By Happy Verma
Customer experience is a crucial aspect for business success. It is the factor that determines the average revenue per user and the customers’ lifetime value with your business. With numerous options available on the market, a single unresolved customer issue can prompt them to switch between brands.
Businesses have begun adopting advanced AI-based technologies, including chatbots, automation, and AI agents. According to a recent IDC study on conversational AI, 41% of organizations use AI-powered copilots for customer service, and 60% have implemented them for IT help desks.
This blog discusses how AI agents have transformed the customer experience landscape and improved their journey. We will also understand how traditional AI, such as AI chatbots, is different from AI agents.
Limitations of traditional AI for customer experience
Traditional customer service involves using technologies such as machine learning and chatbots to automate tasks, process data, and provide data-driven, predefined responses. Traditional customer service is reactive in nature. It includes customers contacting support after experiencing an issue. Here are the drawbacks of conventional AI for customer experience:
They rely on predefined rules and scripts. Any changes in the input or process cause the automation to fail.
For example, a chatbot built on fixed scripts can only answer specific questions, like, “What are your working hours?” If the customer asks, “Are you open on national holidays?”, the bot fails to answer, as this phrase doesn’t match the script.
Have limited problem-solving. They can not handle problems that require problem-solving skills or contextual understanding.
For example, A customer reports, “I was charged twice because I tried paying on your app and then on your website.” A traditional chatbot or rule-based system cannot cater to this inquiry, as it requires contextual understanding. A human agent or advanced AI (AI agents) can trace and correct the billing errors.
Evolving processes or business rule changes need manual updates, which are time-consuming and costly.
For example, a bank updates its loan approval criteria. The traditional AI must be reprogrammed to reflect these changes. Until then, customers receive outdated or incorrect responses, leading to frustration.
How are AI agents revolutionizing customer experience?
Modern AI in customer service employs technologies such as machine learning, natural language processing, and predictive analysis. It can anticipate customer needs, automate inquiries, gain insights from data, and offer customized solutions to your customers. Offering a tailored experience to your customers can help you foster deep customer loyalty and operational efficiency.
Predictive analysis
AI agents can analyze customer behavior, historical interactions, and data in real time to identify potential issues before they escalate. They can notice if the customer repeatedly adds items to the cart but abandons it at the payment step. Based on the cart-abandonment behavior, the agent can identify payment-related issues and offer a live chat option or a discount code to foster purchase.
Streamlined customer service
The agents can automate repetitive routine tasks, including resetting passwords, tracking orders, or updating account information. This enables your customer service representatives to focus on tasks that need more personal touch. Automation can accelerate resolution time and improve operational efficiency while reducing costs.
Highly personalized customer experience
AI agents can analyze customer data, previous interactions, preferences, and account history to provide tailored solutions. This eliminates the need for your customers to repeat themselves, ensuring consistent and relevant responses or solutions.
Beyond automation: Autonomous decision-making
AI agents not only automate tasks but can also make autonomous decisions to resolve your customers’ issues. Autonomous decision-making is the crucial aspect that distinguishes traditional AI from modern AI agents. According to Gartner, “By 2029, AI agents can autonomously resolve 80% of common customer service issues without needing human interaction, leading to a 30% reduction in operational costs.”
AI agents to enhance customer experience can cater to simple or complex tasks, depending on how they are developed and the tools they access. Many AI agents work together, also known as an agentic network. Each agent performs a separate task before forwarding it to another task-based or goal-based agent.
Imagine: A customer wants to return a product and seek a refund. Instead of rerouting this request to your customer service representative, the agent reviews the customer order and issue, as well as the applicable refund policy, and then approves or denies the refund request autonomously. These agents are integrated with customer relationship management (CRM) software or communication tools to enhance the functionalities of existing systems and collect relevant data.
Use cases of AI agents that enhance customer experience
AI agents are transformative and autonomous bots that provide a proactive approach to enhancing the overall customer experience. Many AI agent development companies, such as Master Software Solutions, have created ready-to-use AI agents and also develop custom agents based on the business’s use case. Here are some real-world examples of AI agents to improve customer experience.
Complaint resolution
AI agents decide autonomously whether to issue a refund, replacement, or store credit. They analyze product return policies, past purchase behavior, and customer loyalty status without needing a human manager’s approval.
Proactive after-service recovery
Before the customer complains, agents can predict service failures (delivery time or downtime) and compensate them with a discount, loyalty points, or accelerated reshipment.
Smart escalation management
AI agents are capable of detecting the urgency and sentiments of the customer’s message. In case the agent detects frustration, they can autonomously decide to escalate the issue to a senior agent or initiate a callback. This ensures priority handling.
Dynamic personalization
AI agents can analyze customers’ profiles, browsing history, and purchase intent to decide which product recommendations, offers, or content to display during interactions.
Self-optimizing knowledge base
AI agents can detect recurring customer queries and autonomously decide to update FAQs, tutorials, and chatbot scripts. This ensures faster resolutions without waiting for manual updates from the support team.
Autonomous routing across multiple channels
If a customer starts an issue on chat, however, it requires visual inspection. Agents can seamlessly switch the interaction to video support or email without requiring the customer to restart the conversation.
Conclusion
With increasing customer demand for a personalized experience, many businesses have already started adopting AI solutions. If you have not considered implementing AI-based solutions in your business, you risk falling behind the competition. Embracing AI agents can improve service quality, reduce costs, and create an agile system to meet all your modern customer expectations.
Customer expectations are evolving quickly, and AI agents are already shaping the future of customer service. Want to dive deeper into topics like this? You’ll find more insights on the TrackMySubs blog.