Copilots & Virtual Assistants: Emerging Use Cases, Trends, & Strategies

Customers Reject AI for Customer Service, Still Crave a Human Touch

With generative AI solutions built into the contact center, agents can reduce the time they spend on post-call work, such as summarizing conversations or highlighting action items. Generative AI agent assist tools can even advise agents on when to follow up with a customer based on interaction context. AI solutions give companies a powerful opportunity to enhance and optimize their customer support strategy. From bots that deliver 24/7 service, to solutions that enhance employee productivity, reduce operational costs, and deliver valuable insights, AI can play a role in every aspect of your CX strategy. Finally, while AI can enhance customer support processes, it shouldn’t replace your human support team.

Identified by some analysts as critical to the future of contact centers, conversational analytics extracts data from text and voice conversations between customers and human agents or chatbots. Customer interactions are evaluated in real time so agents can discover behavioral patterns instantly. Agents acquire a more in-depth understanding of each customer through past data and journey insights. The rise of AI in customer service is not about replacing human agents but augmenting their capabilities to provide better customer experiences. AI-driven platforms like Telnyx’s excel at managing routine tasks with speed and precision, enabling human agents to focus on more complex, empathetic, and high-value interactions. The evolution of IVR systems underscores the importance of integrating advanced AI technologies to enhance customer experiences.

AI-based software, Lazar added, also reduced “after call” work in which agents must trace back after a call to capture their notes and sort out what action items they need to pursue. Agent after call work dropped by 35%, potentially enabling agents to handle more calls effectively. AI is the most significant contact center trend in 2024 and should remain so well into the future.

Most organizations are transparent about whether a customer is speaking with an AI or a human but may not always be legally obliged to be so. AIs will become adept enough at handling human-like exchanges and any required handover to a human will be warm and seamless. Contact center leaders will need to focus on training and upskilling their workforce, to help them unlock the full benefits of AI, rather than automating every task. This will be particularly crucial if new regulations emerge that give customers the “right to speak to a human”. In the contact center, this means business leaders will need to implement strong governance that combines advanced cybersecurity strategies with tools that protect against data breaches.

Using historical data, you can check the potential impact a game or competition will have before you roll it out to your team. You can also consistently monitor the impact of gamification strategies on agent performance and satisfaction levels. The ability AI has to examine large amounts of contact center data and identify key trends makes it excellent at predicting future behaviours. It can help companies to deliver proactive service, by monitoring when and why customers are likely to reach out for support. Chatbots can easily tackle automated and routine tasks such as fielding FAQs, resetting passwords and other jobs that don’t truly require live agents.

For instance, conversational AI bots can generate better answers to customer questions by calling on the insights of back-end generative models. Generative AI solutions can automatically create responses to questions on behalf of an agent and recognize keywords spoken in a conversation to surface relevant information. It can even draw insights from multiple different environments to help answer more complex queries. Conversational AI is a type of artificial intelligence that allows computer programs (bots) to simulate human conversations.

Overlooking the Human/ AI Balance

AI holds the promise of eliminating many barriers that have prevented contact centers from turning a profit. Now, vendors can take this to the next level with AI-augmented QA systems – which surface new agent performance data across all customer conversations. Using the proper tools, LoCascio told CMSWire that brands are even able to elevate future conversations by analyzing performance metrics and performance benchmarks.

That extends beyond agent-assist and across the whole spectrum of contact center AI. In January, prominent CX futurist Blake Morgan predicted that 25 percent of AI agent-assist deployments will fail in 2024. The wireless provider will then begin implementing the platform in 2025, with a strategy to continually advance the offering with OpenAI’s latest LLMs. As such, T-Mobile’s data will remain theirs, and their customers’ data will stay theirs. That’s a critical point, especially given the recent lawsuits within the contact center space.

• Intelligent tools can automatically update customer records, fill out forms, schedule follow-up calls, and even manage proactive outreach on behalf of agents.
• Some may even “hallucinate” and make up information for which the contact center can be found liable.
• According to a DimensionData survey, 44% of customers prioritize quick and convenient service over detailed, customized interactions.
• Because they can process language and analyze interactions, they can offer companies insight into customer sentiment, track customer service trends, and highlight growth opportunities.

Nowadays, a lot of contact center platforms allow supervisors to automate things like “quality assessments” and scoring. However, sometimes, it’s important to manually review conversations that potentially hold the biggest lessons for the customer service team. Automated customer service interactions sometimes break down when customers change their intent halfway through a conversation – confusing the virtual agent. These aim to enhance many facets of customer service, from workforce engagement management (WEM) to conversational AI.

The Pros and Cons of Conversational AI

By pulling this all together, RingCentral creates an enterprise communications “super-suite”, with workflows running across the platform for differentiative innovation. Whether it includes new hires or uplevelling supervisors, the team must continually refine these tools. Since AI solutions can analyze and surface trends in handling times, contact volumes, and resource requirements, they can have a significant impact on the accuracy of your contact center forecasts.

A reliance on access to high volumes of data, alongside unpredictable models, and ever-evolving capabilities makes preserving compliance, security, and privacy standards complex. AI Agents are transformative, proactive, and autonomous bots capable of taking action and achieving goals without constant user input. They bring conversational systems closer to fully independent problem solvers or assistants. This enables them to proactively service customers – resulting in higher satisfaction and loyalty. The Smart Tasks solution even allows companies to develop valuable automated workflows, to streamline processes like data entry. Team members can use AI to automatically extract information from transcripts, fill out forms, and reduce the risk of human error.

Since the solution is cloud based, firms don’t need to manage any infrastructure, and they can easily deploy AI capabilities into their existing applications. There, AI models transcribe and evaluate the customer’s query and emotional state, with the information accessible to both Patagonia and Talkdesk. Back in September, Talkdesk introduced two generative AI (GenAI) solutions to its CX Cloud platform.

Then, when an agent receives a specific contact, the desktop defaults to its related layout, which agents can easily flick through as they complete the resolution journey. I believe you have to jump in, even if the timing isn’t perfect, or you’ll be left behind – especially in terms of cost efficiency, but also customer satisfaction. She then shared a vision for focusing on meaningful interactions rather than routine calls and of how AI will complement their current roles.

“We see a future where customers want to bring their own AI models or a combination of models and bots to solve specific use cases and service requirements. With our Engage platform we have architected a solution to be able to handle our customers’ complex and multi-vendor landscapes to allow them to not be locked in to any one model or bot. As the pace of change in Generative AI is so rapid we aim to help our clients position their platform for success irrespective of where the technology ends up,” says Local Measure CEO Jonathan Barouch.

Businesses also need to connect their virtual assistants to their CRM and sales database so that they can access everything they need to know about customers and their previous interactions to solve their challenges. Contact center Voice AI allows organizations to design voice bots that can streamline the IVR experience, and enhance customer conversations. Instead of offering core communication channels, routing, and a dialer, they’re now often expected to cover workforce engagement management (WEM), conversational analytics, knowledge management, and more. Contact centers are now focusing on mobile-first capabilities that could transform business processes and improve agent productivity, particularly among remote agents. Some 10 billion devices are actively in play and connected to IoT with expectations of 25.4 billion units by 2030, presenting enormous opportunities for contact centers. A mobile-first strategy provides agents access to customer data from a central repository using any device from any location.

Healthcare call centers, under immense pressure to manage vaccination efforts, quickly integrated AI to automate repetitive tasks like answering vaccine-related questions and scheduling appointments. Wolverton emphasized that AI’s potential extends far beyond these initial use cases, highlighting its ability to identify trends and improve resolution times through natural language processing and predictive analytics. Most of us are familiar with IVR when calling customer service centers, and have found them to be annoying, time-consuming and not very helpful.

Crafting highly personalized responses to queries across digital channels takes time, but a virtual assistant can help. Leveraging GenAI, they can immediately suggest a response to a question, which an agent can then review, edit, and send in seconds. Indeed, the developer can explain – in natural language – what information the bot should collect, the tasks it must perform, and the APIs it needs to send data. Then, the platform spits out a bot, which the business can adapt and deploy in its contact center. Many contact center providers offer the capability to score conversations via sentiment. Finally, the QA team can review, edit, and finalize that scorecard before repeating the process across other channels (and perhaps specific customer intents).

For instance, the Flow Modelling system by Cresta can determine “troubleshooting paths” for specific challenges based on the impact steps will have on business outcomes. Contact center workforce management can be difficult to navigate, particularly as employees value more flexible schedules. Additionally, it can even show supervisors which conversations they need to review to ensure compliance guidelines are being followed, reducing risks. 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. The faster a live agent can address a query, the quicker they can move on to the next conversation in the queue, improving efficiency.

Measuring Customer Moods, Not Just Sentiment

That QA data could then inform a triage system, which routes contacts based on the likelihood that the agent will solve the customer’s query. Nevertheless, the company promises a “meaningful leap” from today’s next best action solutions, which pre-program customer intents and match them to a limited set of responses. While using AI to modify the tone of customer-agent conversations may seem like a new concept, many contact centers have leveraged such applications over the past two years. SoftBank also noted that it hopes the technology – which blends voice processing tech and AI-enabled emotion recognition – will allow it to boost customer retention. However, AI-driven self-service and workflow automations are not the only tools that could help to combat the causes of customer anger.

Consider solutions like agent-assist and copilots to make it easier for reps to access information, receive real-time coaching, and solve problems efficiently. When people are called on to perform repetitive tasks, the quality of performance will drop. Given AI requires good data to make decisions, allowing AI to input data will likely lead to better AI and even higher-quality automation.

Agentic AI transforms this by enabling AI Agents to adapt flexibly to each customer’s situation in real time. Rather than having supervisors sift through countless transcripts and calls, the AI will detect anomalies in real-time, surfacing issues only when human oversight is truly needed. Notably, make sure that the voice AI solution you choose gives you the freedom to consistently customize your bots, with developer APIs, integration options, and flexible frameworks. Some solutions can also automatically transcribe and translate calls, which can be ideal for enhancing compliance, as well as training initiatives.

He also asserts that by not having AI-powered features like automated meeting notes, ULAP Networks’ customers don’t have to worry about the data privacy implications of that data being accessed. On the one hand, its Enlighten Copilot technology supports agents in every step of their journey, guiding them through real-time interactions with contextual guidance to drive optimal outcomes. Lastly, Avaya’s “Innovation Without Disruption” approach allows customers to deliver GenAI agent assist without ripping and replacing their on-premise or private cloud contact center.

Meanwhile, their flexibility is dynamic and proactive, with deep contextual understanding and foresight. Having a plan from day one to dedicate time and resources to AI maintenance is crucial. Yet, ultimately, before any AI project, leaders must first clarify what’s most important to achieve. It’s easy to be reactive to urgent issues, but focusing on long-term priorities drives sustainable progress. The lesson is to evaluate use case fit and determine when to bypass automated processes.

This is extremely useful for contact center managers who need to identify “trends” in customer feedback to help them make better decisions to improve service. Indeed, as contact centers start applying automation, they can boost efficiency, enhance employee experiences, and improve customer satisfaction. Supervisors need to give agents the freedom to improve engagement and satisfaction levels, but they can’t risk approving shift swaps and time off if it means that customer experiences and team morale will suffer.

Contact Center AI: The Story So Far, and What Comes Next? – CX Today

Contact Center AI: The Story So Far, and What Comes Next?.

Posted: Mon, 20 Jan 2025 12:02:45 GMT [source]

Though conversational AI and generative AI have different strengths, they can both work in tandem to improve customer experience. Tools like Microsoft Copilot for Sales are considered generative AI models, but they actually use conversational AI, too. One major use case for generative AI in the contact center is the ability to automate repetitive tasks, improving workplace efficiency. Generative AI bots can transcribe and translate conversations like their conversational alternatives and even summarize discussions. Most of these solutions build on the foundations of conversational AI, enhancing bot performance with access to large language models (LLMs).

Surfacing Valuable Information in Real Time

Their innovative software listens to conversations in real time and offers immediate feedback to agents, advising them on potential adjustments in tone, pace and conversational style. Seamless, hyper-personalized experiences across all channels are becoming table stakes for modern customer expectations. AI-enhanced call centers, where AI provides human agents with real-time decisioning and actionable insights, enable agents to create the “next best” action for every interaction.

A virtual assistant may then create this necessary content and even translate it into different versions for agents and consumers. This not only gives agents better step-by-step processes to follow but also ensures that business leaders can develop stronger onboarding and training solutions for new employees too. Moreover, long periods of poor employee sentiment could indicate a risk of burnout, disengagement, or frustration that could affect customer experiences. Negative customer sentiment isn’t the only thing that contact center supervisors need to worry about. When an agent’s mood suffers, perhaps as the result of a difficult conversation or high periods of stress, this can create well-being and performance issues, too.

Agentic AI is the next frontier in customer service – providing numerous benefits in contact centers to deliver seamless, efficient interactions for customers. They have higher flexibility, contextual awareness, broader and more adaptive use cases, and more dynamic learning capabilities that can improve with data. However, they offered limited proactivity and only a reactive decision-making capacity, with moderate integration capabilities and adaptive use cases. We’ve had chat and voice bots in customer service, sales, and marketing for a long time. These tend to be based on tech that follows rule-based scripts and logic and, as a result, have rigid flexibility. Whether companies are looking to improve interactions with enhanced personalization and consistent agent support, reduce operational costs, or simply improve their decision making capabilities, AI is a powerful tool.

The unique solution facilitates the voice enablement of conversational AI solutions for a range of use cases, with comprehensive flexibility and support. The right Voice AI solution provider will help you to build and implement best-of-breed bots and systems with ease, and customize those tools to suit different requirements. They’ll give you the freedom to choose how you want to deploy your AI systems, and provide the back-end technology to ensure consistent quality.

It can enable more intuitive self-service experience via voice channels, and reduce the number of customers routed to human agents for common queries. Like conversational AI, generative AI is becoming a more common component of the contact center. CCaaS vendors offer companies access to generative AI-powered bots that can provide real-time coaching and assistance to agents or enhance the customer service experience.

Contact centers must clearly label exit options in their automated systems to avoid this. To fix this, consider tools that allow the CX leaders to personalize tone and tenor through techniques like prompt engineering. Additionally, 90 percent of people in a SurveyMonkey poll said they’d rather deal with a human than a chatbot. In a recent Gartner study of 6,000 consumers, 64 percent preferred companies not to use AI, and 53 percent disliked AI so much they’d consider switching to a competitor. That said, there’s no shortage of ideas as to what AI in the contact center could do. The problem for most contact center leaders is that it’s unclear where to start, as there are many possibilities for use cases.