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Leveraging AI Chatbots for Customer Service to Transform Customer Experiences

March 26, 2024
min read

Maya Angelou, the American writer and poet, once said, “People will forget what they said, what you did, but will never forget how you made them feel.” The famous observation summarizes the importance of customer service in today's business world. 

The current consumer behavior is fickle due to the vast array of options available. Even minor customer service slip-ups can lead to a change in brand loyalty.

An excellent customer service experience will attract loyal customers, increase your brand’s reputation, reduce advertising costs, and boost sales. 

This is where AI chatbots for customer service come into play. With their instant responses and ability to be continuously updated, AI chatbots have made transformative changes in delivering excellent customer experience. 

The Evolution of Customer Service

Traditional customer service meant going to a store or dialing up for help, which is time-consuming! Customers crave speed, and digital solutions like Email, IVR, live chat, and AI chatbots have significantly enhanced customer service experiences.

AI chatbots have brought a paradigm shift in customer service by becoming the single point source for information and guidance to customers. An AI chatbot works round the clock, delivers answers in real-time, and optimizes its responses as it interacts more to reduce misunderstandings.

For example, Walmart uses an AI shopping assistant to help its customers locate products and brands in-store. It answers customers’ queries quickly, enhancing customer satisfaction.

Understanding AI Chatbots

AI chatbots interact with customers using Natural Language Processing (NLP) techniques, such as speech recognition, OCR document reading, speech synthesis, and other Artificial Intelligence (AI) systems abilities.

How an AI chatbot works?

AI chatbots understand the intent of incoming customer queries and communicate accordingly. For example, an AI chatbot can recognize key phrases in customers’ queries and respond by providing detailed information, accurately meeting the customers’ needs. 

AI chatbots are of various types. These include:

1. Hybrid chatbot

It is a combination of live chat by an executive and an AI customer support system.

 Hybrid chatbot

2. Keyword-based chatbot

The chatbot recognizes the keywords in the customer inquiry and responds accordingly. Lara is one of the best examples of keyword-based chatbots. 

3. Social messaging chatbot

This chatbot is deployed on social media messaging handles to respond to customer queries while unburdening the business’ contact center. Domini’s Pizza chatbot is a great example of a social messaging chatbot.

4. Voice chatbot

This AI-powered virtual assistant recognizes voice signals, determines the context, and delivers responses similar to human speech. Popular voice assistants like Siri, Alexa, and others are examples of AI-powered voice chatbots.

5. GenerativeAI chatbot

The bot provides a human-like text response to the customer query. ChatGPT, Claude, and Bard are some powerful GenerativeAI chatbots.

Benefits of AI Chatbots in Customer Service

From round-the-clock availability to automation of tasks, AI chatbots for customer service have many benefits for businesses. 

1. 24/7 Availability

AI chatbots offer round-the-clock customer support, ensuring assistance is available even outside of business hours. Whether addressing product inquiries or resolving issues, they provide prompt assistance, contributing to customer satisfaction at any time of day.

Waste Connections is the best example of deploying an AI chatbot for round-the-clock customer support. The company’s AI agent Trina handles high-volume waste management requests 24/7 with ease, only escalating complex issues to live agents when required.

24/7 Availability and support

2. Improved Response Times

An AI chatbot provides instant responses, unlike a traditional customer support executive. It doesn't require putting customers on hold and can engage with multiple inquiries simultaneously, preventing any potential loss of customers due to delays.

An illustration of this can be seen with the Swiss Red Cross's utilization of the AI Chatbot Landbot during the COVID-19 pandemic. Faced with a shortage of volunteers, they deployed this tool to rapidly screen potential volunteers. The chatbot handled multiple applications at once and successfully onboarded 500 volunteers within a week.

3. Cost-Effectiveness

An AI chatbot minimizes labor expenses by operating continuously without breaks or additional salaries. Its ability to handle multiple customer inquiries simultaneously ensures scalability, managing higher volumes without increasing staffing costs. Furthermore, its efficiency in swiftly resolving queries results in shorter handling times and potential reductions in operational expenses.

For example, Chatfuel works efficiently to provide support to multiple customers simultaneously on multiple channels. It answers simple questions and sends customized messages based on a customer’s actions.

4. Personalized Interactions

AI chatbots, when connected with other business systems like a CRM, can deliver personalized interactions with customers. The AI bot can swiftly authenticate the customer and engage using the customer's data to provide personalized responses.

Zendesk AI is the perfect example of an AI chatbot that provides personalized customer support. It has a natural human tone to its conversations and integrates with the business's CRM to provide targeted responses.

5. Scalability

Handling a sudden traffic surge can be overwhelming for support staff members. This may cause delays, miscommunication, and errors. But with AI chatbots, you can ensure your business runs smoothly even during busy times. 

Additionally, consumers may seek support from multiple channels. They use social media, email, websites, IVR, etc, to contact customer support. You can deploy AI chatbots on all of these channels to engage with customers without straining your human agents.  

Fin, Intercom's advanced AI chatbot, is a prime example of this, streamlining customer service with instant, accurate answers derived from your company’s knowledge base. With support for 43 languages, Fin ensures seamless assistance for a global customer base, contributing significantly to scalability by efficiently handling diverse queries across different regions.

6. Data Analysis for Insights

An AI chatbot's work is not limited to responding to customer inquiries. It can also track customer behaviors, common complaints, popular items, products with issues, etc. It then conducts a deep analysis of the data and provides insights to guide your business decisions. 

Tars is an AI chatbot for data analytics. Avec, a global healthcare provider, used Tars to check patient symptoms. The data gathered by Tars gave Avec’s physicians and doctors more context to provide accurate treatment.

7. Multilingual Support

Multilingual support is one of the major reasons businesses use AI chatbots for customer support. KLM Royal Dutch Airlines is one of the best examples of a company using a multilingual AI chatbot. 

KLM Airlines deployed a travel chatbot called Blue Bot (BB) that supports multiple languages to handle customer inquiries from Facebook Messenger. BB handles 15,000 service requests a week from social media alone. 

8. Consistent Customer Experience

AI chatbots provide consistent responses, unlike human agents, who might err in giving accurate responses. The responses to common queries are predetermined, due to which the possibility of discrepancies becomes lower.

For example, IBM's Watsonx Assistant assists Humana, a health insurance provider, by swiftly delivering precise information to customers through a user-friendly interface. Watsonx manages inquiries about coverage, claims, and healthcare matters. It uses historical data and FAQs to generate response templates, customizing them based on individual customer data when needed.

9. Task Automation

AI chatbots streamline various tasks including scheduling, reminders, order processing, tracking, and gathering customer feedback. For instance, when providing personalized product recommendations, the AI bot generates a list of suitable products for the customer, schedules delivery, and sends the recommendations seamlessly.

Sephora, the beauty brand, is the pioneer in using AI for eCommerce. Their AI chatbot performs various tasks, including booking makeovers, sending alerts, offering personalized product recommendations, and addressing customer inquiries. These features are all geared towards enriching the shopping experience through cutting-edge technology.

10. Enhanced Customer Engagement

AI chatbots engage with the customer in real time, provide deep personalization, send timely messages, understand gaps in conversation, and increase customer interaction with the business. According to Tidio, AI chatbots can boost customer engagement by 35-40%.

An example of enhanced customer engagement because of an AI chatbot is AirHelp, a flight claims management company. AirHelp receives thousands of queries from travelers through multiple channels.

Enhanced Customer Engagement by Airhelp's Chatbot

Challenges and Limitations

While the benefits of AI chatbots for customer service are significant, the technology presents a few challenges and limitations. These include:

1. Incorrect or Biased Results

Since AI chatbots learn from multiple sources and cannot differentiate between good and bad sources, they may present biased information. As a result, bias is one of the biggest problems with AI, with Gemini being the latest example

Google is now facing the heat because of its Gemini AI chatbot, which produced historically inaccurate images, choosing to remove white people from its results. 

Overcoming the challenge:

  • Make sure that the training data integrity is rigorously vetted for bias.
  • Conduct thorough testing of the algorithm to identify and rectify any potential discriminatory outcomes before public release.
  • Adapt the algorithm based on customer feedback to continually refine and mitigate bias.

2. Limited Creativity or Originality

Unlike a human agent who can improvise a response, AI chatbots are incapable of being creative or delivering original responses. They have a limited understanding of language and concepts and may deliver incorrect responses.

For example, the AI chatbot may not understand a customer’s joke metaphor to explain the grievance unless it is deeply trained in the context. In such cases, it delivers irrelevant responses. A research paper published by Harsh Jhamtani revealed this. 

Jhamtani, along with his colleagues, experimented with five AI models. In one funny incident, the AI model GPT-2 misunderstood the input and gave a hilarious reply.

The experimenters gave a grammatically incorrect statement, “Maybe we can get together sometime if you are not scared of a 30-year-old cougar!” to which the GPT-2 replied, “I’m not scared of any cats. I’ve two dogs.”

Overcoming the challenge:

  • Implement advanced algorithms and AI technologies to enhance the capacity of AI systems to identify patterns and generate solutions.
  • Foster creativity in AI by augmenting data analysis capabilities with innovative approaches and methodologies.

3. Inability to Handle Complexity

AI chatbots, while advanced, have limitations. They struggle with nuanced conversations, complex problem-solving, and unpredictable scenarios.

Recently, a delivery company called DPD's AI chatbot caused a stir by generating poems and even swearing when prompted by the customer. 

This incident shows that AI chatbots struggle with complex conversations, adaptability to unpredictable situations, and secure handling of sensitive information. It highlights their current limitations, requiring human oversight for effective operation.

Bots vs chatbots at handling complexity

Overcoming the challenge:

  • Expose the AI model to extensive and high-quality datasets to facilitate learning across numerous scenarios.
  • Mitigate decision-making lapses by ensuring sufficient volume and diversity in the data used for training the model.
  • Implement continual learning mechanisms to adapt the AI model to evolving scenarios and maintain decision-making accuracy over time.

4. Lack of Human Connection and Empathy

Over 75% of customers who use chatbots end up requiring human assistance. It is more so due to a lack of empathy and an inability to understand human emotions. 

Woebot stands out as an exemplary instance of Emotional AI, offering significant benefits in mental health support. With its advanced capabilities, Woebot can detect emotions from non-verbal cues and provide empathetic responses. 

However, it's important to recognize that despite these advancements, AI still grapples with inherent limitations. At its core, AI lacks the depth of human emotion and experience, posing challenges in fully understanding and empathizing with human emotions. 

Overcoming the challenge:

  1. Incorporate sentiment analysis and advanced NLP techniques to enable AI models to deliver appropriate emotional responses.
  2. Enhance AI development efforts by training models with diverse datasets encompassing various social situations encountered in real-life scenarios.

5. Decision-Making Limitations

AI chatbots are rule-bound and are restricted in their decision-making abilities. For example, if a chatbot cannot resolve a customer query, human agents must be the second line of defense to avoid an unfavorable customer experience. 

However, entrusting full decision-making authority to an AI chatbot can yield unfavorable outcomes. A case in point is Microsoft's TAY chatbot, designed as a self-learning AI operating independently of human interaction.

TAY's learning from human interactions resulted in the adoption of offensive language and the dissemination of inaccurate information.

Overcoming the challenge:

  • Expose the AI model to extensive and high-quality datasets to facilitate learning across numerous scenarios.
  • Mitigate decision-making lapses by ensuring sufficient volume and diversity in the data used for training the model.
  • Implement continual learning mechanisms to adapt the AI model to evolving scenarios and maintain decision-making accuracy over time.

6. Ethical Concerns

The ethical concerns surrounding AI chatbots encompass a range of issues, such as bias in decision-making algorithms and potential breaches of privacy.

Moreover, the lack of accountability and transparency in AI systems can exacerbate these concerns, raising questions about their impact on societal values and norms.

A study on four AI chatbots conducted by researchers from Stanford School of Medicine found that the chatbots were spreading racially biased medical information.

Overcoming the challenge:

  • Enhance explainability in AI through adopted methods to alleviate ethical concerns.
  • Promote the development of AI algorithms with robust explainability, enabling detailed explanations for decision-making processes.
  • Utilize feedback from AI responses to training models in ethical decision-making, thereby improving overall ethical standards in AI development.

AI Chatbots in Action

Here are a few case studies of the best AI chatbots for customer service in operation:

1. Chatbots in Hospitality Industry 

The hospitality industry is one of the most frequent customer-facing industries and has a high focus on customer service. Marriott International uses an AI chatbot called ‘Marriot Moments’ that helps its guests make reservations, request hotel services, provide information, and more. 

The AI bot reduced the involvement of middlemen and ensured customers’ requests were met quickly and effectively, leading to increased customer engagement and satisfaction. 

2. Chatbots in E-commerce

H&M uses a chatbot named Kik Bot to guide its mobile customers through the selection of outfits in its online store. The chatbot is a conversational AI chatbot that engages the user in their language and delivers a personalized shopping experience.

Chatbots in E-commerce

Healthcare, retail, transportation, and other heavy customer-facing industries can use AI chatbots for customer service.

Future Trends in AI Chatbots

Top Conversational AI Trends for 2023 Nitor Infotech

AI technology is rapidly advancing. Going into the future, developments in creating empathetic algorithms, deep learning techniques, NLP, etc., will create more powerful AI chatbots.

  • Emotional Intelligence: The new AI models will become more emotionally intelligent, with the ability to comprehend context and conversation accurately.
  • Accurate Response: As deep learning techniques evolve, AI chatbots will become capable of answering complex queries accurately.
  • Personalized Experience: AI chatbots with superior data models will accurately map a customer’s behavior and provide a deeply personalized customer service experience.
  • Mimics Humans: The improved natural language processing abilities in the future will make AI chatbots express themselves as humans.

Even small businesses like Urbanstems, and Warby Parker deployed AI chatbots for customer service. The value AI-driven customer service delivers will make AI chatbots across industries for customer service delivery.


AI chatbots for customer service have been transformative for businesses. AI has led to reduced costs, enhanced efficiencies, and increased revenues. It is the future of customer service for any industry. 

The unparalleled speed at which AI responds, the quality of responses, and its availability make it the must-have tool for customer support. And with new developments in AI technology, AI chatbots will evolve to provide even better customer service.

Are you planning to build an AI chatbot for customer service or want your AI model assessed? MarkovML is here to help. Our platform empowers you to streamline the training and evaluation of your models with ease.

Collaborate with our experts for AI and ML development, create a generative AI app with your data sets, or get a third-party assessment of your AI.

Get in touch with us today!

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