How AI can respond to support tickets

Table of Contents:

• Decide Whether AI Should Respond Directly to Customers
• Use AI to Formulate Responses for Human Agents
• Provide a Rich Knowledge Base for AI to Draw On
• Implement a Feedback System to Continuously Improve AI Responses
• Consider AI’s Role in Both Customer and Internal Support
• Leverage Data Labeling for Precise Knowledge Selection
• Use Bazinga AI for Tailored Knowledge Management
• Optimizing Support with AI: Key Takeaways

Studies and surveys have discovered that 80% of customers feel that a company’s experience is as essential as its products and services. A huge part of this experience is customer service.

Customers these days are looking to have their issues resolved immediately. A Super Office survey found that 46% of their respondents wanted companies to reply to their queries or issues within four hours, while 12% wanted a response within 15 minutes or less.   

This can put immense pressure on the customer support department of a company. Fortunately, AI-powered assistants are now making a case as a viable solution for this. 

These tools can now help customer support teams save time and resources while improving response times. However, implementing AI in ticket responses requires careful planning to ensure accuracy, consistency, and security. 

In this article, we’ll explore best practices for effectively integrating AI into your support ticket processes.

Decide Whether AI Should Respond Directly to Customers

Remember, aside from response times, customers also judge a company’s customer service support by the value it provides. 

If you’re planning to incorporate AI into your ticket response system, it’s essential to be transparent about its limitations.

Including disclaimers about response accuracy can help manage expectations, as AI, while highly efficient, is not always error-free.

For instance, a disclaimer can inform customers that while AI is designed to provide accurate information, complex or sensitive inquiries may require human review. 

This transparency helps set expectations and encourages customers to understand that AI is a tool, not a flawless resource. 

Clear guidelines should also be in place to escalate issues such as account or security concerns to human support for appropriate handling.

Use AI to Formulate Responses for Human Agents

In most cases, a balance between AI and human work is ideal for both companies and customers. 

Let AI draft the responses but before being sent to customers, it should be reviewed by human agents. 

This allows AI to perform the heavy lifting such as pulling information from knowledge bases, drafting responses, and ensuring timely communication. It then leaves support agents to refine the message, adding a personal touch and ensuring accuracy. 

This assisted-response model is especially useful for handling complex or nuanced inquiries where an automated response alone might not be enough to address the customer’s concern. 

For instance, AI can quickly draft answers for technical questions based on similar past cases or documentation, while agents can refine these responses with clarifications or specific details.

Provide a Rich Knowledge Base for AI to Draw On

The output of AI can only be as good as the resources it is fed. 

The richer and more varied the knowledge base, the more accurate and comprehensive the AI's responses will be.

To maximize AI’s effectiveness, organizations should provide it with diverse sources, including:

• Past Support Tickets: Real-life examples of resolved issues that provide practical context.
• Public Documentation and FAQs: Ensures AI can address frequently asked questions and common product inquiries.
• Training Videos and Tutorials: Adds multimedia resources, enabling AI to guide customers through step-by-step processes.
• Internal Wiki/Documents: Within organizations, knowledge tends to exist in silos in various documents or wikis.
• Meeting Notes: Certain meeting insights can aid in understanding complex resolutions for unique issues.

Keeping these knowledge bases updated is also essential. Regular updates ensure AI responses align with the latest policies, products, and services, maintaining accuracy over time.

Implement a Feedback System to Continuously Improve AI Responses

Just as we humans improve our work through review and feedback, AI does the same. 

By allowing both customers and support agents to provide your AI system with feedback through a rating system, you can further refine the response accuracy, tone, and usefulness of your AI system. 

For example:

• Customer Ratings: Reveal how well AI meets customer needs. Consistent low ratings on specific responses can indicate knowledge gaps.
• Agent Feedback: Allows agents to flag inaccuracies and suggest improvements, which can be used to fine-tune AI responses.

Over time, this feedback helps the system to evolve to better meet customer expectations and respond more accurately.

Consider AI’s Role in Both Customer and Internal Support

AI is not only valuable for customer-facing support. It can also play a crucial role in internal support.

For example, an internal AI assistant can answer common questions about company policies, troubleshooting procedures, or product information, reducing the load on HR or IT departments.

An internal assistant can also handle repetitive queries like “how to request leave” or “how to access software.” This makes access to such queries so easy, while allowing other employees to focus on more complex issues and improving overall workplace efficiency.

Leverage Data Labeling for Precise Knowledge Selection

An effective AI system relies on well-organized data, and data labeling is essential for precision in retrieving crucial information. 

Labeled data allows AI to quickly filter information based on categories like product type, issue severity, or keywords, allowing it to provide the most relevant responses.

For example, categorizing support tickets by keywords or issue type enables AI to retrieve tailored information for each query. 

Over time, more detailed labeling helps AI accurately match customer questions with appropriate solutions, improving response quality and efficiency.

Use Bazinga AI for Tailored Knowledge Management

Bazinga AI allows companies to gather knowledge from various sources and assign it to specific assistants based on their roles. 

Here’s how it works:

• Internal-Facing Assistant: Designed for employees, Bazinga can access a broader scope of knowledge covering all products, services, internal procedures, and other proprietary information.
• Public-Facing Assistant: For customer support, it’s critical to restrict AI to public knowledge, ensuring no sensitive data is disclosed accidentally. Bazinga AI also allows for the use of anonymization filters to maintain data privacy and compliance.

This segmentation of knowledge sources allows AI to be a specialized resource for both customer-facing and internal support, while making sure responses are appropriate for each audience.

Optimizing Support with AI: Key Takeaways

AI holds great potential in enhancing support ticket response systems, whether by responding directly to customers or assisting human agents. However, it’s important to remember that it still has plenty of limitations. 

Working around these limitations through assisted-response models or rich databases still allows you to maximize its effectiveness. 

By leveraging AI tools like Bazinga for support, businesses can enhance customer experiences, streamline workflows, and boost internal efficiency.

When supported by a robust knowledge management system and continuous feedback, AI can play a key role in modernizing support functions, benefiting both customers and employees.

Contact us today to learn more about Bazinga or the role of AI in customer support.