AI in Customer Service: 11 Ways to Use it + Examples

Companies are increasingly adopting AI to identify trends and gain insights from the huge volumes of data they hold in order to aid decision-making. AI-driven holistic solutions are being utilized to automate business intelligence and analytics processes based on transactional data found in their databases. By detecting patterns and changes, companies can use the resulting insights for a wide range of business applications, such as new service requirements, location-based trends or new product development.

Above mentioned technologies unfold enormous possibilities to enhance the customer experience across customer purchase journeys. Businesses need to develop an AI-driven customer journey to provide a magical experience to their customers. Augmented Reality (AR), Virtual reality (VR), and Mixed Reality (MR) enhance our view of the real world and are known as extended reality. AR enhances the real view with the help of computer-generated information, Instagram filters, Lenscart 3D mirror, IKEA applications are perfect examples for (AR).

Generative AI tools are powerful engines for creating content based on a wide variety of input. The methods used by these tools are highly effective but have limitations and flaws. Whether it’s too many fingers on the image of a hand or fictional jobs on a resume, AI tools have demonstrated an occasional tendency to produce inaccurate content. Due to these limits, it’s important to have a process for vetting the output of AI tools before relying on it for business decisions or publishing the content publicly.

Customers simply tell the AI what they want to accomplish and the bot completes the request. We’ve all been in a situation where we need to get an issue resolved ASAP – and it’s the worst when you get an automatic message saying that the wait time is over an hour. AirHelp has assisted over 16 million passengers experiencing canceled, overbooked, or delayed flights. As a leader in the traveler claims category, it’s always received a high volume of queries.

New research into how marketers are using AI and key insights into the future of marketing with AI. It’s an AI bot that you can connect with your CRM to perform tasks, like writing messages, or drawing information, like your latest Net Promoter Score results. This can come in handy when you communicate with a single client or a larger customer segment. It’s even more frustrating when it’s a simple question or task, like paying a bill or checking a balance. These tasks can now be handled by an AI system that responds to numbers and audio prompts.

How AI is used in customer service automation

In addition, tools like chatbots make it easy to offer 24/7 support so consumers can seek solutions right when they need them. For more pressing issues, a late-night chatbot can schedule a call back when a live agent becomes available to reduce the amount of time spent waiting when the next day. The company claimed the chatbot was more accurate in “errand resolution” and on par with human agents when it came to customer satisfaction. The post also said the tech was estimated to drive a $40 million profit improvement for the company in 2024.

Klarna says its AI assistant is doing “the equivalent work of 700 full-time agents.” Comparing data taken from different systems is “something every finance team on the planet does a lot of,” said Cory Hrncirik, modern finance lead in Microsoft’s office of the chief financial officer. A couple of thousand people on a financial planning and analysis team each spend one or two hours doing reconciliation each week, and with the new Copilot, that takes more like 10 or artificial intelligence customer support 20 minutes per week, he said. The Copilot for finance will initially run a variance analysis, reconcile data in Excel and speed up the collections process in Outlook. Remote Visual Assistance enhances the product registration process and warranty management, increasing brand loyalty and post-sale revenue. Imagine your refrigerator automatically orders fruits, milk, eggs, bread, butter, and vegetables online, according to the stock left in the specific containers.

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Your customers will be able to solve a problem at any time of the day with AI-powered customer service bots. For example, customers inquire and support staff respond to those queries which create enormous volumes of decently organized data in customer service. Machine Learning helps a program collect and process this data, and train itself to understand and respond to client requests. They can answer general questions or offer self-service resources—like help center articles—so customers can find answers or complete simple tasks. As businesses scale toward global markets, always-on support is crucial to maintain an excellent customer experience.

Customers signals – such as clicks, views and purchases – are translated into predictions that deliver value-added personalization before customers even request it. Predictive solutions combine customer data with AI to determine intent and select the right next step to deliver the relevant customer support. There can be many pain points in the journey, identifying and resolving the most significant one is the job.

AI, then, as well as being a great way to streamline operations and help agents out, is also the method by which you turn customer data into insights that can transform your business. These advanced technologies can detect a customer’s native language and automatically translate the conversation in real time. Is it possible for customers and bots to engage in rich, personalized conversations? Zendesk AI is built on customer intent models that are specific to customer service.

Agent assist

87% of reps say AI tools that help them prioritize customer service requests in terms of urgency are effective. The top challenge for customer service leaders in 2022 was prioritizing customer requests. As companies are dealing with rising numbers of support requests, it can be harder to sift through incoming queries to identify the ones that require the most immediate attention. TELUS International’s Fuel iX drives value for clients by providing end-to-end, next gen CX solutions fueled by GenAI. Its suite of services and capabilities include digital consulting, data analytics, self-service applications, and AI-enabled platform integration.

• Sure, you can use AI to run an effective chatbot, but that’s just one of its many abilities.
• As with customer conversations, these tools are great for giving your agents a place to start.
• If your chatbot has sentiment analysis capabilities, use it to gauge how frustrated a customer is and when your team should intervene.
• Rather than being restricted to a set of rote responses, Chat GPT is “trained” on existing human content published on the internet.
• Transformation requires a cross-functional team consisting of data scientists, process engineers, business managers, technology specialists, domain specialists’, etc.

AI has shown up everywhere in recent months, even taking fast food orders in drive-thrus. And with it come many ethical gray areas and calls to slow down the speed of its development. Zapier can make automating customer service apps about as simple as ordering your favorite breakfast meal from your favorite local fast food chain. Adding AI to the mix is like getting extra green chile on the side—without even having to ask for it. AI-generated content doesn’t have to be a zero-sum game when it comes to human vs. bot interactions.

They can pluck fruits and veggies from the crops and trees that are nothing but the part of a hoax environment created with mixed reality. Artificial intelligence can be an incredibly powerful tool for customer service teams, but it’s a quickly evolving field. Ultimately, much of your success with AI will come down to vetting tools well and ensuring they’re a good fit for your team. Automating your quality assurance (QA) program using AI is another way to save time and continually improve your customer conversations. Many AI-powered QA tools — like Klaus or MaestroQA — automatically review conversations, conduct root cause analysis, and gauge customer sentiment.

You can meet this expectation by integrating AI-powered chatbots into your customer service strategy and providing uninterrupted, 24/7 support. In this article, we’ll dive into some examples of AI in customer service and learn how these companies use AI to improve customer experience. By automatically identifying incoming service requests, Levity helps your customer care professionals to spend more time on essential clients. Sign up for Levity today and find out how you could improve your customer support with easy-to-use, no-code AI workflows. By creating an AI-powered chatbot to answer frequently asked questions with customer-specific information, your customers will be able to get answers to their questions more quickly and simply. In turn, this enables the customer support staff to focus on more complex issues and provide a better overall experience while lowering operating expenses.

Engaged customers are more loyal, have more touchpoints with their chosen brands, and deliver greater value over their lifetime. From customer service agents to the enterprises employing them, here’s what users on the back end can gain from AI. If there’s a tenth circle of hell, it probably involves waiting for a customer service representative for all eternity.

Text/NLP analysis

IBM can help you build in the advantages of AI to overcome the friction of traditional support and deliver exceptional customer care by automating self-service actions and answers. There are still countless issues and regulations to address with its use, plus building systems that seamlessly move customers from AI to humans. Still, the foundation has been set to revolutionize customer service and create an excellent experience for customers and agents. If you’ve ever tried to order an item that’s out of stock or been notified that a product you already ordered is going to be back-ordered, you know inventory management relates to customer service processes. And by keeping items reliably in stock, effective inventory management can keep stock-related inquiries from ever reaching service agents.

Organizations now have access to huge amounts of data about their customers that can be used to provide personalized service and recommendations to targeted consumers. Businesses must design intelligent experience engines, which assemble high-quality, end-to-end customer experiences using AI powered by customer data. When you have a small customer service team or you’re just getting started with your QA program, tools like these can be invaluable. For instance, Help Scout’s AI assist acts like a personal writing assistant in email conversations, helping agents match your company’s support voice and style.

With the help of tools like HubSpot’s ChatSpot, which harnesses the power of Generative AI, the possibilities extend beyond mere conversation. This eliminates the need for predefined dialogue flows, giving your customers a more lifelike, engaging interaction. When you are serving a global audience, your customers can hail from any corner of the world. Catering to such a diverse customer base can be challenging, especially regarding language barriers. For instance, a scenario where a customer asks, “Where is my order? It was supposed to reach me yesterday.” The AI can sense from the tone that the sentiment is negative and the customer is displeased. While many companies are still experimenting with AI to serve their customers, some have already seen positive results.

With AI, you can create powerful intelligent workflows that provide faster support for customers and create more efficient agents. This eliminates wait times as customers get intelligently routed to the agent best suited for the task. From gathering data to speech recognition and message response times, AI can enhance the customer experience in nearly every way when it’s applied correctly. Here, 15 members of Forbes Business Council share their expert insight on how organizations can leverage AI to enhance their customer service. Empower your customer service agents to easily build and maintain AI-powered experiences without a degree in computer science. Meet customers’ needs by solving their most pressing issues quickly, accurately, and consistently across any digital or voice channel.

What ChatGPT brings to the table, however, goes far beyond the capabilities offered by legacy chatbots, and it has the potential to improve customer service in ways that were not previously possible. Post-call reporting, for example, can easily be handled by artificial intelligence platforms capable of logging summaries rich in detail and built for trend spotting. Important information like call time, issue resolution, customer frustration and next steps can all be automated if your contact center management solution has natural language processing built in. In today’s customer support environment, artificial intelligence isn’t a way to replace human agents – it’s a way to help them do their best work by providing assistance across the entire spectrum of customer interactions. Holistically transforming customer service into engagement through re-imagined, AI-led capabilities can improve customer experience, reduce costs, and increase sales, helping businesses maximize value over the customer lifetime. To achieve the promise of AI-enabled customer service, companies can match the reimagined vision for engagement across all customer touchpoints to the appropriate AI-powered tools, core technology, and data.

McKinsey’s global survey on The State of AI in 2021 indicates that AI adoption is continuing to increase with 56% of respondents reporting AI adoption in at least one function, up from 50% in 2020. The report states that the most common business function for AI usage is related to service operations. This is underscored by Gartner’s 2021 Technology Roadmap Survey, which indicates that 65% of customer service leaders plan to substantially increase their adoption of AI capabilities by 2023. This level of forward-thinking explains why the global AI market size is expected to grow from $93.53 billion in 2021 to $997.77 billion in 2028. Prior to 2023, most of these so-called chatbots weren’t actually artificial intelligence.

It’s the process of analyzing large quantities of data and pulling out actionable insights that forecast trends, anticipate customer sentiment, and solve future problems. AI can detect a customer’s language and translate the message before it reaches your support team. Or you can use it to automatically trigger a response that matches language in the original inquiry. These types of tools use AI to synthesize existing information and output copy based on a desired topic. You can then use this copy to create knowledge base articles or generate answers to common questions about your product. You can foun additiona information about ai customer service and artificial intelligence and NLP. We’ve mentioned chatbots a lot throughout this article because they’re usually what comes to mind first when we think of AI and customer service.

AI continues to make significant improvements to machines’ biometric recognition capabilities, especially when it comes to challenging lighting conditions, angles, and backgrounds. Using biometrics, agents can recognize customers, and greet them in a personal manner. Companies can use biometrics to verify warranties, ensuring that customers receive service for their devices without requiring them to save receipts or other documentation. Agents representing financial institutions or insurance companies can use biometrics to quickly authenticate customers while minimizing the risk of fraud.

AI can analyze an entire archive of past interactions and tickets, calibrate them to current resolution processes, and then churn out dynamic wait times based on parameters like ticket type, agent, agent workload, and more. These measures don’t solve anything for customers, but they go a long way in setting expectations and keeping them satisfied. While this process doesn’t directly address users or resolve active issues, it can still be an incredibly useful tool for identifying common friction points for customers. By using these analyses to find trends in service processes, enterprises can fix problems by changing workflows, creating new resources for self-service, or giving agents the training or tools they need to address them. Many business software providers, including HubSpot and Salesforce, have been working to supercharge existing products with generative artificial intelligence, in the hope of making clients more efficient.

When Helsinki and over 38,000 employees needed time-saving automation, the city turned to IBM Consulting to co-create an AI solution with watsonx Assistant to deliver more flexible customer experiences with a digital assistant network. With many repetitive tasks removed, customer service agents can focus on more creative and fulfilling jobs, such as providing personalized service, working through complicated issues, and building relationships. Agents can use as many tools as possible to help them bring a ticket to resolution efficiently, and AI can expand that toolbelt dramatically. By synthesizing data based on factors like ticket type, past resolution processes across team members, and even customer interaction history, AI can automate action recommendations to agents. If queries like these comprise half a company’s total customer support request tickets, that’s a huge time savings for its agents.

Nevertheless, an estimated 75 percent of customers use multiple channels in their ongoing experience.2“The state of customer care in 2022,” McKinsey, July 8, 2022. In customer service, AI is used to improve the customer experience and create more delightful interactions with consumers. Technologies like chatbots and sentiment analysis can help your support team streamline their workflow, address customer requests more quickly, and proactively anticipate customer needs.

For unresolved questions, chatbots can connect customers to available agents, helping ensure that those agents are only getting the more complex or higher-value tickets. AI makes personalization possible through automated consumer data tracking and record keeping, helping teams build well-rounded profiles of their customers. For example, HubSpot’s CRM automatically stores customer details, interaction history, and all relevant information in one place to give you the context to personalize experiences.

For instance, some can automatically take step-by-step screenshots as you work in your product (like Scribe). Even the most powerful large language model currently available to the public (Open AI’s ChatGPT) isn’t actually artificial intelligence. It can only use the information it’s been given to predict the word that is most likely next in a sequence. Major corporations are investing heavily because they are sure that voice-activated and AI-integrated chatbots can consistently handle simple requests.

By implementing machine learning to datasets that include a breadth of customer information and behavior, sellers can send customers personalized recommendations, timely promotions, or targeted check-ins. These tools can automatically detect an incoming language and then translate an equivalent message to an agent and vice versa. Paired with neural machine translation (NLT) services, they can even detect the customer’s location and tweak the phrasing according to localized linguistic and cultural nuances.

• Typically, the use of AI tools involves a third party handling data provided by their customer.
• While this process doesn’t directly address users or resolve active issues, it can still be an incredibly useful tool for identifying common friction points for customers.
• And with it come many ethical gray areas and calls to slow down the speed of its development.
• Your average handle time will go down because you’re taking less time to resolve incoming requests.
• For example, banks observed customers’ frustration while standing in a long queue and provided a digital token system to their customers, eliminating the need to stand in the queue.
• CU endeavors to develop policies that apply to a wide range of technologies rather than specific policies about different technologies, and this applies to AI technologies as well.

This means that you can keep monitoring the model and its performance by evaluating a percentage of its predictions or leave it to work independently. The process of training your data involves uploading data—whether that’s text or images—to one of your predetermined labels. This data is called ‘training data’, and it essentially gives the AI examples to learn from. You can use internal data—your own data, or external data—data taken from other sources. These labels give meaningful information for the algorithm to utilize as a benchmark, which includes the input data points and the final outcome you’re looking for in your model. For example, if you’ve sent someone a welcome email with a Call to Action, you’re probably tracking whether they’ve clicked or not.

AI can even analyze a customer interaction and understand the customer’s sentiment and intent. This allows the bot to identify positive, negative, and neutral language so it can route tickets to an agent accurately if a handoff is necessary and reduce escalations due to sentiment detection. This lets the agent know how to approach the interaction, preparing them to avoid an escalation or de-escalate an elevated situation.

Pursue your passion and change the future of business using all things AI, analytics and automation. Apply a data-driven approach to identify and prioritize customer intents for automation. Blake Morgan is a customer experience futurist and the bestselling author of The Customer of the Future.

Artificial intelligence is the key to enabling real-time service for customer support platforms. What’s more, this technology has the potential to shift the way customer service solutions are developed. Leveraging AI to boost customer happiness, enhance the employee experience, and simplify support can help your business grow and thrive. These bots can be deployed on messaging and email channels to deflect customer questions and handle repetitive tasks—like troubleshooting or collecting feedback—so agents can focus on customer queries that require a human touch. Rather than spending hours manually configuring your chatbots, you can set up an advanced bot in a few simple clicks. To provide 24/7 support, Photobucket uses Zendesk bots, which answer frequently asked questions and hand off conversations to a live agent when appropriate.