IBM AI in Customer Experience CX

Moreover, the company enhanced agent productivity with 100+ work hours saved monthly by AI conversation summarization and a 20 percent reduction in operational costs. Running a conversational intelligence initiative, NTT helped the client pinpoint areas within the call flow where agents could make “save attempts”. With advancements in Large Language Models (LLMs) and Retrieval Augmented Generation (RAG), these use cases can understand and respond to natural language with information and knowledge that is custom or specific to an industry or domain. Additionally, conversation intelligence can detect subtle cues, emotional shifts, and patterns that indicate potential concerns or desires of customers in a single interaction. For example, by detecting a happy customer during an interaction, agents can be alerted in real-time to potential up-sell opportunities – driving loyalty, retention, and lifetime value. Inversely, a frustrated customer could be quickly routed to a supervisor, reducing churn.

Yet, perhaps most fascinating is how contact centers can look at correlations between sentiment and NPS (Net Promoter Score) as well as correlations with different customer outcomes. Yet, the tool also showcases which agents typically perform best across specific intents. However, thanks to agents manually logging these intents, without sufficient time or coaching, those strategies often fail to meet expectations.

See how genAI impacts how organizations design and implement experiences for their users. That may mean taking on some of those less prominent queries or diving deeper into the world of AI to automate the more complex queries, too. After all, as Gartner highlighted, organizational resistance is a primary challenge for organizations when implementing conversational AI. The first phase of any conversational AI project is securing buy-in from critical business stakeholders. For instance, businesses could already auto-summarize contacts with NLP models that vendors spent hundreds of hours engineering. Alternatively, an enterprise may want to build its own Voice of the Customer tool, intent engine, or something else.

Additionally, multimodal solutions will allow companies to create bots and virtual agents that are more intuitive, creative, and dynamic. Enabling these improvements is no small feat for enterprises, though, says senior product marketing manager at NICE, Michele Carlson. With large data streams and the demand for personalized experiences, artificial intelligence has become the key enabler in fostering these better customer experiences. But improving the quality of customer experiences means becoming more customer-centric and data-driven and scaling available human representatives for round-the-clock assistance.

The Role of AI in Customer Support: A New Era of Efficiency

Through its partnership with OpenAI, this company has embedded cutting-edge AI capabilities into platforms like Azure, Microsoft 365, and GitHub. Microsoft Copilot, its AI assistant, helps users with coding and content creation by bringing smart, context-aware suggestions. Microsoft’s widespread implementation and continuous expansion of generative AI functionalities position it at the forefront of AI innovation. Netflix relies on generative AI to enhance user engagement by creating personalized content previews and thumbnails tailored to individual viewing preferences. This technology analyzes user data, including past viewing habits and ratings, to make visuals that highlight aspects of the shows or movies predicted to resonate with certain viewers.

In trawling these, GenAI automates a relevant customer response, which the agent can evaluate, edit, and forward to customers. Additionally, these advanced capabilities reinforce Tesla’s reputation for innovation and reliability, as they continue to set a high standard in the automotive industry by integrating cutting-edge technology into their vehicles. “I know a lot of people get a little concerned about, ‘Should I be using AI? Is it going to freak out my agents?’ … But we’re seeing it’s a benefit. It improves longevity. It gives them answers they need,” Gareiss said in the webinar. ZestyAI claims that Z-FIRE can derive risk scores at various levels for an address using the insurer’s underwriting criteria.

Bottom Line: Embrace Generative AI in the Contact Center to Elevate Service Quality

You can foun additiona information about ai customer service and artificial intelligence and NLP. Indeed, only software development and marketing teams have experienced greater GenAI investment than customer service – according to Gartner research. Global businesses are pumping funds into generative AI (GenAI) use cases for customer service. For over two decades CMSWire, produced by Simpler Media Group, has been the world’s leading community of digital customer experience professionals.

Instead, it’s better to map customer intent – as new auto-summarization tools enable businesses to do – and quantify the most common transactional queries to tackle. In many cases, that excitement has created pressure on service leaders to deliver such solutions. Like all the other capabilities, contact centers can supplement this with ElevateAI Explore, a built-in reporting platform that provides data visualizations of the suite’s business impact. Users can access Explore without leaving the ElevateAI suite, removing the need to port data into a third-party BI tool. Trained on NICE’s anonymized set of over a billion contact center data points, the APIs democratize access to the latest AI innovations. As such, they can do everything from summarizing conversations to coaching agents (and much more).

But a lot of contact center functions are siloed or controlled by other departments with different priorities, according to Eric Buesing, partner at McKinsey & Company. Customer centricity, as its name implies, focuses on understanding customer needs and creating a positive contact center experience. Enterprises are discovering that modern contact centers can best fulfill this objective with the aid of sound business goals, advanced technologies and effective agent training techniques. Any Voice AI system you implement should be able to easily transfer a customer to a live agent, when necessary, to preserve the customer experience.

So you want to make sure it has as much data, and relevant data, for your use case as possible. So for supervisors, they can get a notification about which calls are in need of escalation, and where they can best support their agent. And it’s difficult for supervisors to keep a pulse on, who is on which interaction with what customer? As much as we try with modern technology to do many things, we can only do one really well at once. Another example supervisors are also burdened with; they usually have a large team of somewhere up to 20, sometimes 25 different agents who all have calls going at the same time.

In addition, global organizations with customers all over the world can cater to the needs of their customers, irrespective of the time zone. Implementing generative AI in contact centers leads to substantial cost savings by decreasing the reliance on live agents for every customer inquiry. GenAI systems can automate tasks and supercharge self-service options, decreasing staffing needs and operational costs without compromising service quality. Technically, this works, and agents and customers can engage in phone conversations while speaking different languages. After years of call and contact monitoring and CSAT/sentiment analysis, experienced team leaders and quality analysts understand what an excellent customer conversation looks like.

KEY TAKEAWAYS

When implemented together, AI agents can give customers seamless experiences that are just as contextual and flexible as human interactions, yet faster and more consistent. Call center automation systems complete repetitive, and possibly time-consuming, tasks without human intervention so agents can turn their attention to more important actions like solving a complex customer issue. Technologies such as voice AI, ACD and robotic process automation typically lower contact center costs and help improve customer experience by providing prompt, seamless service. AI is a powerful tool for the contact center, but it can’t completely eliminate the need for human agents – at least not yet. Voice AI in the contact center can accelerate response times, improve customer service, and automate repetitive tasks. However, some consumers will still want personalized, humanized interactions with live agents.

Around 71% of buyers say they want personalized interactions with contact centers, and 66% are frustrated when brands don’t deliver. Voice interactions are about a hundred times more expensive than a web or chatbot interaction. So by building effective chatbots, by building effective IVAs, they are also, in turn, improving their overall cost goals for their organization. Once the call is over, artificial intelligence can do things like summarize the interaction. And it is difficult to then decipher that, and their next call is going to be coming in very quickly.

As a result, Altshuler Shaham recorded a 760 percent growth in new customers and a 540 percent increase in incoming leads. “This level of insight will enable you to make informed decisions on changes in your business which can reduce contact volumes being presented to your human workforce,” added Budding. Almost half of the contact center agents don’t have the skills they need to deal with the current demands of their job – according to Calabrio’s recent State of the Contact Centre report. So, let’s roll up our sleeves and dive into how AI in the contact center is flipping the script, turning those frustrating experiences into moments of delight.

To ensure generative AI is used safely in the contact center, government regulators and the tech industry will need to work together to implement comprehensive frameworks. “The EU and US have shown themselves keen to collaborate with one other and with industry to develop standards further. A formal global framework would be much harder to achieve, of course, with universal agreement on every detail unrealistic. However, there is enough information available for organizations to begin adapting their approach to fall in line with potential changes in regulation.

LLMs employ natural language processing capabilities that let the contact center software understand the various nuances of written and verbal communication. This capability makes conversational AI a good fit to bolster the customer service engagement and service fulfillment process without increasing staffing levels. The ability of conversational AI to analyze, retrieve, predict and pass on information in multiple written or spoken formats helps take the customer contact center experience to a more efficient level with little Opex overhead. Through facilitating AI-powered self-service options, giving agents instant access to relevant information, and enabling round-the-clock support, generative AI provides customers with quick answers to their questions.

Nevertheless, transferring that knowledge into specific, measurable, and fair quality assurance (QA) scorecard criteria is easier said than done, not to mention time-consuming. That makes it easier for future agents – handling follow-ups – to get to grips with what happened on the previous call. Google Cloud’s Generative FAQ for CCAI Insights allows contact centers to upload redacted transcripts to unlock this capability. The tool may also generate conversation highlights, summaries, and a customer satisfaction score to store in the CRM.

By using Google’s LLM Summarization tool instead, companies are able to bring the time down to near zero. By proactively providing suggestions to agents during the conversation, the agent doesn’t even need to search – saving the agent time and improving the customer’s experience. By using voice biometrics, customer support systems can quickly and securely verify a customer’s identity, speeding up the support process and enhancing security. This is particularly useful in sectors such as banking and finance, where secure and swift customer verification is crucial.

In this shifting and uncertain business environment, companies refocused their attention on not only gaining new customers but also reconnecting with existing customers in the most convenient and cost-effective way possible. Business strategies dictated upgrading and modernizing the contact centers we see today. “Conversational analytics will be something customers can benefit from,” Cleveland said. “But [contact centers] must scrub existing data to make sure the data is accurate and up to date. Otherwise, agents could be handing out bad information.” That process involves overlaying the model with data and insight from the CRM, customer conversations, and various knowledge sources.

As consumers, we’ve all experienced a scenario when an AI-powered chatbot fails to meet our expectations, and we want to contact a live agent. Indeed, Altshuler Shaham integrated CommBox AI into all chat functions, which enabled 24/7 instant support for customers. By embedding coaching in these “save attempts”, NTT’s client experienced an eight percent improvement in their customer retention rate that sustained for over ten months. These insights can drive action, such as identifying agent behaviors to improve coaching.

By automatically producing these personalized previews, Netflix not only increases the likelihood of users clicking the suggested content, but also elevates the overall platform experience. By scanning financial reports, news, and other relevant data sources, generative AI can spot trends, collect competitive intelligence, and produce insights for customer behaviors. As a result, financial analysts can stay ahead of the market shifts and competitor strategies. GenAI can also customize these insights based on specific markets, regions, or customer personas, promoting more targeted strategies and forecasting. For the finance sector, generative AI technologies support decision-making and bolster security through automating complex processes. GenAI use cases in this field include gathering market insights, making budget predictions, and detecting fraud to safeguard financial operations.

Look for tools that can analyze customer sentiment and intent, and determine when to transfer a call. According to EU rules, companies will need to disclose which content is created by generative AI, publish summaries of data used for training, and design models to ensure they don’t generate unsafe or dangerous content. US guidelines also require companies to leverage tools to detect AI-generated content, deepfakes, and other solutions used for fraud. They can handle an unlimited number of conversations simultaneously, and can even leverage advanced analytical tools and data to personalize interactions.

Large Dutch financial institutions, particularly in insurance, pensions, and mortgages, often grapple with the operational challenge of manual logging of queries in their call centers. This not only varies the quality of the legally required call summaries but also increases costs. It leverages strategy documents, brand guidelines, and other assets to build customer questionnaires for review in seconds. The Customers’ Choice conversational AI vendor – as per a 2023 Gartner report – defines an “assertion” as the conditions a bot must meet to pass a test. By pairing this with the Cognigy Playbooks reporting platform, service teams can verify bot flows, validate outputs, and add assertions. Another advantage of these auto-generated articles is that they’re in the same format, allowing agents to quickly comprehend and action them.

Five9 Genius AI is a four-step process for implementing AI, centering on the Five9 data lake. Customers can action that process – as outlined below – by leveraging elements of the Genius Suite, which centralizes the vendor’s AI solutions. Here’s where most businesses go wrong with their strategies, and how you can boost your chances of success. Artificial intelligence, particularly generative AI and large language models is evolving too quickly for a classical top-down regulatory approach to be effective. When generative AI was first introduced, we only had a few existing rules surrounding data protection in the contact center, like GDPR. In the contact center, many of the concerns regulators have about how AI might be used may not come into play.

Virtual Agents Support Employees, In Addition to Customers

Because of this, they are more likely to become trusted advisors for AI investments, as they have experience implementing AI strategies for themselves and their clients. Organizations wondering how to embrace AI should look no further than business process outsourcers. Verizon is the second-largest telecommunications company by revenue and the largest by market capitalization. The company is also the largest wireless provider in the United States with a reported 143 million subscriptions. Per the Zesty-published case report, the company needed more scientific tools to evaluate wildfire risk accurately.

They should also factor in each agent’s skills so the contact is routed to the agent best equipped to handle a call, email, chat or other message and provide more personalized support. Contact center software can assess the likely purpose of a customer request based on data, such as the customer’s purchasing and contact history, by automatically parsing initial messages. Even if agents speak a variety of languages, customers might not be able to interact effectively if the contact center software doesn’t accommodate the language of the customer. By automatically placing inbound calls in virtual queues until live agents can take them, call queuing reduces the risk of dropped calls during periods of high call volume. Without software-based call queuing, the contact center would ask customers to leave a message, call back later or task agents with queueing calls manually, increasing the burden on agents already handling substantial volumes of calls.

By determining whether a customer is frustrated, satisfied, or neutral, GenAI helps companies prioritize important issues, making sure that urgent cases are handled swiftly. Sentiment analysis extends to social media monitoring, where generative AI systems can detect shifts in customer sentiment and allow organizations respond proactively to emerging issues. Today’s customer service agents face increasing pressure to deliver expert support across multiple channels, at speed. For instance, chatbots can handle simple requests, and automate processes for employees, like scripting or call transcription, allowing employees to focus on more valuable tasks.

Experts agree one doesn’t want to just throw jobs in the hands of AI without human beings monitoring and approving the work. Brands can use Sprinklr AI to identify leading contact drivers and create bot responses that are accurate, human, and helpful. GenAI has enormous ChatGPT App potential to boost efficiency and elevate CX when applied correctly. But, if misapplied or non-integrated, it can become a costly distraction rather than a driver of real value. Additionally, focus on game-changers like knowledge retrieval or automatic summarization.

In addition to providing satellite imagery and merging that with third-party data, it helped Metlife understand what happened at that property over time, allowing for synthesizing that into a score. The case report also mentions that the tools Metlife used before Z-FIRE only had a broad-brush approach to wildfire, but with ZestyAI, the company got in-depth details and insights. MetLife expected that implementing an AI-enabled system would help skilled examiners focus on complex cases to increase accuracy and efficiency.

The conversation intelligence tool parses a company’s existing conversational data and outline the keywords, phrases, topics, and questions. That information then many inform various workforce ChatGPT engagement management initiatives and enhance reporting. In his talk, Misra revealed that some agents spend 90 seconds at the end of every customer conversation completing the summary process.

• When a customer gets the right answer on the first contact, and it is delivered quickly and accurately, they will be pleased, and the agent will benefit from the positive interaction.
• For many contact centers, however, adopting this game-changing technology has been difficult due to the complexities of integrating AI with on-premises infrastructure.
• Per the Zesty-published case report, the company needed more scientific tools to evaluate wildfire risk accurately.
• So if you or me, if we were to call into a contact center, they would know where our journey has gone.

According to Opus Research, 49 percent of organizations say using a conversational intelligence solution has helped them support customer satisfaction. Imagine giving each of your agents a genius sidekick who whispers ai use cases in contact center the right answers in their ear. That’s essentially what AI in customer service, specifically AI-powered agent assist, does. The most effective routing capabilities are based on more than just agent availability.