AI in customer service
AI and ML / December, 05 2022

7 Common Mistakes to Avoid when using AI in Customer Service?

Customer Service is no longer the same with evolving customer interactions and touch points. AI in customer service has grown rapidly in recent years. 47% of customers chose face-to-face interactions before the pandemic and now it is less than 25%. This translates to more live chat and contact center operations in the upcoming years.

Irrespective of the industry, customer service is transforming by leveraging data, automation, and AI. How do we improve satisfaction scores while attending to more requests from the same employees? AI in customer service with different bots helps in understanding different questions and answering them based on conversation patterns. Let us look at the use cases before analyzing them further.

Best Examples of AI in Customer Service

Organizations leveraging AI in their internal processes synchronize it to efficiencies, experience, and accuracy. It is the same in customer service too. Chatbots are at the forefront but there are a few more areas with machine learning, natural language processing, and voice response systems. AI is creating a new paradigm shift in customer service.


The most common use case for AI in customer support is chatbots. Organizations already use chatbots to handle routine customer inquiries like order status, delivery issues, and rating the product/service. Most of the frequently asked questions are transitioned to a chatbot to enhance customer experience. It is also the most efficient way while cutting down employee-related costs.

Agent Assist

Customer service agents may not be able to manage all the knowledge through the processes to assist the customers. AI in customer support may augment their abilities by displaying the right information on their screens and analyzing customer queries and past interactions. It reduces the average turnaround time for customer requests and errors from the agents.


Customer self-service options are the emerging options in customer service. Millennials prefer to assist themselves without speaking to an agent. With proper AI-enabled tools and information, customers can resolve their issues as per their schedules.

Process Automation

AI-enabled automation not only improves the workflows but improves request processing time. Creating task-based bots that can provide alerts and notifications to the processes is one such example. Managing historical records, incident support and proactive outreach to customers can be the other focus areas. AI-enabled automation tools can also help in existing workflows to automate repetitive tasks to reduce the timelines and improve the experience for the agents and customers.

Machine Learning

Why do organizations leverage machine learning? Mostly for analyzing large data sets. Do you think we have such data in customer service? It has large volumes of questions and answers. Machine learning can help in analyzing these Q&As to suggest the best response. Moreover, it can assist agents with any missed communication. Also, it can help chatbots to provide the best solution based on historical data analysis and enable self-service.

Natural Language Processing

How do you analyze customer interactions? With evolving AI in customer service, all the text fed to the chatbots in any language can be analyzed in real-time to provide the best solution. Also, there are many interactions across different channels – web, mobile, social, and chat. Analyzing this data is critical for the agent to satisfy the customer’s needs at different touchpoints. Language detection algorithms are an additional advantage in providing a unique customer experience.

Sentiment analysis

How do you detect customer satisfaction scores from the interactions with the chatbots or agents? Analyzing the mood and sentiment scores for each interaction has become critical in enhancing customer satisfaction. Based on the analysis, you can escalate the issue or reroute the request to different business functions. Sentiment analysis and analytics can create a 360-degree customer view for each interaction.

Mistakes to Avoid with AI in Customer Service

While implementing AI is crucial for customer service, you may not risk making some costly mistakes. The RoI of the implementation and end user experience may be impacted heavily with such issues.

Striking the right balance between human and automated interactions

AI in customer service is supposed to increase customer satisfaction. Quicker response times and faster issue resolution have led to the implementation of AI in customer support. But customers feel valued only if they receive personal interactions. How do we personalize interactions? With evolving capabilities, AI can analyze past interactions, preferences, and demographics. But eliminating the human touch through the interactions at times may overwhelm the customers.

Organizations should identify all the touchpoints and implement AI without excessive use of technology. Businesses should exploit the usage of AI while also staying intact with the personal touch.

Measuring AI’s performance

Organizations should stop perceiving AI as another technological advancement. They should gauge the performance of AI projects at regular intervals. Most organizations fail to recognize the RoI from AI implementations as fail to assess the project or follow a wrong methodology for assessment.

In customer service, AI is often replicating human behavior. Hence the performance also needs to be measured against the customer satisfaction levels and mimicking human behavior.


Automation on the front looks lucrative for customer service processes. Though it looks like a comprehensive approach, there are nuances to it too. Whenever you think about a process to be automated, consider the cost approach and understand the impact it creates. Never approach automation for the sake of doing it. It may look promising at the initial stage but later it could affect employee morale and customer satisfaction.


When you want to replace employees with Chatbots and related services, do you need training? You may not end up automating everything, only routine tasks are automated. Hence it is important to initiate the knowledge sharing between employees to make the processes better with Chatbots, AI, and related services.

Monitoring and maintaining AI

The deployment of a model is not the end of the story. You need to train the model continuously to improve the results. As the complexity of the AI project grows manifold, the potential results for failure also increase. Hence, organizations should allocate resources to the maintenance and monitoring of AI projects while they plan for the implementation.

Chatbot strategy and self-service options

Now, let us move on to the specific mistakes related to the Chatbot implementation. Most organizations deploy Chatbots on the go without proper planning and visualization of the processes. Before you plan for chatbots, you should plan the resources, processes to be automated and the options that you want to provide the customers. Most of the time, organizations overlook key features like self-service in the plan for chatbot implementation.

Leveraging data to improve chatbot interactions

Implementing AI is a continuous process in customer service. You should continue to learn from the interaction to offer the best customer experience. It is not a one-time task like many others. Hence, organizations should leverage data to analyze the interactions and offer feedback. Customer experience with Chatbots can become overwhelming if you do not understand the previous chat interactions. Do you want to avoid these mistakes? Implement a chatbot in a week with comprehensive consulting and assessment. Initiate a conversation now.

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