ai use cases in contact center 4

The Development of Sentiment Analysis: How AI is Shaping Modern Contact Centers

AI Agent Assist Trends for the Contact Center

It can reduce operational costs, allowing agents to automate various tasks, and even provide insights into customer preferences and sentiment. The shift to increasing use of digital agents in CX is not about replacing humans—it’s about using AI to enhance and smooth interactions. AI can take care of simple queries and automatically provide information or make bookings – freeing up humans to deliver empathy and make personal connections with customers  on more complex, nuanced tasks. By leveraging voice AI, companies can transform routine exchanges into valuable experiences, driving both customer satisfaction and loyalty.

The innovation also inspires cooperation between quality assurance and coaching teams, who can create a connected learning strategy to bolster agent performance. 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.

The Collaborative Customer Experience Platform by Zingly.ai

These agents also are increasingly serving as extensions of a company’s salesforce, which is seen as another way to help contact centers become profit centers. “An increasing number of companies are not implementing AI for AI’s sake,” Lazar reported. Thankfully, new natural language processing (NLP) and generative AI (GenAI) models can spotlight knowledge improvement opportunities and even draft new knowledge articles for review and publication.

Adopting AI is not about outpacing the competition, it’s about meeting the growing expectations of the customers of today and tomorrow. Conversational IVR systems can interact with callers in a natural format, responding to their spoken queries instantly, and helping to guide them towards the right solutions. Contact center virtual assistants can identify when conversations are beginning to go downhill, identifying negative customer sentiment or specific keywords in real time. Since AI-powered tools can more accurately identify customer intent and sentiment, they can also streamline the routing process.

That’s why many vendors offering AI agent assist tools, like Five9, Nice, and Talkdesk, are building automated compliance and security solutions into their technologies. For instance, Talkdesk Guardian tracks the words spoken in a conversation, creates a baseline of secure user behaviors, and flags unexpected events for supervisors and managers. One of the biggest benefits of leveraging AI agent assist tools in the contact center is that they empower staff members to deliver more personalized experiences to every customer. More than just simply surfacing a customer’s name or account information at the start of a discussion, the right tools can provide unique insights into a customer’s journey, previous purchases, and other data. AI agent assist software is emerging as a vital resource for today’s customer-focused teams.

QA Automation – How Far Can We Push AI?

Because they leverage speech-to-text to create a transcript from the customer’s audio. It then passes through a translation engine to pass a written text translation through to the agent desktop. Some may even share insight on how that sentiment has changed over time so contact centers can decipher – across intents – what is driving positive or negative emotions.

20 Contact Center AI Use Cases to Transform Agent and Customer Experiences – CX Today

20 Contact Center AI Use Cases to Transform Agent and Customer Experiences.

Posted: Fri, 24 May 2024 07:00:00 GMT [source]

Although conversational AI tools are more advanced than traditional chatbots, they can still struggle with complex linguistic nuances and requests. By analyzing previous discussions and real-time sentiment or intent, conversational AI can help ensure every customer gets a bespoke experience with your contact center. Conversational AI has become the backbone of many advances in the customer experience and contact center landscapes. It forms part of the tech behind conversational intelligence tools, such as those offered by CallMiner, Calabrio, and Talkdesk. Also, it’s essential to support agents in using the software effectively, ultimately improving their interactions with customers. “Some users I talk to find chatbots infuriating and will hang up on a call when they sense their questions can’t be answered,” Gold noted.

LoCascio said that AI-powered call centers enabled brands with no set infrastructure in place to immediately begin to improve the customer experience even as more and more lockdowns shuttered businesses. 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. A large language model is a probabilistic model of natural language trained on massive amounts of data that can understand and generate phrases based on the text it was trained on.

GenAI in Marketing

The company claims that the implementation of this tech will allow users to deploy AI agents to handle more complicated tasks without the need for human monitoring or intervention. The newly named Talkdesk Ascend AI platform will use “agentic AI” to power all of the vendor’s major AI solutions, including Talkdesk Autopilot, Talkdesk Copilot, and Talkdesk CX Analytics. With AI’s ability to process vast amounts of data quickly, there’s an increased risk of sensitive customer information being mishandled. “Businesses who don’t do analytics in-house can significantly benefit from hiring a team of experts to do analytics for them, then present quality data back to them regularly,” Creasey notes.

As a result, resource planners increase their schedule efficiency while agents gain greater schedule autonomy. A win-win that – according to Verint – will “revolutionize” contact center scheduling. That is either positive or negative, depending on whether that impact is beneficial or detrimental to core workforce management (WFM) metrics. Since then, those vendors have rearchitected their solutions so that contact centers can plug in different LLMs to power GenAI use cases and optimize their outcomes.

He notes that most chatbots are operating on fixed algorithms with clearly defined parameters, and not on AI – which is possible, but not currently widely done. Automation is widely used in UC – whether it’s automatic call transcription in a call center or chatbot integration on webpages. McDonald asserts that many technological features that are referred to as AI are automation, and the two terms are being used interchangeably to “jump on the bandwagon”. This further complicates compliance for companies operating globally, since it becomes more difficult to adhere to a uniform standard of data protection and ethical AI use. “By telling your BI tool which KPI you want to look at, you immediately get automatic visualisations of the data and relevant insights, making data-driven decisions easier than ever,” Creasey says.

Security and compliance are critical in the contact center, and AI gives business leaders the guidance they need to protect their teams, customers, and data. It can increase visibility into potential security risks, optimize authentication methods with biometrics, and automatically alert business leaders when it detects suspicious activity. There are various ways contact centers can connect generative AI and conversational AI.

Now that customers have access to more self-service solutions than ever before, by the time they reach an agent, they expect fast, insightful, and convenient support. Contact center virtual assistants in WFM systems can rapidly assess situations as they occur and recommend intraday management practices to boost team efficiency. They can suggest how to distribute resources between teams and contact center channels. Or they can switch up scheduling strategies in real-time to minimize the risks of understaffing or overstaffing.

Finally, NICE has been developing its AI technology so human agents can become overseers of bots, monitoring bot-led interactions and training bots to perform better. By removing many administrative tasks and simplifying knowledge access, agents can allocate more of their headspace to providing empathetic, emotionally intelligent customer service. Yet, with the rise of generative AI (GenAI) and virtual assistants – like Copilot – agent assist has become a central area of contact center AI investment. According to 8×8, the CX capabilities will allow business leaders to bolster experiences and engagement across organisations for customers and employees.

Agent assist tools with predictive analytics capabilities can also determine when an agent should follow up with a customer or proactively reach out with a new offer or deal to increase sales. Plus, their ability to collect data from every interaction means agent assist tools can give businesses valuable insights into new ways of personalizing and enhancing customer service. Advancements in NLP algorithms, drawing on deep learning capabilities, and pre-trained language models, will make NLP systems even more effective at understanding nuances in customer voice and language. These solutions will pave the way for more advanced speech analytics processes, allowing companies to access insights into customer sentiment and emotion throughout the customer journey. They use advanced AI technology to elevate call center interactions by providing a sophisticated analysis of voice tones.

Automating Post-Call Processing

Even if your AI bots have the capacity to improve automatically over time, with machine learning, you still need to ensure you’re actively reviewing their performance and looking for opportunities to improve. You can even use voice bots to enhance the employee experience, and boost productivity. For instance, “Agent Assist” tools can monitor conversations and send real-time guidance and directions to your employees and supervisors, boosting workplace efficiency. These cloud-native platforms – like the Zoom Contact Center – include low-/no-code interfaces that allow businesses to compose new and improved contact center experiences for customers and agents.

In fact, many businesses are discovering that a combination of on premises and as a service is producing more than satisfactory results. Many companies using AI agent assist tools today already use these solutions to automate various repetitive tasks for team members. Powerful AI solutions can immediately record and transcribe conversations and extract data from discussions, ensuring agents can focus more on delighting their customers.

Generative AI in Customer Experience: The 11 Most Implemented Use Cases

With these use cases, Grubb has seen big advancements in AI for contact centers in recent months. The trend will continue throughout 2025, improving both customer experience and the government agent work environment, he says. These capabilities empower sales teams to work smarter, not harder, resulting in increased revenue and improved customer relationships. Some GenAI applications can assess a conversation, summarize it, and then send it to the CRM.

Consider the additions to Copilot, which supports agents and supervisors on the platform. To start, Microsoft added Copilot-generated notes, which assist human agents in capturing the key facts of an issue. For instance, Verint provides three “bots” that the business can chain together to create a comparable continuous learning cycle. Customer service teams – including both human and AI Agents – may then leverage the knowledge articles to autonomously solve customer issues.

AI is reshaping the contact center landscape – transforming a largely reactive operation into a proactive, intelligent, and personalized customer experience. AI will continue to improve efficiency, empower human agents and pre-empt customers’ needs before they identify them. Here we explore the key benefits of AI in the contact center and how its role is set to set new standards for CX in 2025.

Advances in generative AI and conversational AI algorithms also pave the way for a more comprehensive level of agent support across multiple channels. Today’s agents need to be able to provide consistent, immersive experiences across chat, email, social media, voice, and video. 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. AI agents can book hotels and other stays, flights, and process service and support requests.

Customers are evolving, their needs are changing, and self-service is critical to the next generation of consumers. Because, unlike traditional technologies, AI tools require continuous evaluation and improvement. Understanding intent data holistically enables contact center teams to uncover opportunities for significant improvement.

Personalized service has long been the North Star for not just contact centers but for everything CX. This can help supervisors ensure they constantly recognize and reward high performers and offer the right assistance to agents with issues. Thankfully, with this process automated, agents have one less task to complete across every contact. Moreover, the contact center can more accurately track customer intent and implement better customer contact strategies. A contact center virtual assistant can handle these tasks automatically, populating forms with information given by a customer and even completing tasks like scheduling an engineer to visit a client’s home. In the past, companies that wanted to scale into new geographies would have to spend a fortune hiring multilingual customer service team members.

AI tools can pinpoint the ideal agent for an interaction based on agent expertise, availability, query complexity, and even customer value. What’s more, they can distribute conversations across a range of channels, empowering omnichannel service. Smart conversational assistants can analyze inbound ticket information and assign issues to specialized generative models to help with customer service. Conversational bots can even draw insights from FAQs and knowledge bases created by generative AI during discussions.

Moreover, many vendors have vastly expanded their agent-assist capabilities to meet this demand. 8×8 has introduced its latest artificial intelligence and customer engagement features to its cloud CX platform. 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.

• Here are three use cases of AI in customer experience that can transform how businesses interact with customers.
• Keyword-based sentiment analysis (commonly referred to as “rule-based”) scans transcripts for specific keywords from a predefined list of “positive” and “negative” terms.
• Such actions may include improving agent support content, solving upstream issues, or adding conversational AI.

For instance, a customer saying “great” may be worth a score of +5, while a customer cursing would be -10. Sentiment analysis is a tool that uses natural language processing (NLP) to analyze calls and transcriptions to understand how the callers are feeling, how agents performed, and if the call was resolved properly. With that in mind, let’s take a closer look at sentiment analysis, the role large language models (LLMs) play in improving sentiment analysis tools, and how companies like MiaRec are changing how we look at calls. Some solutions can even generate content in various formats, such as graphs or visuals that agents can share with customers during sales or support calls. Any AI solution you implement into your customer support strategy should be intuitive and user-friendly.

More than just an effective solution for automating common tasks, like performance monitoring and quality scoring, these tools augment and empower agents on a massive scale. Perhaps one of the biggest use cases for AI in customer support, is that it allows companies to offer 24/7 assistance to customers on a range of channels. AI chatbots, for instance, are available to answer questions and deliver self-service resources to customers around the clock.

Indeed, their intelligent contact center platforms could scour that automated QA data to uncover which agents best handle specific queries. These models analyze contact center data to predict which agent is most likely to deliver a particular outcome – such as a high CSAT score – for the specific customer reaching out. Indeed, recent Gartner research found that 61 percent of customer support leaders expect generative AI (GenAI) to reduce their agent headcount by less than five percent. Only last week, CX Today reported that 12 of the top 20 customer service BPOs are modifying their agents’ voices with AI, so reps from around the globe sound as if they are speaking with non-accented American English.

5 Disruptive Use Cases for AI in Customer Experience – CX Today

5 Disruptive Use Cases for AI in Customer Experience.

Posted: Tue, 21 Jan 2025 16:13:02 GMT [source]

With this, a QA leader can input simple prompts as to what a top-notch customer-agent interaction looks like on a specific channel. Generative AI solutions can now automate this process, shaving seconds from every contact center conversation and – therefore – saving the service operation significant resources. When a service agent ends a customer interaction, they must complete post-call processing.