5 Disruptive Use Cases for AI in Customer Experience

The Future of CCaaS Platforms: 5 Expert Takes

In the contact center, many of the concerns regulators have about how AI might be used may not come into play. For instance, the US requires companies to avoid using AI to engineer dangerous biological materials and create deepfakes. However, the US and EU also require companies to be cautious about the content they create with AI tools. It almost goes without saying that every new AI regulation will focus on data security. The EU and US mandates already restrict companies from leveraging and using sensitive data, such as biometrics scans to train AI models.

CX Today’s Charlie Mitchell introduces five AI use cases for customer experience, with a specific focus on contact centers. CX Today’s Charlie Mitchell introduces five AI use cases for customer experience, with a specific focus on contact centers. After all, contact center providers promised that this would be a simple first use case for service teams to help build their confidence in GenAI. Even with the best scheduling and workforce management (WFM) strategy, the contact center can be unpredictable. Sometimes, spikes in contact volume happen without warning, and business managers need to make quick decisions to handle an influx of requests. While this may be too much for a virtual assistant alone right now, it’s a possible future use case.

Agent Assist: The Use Cases

Alongside sentiment, contact centers may harness GenAI to alert supervisors when an agent demonstrates a specific behavior and jot down customer complaints. Alongside this, the solution provides a rationale for the automated answer in case quality analysts, supervisors, or coaches wish to delve deeper or an agent wants to challenge it. As generative AI monitors customer intent, many vendors have built dashboards that track the primary reasons customers contact the business and categorize them. That final part is crucial, keeping a human in the loop to lower the risk of responding with incorrect information and protecting service teams from GenAI hallucinations. Well, many tangible use cases were already in the space before the advent of the tech.

Future Trends: What’s Next for AI in Contact Centers? – CX Today

Future Trends: What’s Next for AI in Contact Centers?.

Posted: Tue, 10 Sep 2024 07:00:00 GMT [source]

This bottoms-up approach avoids the pitfalls of rolling out unproven AI to customers and ensures customer service is automating processes that are ready. It’s not about turf wars; it’s about leveraging expertise to create consistent, impactful customer interactions. A virtual assistant available to managers in their voice of the customer (VoC) platform may be able to ingest feedback from conversation transcripts and surveys to generate a “trend overview”. A contact center virtual assistant can identify which conversations are most likely to hold the most insights for training purposes.

Measuring Customer and Employee Sentiment

This approach helps IoT make any agent the „right agent“ for a customer to contact because all parties have access to the same information. In conversations with contact center managers over the past couple of years, Metrigy president and principal analyst Irwin Lazar said the biggest high-level trend has been to improve agent efficiency. But managers said their agents were feeling frustrated because they couldn’t get the information customers needed, resulting in poor customer service. The most effective customer experiences are those where AI and human insights work hand-in-hand to deliver value, empathy and satisfaction. PBR is increasingly being integrated with advanced AI capabilities such as real-time sentiment analysis and omnichannel journey mapping.

Beyond simply transforming self-service experiences, generative AI empowers companies to deliver more personalized, efficient service at scale, while improving employee productivity and reducing operational costs. The second solution, AI Translator, automatically translates text, enabling agents to communicate with customers in any language. This evolution sets a new standard for automation in the contact center, enabling unparalleled efficiency, adaptability, and precision in customer interactions. AI-powered UC systems have a higher responsibility to prevent exploitation, manipulation, and misuse of sensitive data, which may impact individual customers or severely damage a business’ reputation and legal standing. AI-driven tools in UC can improve speed and efficiency but require it to capture and store large volumes of personal data – everything from financial details to private conversations. Businesses should consider the risks involved in new innovations to protect privacy.

The Creation Of More Robust Knowledge Centers

Both solutions are available across digital channels, including email, chats, and SMS. All-in-all, Talkdesk calls this an “essential capability”, particularly for accelerating new agent proficiency or supporting those who have failed to adhere to company communication guidelines. Alternatively, they may use it to personalize knowledge articles before sending them off to customers. With this, agents may simply write out the bulk of their responses and use the tool to ensure a professional, friendly, or empathetic tone. Maintaining compliance with industry standards in the contact center has always been complicated.

18 Use Cases for Agentic AI in Customer Experience – CX Today

18 Use Cases for Agentic AI in Customer Experience.

Posted: Fri, 13 Dec 2024 08:00:00 GMT [source]

While this use case is usually reserved for digital channels, some contact center virtual assistant providers are taking steps to translate voice calls, thanks to the GenAI advancements. This ensures agents can deliver the same quality of service to customers from different locations. To do so, the virtual agent can leverage insights from various knowledge sources, such as knowledge bases, CRMs, and web links. All the agent needs to do is ensure the response is relevant, and accurate before clicking “send”.

Monitoring Customer Sentiment Signals

The vision is to move from a human-centric model to a hybrid human-bot model, which involves having “human-tending bots” that can assist and oversee multiple human agents. Indeed, a recent study found that 42 percent of businesses have fully integrated AI into customer interactions. Converting call recordings into searchable transcriptions and summaries can be a time-consuming process. AI-driven solutions can offer real-time or post-call transcriptions of every interaction.

• AI can surface valuable insights to agents from CRM solutions and databases, helping agents resolve issues faster, and personalize experiences based on profiles and previous discussions.
• Security is also critical to how AWS starts with the development of all its AI services, as it’s a lot easier to start with security in the development rather than bolt it on later.
• Finally, the QA team can review, edit, and finalize that scorecard before repeating the process across other channels (and perhaps specific customer intents).
• These keywords are assigned scores, typically based on how positive or negative they are, which are used to determine overall customer satisfaction.
• AI agent-assist tools could even help companies deliver a more proactive level of service by leveraging IoT capabilities to monitor the performance of devices and other technologies in real-time.
• This will allow business leaders to craft customer experiences based on a deep understanding of a customer’s emotional state, leading to more empathetic, personalized interactions.

Of course, it’s unlikely we’ll see a universal agreement among governments and regulatory bodies any time soon. In a recent webinar with Observe.ai, I discussed how companies like Accolade Health and Affordable Care are beginning to automate their contact center with Voice AI Agents. The AI identifies when a customer qualifies for a better loan rate and provides a detailed breakdown of how to switch, including pre-filling applications.

The Sprinklr Digital Twin is a conversational AI offering that allows different departments within a business to build their own AI Twin. It has created a software overlay that aggregates all that customer data, puts it into what’s called a “room”, and applies AI to be much more predictive. NICE Enlighten XM unifies customer interaction data, metadata, and insight from the business’s broader CX ecosystem to build a unique memory graph for each customer. That feature enables cross-region failover, ensures business continuity, and provides that all-important uptime.

Some generative AI tools can even create intuitive call summaries, highlighting important topics and action items for customer follow-up, and training purposes. Additionally, make sure your agents know how to take full advantage of the AI solutions available to them. Show them how they can use AI tools to streamline processes, automate routine tasks, and achieve their professional goals. This will help to strike a better balance between the AI tools and human employees in your ecosystem. 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. Look for tools that can analyze customer sentiment and intent, and determine when to transfer a call.

Beyond real-time supervision and reporting, supervisor copilots enhance knowledge bases, facilitate AI training and feedback loops, and support compliance monitoring. Here’s where most businesses go wrong with their strategies, and how you can boost your chances of success. The question is, which of these two solutions do you need, and do you need to choose between one or the other? Though ChatGPT, Microsoft Copilot, and even solutions like NICE’s Enlighten AI suite are driving focus to the rise of generative AI, it’s not the only intelligent tech making waves.

They can also be configured to route conversations based on various factors, such as customer sentiment or agent skill level. Using advanced computer vision and voice analysis, AI systems have the capability to detect and analyze human emotions in real time. These systems can interpret facial expressions, tone of voice and even subtle gestures to gauge a person’s emotional state. The insights gained from this analysis can provide valuable context and help create more personalized and empathetic interactions during customer engagements. Agent assist technologies enhanced by cutting-edge artificial intelligence can transform the contact center.

Still, Google has pledged to make such a feature available on its Google Contact Center AI Platform soon. OpenAI demonstrated earlier this year how this works using ChatGPT, as shown below. With this insight, brands can deep dive into how their agents evoke all sorts of emotions and uncover new best practices to coach across the agent population. The Forrester Wave CCaaS leader then applies GenAI to monitor the trend in sentiment and alert the supervisor when it drops significantly. Knowing this, they can stay focused on what the customer is saying, not trying to remember what they said previously, which should improve their call handling. While the distinction between contact center AI features is often lacking, innovation advances, and – in 2025 – vendors will continue releasing new capabilities.

AI in the contact center offers an incredible opportunity to automate various tasks that would otherwise drain employee productivity and efficiency. Local Measure’s Engage platform, for instance, empowers companies to rapidly summarize call transcripts with Smart Notes, reducing after call work time, and boosting productivity. For instance, the Smart Composer solution from Local Measure empowers agents to rapidly generate responses to customer queries, optimizing tone, grammar, and communication quality instantly. By analyzing why customers make contact and identifying points of frustration in their journey, AI provides insights far beyond what call deflection through automation can achieve.