21 Unknown Applications of Gen AI in Customer Service

Rather, AI should be viewed as a tool to augment the human experience – not take it away. You can foun additiona information about ai customer service and artificial intelligence and NLP. In fact, AI technologies are projected to increase business productivity by up to 40% in the coming years. It’s no secret that AI can be leveraged to boost call center efficiency and productivity. You’ll also decrease the chance of cart abandonment as customers will always have somewhere to turn when they need their questions answered. It’s why 91% of call center leaders have made automation a critical priority for 2023.

Call scoring is when contact center supervisors review agent calls to measure the agent’s performance and review script effectiveness. Your contact center has a Quality Management (QM) process to make sure all contact center conversations are up to your organization’s standards. Here are a few AI tools you can use to get a more comprehensive view of how your contact center is operating. Supervisors can then skim the call transcripts to quickly understand agent calls, rather than having to listen to the entire audio. During our recent AI webinar, we found that almost 50% of poll respondents felt “increased efficiency and productivity” would be the biggest impact of deploying an AI contact center.

Manual analysis has given way to automated sentiment analysis solutions powered by AI. AI analytics tools can quickly transform customer sentiment into valuable customer insights and help them map the customer journey and even predict market trends. Generative AI chatbots can play a vital role in training and developing call center agents.

If you need to make a case for your business to transform its traditional call center into a future-forward, AI-powered operation, this blog can help to support your efforts. It includes several examples of how leading companies in various industries are using AI in contact centers. But before we get to those stories, let’s look at why AI is important in delivering a modern customer service experience — and what types of contact AI solutions are commonly used today. An Interactive Virtual Assistant (IVA) is a virtual assistant that automates call center processes.

Automation, while reducing the need for human resources, is still just as accurate and consistent as a live human agent. Generative AI is a form of AI that can produce content or get content from the internet, such as text, images, audio, and data. Generative AI continues to improve through machine learning, and technologies like ChatGPT show just how useful this technology can be to our everyday lives. This is no different in the contact center industry, where contact centers AI has the potential to make a huge impact. The future of artificial intelligence is set to revolutionize customer service with predictive analytics and hyper-personalization. Contact center AI is advancing towards managing current demands and anticipating them, including predicting surges in call volume and identifying customers at risk of churn.

They’re turning to AI-powered solutions to meet common challenges that are becoming more difficult to handle, like increasing interaction volumes, rising customer expectations and demands, and agent burnout. Innovative – The transformative GenAl functionality that creates value multipliers across the service funnel. These include capabilities like recommending products based on customer preferences, servicing co-pilot, a personal coach for agents that automates call summaries and assessments, Chat GPT and workforce management. Knowledge base search – Enhancing service agents’ capabilities by providing real-time ChatGPT-style access to all relevant information, including product guides, policies and call transcripts. AI interactions are expected to constitute 30%-50% of customer care interactions in the next five years. This will include more voice-to-voice interactions and a consolidation of channels, like IVR, chats, visual assistant and others, into a channel-agnostic AI solution.

Speech analytics and text analytics isn’t new, but they’re taken to a whole new level within an AI call center. Namely, it can be incredibly time-consuming to listen to many calls and evaluate every single one.

By resolving basic requests with artificial intelligence, you speed up workflows and reduce workload for your team. As a result, their time is freed up to focus on handling emotionally complex or sensitive problems. Overall, our data illustrates that AI for customer service is positively contributing to enhanced support across many leading organizations today.

Integrating AI into contact center functions has also paved the way for more personalized customer experiences. AI systems can now tailor interactions based on a customer’s previous history, preferences, and even sentiment, making each interaction more relevant and meaningful, which, in turn, enhances the customer experience. AI (artificial intelligence) has the power to enhance customer connections, streamline support processes, and provide valuable insights from customer data. In this post, we’ll explore the benefits of integrating AI into your contact center, common use cases for AI-based call centers, and future trends to help you stay ahead of the curve. We already specialize in CX, so adding call center AI to our toolset made perfect sense.

Digital Immune System

Businesses looking to stay competitive in the digital age need to look beyond traditional models and embrace the transformative power of intelligent automation in their contact centers. It’s not just an upgrade; it’s a necessity for thriving in the modern, customer-centric market landscape. Many call centers still operate on legacy systems that might not be readily compatible with the latest AI technologies. Upgrading these outdated systems requires significant investment in terms of financial resources, time, and effort. Ensuring seamless integration while maintaining uninterrupted service is crucial to avoid customer frustration and abandonment.

Secondly, AI can significantly decrease the average handling time for customer inquiries, allowing agents to handle more customer interactions in less time. By maximizing agent efficiency with AI, businesses can also reduce average handling time, decrease wait times, and provide an improved customer experience overall. Azam Mirza, president and co-founder of Akorbi, a multilingual digital transformation group, told CMSWire that generative AI has been around for a few years now in the contact center industry. “Initial use cases were around emotion deduction based on tone or voice, speed of conversation, and choice of words used,” said Mirza.

As such, call centers need to bring disparate sources of knowledge content (knowledge base, help center, product documentation, and so on) into a single source of truth. Across what has become a non-linear customer journey, customers expect to find relevant information where and when they need. Through intelligent recommendations and search experiences, conversational AI can create relevance across website entities, self-service portals, and other customer self-service experiences. Salesforce research shows that 61% of customers prefer self-service tools for simple service issues. However, to do that, a business needs a large knowledge base that customers can search through to find a solution.

Integration with existing systems

Such automation goes beyond just reducing call handle times; it empowers agents with the right tools and information, enabling quicker and more informed decision-making. AI-based contact center solutions help organizations deliver exceptional customer service experiences via shorter call handle times, faster first-contact resolutions, and increased agent competency. To learn more about how KMS Lighthouse can help your business become a leader in customer experience, request a demo today. With AI now making it possible to manage more customer interactions ranging from simple FAQs to more complex inquiries, the technology is inching ever closer to mirroring human exchanges. Generative AI models examine conversations to grasp context, produce coherent and contextually fitting replies, and manage customer inquiries and scenarios with greater efficiency.

In a 2015 study by Gartner, it was found that 89% of the companies believed the customer experience shall soon become the foremost basis of competition. Years later, the study is proving to be accurate as most businesses and leading businessmen lay emphasis on the importance of customer service and customer experience. Generative AI can assist in scheduling appointments or bookings, reducing the workload on human agents. Freeing customers from such manual processes is good for both customer autonomy and resource utilization. Begin by launching a focused pilot project or targeting a small area where you can track the impact of your call center AI. This allows for more controlled management and observation, providing a detailed preview of AI’s potential transformative effects on your wider customer service delivery.

Additionally, businesses can take advantage of improved contact center visibility through AI-derived analytics, metrics and KPIs. Learn how they can boost customer satisfaction, improve service efficiency, and drive revenue. AI enhances customer self-service and knowledge management, significantly reducing call volume. Implementing AI knowledge base software allows customers to quickly access accurate information and troubleshoot common issues independently while streamlining knowledge management for your call center team.

Hold up Google Lens to that old Slovak-language cookbook on Grandma’s shelf and right away you have the real-time translation of her famous whipped-cream fruitcake recipe right there on screen. Frontline Care is the easiest and most powerful way to bring AI into contact and call centers, and empower agents to do their best work. Auto dialers are tools that automatically dial customer phone numbers, transferring the call to an agent when the customer answers. This technology maximizes agent productivity by eliminating the need for manual dialing. It requires continuous monitoring and improvement to adapt to changing customer needs and behaviors.

Boomi saw a 300% increase in case deflection during the first three months after implementing Coveo AI within Salesforce. They’ve embraced this technology in their contact center, and it’s paying tremendous dividends. When your agents are in the middle of a service interaction, they don’t have time to read pages of documentation or every detail of a knowledge base article. But, they still need to find the right information to solve your customer’s query.

Unlike rule-based sentiment analysis, NLP-based Sentiment Analysis offers a more nuanced analysis by measuring context. By analyzing context, NLP-based Sentiment Analysis is able to better determine customer sentiment throughout the conversation. With NLP-based Sentiment Analysis, you can understand how customers felt during their call with the agent. In addition, AI contact center software can assess customer emotions, sentiment, satisfaction, and more across multiple communication channels. This provides a more nuanced look into customer satisfaction beyond traditional metrics, helping call centers improve CX and service quality together. By understanding the context and urgency of each call, AI contact center software can route customers to the right agents or departments.

For instance, start by automating straightforward yet high-volume tasks to relieve your reps’ workload. AI customer service is a game-changer, and its benefits ripple across both customers and support teams alike. Chances are your support team has put a ton of work into documentation over the years, and using AI in customer service can unlock the potential of your existing content. If the chatbot can’t offer a solution, it hands the customer over to human support.

When it comes to the human aspect of the contact center, however, a different form of AI is helping to improve the customer service experience. Today, nearly every aspect of a human agent’s contact with customers can be analyzed. 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 see 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. Generative AI in customer service refers to applying artificial intelligence technologies that can generate human-like responses, enabling automated customer interactions.

Large language models may struggle with complex inquiries, leading to inaccurate responses. Additionally, there are concerns surrounding data privacy, security and potential biases in AI training data. To further improve customer experience, emotion AI solutions https://chat.openai.com/ can estimate customer emotions by analyzing visual, textual, and auditory customer signals. This allows customer service reps to be more conscious of customer emotions and for example pay special attention to angry customers with the intent to churn.

• When grounded in your customer data and knowledge base, you can personalize these generated replies, making them more trustworthy.
• Also, contact centers can deploy technology to enable smoother audio quality, even when caller bandwidth is low.
• RPA, or Robotic Process Automation, in the context of a contact center, involves using software robots to automate mundane and repetitive tasks.
• Proactive outbound messaging involves sending automated messages to customers based on specific triggers or events.

The AI system can understand when information is missing, make best-fit decisions based on incomplete information or ambiguous circumstances, and can easily make mid-course adjustments when new information comes to light. When this flexibility is combined with an intuitive drag- &-drop process builder, it becomes easy to define, maintain, and extend complex processes and nuanced decision-making. Watch out for pretenders like rigid scripting and rule-based systems—they tend to put agents and customers in conversation cul-de-sacs and dead ends, especially when the customer goes off-script (which is quite common). Also called virtual customer assistants, chatbots, avatars, virtual agents, or concierges, VAs help businesses wow customers with distinctive self-service, while helping them cut costs and build brand equity. The best VAs are also multilingual and communicate in multiple modes—text-to-text, text-to-speech, speech-to-text, and speech-to-speech.

Sometimes, you’ll combine use case 2 and 3 so that the agent will answer questions, until it reaches a point of action, then it’ll guide the user to the channel of choice for fulfilment. Here, AI can help in reducing wait times and agent workload, effectively filtering out calls that can be resolved through existing self-service options. You can use Topic Analysis to organize calls by topics such as products, competitor mentions, and more. If you are new to implementing AI-based solutions for your contact center, or even if you are a seasoned AI-user, we highly recommend checking out our AI Maturity Model. This model can help you to assess where you are in your AI journey and provide you with recommended next steps to further enhance your AI capabilities.

Moreover, contact centers will use additional predictive signals to detect fraud, given that stolen identity can mean bad actors pass through ID&V in some circumstances. Moreover, ongoing monitoring and auditing of AI systems can help identify and address potential security vulnerabilities as they emerge. These attacks effectively manipulate input data to deceive AI systems, leading to incorrect or unintended outputs. Yet, it’s not all sunshine and roses, as DPD’s GenAI chatbot disaster recently highlighted. For example, a customer contacts a call center with a common question about a product return policy. From a sales perspective, AI can also help sales reps identify potential sales opportunities, handle objections more effectively, and ultimately, close more deals.

AI can assist with contact center analytics, which provides the opportunity to spot trends across large sets of customer data while providing insights on whether or not your customers are angry, happy, or dissatisfied. Supervisors can then adjust their strategies ai use cases in contact center for interfacing with customers and improve their services to deliver a better experience. The better a call center can gather data on each customer interaction, center operations and average handle time, the better the search experience will ultimately be.

Improve replies with AI assistance for support reps

To delve deeper into how generative AI has changed customer service – check out the 20 new use cases below. Such innovation has changed how many contact centers build bots, self-service applications, and proactive campaigns forever. National window replacement franchise Renewal by Andersen gets its most valuable sales conversions over the phone and uses a pay-per-call fee model to send leads to its 90 franchise affiliates. However, the firm lacked an effective way to measure and qualify leads or confirm it was billing the correct fees. Additionally, Renewal’s contact center QA was based on just 2% of phone calls graded manually — a time-consuming system that was prone to error. If you wanted to see what customers were saying about a specific product, you could use Topic Analysis to sort calls that only mention that product.

One of the key features of an effective contact center AI platform is a predictive analytics function, with data centralized in one location. You’re able to increase agent efficiency by giving them information right away and providing predictive insights about customers’ next moves. AI capabilities include helping agents in calls with real-time guidance and support, reducing after-call work, improving call resolution and automatically flagging regulatory, compliance or QA concerns. For example, AI can not only help to identify opportunities for self-service, but it can also flag which customer interactions are priority cases that need human agents’ input to prevent customer dissatisfaction or churn. Virtual assistants and chatbots are at the forefront of automation in contact centers, offering immediate assistance to customers.

Voice and live human interaction still play an important role

This will improve customer call quality over time, help you further refine best practices, and reduce instances of churn and dissatisfaction among callers and customers. What’s more, AI can make detailed customer information and behavioral profiles available to all your agents. This information helps customer service teams anticipate customer needs and quickly adjust their approach to customer retention, upsell and cross-sell, or other specific actions in every customer interaction.

This feature democratizes access to advanced AI technology, enabling businesses of all sizes to benefit from automation. Prediction-based software complements automated scheduling by analyzing past data to forecast future demand, allowing for more efficient and proactive staff allocation. Automated scheduling in contact centers dynamically assigns agents based on demand and skill sets, eliminating the need for manual scheduling. Besides saving time, it also ensures optimal staffing for different times and needs. It allocates resources based on forecasted needs, ensuring that the right number of agents are available at the right times, thus maintaining service levels without incurring unnecessary labor costs. With automation, contact centers can effectively maintain an omnichannel presence, ensuring that they are accessible to customers through their preferred mediums, thereby enhancing customer satisfaction and reach.

Generative AI customer support models integrated into interactive voice response (IVR) systems provide natural and human-like responses to customer queries over the phone. Additionally, quality management analytics can make the quality monitoring process more efficient and accurate for those supervisors that play a role in pulling and evaluating interaction samples. Quality management analytics software analyzes and categorizes interactions, making it much easier to pinpoint the right ones to evaluate. Plus, this analytics solution enables efficient problem solving by allowing supervisors to target the analysis of specific interaction types – for example, short calls. Credit these results to user-centered design and management supported by artificial intelligence. Natural language processing (NLP), a form of AI, has transformed how users interact with IVR systems.

And this figure is only expected to grow, with 83% of executives considering AI a strategic priority for their business. These include capabilities like after call insights, tracking repeat call frequency, service knowledge assistant and generating dynamic knowledge articles. Post-conversation insights – Summarizing speech to text in a few succinct points to create a record of customer complaints, actions taken and recommended steps based on company policies. This could enable solutions like ChatGPT that replace entire value chains, low-cost native AI startups that quickly create new solutions at low costs and simplified onboarding and migration processes between products. With the tools covered in this article, you’ll have a solid foundation to get started with AI as part of your customer service strategy. Ultimately, real-time translation is an essential AI tool, enabling businesses to engage a wider audience, improve accessibility, and eliminate language barriers.

Combined with Gen AI’s ability to understand content with context, these scripts can continually improve with changing customer expectations. Generative AI can assist in providing customer support in multiple languages, eliminating the need for language-specific agents. Translate customer inquiries and generate responses in the customer’s preferred language. Multilingual support is not only convenient but also unlocks the possibility to target a much wider user base. Beyond breaking down language barriers, AI tools are capable of identifying personalized coaching opportunities by evaluating agent performance on various metrics.

As a result, the GenAI application has something to work from – as do live agents during voice interactions –enhancing the contact center’s knowledge management strategy. This enables the service team to prioritize actions to improve contact center journeys. Such actions may include improving agent support content, solving upstream issues, or adding conversational AI. Invoca’s platform now provides automated QA based on 100% of calls and provides instant feedback to agents. Invoca’s Google Ads integration has also helped MoneySolver’s marketing team to track call attribution more efficiently, allowing for better optimization of ads and a 30% increase in return on ad spend (ROAS).

When customers encounter friction, or have a problem, they call support, and those interactions cost money. And with only 13% of CX leaders confident they can take action on CX issues in near real time, contact center efficiency will remain a top priority for the foreseeable future. The ability of these systems to escalate more complex issues to human agents while independently handling routine inquiries exemplifies the synergy between AI and human intervention. By implementing contact center automation, businesses not only elevate customer satisfaction but also position themselves for greater financial success.

Contact center AI refers to the application of artificial intelligence technologies, such as machine learning and generative AI, within a call center. Clearly, the world has gone digital and omnichannel, with omnichannel penetration of commerce at anywhere from 35% (healthcare) to 65% (consumer electronics). It is no secret that the next generation of omnichannel, digital touchpoints like mobile, social, and IoT coexist with traditional ones like the call center, field service, kiosks, branch offices, and retail stores.

Additionally, generative AI models must be fine-tuned using data that is relevant to the task, brand or industry. As generative AI continues to evolve, it promises to play an increasingly important role in shaping the future of various industries and society in general. The technology provides new possibilities for content creation and automation and is paving the way for innovative solutions and enhanced creativity in a variety of fields. Cem’s hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

• Contact center AI can automatically generate a knowledge base article after a support case is closed by pulling from case notes, message history, and data from other service tools.
• As businesses continue to adopt and leverage this advanced technology, the focus remains on delivering exceptional customer experiences through intelligent and empathetic interactions.
• With developments like Google Cloud AI, ChatGPT, Microsoft AI, and IBM Watson, it seems as though AI could be taking over.
• Adding AI to your customer service is no problem when you partner with a BPO company like Unity Communications.

Due to Covid-19, call centers took a hit, as call volume increased and the amount of customer service agents decreased. Incoming calls rose by nearly 300% during the first few months of the pandemic, resulting in a rise of hold times and escalations. For reference, escalations are when calls are sent to human agents or other support representatives.

For example, they tailor the search experience in their app marketplace to make sure visitors can quickly find the best solution for their needs (while increasing the chance of engagement, click-through, and conversion). It can help deflect cases too, as people get AI-powered, personalized help directly in their own workflows. Yet, even with some of the capabilities vendors leverage today, arenas such as reporting, routing, and workforce management seem ripe for GenAI augmentation. When an agent types in a question, it can pop up the answer, so the agent doesn’t have to trawl through articles and documents to find it. Meanwhile, the capability uncovers the characteristics that lead to successful resolutions.

They offer intuitive self-service options, handling routine inquiries and providing instant responses. Generative AI is reshaping the customer service landscape, enabling businesses to provide personalized support, streamline operations, and enhance overall satisfaction. Generative AI offers a myriad of use cases that empower organizations to deliver exceptional customer experiences. All three of those uses are highly relevant to customer service operations, making contact centers a favorite sandbox for testing out the potential of AI.

Automating routine tasks frees up agents to focus on more complex and nuanced customer interactions, thereby adding significant value to the customer experience. This transition leads to tangible improvements in key contactcenter metrics like first-call resolution rates and speed to answer. In essence, contact center automation is not just about reducing the workload on human agents; it’s about revolutionizing contact center processes and elevating the customer experience to new heights.

AI is unlikely to replace call center agents completely but will work alongside and empower them instead. A study from the National Bureau of Economic Research has also shown that the staggered introduction of a generative AI-based conversational assistant increased productivity by 14%. With LLMs, contact center generative AI can create new entries that fit your company’s specific format to ensure everyone has access to the same information. This helps ensure that all important information is always current, relevant, and easily accessible.

Interaction summaries are invaluable for follow-up, training/monitoring, documentation, and knowledge sharing within your business. AI-generated transcripts are a valuable resource for training/onboarding, monitoring performance, and ensuring compliance. Real-time translation is particularly useful for call centers that operate in multiple countries or regions with diverse populations. This empowers agents to increase their productivity while forging stronger customer relationships. By offering 24/7 support, you’ll prove that you care about your customers which is essential for cultivating customer delight and building brand loyalty. Find six Contact Center Use Cases for Conversational AI; see how savvy leaders are investing to save time and money and make their teams more efficient.

One of the great things about AI is that it can collect and analyze large volumes of data in real time. Customer service AI boosts agent efficiency by automating repetitive tasks and reducing workloads. This contributes to increased customer satisfaction and an improved call center CX. That being said, you might be wondering if AI-powered automation really lives up to the hype – and whether it’s the best fit for your contact center. Thanks to these emerging call center technologies, businesses can maximize productivity, streamline operations, and engage more consumers than ever before.

This is done through conversational AI that can translate speech into text through a speech analytics solution. One of the best ways that AI is used to improve customer service is through agent assistance. AI can be used to supplement the productivity of call center agents through real-time assistance. This is done through automatic suggested responses, real-time customer sentiment analysis of customer mood, and more. Virtual agents powered by AI are revolutionizing the way customers interact with contact centers.

Your agents get more done with less busy work and your customers get a quick and easy resolution to their problems while having a personalized experience. Contact center AI can automatically generate a knowledge base article after a support case is closed by pulling from case notes, message history, and data from other service tools. From there, your agent just needs to review the article to ensure accuracy and add it to the queue for approval.

The AI system could respond by expressing gratitude for their positive feedback and reinforcing your commitment to maintaining this efficiency level. Powered by generative AI, it summarizes the topics discussed during an interaction, saving valuable time and providing crucial information follow-up conversations with the same customer. From there, you’ll need to build a process for capturing and reusing knowledge, both agent-facing and customer-facing sides of support.

In the meantime, the AI is using the data from the message thread and the actions that Katie took in Jane’s account to generate a case summary. The hottest topic in service today is generative AI, especially AI in the contact center. The contact center is where customers can turn to for questions or help – by chat, email, or self-service portal so they can take matters into their own hands. Adding AI to your customer service is no problem when you partner with a BPO company like Unity Communications. Over a million customers called in before the AI rollout, most of whom bypassed the IVR system to speak with real people about their health plan’s benefits and eligibility. That new LLM feature may further enhance automated customer replies by ensuring they align with the brand’s tone of voice.