AI will not replace customer support teams.
There is talk in the corridors of customer service that artificial intelligence will replace support teams.
This is far from the truth. In fact, AI for customer service will augment support teams, in turn, boost productivity.
Let’s take customer care agents working with cloud contact center software as an example. Customer service AI for these agents is set to assist, coach, and review their workflows for exceptional customer support delivery.
In this age of AI, small and medium-sized businesses (SMBs) cannot compete on features or solutions. Exemplary customer service is the differentiating factor that will keep customers around and even refer their friends.
Now, this article covers the basics of artificial intelligence for customer service and how businesses can leverage AI to improve support teams’ productivity.
Table of Contents
What is AI for customer service?
AI for customer service is an artificial intelligence system programmed to interact with customers as human-likeas possible. It often takes the form of either self-service AI support tools or customer service chatbots.
The goal of AI in customer service is to respond to customer queries and requests in a conversational manner using natural language processing.
In broad terms, two types of AI are used in customer service.
As the term suggests, narrow AI focuses on specific tasks without necessarily being programmed. Such AI tools include voice recognition devices like Alexa, Siri, and Google Assistant.
Other narrow AI examples include:
- Search on the internet
- Facial recognition
- Disease detection
- Image recognition
- Language Translation
Artificial General Intelligence (AGI), or Strong AI, seeks to mimic complex human behavior and thought processes. Although most true general AI applications are not publicly available yet, here are examples of AGI being applied:
- OpenAI’s GPT-3
- Autopilot in cars
- IBM Watson
| Narrow AI /Weak AI | General AI / Strong AI |
| The currently used artificial intelligence | Future artificial intelligence |
| Reflexive tasks with no understanding | Full range of human cognitive capabilities |
| Limited to specific tasks | Perform general human intelligent tasks |
| Knowledge does not transfer to other tasks | Leverage knowledge transfer to become more intelligent |
Why conversational AI for customer service?
First, what is conversational AI? – Conversational artificial intelligence is the combination of technology (machine learning and natural language processing) that enable people to interact with computers in a human-like manner.
Conversational AI for customer service is designed to improve support operations efficiency and customer experience.
Research indicates that 90% of customers with a bad support experience tend to leave without warning. With the constant rise of customer acquisition costs (CACs), it’s vital for SMBs to retain customers through exceptional customer service.
Some notable benefits of conversational AI in customer support include;
- Lower response time in solving customer issues,
- Increased agent productivity,
- Improved personalization for customers,
- Executable omnichannel strategy,
- Better self-service experience,
- Easier scalability, and management.
- Reduce support teams’ burnouts,
- Save overhead costs,
- Provide multilingual customer support.
Stats on conversational AI for support teams
- Research by SalesForce indicates 42% of consumers expect chatbot responses within 5 seconds, while 36% expect them within 5 minutes.
- According to Invesp, 32% of customers value chatbots that evoke ‘friendliness and approachability’ as the top benefits.
- As per HubSpot, 40% of shoppers don’t prefer being helped by a conversational AI tool as long as it resolves their issues.
- Research by Salesforce indicates three of four customers anticipate conversational AI solutions will influence their expectations of companies in the next five years.
- As a result of implementing conversational AI, 80% of executives demonstrate improved customer satisfaction, as per MIT Technology Review.
How does AI for support teams work and use cases?
The rise of automated customer support teams is just the beginning of AI applications in customer service.
On the side, it’s important to understand how AI for support actually works. There are a couple of technologies that power conversational AI in customer service:
- Machine Learning
According to IBM, machine learning is a sub-field of AI that consists of a set of features, datasets, and algorithms that are continuously improving themselves with experience.
As AI customer service solutions solve support issues like ticketing, for instance, it keeps growing and becoming smarter in effectively sorting support tickets.
- Natural Language Processing
Natural Language Processing or NLP, in short form, is the method of analyzing language with the help of machine learning used in conversational AI. It consists of four steps to output desired customer resolution.
The customer support AI process for sorting the support ticketing issue follows these steps:
Step 1: Input generation – a voice or text from the customer looking to book a support ticket.
Step 2: Input analysis – If the input is text-based, the natural language understanding (NLU) to pull the meaning of the text provided.
Step 3: Dialogue management – Natural language processing creates a response and provides the most relevant support ticket depending on the query.
Step 4: Reinforcement learning – Here, the user inputs are analyzed to refine replies over time and ensure the ticketing process is smooth and optimized for fast ticketing resolutions.
What is an example of AI customer service?
- Support ticketing
AI revolutionizes customer service by helping companies manage support ticketing more efficiently and effectively.
With the help of AI-powered ticketing systems, companies can provide faster and more personalized support to their customers, leading to improved customer satisfaction and loyalty.
One way in which AI is helping support ticketing is through automated ticket routing. With the help of machine learning algorithms, AI-powered ticketing systems can analyze the content of support tickets and automatically route them to the appropriate support team or agent based on the nature of the issue.
This helps to reduce response times and improve the accuracy of ticket routing, leading to faster resolution times and improved customer satisfaction.
- Sentiment analysis
One of the most common applications of AI sentiment analysis in customer service is analyzing social media posts and comments. Companies can quickly identify and respond to customer feedback by monitoring social media channels.
This helps improve customer satisfaction and loyalty. Sentiment analysis can also identify emerging trends and issues, allowing companies to address them proactively before they become major problems.
Overall sentiment analysis in AI for customer service to help deepen an understanding of customers’ needs. And by analyzing customer interactions across various channels, companies can gain valuable insights that can help improve customer satisfaction, loyalty, and retention.
- Self-service
Customer support AI is enabling self-service options. By using AI-powered chatbots and virtual assistants, companies can provide customers with 24/7 support and a more personalized experience.
One way AI is powering customer self-service is by using virtual assistants.
AI-powered virtual assistants are similar to chatbots but are more advanced
and can handle more complex requests. They can assist customers with product recommendations, troubleshooting, and even complete transactions on behalf of the customer.
How do you scale customer service with AI?
The traditional way of scaling customer service is tedious and expensive. You have taken customer agents through rigorous interviewing and training. With high customer service agent turnover, you may spend a lot of resources and lose agents after a couple of months.
However, customer service AI minimizes some of these agent service delivery shortcomings. Artificial intelligence (AI) can help businesses efficiently manage and scale their customer service operations while providing personalized experiences to customers.
An example of AI for customer support solutions is predictive analytics for proactive support. This AI tool is used to anticipate customer needs and provide proactive support.
By analyzing customer data, AI can predict potential issues before they occur and provide solutions before the customer even realizes a problem.
This can help businesses prevent customer churn, reduce the number of support inquiries, and improve customer satisfaction.
Why is AI the future of customer service?
Apart from automating repetitive tasks like support ticketing, AI is the future of customer support for these reasons:
24/7 Availability: AI-powered chatbots and virtual assistants can be available 24/7, allowing businesses to provide round-the-clock support to their customers.
This means customers can get their questions answered and problems resolved anytime without waiting for business hours.
Cost-effectiveness: By automating routine tasks, AI can help businesses reduce the costs associated with customer service. This can lead to significant cost savings over time, especially for large organizations.
Data-driven insights: AI can analyze large amounts of customer data and generate insights to help businesses improve customer service. For example, AI can identify customer complaints and feedback patterns, allowing businesses to address common issues and improve their products and services.
