AI Customer Support: The Use Cases, Best Practices, & Ethics

The AI revolution isn’t just driving the creation of higher quality chatbots (with generative AI). There’s also an incredible opportunity for businesses to leverage AI across a range of channels, including in the voice landscape. With the right support, business leaders can stay ahead of AI trends, implement the latest technology, and ensure they’re future proofing their approach to compliance.

It’s allowing users to build applications using natural language alone instead of drag-and-drop tooling. Elsewhere, a Japanese telecoms provider is trialing a similar software that modifies the tone of irate customers. Finally, the QA team can review, edit, and finalize that scorecard before repeating the process across other channels (and perhaps specific customer intents).

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CMSWire’s Marketing & Customer Experience Leadership channel is the go-to hub for actionable research, editorial and opinion for CMOs, aspiring CMOs and today’s customer experience innovators. Our dedicated editorial and research teams focus on bringing you the data and information you need to navigate today’s complex customer, organizational and technical landscapes. This is likely because BPOs embrace digital transformation — and, therefore, innovative technologies like AI — for internal CX strategies, according to Robin Gareiss, CEO and principal analyst at Metrigy. While AI is not new to contact centers, BPOs that invest in newer technologies often have happier agents, leading to happier customers.

• They can understand natural language, interpret intentions, and minimize call queues.
• Indeed, this list of generative AI use cases for customer service originally included 20 examples.
• Generative AI enables accurate budget forecasting by analyzing historical financial data, market conditions, and economic indicators.
• What you can do is, instead of emailing a data analyst back and forth for a report, you could interact with generative AI.
• These speech-enabled, automated systems use voice prompts to help callers navigate call tree menus or access information without the need for a human operator.

The fact that AI solutions rely on large amounts of data means that businesses need to take a proactive approach to using that data, and their tools ethically, and securely. AI-powered smart tools like “Smart Translations” enable agents and customers to connect in their preferred languages, streamlining communication and improving customer satisfaction. This feature also allows companies to reduce the costs of hiring additional global agents to serve international consumers. Companies need to determine how to implement this technology in a way that enhances customer experiences, augments agents (without replacing them) and adheres to compliance standards. NICE is also leveraging AI to support supervisors and CX leaders, providing real-time and historical insights into CX operations along with actionable steps to optimize employee and customer experiences. With both in-depth historical analytics and real-time dashboards, organizations can take a more data-driven approach to delivering exceptional customer experiences.

Conversational Intelligence Enables a “Near Self-Sufficient Support System”

“For companies in the primary stages of GenAI adoption, putting it directly in front of the end customers often sounds intimidating,” Caye explains. The first pillar to consider, he suggests, is actually preempting the need for customer contact. Or, as he puts it, “The best service is when you don’t need service,” meaning that the primary objective is resolving issues ai use cases in contact center before they arise. While it already does a good job of this via its pre-packaged, sector-specific offerings and client experience (as G2 recently noted) – the Ascend Connect suite will put it inside more contact center operations. As a result, businesses don’t need to hire in-house data scientists or AI/ML specialists to drive maximum value from the offering.

As generative AI solutions become more customizable, with no-code and low-code bot-building platforms and tools like Microsoft’s Copilot Studio, the options for personalization will grow. Companies can create generative AI tools designed explicitly for specific customer segments and use cases. However, evolving generative AI trends in the “multimodal” landscape could change all this. Some of the most advanced generative AI solutions today, such as Google’s new “Gemini” model, can understand and respond to content in various forms. They can even create different types of content, from videos and images to human-sounding speech. And then you get into some measurements that are more around the length of the call or efficiency-driven, like an average handle time or an average talk time.

Talkdesk Announces an “Industry-First” GenAI Suite for On-Premise Contact Centers

Tools capable of predictive analytics can help companies forecast future contact center needs, and determine how to distribute their agents across different channels. Companies such as LivePerson and IBM ChatGPT Watson Assistant are leading the charge in this space. LivePerson’s AI-driven chatbots can handle a wide range of customer queries, from answering frequently asked questions to processing transactions.

Some contact center software vendors also offer CRM platforms, along with built-in integrations. Call whisper delivers important information about customers to agents before beginning a contact response. This feature, for example, could be configured to report information about the purchasing history of a customer making an inbound call so the agent taking the call will have potentially valuable information when servicing the customer. Providing this information automatically through a call whisper feature can save time during calls, build customer loyalty and improve an agent’s efficiency. IBM customer experience consulting provides deep expertise in customer journey mapping and design, platform implementation, and data and AI consulting so you can harness best-in-class technologies to drive transformation and growth.

Empower your team to build and deploy AI chatbots that understand your customers requests the first time. Wimbledon, one of the best-known tennis tournaments in the world, partnered with IBM Consulting® to create AI-generated insights and world-class digital experiences. Implementing AI into the customer experience area of the business is exciting, but it also produces several challenges. For instance, perhaps ChatGPT App they could place a bot in the contact queue to gather information, as the customer waits, that’s relevant to their stated intent. That could streamline the subsequent customer interaction and improve the agent experience. Indeed, businesses can then take a much more targeted approach to mapping out customer journeys, building bot flows, and ensuring relevant data inputs with the help of their teams.

By empowering agents with these insights, Cogito not only increases individual performance but also transforms the quality of service across entire organizations. AI in customer experience relies on algorithms that sift through massive datasets to understand individual customer preferences, behaviors and purchasing habits. By analyzing variables such as browsing history, past purchases and interaction patterns, these algorithms detect subtle trends and patterns. This deep learning process enables the creation of highly tailored marketing campaigns that resonate with each customer on a personal level, ultimately enhancing their engagement and satisfaction. Companies are even introducing new “small language models” specifically tailored to address specific tasks, like responding to customer queries on a messaging app. These tools may be more attractive to smaller companies with limited bandwidth and storage resources.

Generative AI solutions have already proven their ability to enhance personalization in the contact center. The right tools can leverage in-depth data about customers to coach employees on how to respond to questions or deliver personalized guidance in a self-service interaction. Most of today’s business leaders know they must consistently serve consumers on a range of channels to earn their loyalty. However, fewer than a third of contact centers deliver truly “omnichannel” experiences.

• This technology, which allows computers to understand and process human speech, is increasingly being integrated into customer support systems for various purposes.
• Partnering with Shift Technologies, the company launched Force, an AI solution to detect claims.
• It expedites product development, keeps their quality in check, and predicts equipment features, improving the way manufacturers approach production and maintenance.
• And agents, it’s difficult for them to get conflicting feedback on their performance.

That may enable more emotionally intelligent virtual agents to empathize with customers and adapt their communication style accordingly. Indeed, contact centers can now instantly generate objective and digestible post-interaction summaries with the relevant context for the next agent. It’s easy to see why, as AI tools have the ability to streamline operations, make teams faster and more efficient, and greatly improve customer satisfaction rates. However, for companies making the transition into the new age of AI-powered contact centers, it’s important to look beyond the hype. AI can accurately and conveniently service contact center customers across several communications channels using voice and text.

Nearly every aspect of a human agent’s contact with customers can be analyzed using AI. Examples of collected metrics include call and chat logs, handle times, time-to-service resolution, queue times, hold times and customer survey results. All this information is collected and analyzed to determine how customer satisfaction can increase, while simultaneously decreasing time-to-service resolution. AI is used to track these statistics, formulate performance profiles and make automated coaching suggestions to agents.

Make sure you also have a plan in place for how you’re going to consistently monitor and optimize your AI solutions. A well-developed AI customer support plan should include a process for consistently fine-tuning chatbots, voicebots, and other AI solutions, based on feedback and insights. You can foun additiona information about ai customer service and artificial intelligence and NLP. Evolving customer expectations have led to a phenomenal increase in the number of companies leveraging innovative technology to optimize buyer journeys. Artificial Intelligence has emerged as one of the most valuable tools business leaders can access to boost satisfaction rates, streamline contact center processes, and access valuable insights. Automation and AI offer incredible opportunities to improve the efficiency and overall productivity of the contact center. The right technologies can automate repetitive tasks, such as summarizing call transcripts and data entry, giving agents more time to focus on valuable tasks.

Predictive analytics also plays a vital role in resource allocation within customer support departments. By forecasting periods of high demand, businesses can optimize staffing and resource allocation, ensuring that they are prepared to more efficiently handle peak times. And where AI and machine learning really help here is finding areas of variability, finding not only the areas of variability but then also the root cause or the driver of those variabilities to close those gaps. And a brand I’ll give a shout out to who I think does this incredibly well is Starbucks. I can go to a Starbucks in any location around the world and order an iced caramel macchiato, and I’m going to get that same drink experience regardless of the thousands of Starbucks locations.

AR and VR extend beyond traditional support methods by providing visual and experiential means of assistance, which can be especially useful in complex or technical scenarios. AI is able to analyze customer data, including past interactions, preferences, and behavior, to offer personalized self-service options. Consideration was given to research by IDC and independent user reviews appearing on G2. Based on this analysis, we created an unranked, alphabetical list of 15 top contact center software features. A contact center that engages with customers primarily via voice calls, for example, might not need support for multiple communication channels. Lastly, Sprinklr helps contact centers connect virtual agents with existing CRM, CDP, and knowledge base systems to provide agents with critical customer information in real-time.