The Top Customer Service Trends and Technologies for 2024: In Customer Service, AI Is Everywhere

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The past two years have seen a massive surge in artificial intelligence, especially generative AI and the large language models (LLMs) that fuel it, leaving companies and customer experience leaders to rethink the ways in which they create exceptional experiences.

With the huge amount of personalization that AI enables (and customers have come to expect), it’s now possible to anticipate customer needs and address them across channels.

“Today conversational AI chatbots, human agent-assist copilots, automated employee and customer workflows, and conversational business intelligence analysis of rich engagement data are helping brands level up their contact center capabilities to meet these goals,” says Frank Schneider, a vice president and AI evangelist at Verint.

Here, we’ll explore five critical ways that customer service leaders are using AI technology to provide better customer service:


“One of the hottest trends we are seeing now is the use of predictive analytics for CX, to anticipate customer issues and provide solutions before problems escalate,” says John Sun, founder and CEO of Spring Labs, a data solutions provider. “However, the elephant in the room when it comes to predictive analytics for CX is how to obtain accurate, relevant data with strong predictive power. Having a strong data science practice is a must for any company serious about using predictive analytics to level up their CX.”

Too often, though, companies focus on predictive analytics before they have their data structures in order, and those attempts rarely yield results, Sun adds. “One way to start is by consolidating existing tooling as much as possible to develop consistent sources of truth for every step of the CX journey. We’ve seen companies with as many as a dozen different overlapping systems where data is collected and aggregated, which obviously makes it harder to know what’s reliable data and what isn’t.

“AI might provide the biggest quantum leap in predictive analytics for CX in history, and we’ve barely begun to scratch the surface,” adds Sun, who sees unstructured data as a major problem in CX and AI as a great solution for it. “The popular LLMs, as well as less-explored small language models, both provide the necessary tools to understand your unstructured data and create meaningful structured data points in a fraction of the time and resources it would’ve taken in the pre-AI world.”

Financial service firms, which are Spring Labs’ main type of customer, use predictive AI to evaluate customers’ previous behaviors, payment patterns, and previous interactions to proactively determine when they might be at risk of falling behind on payments. By knowing this early, financial service firms can offer changes in payment plans, such as lengthening the terms of loans, to help customers before minor issues turn into serious delinquencies.

Such predictive AI involves gathering and analyzing relevant customer data from a variety of sources. Though that was technically possible before the current age of AI, today’s technology does it so much quicker and easier.

Similarly, AI helps with casual inference to understand and predict customer behavior, according to James Briggs, founder and CEO of AI Collaborator, an AI procurement marketplace provider. “Causal inference techniques are changing the way companies analyze customer data and identify the underlying factors that drive customer actions and preferences. By understanding cause-and-effect relationships, businesses can predict future behavior more accurately and make informed decisions to enhance customer satisfaction and loyalty.”


Another use for AI centers on digital adoption platforms, which are capable of delivering real-time, bite-size, easily digestible learning content precisely at the time when it is needed. Though often used for customer and employee onboarding, DAPs are increasingly finding their way into customer service, a trend that has not escaped the attention of research firm Gartner.

Gartner has predicted that by next year, 70 percent of organizations will use DAPs within their technology stacks to overcome insufficient application user experiences, particularly for customer support. Fellow research firm IDC, in its “Future of Work Trends 2024” report, predicted that by 2027, 80 percent of top companies will have mitigated technical skills shortages and conduct employee upskilling with a DAP.

DAP technology, it turns out, can also be used for reminders, alerts, announcements, and information bubbles with supporting learning and development-related links or videos.

DAP’s today can act as a total companion for customer service agents, according to Krishna Dunthoori, CEO and founder of Apty, a DAP systems provider. “The end user is no longer dependent on multiple sources to get the task done right,” he says.

Now that the DAP is infused with AI, it provides full agent support for customer service. If an agent encounters a problem, the DAP can provide instant, step-by-step solutions without leaving the application, according to Dunthoori.

Among the benefits cited by companies that have used DAPs in customer support contexts is a reduction in customer support tickets due to self-help, reduced friction, and better overall customer experiences and higher satisfaction rates. Built-in analytics and feedback capture also help companies understand where customers are having problems so they can avoid them in future product iterations.


AI has also advanced in the past few years to the point where it is now multimodal, meaning that it can process and combine multiple types of data, including images, videos, text, code, audio, and voice, to produce more accurate outputs. Multimodal AI systems are trained to identify patterns between different types of data and can use this information to make predictions or generate new content.

Multimodal is the most user-friendly iteration of AI, according to Raghu Ravinutala, cofounder and CEO of Yellow.ai, a provider of AI-first customer service automation solutions. “It can also produce outputs that are more precise, more adaptable, and, subsequently, much more consumer-friendly,”

The point of multimodal AI is to simplify customer interactions, “to make the process seamless for all involved,” Ravinutala says.

Customer service uses of the technology are many. “Consider a consumer who receives a package whose contents are broken or incorrect. With multimodality, the user can send a photo of the broken or faulty item, which becomes an input the chatbot can use to take the next required steps and subsequently, resolve the query,” Ravinutala explains.

Multimodal AI can also make virtual agents more versatile and intelligent by drawing from a larger dataset and allowing customers to ask questions in different ways, enabling more personalized interactions and resolving customer issues faster and more accurately.

“In voice, multimodal AI also helps match accents and regional nuances,” Ravinutala adds.

The technology is also helping companies fill their customer service staffing needs more quickly by enabling active engagement with multimedia content, shortening the learning curve, he continues. “With multimodal capabilities, companies onboarding new customer service agents can accommodate diverse learning preferences, ensuring a smoother experience for new hires who may prefer reading, visuals, or auditory explanations.”

“The closer AI can come to replicating the perception and output of a human being, the better it will be at meeting the multiplying needs of businesses and their customers.”


The contact center industry has been talking about omnichannel for years, and today, interaction channels are finally coming together, “driven by the move of contact centers to the cloud and open architectures that enable companies to integrate and manage multiple channels from one interface,” says Rebecca Wettemann, founder and CEO of CRM consulting firm Valoir.

Smart use of cloud technology has made better ominichannel customer service a reality, Wettemann says. Though most companies started employing cloud technologies for customer service as a result of the pandemic, it hadn’t been until the past year or so that many have implemented some of the best strategies to leverage the cloud, including cross-training agents in the use of the various technologies.

And the cloud contact center market is only expected to grow even more dramatically going forward. In fact, research firm MarketsandMarkets recently projected that the worldwide cloud contact center market will expand from $26.2 billion today to $86.4 billion by 2029, seeing a compound annual growth rate of 26.9 percent.

Also growing alongside the cloud contact center market is the use of social media for customer service, according to many experts.

The social media aspect of omnichannel customer service has become increasingly important, Wettemann adds. In some parts of the world, WhatsApps is the most popular channel for customers.

“When social media first came out, a completely different team or sometimes an intern was handling things that came through on Twitter. Now we expect that channel to have the same level of consistency, professionalism, and responsiveness as all other channels,” she says.

“It’s really dumb that we’ve had separate technology processes and teams to manage different channels of customer inquiry,” Wettemann goes on to explain. “The only reason we did it that way was because everybody built out call centers that were so inflexible that there was no way to bring other channels in [prior to implementing cloud technology].”

Full omnichannel contact center operations are still not omnipresent, but incredible progress has already been made, according to Wettemann. “While we’re not there yet, the future is a set of virtual and human agents that manage flexibly across channels based on real-time demand and resource availability and a seamless experience for the customer.”

To help companies get past the last few hurdles, Wettemann offers three tips: “The first step is enabling the channels; the second step is really making sure that you’re consistent across all of them; and the third is being able to optimize and understand every customer interaction so they get the best experience no matter how they try to reach you.”


Like the fuller realization of true omnichannel customer service, improved personalization is a goal that customer service providers have chased for many years. AI has brought about marked improvements in that effort in the past year, according to Daniel Harding, director of MaxContact Australia.

“We are in the age of personality, and that is true for the hottest trends in customer service, too,” Harding says. “Customers no longer tolerate an impersonal approach; they have options to take their business elsewhere if the customer experience is not up to standard. This is particularly true in industries where there is no differentiation between the end product and the only difference is customer service. If your customer service is not up to scratch, then consumers will not be patient and will exercise the option to switch suppliers.”

Conversational and generative voice AI are the biggest trendsetters for providing better personalization right now, Harding adds. “We are seeing organizations invest heavily in new technologies to better serve their customers. ‘AI’ has been the buzzword for some time now, but this is certainly the case in the customer service industry whereby companies are using various AI tools to better serve their customers.”

These tools also help customers who want self-service to request information, return products, update address details, and perform other tasks without waiting for a human agent, Harding says. “This allows customers to get a better experience by having a quick resolution via the customer’s communication channel of choice.”

Personalization is expanding in other ways. The era of superficial personalization based solely on basic details like names and recent browsing history is over, agrees Amy Jerusalmi, chief customer officer of Cordial, a marketing technology provider. “Personalization requires honest conversations and a genuine understanding of customer needs, interests, and preferences. This is where AI becomes a powerful enabling technology, not by replacing humans but by facilitating meaningful dialogues using relevance derived from intelligent data analysis.”

And AI isn’t done upending the contact center just yet. That comes with a few caveats, though, according to Verint’s Schneider.

“Organizations must be mindful of a number of new challenges and factors related to the deployment of the latest generative AI and LLM capabilities,” Schneider says. “It is important for organizations not to expect miracles in the coming weeks and months, but to use genAI in pragmatic ways as conversational middleware across AI and contact center automation tools to ensure the right AI capabilities will meet present and future customer service needs.

“As the market looks to the future of conversational AI solutions that leverage generative AI and LLMs, we are entering a world where best practices are being developed as we go along. CX practitioners are now daring to be bold and do great things,” Schneider continues. “However, transparency, agility, and control will be paramount to the success of delivering elevated customer experiences through AI-powered solutions.” 

Phillip Britt is a freelance writer based in the Chicago area. He can be reached at spenterprises1@comcast.net.

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