Consumer expectations are shifting, and providing high-quality service has become essential for businesses in most markets. As a result, companies are compelled to make technological investments to improve customer experience (CX) and customer support delivery.

Artificial intelligence in customer service has the potential to be entirely transformational for both business and customer. Integrating AI into the customer service workflow has already delivered impressive results for some early adopters—it can boost productivity, streamline customer interactions, and offer assistance to customer support personnel in real time.

Early adopters have integrated AI into customer service roles, using it for workflows ranging from chat support that can answer simple customer questions, to predictive analytics that anticipates customers' needs. This union of artificial intelligence and customer service can improve response times and make it possible to offer some form of customer support around the clock.

Artificial intelligence and customer service go hand in hand, delivering a synergy between humans and technology that can improve the customer journey across all touchpoints. This article will examine how to improve customer experience with generative AI.

What is customer service AI?

Artificial intelligence for customer service refers to the use of advanced AI technologies such as natural language processing (NLP) and robotic process automation (RPA) to augment and enhance the customer support process, and deliver a better CX.

These technologies can be used to intelligently automate specific customer experience-related tasks and assist professional customer support specialists. This approach supports a more personalized customer journey and improves the responsiveness and efficiency of customer support services.

Some examples of how AI can be used to improve CX include:

Workflow automation: Using AI and agentic workflows to automate standard, manual tasks, including categorizing support tickets, updating a CRM, and automatically routing support calls.
Tools to assist support specialists: Summarizing long support conversations, suggesting possible responses and solutions, and providing rapid access to relevant knowledge.
Predictive analytics: Intelligently analyzing customer data to detect patterns and forecast possible future support issues.
AI virtual assistants: AI-driven virtual assistants can tackle general customer support inquiries and answer frequently-encountered problems instantly.

Implementing AI-powered customer service can help a business to improve ROI while also improving customer experience. It can help build customer trust through better personalization and quicker responses.

Principal benefits of AI in customer service

AI-powered customer support tools can help a business provide faster responses to customer inquiries without the immediate need for human intervention, and with 24/7 availability. AI customer service can also improve service delivery speed and scalability, allow for more personalization, and offer continuous assistance to professionals in the role.

Implementing AI into the CX workflow can benefit a business in many ways, including the following.

Improved customer experience

Integrating AI in customer service can help to deliver some significant improvements in CX. Customers can access omnichannel support, fully personalized customer journeys, reduced wait times, and around-the-clock support availability. With service expectations now high across every sector, these improvements could help companies deliver the level of support that customers demand.

Faster response times

Many customers rate waiting times for support requests as a major complaint, especially when platforms experience high traffic. This is especially true of accessing phone support for B2C businesses; call queuing techniques can lead to customers waiting on the phone for extended periods before they even speak to a customer service agent.

Deploying AI virtual assistants as a first line of defense for customer support requests can help to address the waiting period issue. AI agents could be used to answer basic support inquiries, immediately providing customers with answers from the knowledge base. It pays to deploy the virtual assistants as triage for faster initial responses, and then hand the more complex requests to human support specialists as soon as practicable. Taking a collaborative human-in-the-loop approach can bring the best of both worlds, integrating human input and expertise into the process to help enhance the accuracy, reliability, and adaptability of machine learning systems.

Personalization at scale

Providing fully personalized, large-scale customer support services can be a challenge. However, artificial intelligence for customer service has changed the landscape. The combination of retrieval-augmented generation (RAG) with AI’s ability to analyze huge volumes of customer data has made a real difference. RAG-enabled AI platforms help to personalize the customer journey based on their previous interactions and history.

AI can help to personalize customer support through techniques like these:

Analyzing previous interactions: By recalling the results of previous customer support requests from a specific customer, AI can deliver a contextually-aware response that takes the customer’s support history into account.
Service-aware responses: In some sectors, such as energy supply, AI virtual assistants can be made aware of current service outages and issues that might impact a specific location. The virtual assistant can then provide real-time support responses to concerned customers, empowering employees to focus on resolving the issue and communicating the results.
Referencing prior engagements: Some support issues are ongoing, and a customer may have to make several support requests to resolve an issue. Traditionally, this would often involve the customer explaining a problem from the beginning each time they contact support. Given AI has access to all prior support requests for a customer, it can intelligently refer back to previous engagements without the customer needing to explain the problem again.

24/7 availability

Customers increasingly expect support and problem resolution around the clock. AI virtual assistants and virtual support specialists can fill this service gap. This is a vital capability in some industries, for example:

Companies that operate internationally and are expected to support customers in multiple time zones.
E-commerce platforms, where customers might need at least a basic level of support outside of normal business hours.
Urgent support issues in industries where allowing a support request to sit waiting could result in significant losses, such as lost revenue or diminished customer trust.

Omnichannel support

Today’s customers demand service through a variety of channels—from ticketing systems, email and web chat, to social media, phone support, and more. If you have a presence somewhere, a customer could expect to get support there. But these channels tend to be separated and manually accessed at the point of contact, which can make it difficult to view a customer’s full support history.

AI-powered customer support provides a vehicle for combining these disconnected channels into a unified whole, delivering an omnichannel customer journey. AI can help with this in various ways, including:

Standardizing responses: Ensuring that the support experience is similar no matter which channel a customer uses for help.
Continuity of resolutions: By being able to recall the contextual history of a customer’s support requests, customers don’t need to explain the same problem again and again.
Cross-platform integration: Agentic AI can operate across multiple different platforms in the same way, whether a customer contacts your business via the company website’s chat function, by SMS or social media messaging apps, and so on.

Greater productivity and efficiency in your organization

Adopting artificial intelligence for customer service empowers businesses to boost workforce productivity. By automating routine tasks, support specialists are freed to work on other more meaningful activities. Automation can not only support efficiency, but can also help to reduce human error.

When AI-powered customer service takes a place on the front line, dealing with simple support issues and routing more complex issues to human operators, it can help a business to deal with a higher volume of customer support requests. Some of the support tasks that can be automated using AI for customer service include:

Automated troubleshooting: AI-powered virtual assistants can work in a question-and-response style, walking a customer through several troubleshooting steps to resolve common problems.
Support request categorization: AI can analyze the contents of support tickets and judge them on urgency and customer sentiment. Based on this analysis, tickets can be routed to the proper support channel, so that high-priority tickets receive attention immediately.
Automated data updates: AI can log customer interactions automatically and extract any pertinent details which can be used to update other data silos, such as CRM systems, in turn saving human operators time and effort on manual reporting.

Enhancing worker performance

When using AI in customer service, businesses must remember that the technology isn't meant to replace human support agents completely. Rather, its purpose is to support and empower employees by automating routine tasks.

Professionals have the opportunity to work more productively and self-assuredly if they are given access to customer service AI-powered systems. Here are some examples of how AI can help employees:

Sentiment analysis: AI can monitor customer sentiment and frustrations, providing an agent with real-time feedback on a customer's emotions. The agent can then adjust their approach, if necessary, and more accurately address the customer's emotional needs.
Intelligent knowledge base searches: AI can use RAG to retrieve and/or summarize sources of business and product-specific data, documentation, and service information for staff in real time.
Auto-generated support responses: AI can help workers draft responses to support requests, referencing previous interactions where relevant, and inserting pertinent facts and advice.
Real-time support monitoring: AI can be used to analyze support requests and communications, suggesting which answers might be the best. This can help professionals deal with responses faster and with greater accuracy.

Reducing human error

Using AI to automate specific customer support tasks and ensure that these are handled uniformly and accurately can lead to better accuracy in responses. Customer service AI can be used to automate parts of the support workflow in ways such as:

Automated sanity checks: AI can monitor customer interactions with support specialists and highlight any actions taken that breach security and compliance best practices.
Consistent and accurate responses: When a business uses AI-powered virtual assistants and other AI systems to take on support specialist roles, it increases the likelihood that all customers will receive consistent messaging across all channels and the same level of service.
Minimizing data processing errors: While AI is not immune to mistakes such as hallucinations and bias, it can still help mitigate errors like typos, especially when paired with structured data inputs and clear checks that flag unusual or incorrect information before it's sent out.

Improved ROI

One of the main advantages of implementing artificial intelligence in customer service is improving the ROI of support spend without compromising the quality of service—in many cases, actually improving it. Customer service AI automation can support workflows more efficiently.

Handling higher query volumes with fewer resources

As a business grows, it can be a challenge to scale customer support capabilities; costs also grow with the need for additional support specialists, between salaries and office overheads.

By using AI-driven virtual assistants or virtual agents as part of the enterprise AI tech stack, a company can substantially increase the volume of incoming support requests it can handle. Let the virtual customer support assistants do an initial triage of incoming requests, handling the simple ones (like resetting a password) and passing more complex cases over to human operators.

This flexibility can be especially useful in those temporary and seasonal periods of increased support call volume.

Optimizing resource allocation for greater efficiency

Using artificial intelligence in customer service helps a business ensure its human resources are allocated to the most important, complex, and highest-priority support requests. AI for customer service can help reduce the need for support specialists to handle minor support issues and repetitive manual tasks, while also supporting rostering by predicting required staffing levels at any given time.

AI can provide several benefits in this regard, including:

First-level support becomes automated: By using an AI-powered virtual assistant as the initial point of contact for new support requests, many minor support issues can be resolved without the need for human intervention.
Predicting support needs: AI can be used to predict the demands on support staff based on emerging or historic customer service trends. The business can then adjust staffing levels as required.
Routing support requests intelligently: AI can analyze the content of customer support tickets and automatically route them to the best support resource—such as a particular support agent or another department—reducing the need for manual intervention.

Examples of AI in customer service

Integrating AI into customer support operations is a low-hanging fruit, and it’s difficult to ignore the advantages of improving ROI while enhancing the efficacy of support services. In the following sections, we'll examine some real-world instances of artificial intelligence in customer service.

AI virtual assistants

Perhaps the most popular application of AI in customer service is with AI assistants. These systems become the first point of contact for all new support requests, and can be used to implement intelligent first-level support. Virtual assistants have many benefits, including:

24/7 support: AI assistants can be at work around the clock. They can be used to provide at least a basic out-of-hours support service.
Highly scalable: AI agents and assistants can tackle numerous first-level support engagements concurrently, leading to less waiting and a better level of service for customers.
Immediate answers: An AI virtual assistant can often resolve a customer's simple issue without the need for a human support agent to get involved.

Intelligent support for customer service agents

The application of AI-powered tools to assist human support specialists—enhancing their skills and making it possible for them to respond to support requests faster—is another excellent illustration of artificial intelligence in customer service. Human support specialists can benefit from the help of AI in many ways, including:

Sentiment analysis: Analyzing records of support interactions for real-time sentiment detection or post-interaction review to help support specialists understand customers' emotional state and respond appropriately.
Retrieving knowledge as needed: RAG-enabled AI for customer service can be used to intelligently search known issues and solutions contained within the company’s knowledge base instantly.
Real-time support recommendations: Whilst handling a support ticket, agents can be given real-time advice on how they should respond to the request based on past engagements and the context of the request.

Workflow automation

One often overlooked example of AI customer service is its ability to automate parts of the customer support workflow, reducing the manual effort required and improving the speed of support delivery. This can take many forms, such as:

Updating CRM systems automatically: Support interaction can be instantly appropriately logged to CRM systems, removing the need for agents to make manual entries after each call.
Summarizing support requests: Long, complex support requests can be summarized to make them easier for support staff to understand without reviewing the entire history of a support ticket.
Routing support tickets automatically: Based on the context of support tickets, AI can route them to the most appropriate resource for handling them.

Analytics for proactive customer service

Traditionally, customer support is tackled reactively. AI can be a real game-changer here. Predictive analytics can highlight any potential holes in the capabilities of support services and warn of possible trends that could become problematic. AI can analyze support data and, for example, do the following:

Personalize support engagements: By analyzing a customer's historical support data, AI can anticipate possible needs and present relevant solutions before the customer asks for help.
Tracking customer sentiment: Using NLP techniques, AI can monitor a customer's tone and satisfaction within a specific support engagement. This data can help companies improve their support offerings.
Proactive issue solving: By analyzing patterns in overall customer support requests, AI can detect emerging service issues and alert the business before the problem worsens.

Considerations when using AI for customer service

Any company looking to implement AI in customer service needs to be mindful of potential dangers. AI has already demonstrated its ability to improve ROI, increase support workflow efficiency, and improve response times in many cases. However, adoption in certain AI use cases for customer service may present some obstacles—as seen in the following.

Available budget and resources

Deploying artificial intelligence into customer service is a project that will require a fair amount of investment—not just in technology, but also in training, security, and compliance. Some of the main cost considerations include:

AI model development: A business must decide whether it’s best for them to train a custom AI model, or use an existing third-party model and fine-tune it.
Infrastructure costs: Will the company run the AI model in-house on its own infrastructure, or take advantage of third-party cloud services? Consider not only the initial setup costs, but any ongoing maintenance costs as well.
Workforce training: The company will need to train staff on how to use the new AI tools, and also educate them on the legal and security implications of using AI.

Impact on the workforce

A large concern for many companies is how AI will transform the roles of existing customer support staff. Job roles could be impacted, and additional skills may be required. Some of the main effects of AI on support staff could be:

AI as a collaboration tool: Staff members must be reassured that AI will be used to support them, simplifying their work and increasing productivity.
Resolving concerns about job loss: Many support employees, particularly those working with customers, may worry that AI will replace them. Transparency and clear messaging is essential to allay these anxieties.
AI will be used to enhance support: Staff may not understand how AI will fit into current workflows. Efforts must be made to explain the benefits that all staff will benefit from AI tools, enabling them to handle difficult support requests more easily.

Security and data protection

Any company trying to work out how to use AI in customer service must be aware of the legal implications regarding security and the protection of private data. There are currently several laws that come into play here, including the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and PCI DSS. Any AI deployment needs to take these into consideration by, for example::

Planning for full compliance: This is compliance with all current and any future new laws or regulations that apply to AI security and data privacy.
Implementing access controls: Consider a multi-factor authentication method and role-based permissions for access to AI tools a necessity.
End-to-end data encryption: All data should be kept encrypted at all times. Not every data privacy law insists on this, but this will be a legal requirement for firms that operate globally.

AI and customer service: The future of CX

AI has already revolutionized how early adopters operate their customer support services. In terms of AI use cases, there are many benefits in customer service and AI—the key benefits include increased support capabilities, as well as greater personalization, scalability, and availability of support services.

Many of the current gaps in customer support provisioning are being filled by AI, allowing businesses to assist customers more quickly, accurately, and satisfactorily. As AI progresses, new and important roles for AI in customer support will undoubtedly emerge.

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AI in customer service: How can it improve CX?