How Agentic AI is Empowering Human Agents and Redefining Service Standards

Agent Assist: Use Cases, Benefits, & Providers

Such actions can help agents achieve their goals and targets faster, and increase sales and revenue for companies, too. However, taking notes throughout every customer conversation can drag an agent’s attention away from what matters, the discussion at hand. However, AI can augment these agents in a range of ways, streamlining tasks and boosting productivity on a massive scale. As such, the success of any customer retention project still hinges on human professionals who can creatively solve problems, empathize with customers, and build relationships. 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.

Yet, when it comes to chatbots, many organizations deploy them straight out of the box, making them sound like every other brand using that platform. Yet, the traditional buyers of CCaaS solutions are often contact center leaders who may not have strong existing ties to Microsoft. Either way, the AI Agent may interact with a customer and – if connected to the correct systems – solve many of their problems.

Moreover, the strategy will stop organizations from over-engineering solutions without fully understanding the problem. Investing in these tools also uncovers insights that can improve contact center operations. After multiple visits and a known external issue, the provider could have bypassed the initial troubleshooting steps and escalated the case directly to Level 2 or Level 3 support. However, every time the customer called for support, they entered an AI-led flow that forced them to go through the same troubleshooting steps despite the technicians knowing the issue wasn’t with their equipment. Sometimes, a business can prevent the issue before it occurs and remove the need for customer contact altogether. Also, bring more people into the fold, particularly the marketing team, who are the business’s brand voice guardians.

Grubb says that chatbots as the most prominent use case for contact center AI to date. They can incorporate a customer’s vocabulary, and they can become self-learning,” he says. Grubb, who manages the CDW presales team for contact centers, explains that a technician cannot start chatting with customers about generative artificial intelligence and expect a successful experience. The customer may have the look of a deer in headlights and not fully comprehend the value of the conversation. B2B sales teams especially like to leverage GenAI for lead-gen activities such as building the business’s ideal customer profile. They may then use the tech to determine which companies match it based on location, news, market trends, etc.

Automating Lead Generation Initiatives (42.3 percent)

The ability to rapidly capture customer intent can help with everything from tracking a customer’s interaction history to building more granular contact center forecasts. To achieve a seamless contact center environment, government agencies must support omnichannel communications to ease the experience of citizens interacting with state and local agencies. Now, we sell use cases,” says Rocky Grubb, a CDW solution architect team lead for the collaboration practice.

Emerging Best Practices for Using Generative AI In Your Banking Contact Center – The Financial Brand

Emerging Best Practices for Using Generative AI In Your Banking Contact Center.

Posted: Fri, 06 Sep 2024 07:00:00 GMT [source]

If a contact center can continuously feed such a solution with knowledge sources, contact centers can continually monitor customer complaints and act fast to foil emerging issues. From there, Sprinklr customers may harness the provider’s omnichannel capabilities to distribute these surveys, converge the data, and – again, using GenAI – analyze the feedback. By pairing this with the Cognigy Playbooks reporting platform, service teams can verify bot flows, validate outputs, and add assertions. That will impact many aspects of customer service, and chatbot development offers an excellent early example. Alongside spotting gaps in the knowledge base (as above), some GenAI solutions can create new articles to plug them.

Proficiency-Based Routing by Amazon Connect

These instructions can pop up automatically during an interaction to guide an agent on how to help a customer, enhancing case management. For agents with dyslexia or dyspraxia, this is an especially helpful aid as they can confidently correspond with customers, clients, and fellow employees. As a result, it’s not only easier to respond quickly to queries but also makes the process far less stressful, as people don’t have to spend time reading pages upon pages of company documents to find the right solution. For instance, a virtual assistant can help summarize company information quickly in an easy-to-understand, clear, and concise way. Below, each industry expert shares their favorite agent-assist use case before highlighting several benefits of deploying the technology.

These tools even help to reduce errors in the contact center, reducing time spent on resolving mistakes. AI’s ability to deliver these benefits relies on its access and capacity to continuously ingest and learn from vast data sets. With the risks of inadvertently mishandling sensitive customer data, it’s important for businesses to find balance in their pursuit of AI-driven advantages.

They are also less likely to need to hire someone to take care of these day-to-day tasks for them. From there, the virtual agent will request a recall and send a replacement, utilizing workflow automation on the back end. Furthermore, they can prepare contact centers for the future of contact handling – where they tactfully leverage conversational and other forms of AI alongside various channels and modalities. Yet, a decade after the first CCaaS solutions went mainstream, not much has changed within the service experience.

This gives contact center reps more time to focus on answering customer calls that need more attention and focus wholeheartedly on addressing customer needs. These tools can even integrate with CRM systemsto offer personalized insights into a customer’s purchasing history or previous conversations. In the standard contact center, employees often waste significant time searching for relevant data to solve a customer’s problems. An AI-powered assistant can boost agent productivity, surfacing information from databases and other applications, based on identified keywords.

Whether through Intelligent Virtual Agents (IVAs), agent assist, workflow automation, or other forms of AI, targeted implementations guided by that analysis work from step two will help drive success. But, with agents dealing with difficult situations more frequently, it also creates a need for them to show more empathy and creativity, which can drain their energy. Instead of searching for information and struggling to figure out how to best proceed with an interaction, agents have the necessary information at their fingertips in real time. With these changes, agents become brand ambassadors who are critical to a positive, and therefore successful, customer experience.

At that point, the manager could then join the call and listen in or interrupt the call, if needed. While this has been the norm for decades, it’s obviously highly random and error-prone. Microsoft delivers CCaaS as a solution that offers seamless integration with its widely adopted tools like Teams and Dynamics 365 Customer Service. Nevertheless, Microsoft may demonstrate its customer service innovation streak by releasing these AI Agents so early into its contact center journey. Loading these auto-summarizations into the CRM post-contact has proven helpful in tracking customers’ case history, lowering handling times, and saving costs.

Conversational AI vs Generative AI: Which is Best for CX?

“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.” Some CX providers have developed GenAI-driven solutions that evaluate successful customer conversation transcripts – specific to one query – to define the optimal troubleshooting steps.

AI’s Evolving Role in the Contact Center – CX Today

AI’s Evolving Role in the Contact Center.

Posted: Tue, 21 Jan 2025 15:02:33 GMT [source]

The contact center virtual assistant has evolved, supporting customer service teams in various new ways. Around 71% of customers now expect a personalized experience from every contact center. It can share data on past interactions, preferences, and behaviors with an agent during a conversation, draw data from CRM systems during self-service discussions, and even deliver personalized product recommendations.

Improving Workforce Management

With AI, the frustrations of old-fashioned clunky IVR systems are a thing of the past. AI-powered IVR systems can understand caller requests, analyze their needs, and automatically route them to the right agent or department in seconds. With AI-powered IVRs, customers can express their needs more clearly and even complete simple tasks without speaking to an agent. With NLP (Natural Language Processing) models or generative AI, contact centers can automatically identify a person’s reason for contacting their team.

These deployments highlight how marketing teams are utilizing GenAI to automate much of the underbelly of their operations. Yet, here some brands are also using tools, like Adobe Firefly, for enhanced imagery. Soon, GenAI video models will come, too, with Adobe already releasing this capability in beta. These summaries may include key discussion points, action items, deadlines, and miscellaneous notes. These include utilizing the tech to update sales materials, recommend up/cross-sell opportunities, and make in-call coaching suggestions.

The Conversation Booster by Nuance uses generative AI to combat this issue as users carry out self-service tasks within the bot. These may include making payments, scheduling appointments, or updating their personal information. Another advantage of these auto-generated articles is that they’re in the same format, allowing agents to quickly comprehend and action them. Unfortunately, there are seemingly no purpose-built solutions for contact centers quite yet.

It’s unrealistic to think a CMO will suddenly adopt a contact center solution as a comprehensive marketing tool. In doing so, T-Mobile hopes IntentCX will enable live and virtual agents with “actual solutions” to customer solutions instead of just AI-summarized data. This strategic use of data and technology illustrates the power of AI in customer experience and how it can keep companies competitive. Netflix is a master of hyper-personalization, utilizing advanced AI algorithms to analyze the viewing habits of each user. “Sixty percent of customer service and support leaders are under pressure to adopt AI in their function,” McIntosh explained. David’s Bridal’s concierge bot, Zoey, became a key part of the brand’s strategy, helping drive ecommerce revenue by simplifying repetitive tasks for customers.

Unearthing Customer FAQs

Indeed, GenAI applications – like Service GPT by Salesforce – can do this by first understanding the customer query and sieving through various knowledge sources looking for the answer. The vendor’s RingCX CCaaS solution pairs with RingEX and RingSense AI, its respective UCaaS and conversational AI platforms. Two excellent, often-overlooked examples are automating quality assurance (QA) and mining unstructured data to identify more points of frustration within the service experience.

In the last year alone, we’ve lost count of the number of contact center, CRM, and CX software vendors introducing new AI capabilities for customer service teams. With such tools, the contact center can reimagine its knowledge management strategy and ensure its virtual assistants leverage the latest and greatest knowledge base insights. In the quest to deliver exceptional CX, embracing AI in customer experience offers more than just automation; it provides a canvas for innovation and differentiation.

It then analyzes the case notes, transcripts, and summaries to map issues and uncover the key troubleshooting steps agents may take to resolve each query. As such, they filled the CRM with inaccurate data, which meant that contact centers struggled to track the history of many customer cases. Contact center agents had to manually write up a summary, tag the interaction with a disposition code, and send it off to the CRM.

• 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.
• Traditionally, contact centers have had problems with live agents manually entering the codes, as they may select the wrong code or skip past the problem.
• What I found particularly interesting was the breadth of different AI use cases that spanned all communication channels.
• However, AI can augment these agents in a range of ways, streamlining tasks and boosting productivity on a massive scale.

As companies continue to gather more data from every interaction and hybrid work changes the contact center landscape, security and compliance risks are growing. For utilities, telecoms, and financial institutions, Gather can automate bill payments by amalgamating payment information and verifying account balances. The AI guides users through entering payment details securely, reducing the need for live agent support. By asking callers for necessary claim details (e.g., date, type of claim, policy number), Gather can capture all pertinent information and give real-time status updates on existing claims. Indeed, it provides a global community of specialist contact center consultants, far-ranging expertise, and numerous strategic partnerships to help enterprises create the contact center of tomorrow. Yet, the contact center can slowly build towards Kohli’s vision by first utilizing smart bots to triage customers.

Additionally, with access to in-depth data about contact center performance, call and contact volumes, and historical trends, AI tools can assist businesses in resource allocation. Tools capable of predictive analytics can help companies forecast future contact center needs, and determine how to distribute their agents across different channels. The infusion of generative AI into the contact center will provide a step function in the ability for brands to manage and improve customer interactions.

The 18 Agentic AI Use Cases

After all, they may prefer to handle contacts on channels like live chat, as it’s often cheaper. In the example above, the contact center focuses on handling the interaction within the customer’s channel of choice. Still, customers must repeat themselves, queue for long periods, and – perhaps most frustratingly – get caught up in the IVR.

The top two may come as a surprise, especially as the noise around other AI-led technologies – including virtual agents and assistants – reaches a fever pitch. Potosky recommends that instead of using GenAI to replace human agents, companies should focus on leveraging employee enablement technology. “For example, AI-infused chatbots must communicate to the customer that they will connect them to an agent in the event that the AI cannot provide a solution. It must then seamlessly transform into an agent chat that picks up where the chatbot left off.

“This is an example of how conversational AI can make routine interactions more convenient for customers while enabling employees to focus on providing value in complex situations,” said LoCascio. As a result, contact center teams gain a more representative view of customer experience beyond the individual performance of agents. Many CCaaS providers are developing workforce engagement management (WEM) solutions. But, beyond building unified reports, few vendors are thinking of how to best marry the technologies.

Companies like Content Guru, with a strong background in the AI landscape, can assist businesses in implementing their own comprehensive governance strategies. It offers businesses an opportunity to use bots to rapidly notify customers about technical issues, changes to their accounts, and new products. It also allows organizations to analyze customer history and preferences on a massive scale.

Indeed, while its standard turnkey offers are based on a single LLM, Avaya can enable customers to bring their own LLM via its API-first approach, with transcriptions done by either the customer or Avaya. 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. After all, contact centers use that disposition data to isolate customer trends, identify broken processes, and inform automation strategies.

This will improve operational efficiency and create a more seamless, personalized, and empathetic customer experience. As contact centers leverage generative AI, they stand to gain a competitive edge while fostering improved customer relationships and long-term satisfaction. It can suggest relevant information, recommend solutions, or automate information retrieval, enhancing agent productivity and accuracy, which leads to happier customers. Contact center virtual assistants can leverage large language model (LLM) technology can process huge volumes of information, converting countless reviews, testimonials, and other feedback forms into concise takeaways.