AI in Customer Service

AI in Customer Service: 11 Ways to Automate Support

The state of New Mexico is blessed with many splendors, including the amazing regional fast food chain Blake’s Lotaburger. I don’t know who Blake is, and I also don’t get burgers there, but what I do get is a breakfast burrito served Christmas style—with both red and green chile.

With a small menu featuring locally farmed Hatch chiles, I imagine Blake’s still does things pretty much the same as they did when they opened in 1952—except for one thing.

The last time I called to place an order before a road trip, I was greeted by first name by a disarmingly human computerized voice that recognized my number and suggested the exact order I planned to make. Freaky or not, artificial intelligence is becoming as common as it is rapidly changing—here’s how companies like Blake’s are putting it to use.

Read More: AI vs. Human Agents: How to Strike the Right Balance in AI Customer Service

What is Customer Service AI?

Customer service AI refers to the application of artificial intelligence technologies to improve and automate customer service processes. AI can streamline workflows for agents while enhancing the customer experience. Common uses of AI in customer service include support ticket sorters and chatbots, but the possibilities are vast.

AI in customer service can handle a variety of tasks, from answering simple customer inquiries to providing complex support. These AI tools are designed to assist human agents, not replace them. They can process large volumes of data quickly and provide insights that help improve customer interactions.

The integration of AI in customer service is a growing trend. Companies are increasingly using AI to manage contact centers, eCommerce platforms, and service-related sales phases. As AI technology advances, its applications in customer service continue to expand.

Businesses can implement AI in their customer service operations in numerous ways. This flexibility allows them to tailor AI solutions to their specific needs, ensuring a better fit for their customer service strategies.

Benefits of AI in Customer Service

AI in customer service offers numerous benefits for both companies and their customers. Here are some key advantages:

For Agents

  • Reduced Ticket Volume: AI helps customers resolve issues on their own, reducing the number of tickets that agents need to handle.
  • Fewer Low-Level Tasks: AI handles repetitive, low-impact tasks, freeing up agents to focus on more complex issues.
  • Lower Costs at Scale: AI can manage growing customer needs without significantly increasing costs.
  • More Actionable Insights: AI analyzes data to forecast needs, suggest actions, and maintain inventory efficiently.

For Customers

  • More Efficient Resolution: AI can quickly direct customers to solutions, saving them time and frustration.
  • Cleaner UX: AI streamlines front-end processes, addressing common issues before they escalate.
  • More Predictable UX: Customers can choose resolution methods with accurate response time estimates.
  • Lower Costs: By keeping operational costs down, companies can pass on savings to customers.

AI in customer service not only benefits companies by improving efficiency and reducing costs, but it also enhances the customer experience by providing faster and more reliable service.

How AI is Used in Customer Service

AI in customer service is used to supplement human agents and improve overall service efficiency. Here are some ways AI is transforming customer service:

  • Customer Insights: AI analyzes vast amounts of customer data to provide actionable insights. This helps support teams understand customer needs and trends, allowing them to address issues proactively.
  • Customer Self-Support: AI solutions enable customers to find answers themselves, reducing response times and decreasing the burden on support agents. This self-service approach is particularly useful for resolving common inquiries.
  • Agent Support: AI tools assist agents by providing quick access to resources, communication channels, and relevant data. This support helps agents resolve issues more efficiently and effectively.
  • Process Improvement: AI can identify and eliminate friction points in workflows, adding smart automations that streamline customer support responsibilities. This leads to faster resolution times and improved customer satisfaction.

11 Ways to Automate Customer Service with AI

AI offers various applications in customer service. Here are some notable examples:

1. Customer Service Chatbots for Common Questions

Chatbots are a popular form of AI in customer service. They answer common questions using pre-defined responses or by searching existing resources. This helps reduce the workload for human agents.

  • Example Use Case: A buyer visits a website to check the exchange policy. The chatbot guides them through the process, resolving the issue without agent intervention.

2. Customer Self-Service Chatbots

These chatbots suggest actions based on customer behavior, helping them find solutions faster. They can also recommend products or services, enhancing the customer experience.

  • Example Use Case: A potential customer searches for “Hanukkah gift ideas” on a website. The chatbot suggests a curated holiday gift guide, streamlining their shopping experience.

3. Support Ticket Organization

AI organizes support tickets using natural language processing and sentiment analysis. This automation reduces the time agents spend on administrative tasks and improves scalability.

  • Example Use Case: Support requests are automatically tagged, prioritized, and assigned to agents. Critical tickets are addressed first, ensuring timely resolutions.

4. Opinion Mining

AI analyzes customer feedback from surveys, reviews, and social media to identify trends and friction points. This helps companies improve their customer service processes.

  • Example Use Case: AI processes survey results to uncover common customer sentiments, providing data-driven insights for service improvements.

5. Competitor Review Assessment

AI evaluates competitor reviews and social media mentions to understand customer preferences and pain points. This information helps companies refine their strategies.

  • Example Use Case: AI analyzes competitor feedback to identify gaps in their offerings, informing new product features.

6. Multilingual Queries

AI translation tools support global customer bases by automatically translating messages between languages. This ensures clear communication without requiring multilingual agents.

  • Example Use Case: Agents receive messages in their preferred language, while customers’ responses are translated back, facilitating effective support.

7. Machine Learning for Tailoring Customer Experience

Machine learning personalizes the customer experience by analyzing behavior and preferences. This leads to targeted recommendations and promotions.

  • Example Use Case: AI segments customers based on browsing history and offers personalized discounts, enhancing customer satisfaction.

8. Machine Learning for Inventory Management

AI-powered analytics optimize inventory levels, ensuring products are available without overstocking. This improves customer satisfaction and reduces stock-related inquiries.

  • Example Use Case: AI forecasts demand and adjusts inventory levels, ensuring high-demand products are always available.

9. Wait Time Monitoring

AI predicts wait times for customer service, setting accurate expectations and reducing frustration. This transparency improves the overall customer experience.

  • Example Use Case: AI assesses agent workload and ticket types to provide dynamic wait times, helping customers decide whether to wait or try self-service options.

10. Automating Agent Action Recommendations

AI suggests next steps for agents based on ticket type and past resolutions. This helps agents resolve issues faster and more effectively.

  • Example Use Case: AI provides resolution options and response templates, giving agents a head start on addressing issues.

11. Using AI-Generated Content in Agent Responses

AI-generated content supplements human responses, saving time and ensuring consistency. This approach reduces agent burnout and improves communication quality.

  • Example Use Case: Agents use AI to generate email templates, which are stored in knowledge libraries for future use.

Risks Involved with Customer Service AI

While AI offers numerous benefits, it also comes with certain risks:

  • Privacy Concerns: AI handles large amounts of data, raising concerns about data security and privacy. Ensuring data is anonymized and secure is critical.
  • Long-Term System Maintenance: AI systems require ongoing maintenance and updates. This can be complex and may need specialized skills.
  • Implementation Friction: Integrating AI into existing systems can be challenging. Companies should expect a learning curve and potential trial and error during rollout.
  • Team Resistance: Employees may resist changes to established workflows. Providing training and demonstrating the benefits of AI can help ease the transition.

AI Customer Support Software Options

Several AI tools and software can enhance customer service operations. Here are some options:

  • AI Help Desks: AI help desk software structures and automates support workflows. Solutions like Zendesk and Hiver offer these capabilities.
  • Generative AI: Generative AI tools like ChatGPT and Jasper handle text-oriented tasks, such as writing messages and translating requests.
  • Native CRM Tools: Many CRMs have built-in AI tools for customer service. These tools integrate directly into existing workflows.
  • AI Chatbots: AI chatbots provide quick answers and resources, reducing the need for human intervention. They are a common and effective AI solution.
  • AI Sales Tools: AI sales assistants automate outreach campaigns and enhance emailing. These tools intersect with customer service to improve overall efficiency.
  • AI Virtual Assistants: Virtual assistants like Cortana, Google Assistant, and Microsoft Copilot offer ad hoc support for agents, providing quick problem-solving resources.
  • AI Meeting Assistants: Meeting assistants take notes, make transcripts, and summarize meetings, helping agents stay organized and efficient.
  • Multiple Integrated Solutions: Combining multiple AI tools with automation platforms like Zapier can create powerful, customized solutions for customer service.

Automation and Customer Service AI

Automation complements AI in customer service by streamlining processes and reducing manual tasks. Best practices for implementing AI and automation together include:

  • Identifying repetitive tasks: Focus on automating tasks that are time-consuming and repetitive.
  • Integrating with existing systems: Ensure AI tools work seamlessly with your current customer service infrastructure.
  • Providing training: Educate your team on how to use AI tools effectively.
  • Monitoring performance: Regularly assess the impact of AI and automation on customer service metrics.

Combining AI and automation can significantly enhance customer service efficiency and effectiveness.


AI in customer service is revolutionizing the way companies interact with their customers. By automating support tasks, providing valuable insights, and enhancing the customer experience, AI is becoming an indispensable tool for businesses. As AI technology continues to evolve, its applications in customer service will expand, offering even more ways to improve efficiency and satisfaction. Embracing AI and automation together can help companies stay ahead in the competitive landscape of customer service.

Scroll to Top