environment and recognising customer needs are the factors that business owners must communicate to AI developers.

 

Align AI Objectives With Business Expectations

 

At the very start, determine the goals your company aims to achieve with an AI agent. Reducing response time, improving customer satisfaction and cutting support costs are among the most frequent requests. It is reasonable to assess the challenges in customer service you currently face, like high ticket volumes or slow response times.

Don’t forget to analyse customer needs. Review customer interactions, complaints and FAQs to determine recurring problems that AI can address. Assess preferences for self-service options, response time and resolution quality. Define which types of queries AI should handle (e.g., simple FAQs, account issues) versus those requiring human support.

 

Assess Security and Compliance Considerations

 

AI agents are all about data. Thus, it is crucial to assess security and compliance requirements to AI development at the very beginning. First of all, properly communicate your internal data privacy and security policies to outline proper processing and storage requirements. Determine categories of data the chatbot will process to align the processing with privacy regulations and ethical use.

Second, your regulatory environment also sets limitations. Need to serve customers across the EU? Make sure your chatbot is aligned with GDPR. Targeting the U.S. market? Take into account multiple data protection regulations across the states, such as CCPA, UCPA, etc.

Third, consider regulatory requirements specific to your niche. AI chatbots that handle sensitive healthcare information may need to be HIPAA compliant and those handling financial data should follow PCI DSS requirements.

The EU Artificial Intelligence Act would further frame the legal and ethical requirements for your AI chatbot. It can also help guide development in areas where regulations are still being formed, such as the UK and US. Finally, it would be great to require proper standardisation of your future AI chatbot; ISO/IEC 42001/2023 certification from your AI developer would be a great advantage.

Choose The Right AI Platform or Model

 

Selecting the right platform is necessary to ensure the system is scalable and secure. Depending on your data privacy considerations, business objectives and deployment preferences, you may be offered to build your AI agent on the basis of Open AI, Gemini, LangGraph Studio, or even develop it on cloud platforms like Azure or AWS.

For example, for advanced AI agents supporting complex conversations, OpenAI offers a solid foundation. Open-source frameworks and custom integrations are better developed on the basis of LangGraph Studio. Meanwhile, Gemini focuses on integrating large language models with advanced reasoning.

 

Collect Data For Knowledge-Based AI Integration

 

While many clients refer to the process as training, we prefer saying “knowledge-based AI integration.” To ensure that your AI is both accurate and personalised for your business, grant access to a knowledge base containing information relevant to your products, services and customer interactions.

The bigger and more comprehensive the knowledge base is, the smarter and flexible your AI agent will become. The knowledge base could include FAQs, product manuals, troubleshooting guides and customer service protocols.

 

Test and Optimise

 

Before a full-scale deployment of an AI agent, conducting a pilot test is essential. It would help to assess its performance, identify weaknesses and refine its responses. Cooperate with the vendor to deploy the AI in a limited environment, for example, a small group of customers.

This would help monitor AI-customer interactions to ensure the AI understands queries correctly and provides relevant responses. Remember, regular feedback loops ensure the AI evolves into an effective and customer-friendly support tool.

 

Implement Omnichannel Support

 

Think about preferred communication channels. Web and mobile integration, social media and instant messaging are the common channels for AI agents. Consider incorporating your AI agents across multiple communication channels to ensure consistent support regardless of where customers engage with your business.

Developers will then take care of synchronising AI interactions across platforms to maintain conversation history and personalise responses.

 

Ensure Smooth Human-AI Collaboration

 

AI agents, like any other technology, are good as long as they are in good hands. A successful AI-powered customer service system balances automation with human expertise. That is why it is necessary to collaborate with developers to ensure a seamless transition between AI and human agents when needed.

Clearly outline which tasks the AI should handle (e.g., FAQs, order tracking) and which should be escalated to human experts. With all necessary information, AI developers could ensure seamless handoff of the most complex tasks to humans, so customers don’t have to repeat themselves.

 

Ensure Continuous Improvement

 

AI agents are not a “set-it-and-forget-it solution.” They require continuous improvement and oversight. Regularly update the knowledge base with new information such as product updates, promotions, or customer feedback.

This helps the AI stay current and accurate, ensuring it provides relevant answers based on the latest available data. Integrate customer interaction data (e.g., past conversations, preferences) into the AI system to help it personalise responses and recommendations.

 

Address Biases

 

Addressing biased issues in AI chatbot development means ensuring it represents diverse demographics, locations and interactions. For example, skin cancer detection AI tested exclusively on lighter skin tones will show wrong results when assessing other skin types.

If not addressed, this can result in delayed or incorrect diagnoses. Regulatory compliance helps enforce ethical standards, reducing biases and discrimination in automated processes.

 

AI Agents and Their Uses

 

AI agents for early Alzheimer’s disease diagnosis – AI chatbots are widely exploited in healthcare software development as they help minimise patient anxiety and ensure clear, empathetic communication, improving overall healthcare experiences. A prominent example of using AI agents in healthcare is the early diagnosis of Alzheimer’s disease. This AI agent identifies minor changes that occur in brain MRIs and communicates results. The early diagnosis is crucial for timely intervention and treatment of Alzheimer’s disease.

AI support in managing energy enquiries – Energy providers can take advantage of AI agents to enhance operations and improve customer satisfaction. They commonly exploit AI agents that automate sign-ups, manage routine enquiries and guide new customers by providing consistent information on billing and support. This approach reduces administrative overhead and streamlines the onboarding process.

AI-powered tracking for logistics – For logistics, the example of a helpful AI agent is one that is able to assist customers by providing real-time tracking updates, answering common shipping queries and helping with delivery rescheduling. This type of chatbot understands natural language and can guide users through processes like filing claims for lost packages or estimating delivery times.

Thus, AI assistants significantly enhance customer service by providing instant, accurate responses, improving efficiency and offering 24/7 support.

At the same time, effective communication between businesses and developers is crucial when building AI chatbots, as it ensures that the chatbot aligns with specific business goals and customer needs. Remember, AI is powerful but not limitless; set clear, achievable goals, clearly define capabilities and set boundaries to ensure effective deployment.