The Future of CCaaS Platforms: 5 Expert Takes

Readers will also get a big-picture analysis of what businesses must do to personalize customer interactions and maximize ROI. Leading vendors like XCally give companies access to flexible AI systems that can power everything from chat and voice self-service strategies, to sentiment analysis and predictive insights. With these tools, you can improve efficiency, productivity, and customer satisfaction, without having to compromise on ethical standards, or compliance. AI sentiment analysis solutions can help businesses understand which factors influence the thoughts and feelings of their customers.

So it’s not just the phone calls, it’s email, it’s social, it’s the chatbots on their website. So there’s more places that customers can get information about a business than ever before. Looking ahead, Carlson sees the evolution toward AI-enabled customer centricity as a signal of a customer experience paradigm shift where AI will augment not just operational details but offer insights into high-level business strategy. These are just two examples of many that highlight how GPT-4.o can automate complex workflows and enhance real-time interactions throughout the enterprise. For example, the following demo of two GPT-4os interacting and singing perhaps sheds some light on a future when machine customers and agents converse on behalf of their human counterparts. For instance, consider how many leading conversational AI vendors have augmented their solutions with image recognition (IR) to recognize entities within photos and make automated recommendations.

AI Demystified! Customer Service Bots & Beyond (with a Demo)

Yet, as customers have learned more about capabilities and risks, we see more of our customers using GenAI to augment and empower bots to understand and process ambiguous information where they previously could not do so. However, it’s crucial that CX leaders call AI what it is, or they risk breaking customer trust. Attempting to pass an automated response off as a human, especially in high-emotion scenarios, is a mistake. 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. Matt Hasan, CX strategist and AI solutions developer at aiRESULTS, Inc., told CMSWire that AI enhances personalization through predictive analytics and customer journey mapping.

We recently completed our Emerj AI in Banking Vendor Scorecard and Capability Map in which we explored which AI capabilities banks were taking advantage of the most and which they might be able to leverage in the future. Metlife opted for ZestyAI’s Z-FIRE product, which the company claims uses deep learning algorithms to analyze high-resolution aerial imagery and other data sources to generate insights and predict future property risks. Whether health, life, or vehicle insurance – customers need to feel understood and serviced at the earliest interaction with service funnels and online processes. MetLife Japan management decided to leverage machine learning to expedite the detection of suspicious claims with greater accuracy than relying on human labor. Partnering with Shift Technologies, the company launched Force, an AI solution to detect claims. MetLife Japan further claims in press releases that the solution uses machine learning to detect vast amounts of data, including records of previous fraudulent claims.

Increasing the Scope of Conversational AI

So an auto-summarization tool does that automatically based off of the conversation, saving the agents up to a minute of post-call notes, but also saving businesses upwards of $14 million a year for 1,000 agents. Which is great, but agents appreciate it because 85% of them don’t really like all of their desktop applications. That next time they call, they know those notes are going to go over to the agent, the agent can use them.

Also, contact centers can deploy technology to enable smoother audio quality, even when caller bandwidth is low. Stress and burnout rates are especially high among agents in contact centers, which see twice as much turnover as any other profession. Several prominent CCaaS providers discuss how generative AI will shake up service ai use cases in contact center operations. Our new Gen AI Lab Programme is a dedicated “show me don’t tell me space”, a ‘birth place’ and site for customer events and collaboration. When it comes to Gen AI, we’ll be offering a whole tool kit of collaborative hackathons, labs and design sprints, to help customers make the most of this game-changing innovation.

Forty years has passed since the launch of IVR, we now have more customer service agents than we ever dreamed of, along with a significant number of customers frustrated and annoyed with the experience they have with IVR. Using AI to remove mundane contact center tasks allows agents to focus on up-skilling their capabilities, ChatGPT App empowering them to tackle increasingly complex issues and ultimately providing better customer experiences and outcomes. You can foun additiona information about ai customer service and artificial intelligence and NLP. When all customer resolutions need to happen fast, every minute stuck in your call-handling process can cost you both money, customer satisfaction and possibly customers themselves.

Organizations need to implement safeguards to detect and rectify issues wherein AI might accidentally generate inaccurate, misleading, and potentially damaging information. This is crucial to not only staying compliant but preserving strong relationships with your customer base. The EU and US aren’t the only regions investing in new regulatory requirements, though they do represent some of the biggest markets for many contact centers. Everywhere you look, government groups are working together to craft a future where we can access generative AI without harming data privacy or compromising civil rights.

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. 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.

• These datasets are necessary for testing algorithms, training machine learning (ML) models, and evaluating new health technologies before implementation.
• By forecasting periods of high demand, businesses can optimize staffing and resource allocation, ensuring that they are prepared to more efficiently handle peak times.
• “The idea is not about replacing jobs, it’s about augmenting efficiency and effectiveness,” Yip said.
• The chatbots use conversational AI to act as the contact center for customers seeking quick answers to queries and ways to resolve simple issues at any time of day.
• In recent years, conversational AI vendors have brought various real-time translation models to market, with brands like Cognigy even making them available on the voice channel.
• Agent assist will correct the imbalance in a contact center agent’s time so they can better connect with customers and focus on high-value interactions.

The digital world has empowered companies of all sizes to deliver services and products to customers all around the globe. However, delivering global support can be more complex, requiring companies to invest in dedicated teams to serve customers who speak various languages. AI can reduce the need to hire additional language support, with real-time translation options. AI-powered customer service solutions aim to elevate their contact center operations. Microsoft and Google have both made significant strides in this area, with their recent announcements of AI-driven contact center solutions that promise to revolutionize customer interactions. A combination of automated scripts, LLM algorithms and customer analysis techniques can be used to transcribe, organize and analyze post-call and post-chat summaries.

The Future of CCaaS Platforms: 5 Expert Takes

Maintaining high standards in manufacturing can be challenging, but AI-driven systems can relieve the process by spotting possible product defects instantly. Generative AI tools can be trained to distinguish defective from perfect-quality products and alert teams of possible flaws. This could lead to a decrease in product recalls and ensure output consistency, refining overall manufacturing reliability. GenAI goes beyond traditional static analysis tools in bug detection, doing more than just catching syntax errors—it also identifies potential vulnerabilities and logic flows before they escalate into bigger problems.

In the 1970s, the automated call distributor (ACD) was developed to help businesses manage inbound calls and IVR systems became commercially available. In the 1980s, AT&T agreed to break up into Regional Bell Operating Companies, opening the door to competition, and outbound call centers used predictive dialing to place multiple calls at the same time to get a live person on the phone. “Many contact centers have a full-time channel in place, but not so many have an omnichannel in place and working right now,” Cleveland acknowledged. “It’s important for users who can’t get the information they need and be able to seamlessly move among multiple channels like websites or a mobile app in real time. I see omnichannel as the next necessary trend in AI.” 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.

Not Prioritizing Integration

Lately, administrators, supervisors, workforce planners, and quality managers can all benefit from GenAI-powered assistants that help to interpret data, spot areas for employee improvement and learning, and automate routine tasks. Such tasks include auto summaries to reduce wrap-up time, suggested next-step actions, live transcription, sentiment analysis to ensure you steer the conversation positively to help the customer, and many more features. 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.

The true value of AI happens when AI is used holistically for more than generating text from prompts (although that’s important, too). When used effectively, targeted use of AI can assist agents in their current tasks to achieve their best work. Its solution also detects dead airtime, uncovers cross-talking, and creates alerts and triggers so supervisors can gain even more insights and – crucially – act on them. With that information, contact centers can work backward, dive into the customer journey, and amend the broken processes they’ve grudgingly learned to live with. Service leaders can get to the bottom of what’s causing the issue in the first place, monitoring keywords and phrases from a group of contacts that share the same customer intent.

Sprinklr’s “call note automation” solution aims to overcome this issue by jotting down crucial information as the customer talks. However, even that can impede an agent’s ability to engage in active listening as they multi-task, resulting in increased resolution times. Again, the contact center must plug the solution into various knowledge sources for this to happen – as is the case across many other use cases – and an agent stays in the loop. 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. “That gives them a lot of the experience internally — like ‘Hey, we’ve done this not only for ourselves, but for our clients.’ And it really gives them that foundation [for digital transformation],” Gareiss said. BPOs view AI investments as a higher priority than organizations in other industries, according to Metrigy research.

In fact, 30% of customer service reps are expected to use AI to automate processes by 2026. Investing in AI-enhanced chatbots and virtual assistants also supports scalability, allowing businesses to efficiently manage peak times without ChatGPT compromising service quality. As these technologies continue to advance, their ability to understand context, recognize sentiment and engage in more meaningful conversations will further enhance the customer service experience.