Generative AI

Generative AI will result in fewer agents taking on more complicated work, according to a Forrester report released in June. Members of these smaller teams will need a combination of emotional intelligence and technological know-how, the report found.

Next-generation call center work will require leaders to rethink their hiring and retention strategies, according to Christina McAllister, senior analyst at Forrester. The call center of the future will need agents with relevant training and advanced skill sets, making individual workers more valuable.

"I believe that it's no longer going to be a race to the bottom on how cheap you can get your labor," McAllister said. "There's going to need to be a focus on retention."

It’s still the early days of the generative AI revolution, but it’s as good a time as any for call center leaders to future-proof their workforce and technology strategies.

One way to prepare agents for their changing roles is investing in their artificial intelligence quotient, or AIQ, according to McAllister. Having a human in the loop can be valuable, particularly during early rollouts, but few workers understand what that really entails.

“You need to educate folks on what it means to be the human in that loop and how best to work alongside AI,” McAllister said. “When do you trust the output of an AI solution? When do you question the output of an AI solution?”

Contact centers need to ensure their knowledge management systems are up to the task of supporting these agents as well, according to McAllister.

Generative AI is only as good as the data that powers it, and the technology can’t fix a poorly maintained or outdated knowledge base. A trustworthy copilot that helps agents deliver better experiences needs clean, trustworthy data it can draw from.

However, in the past, many call centers underinvested in their knowledge management solutions, according to McAllister. Generative AI projects often receive bigger budgets, and modernizing the data backbone is a worthwhile use for this funding.

"Folks are coming face-to-face with the need for data,” McAllister said. “It’s one of the investments they need if they pursue generative AI projects. If you don't have the data from which you can generate that information or knowledge, what are you doing?”

The people arriving at “Willy’s Chocolate Experience” at a warehouse in Glasgow, Scotland, this February were expecting an immersive event, an experience that would transport them to a “magical realm,” filled with colorful, larger-than-life lollipops.

At least that was what images of the event, created by generative AI, promised. What attendees got instead was a sparsely decorated warehouse that saw kids cry and led some attendees to call the police, believing they had been scammed.

The Glasgow event is a prime example of “when you promise the world with these AI images, and it completely underdelivers,” Audrey Chee-Read, a principal analyst at Forrester, said during a presentation at Forrester’s CX Summit in Nashville, Tennessee, in June.

Chee-Read, who studies consumer behavior, said that consumer trust in generative AI is exceedingly low and it’s up to brands to bring their customers along — or risk losing the trust they hold.

“With the proliferation of AI, there is a halo of consumer skepticism,” Chee-Read said. “When it comes to AI, consumer distrust is the default.”

The data backs that up. Only a quarter of consumers trust information provided by generative AI, according to Forrester’s research. Three-quarters of people believe companies should disclose when they are using generative AI. And less than a quarter feel comfortable giving up personal information to generative AI tools.

How brands can build trust in generative AI

On a basic level, brands need to make sure that the output meets customers’ expectations. Overpromising and under delivering is a recipe for disappointment and distrust.

CX leaders need to maintain human oversight, analysts urged. 

“Some organizations will fly too high to fast,” said J.P. Gownder, VP and principal analyst at Forrester, during a presentation at the conference. Responsible generative AI use keeps a human in the loop.

Chee-Read offered the example of UberEats using generative AI to produce an image of a fruit pie for a restaurant selling a pie. The problem? The company was selling a pizza pie, not a dessert pie, and customers who were eager to get themselves a dessert pie found themselves with pizza instead. 

When use of generative AI goes wrong — whether from overpromising or lack of oversight — trust is the first thing to go. 

“The first thing that we always see when it comes to backlash is that you lose the credibility that you have built and you have been building,” Chee-Read told CX Dive. “It is very, very easy to completely lose the years of work that a brand might have spent to build that credibility and not to be taken as a joke.”

The company that put on the Willy Wonka experience, for example, lost the trust of its customers, and turned into the butt of a joke across the internet, she said. But such errors in judgment can also stifle experimentation and innovation for years to come.

“When a company then tries to bounce back and share new innovation, they can't be taken seriously, and there is more work to be done when they're trying to come back,” Chee-Read said.

Customers want to know when generative AI is used. For Chee-Read, the answer is simple: “disclose, disclose, disclose.” 

“You need to not be afraid to share when AI is being used,” Chee-Read said. “If you’re gun shy to tell people, then you need to scrutinize why and whether you should be using it.”

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Customer experience (CX) teams are your company’s frontline ambassadors. They talk to your customers daily and handle all questions, issues, and complaints in real time. It’s safe to say that customer satisfaction, loyalty, and, ultimately, your bottom line hinge on their effective, efficient communication. 

But what happens when communication breaks down? This year’s State of Business Communication report revealed how communication overload and poor communication are impacting knowledge workers—but not all workers experience things equally. For CX teams, communication impacts productivity, stress levels, work satisfaction, and retention more than most.

Let’s dive into the latest findings about CX communication and explore how AI might just be the solution CX teams need to thrive.

CX teams are frontline champions

CX teams face unique communication challenges compared with the rest of the organization. However, those challenges have forced them to build communication skills that few others possess. CX agents spend the majority (58%) of their week communicating with customers in real time—this is 16% more than the average knowledge worker. CX teams spend an average of 19 hours each week on written communication.

CX workers use channels like email and chat most frequently. They communicate via these channels more than any other team, spending an average of seven hours weekly on email and four hours weekly communicating via chat.

Because of the urgency that CX agents face in their role, they simply don’t have the time to trudge through unorganized or unclear communication. This has forced them to be effective managers of information. In fact, CX workers report spending 22% less time misinterpreting communications and 14% less time searching for company information to include in communications than the average worker. These agents know where to find the information they need and feel confident that they can convey their message effectively to customers.

CX teams are professional frontline communicators who rely on effective communication to succeed in their jobs. But what happens when communication doesn’t meet their high standards?

Poor communication takes a toll on CX job satisfaction and well-being

When customers reach out with a question or issue, they often expect (and sometimes demand) real-time responses. This leads CX agents to feel more stressed than the average knowledge worker when communication does break down. Nearly two-thirds of CX workers (64%) say that poor communication increases their stress at work. 

More than any other team surveyed, CX teams report being frustrated by a lack of organization in finding and using information to communicate effectively at work. They’re also frustrated by information overload, communication channel overload, and inefficient tools for their jobs.

All of this adds up, taking a toll not only on their stress and well-being but also impacting their overall job satisfaction. One in three CX workers say that poor communication has made them consider finding a new job. To avoid burnout and turnover, it’s time for CX teams to embrace a solution to communication overload.

The power of generative AI for CX

Last year, the notable explosion of generative AI (gen AI) was game-changing for the world of communication. Early adopters of gen AI in the CX world report saving an average of seven hours each week in productivity alone. This is huge for customer-facing teams in particular because it gives them time back to put into delivering the best possible customer experience.

CX agents who use AI in their daily work experience reduced workloads and lower stress. They believe that AI makes them better at their jobs, improves the quality of their work, and helps them avoid miscommunications that could cost them both time and stress.

AI has already shown tremendous potential to transform the ways we work. But these benefits are largely untapped—particularly for CX teams.

CX teams are slow to adopt AI

Compared to any other team surveyed, CX workers are less likely to embrace using gen AI at work. Only 37% of CX workers reported experimenting with gen AI in their role. This could be because 81% of CX agents are skeptical about AI’s ability to help convey empathy in their communication, which is a critical component of building relationships with customers. Their job hinges on understanding customer needs, recognizing customer emotions, and addressing both in a compassionate manner, so a perceived lack of empathy in gen AI results is a major hurdle in CX adoption.

Other blockers to fully embracing AI include security and privacy concerns (67%), fear over content quality (59%), and worries over skills gaps (59%). CX workers worry that gen AI results will be outdated, inaccurate, misleading, or irrelevant. These teams care deeply about content quality, as poor or inconsistent interactions risk brand reputation, customer churn, and their own future career growth.

Finding the right AI tool for CX

However, as the early adopters above reported, generative AI actually has the power to help CX teams communicate more effectively and improve the quality of their work. Leaders should look for generative AI tools that help their CX agents overcome these blockers so they can tap into AI’s benefits and embrace its power at work. 

When researching tools, you’ll want to look for an AI communication assistant that can:

• Personalize results for context, tone, clarity, fluency, and more
• Be customized to your specific brand, so you can better enforce brand guidelines, terminology, and standards as CX workers communicate with customers across channels
• Work across every layer of communication, such as email, chat, text, and any others that CX teams use to interface with customers
• Upskill not only their CX team but the entire workforce

As CX teams scale to meet customer needs, they’ll need to navigate how to deliver a great and consistent customer experience (in a cost-effective manner). Generative AI offers a promising solution to help CX workers elevate content quality, create consistent brand experiences, and provide real-time coaching that helps to upskill the entire CX workforce.

It’s time for CX teams to harness the power of AI to communicate better, not more. Get even more CX highlights from the report in our latest infographic, Customer Experience Teams Have The Most to Gain from Gen AI.

Curious to learn about trends in your team’s communication trends and AI usage? Check out our blogs that dive into the worlds of marketing, HR, and IT communication.

Klarna’s OpenAI-powered customer service assistant is making headlines for its promising early results — and the apparent threat to customer service agents' jobs.

In February, the buy now, pay later provider reported that the AI assistant managed to handle the work of 700 full-time agents during its first month of operation.

But business leaders need to keep the bigger picture in mind when analyzing apparent AI success stories, according to Christina McAllister, senior analyst at Forrester. Four weeks is too little time to determine the long-term impact an AI-led strategy will have on outcomes like customer retention and lifetime value.

Klarna says its AI assistant can handle a range of inquiries in 35 languages and has handled 2.3 million conversations, or two-thirds of customer service chats, since its launch a month ago. The company reported a 25% drop in repeat inquiries with average resolution times of less than 2 minutes, compared to 11 minutes previously.

While these initial results may make going all in on the technology tempting, replacing human agents with AI can open companies up to unnecessary risk in the short term, McAllister said. The technology is improving, but high-profile cases of chatbots going haywire provide examples of why a balanced human and AI strategy is important to mitigate risks.

Klarna says it isn’t cutting humans out of the loop despite the early success of its assistant. Customers can still choose to speak with human agents if they prefer and to handle inquiries that require nuance beyond what AI can provide.

“With the AI assistant, our customer service can operate with fewer people and require significantly less resources,” a Klarna spokesperson told CX Dive in an email. “However, there still is a need for more experienced and senior staff, for example with specialized training in complex or sensitive cases.”

There are other reasons to maintain the human element as well. Senior contact center leaders say their brands only interact one-on-one with a customer two to three times per year on average, McAllister said. 

That can represent an enormous volume of calls for a brand with millions of customers, but it offers a few chances to directly engage with consumers. Business leaders, McAllister said, need to ask if the goal of AI assistants is to save money by eliminating that human-to-human experience.

“If so, this strategy appears short-sighted, given both the value consumers place on those customer service interactions, and the limited opportunities to develop one-to-one customer relationships,” McAllister said in an email to CX Dive. “Brands on this path may achieve short-term cost benefits, but risk sacrificing long-term customer value.”

Generative AI is still in its infancy, and often lacks established blueprints for business adoption. Many CX leaders, alongside technology executives, are still exploring how they can tap into AI’s potential without creating a bloated tech stack.

A successful generative AI rollout involves a combination of careful planning, patience and flexibility, business leaders said during Customer Contact Week 2024 in Las Vegas in June.

Cross-departmental coordination is essential when implementing generative AI, according to Lori Bradshaw, director of guest CARE at Alaska Airlines. Leaders across the company need to share reasonable expectations — AI is a powerful tool, but it isn’t foolproof.

"I think some companies have a misperception that you can just plug in AI, flip a switch and watch it work,” Bradshaw said. “It takes coordination and calibration and a clear, thoughtful rolling out of these capabilities.” 

Leaders should implement generative AI in a controlled, efficient manner, according to Enda Murphy, chief technology officer at US Radiology Specialists. One approach is to examine which CX use cases are truly improved when automated by generative AI. 

Many customer service calls are too complicated for automation, but others are simple enough to save time for agents and customers. US Radiology Specialists uses AI for giving out directions to imaging centers or scheduling appointments, but not for conversations involving sensitive medical information.

“It's about using the right tools in the right place for the right part of the workflow, and it really depends on exactly what you're trying to achieve,” Murphy said.

Flexibility is paramount with generative AI plans, according to Murphy. Approaches that work well today may become outdated next year, making platforms that can quickly and easily swap out algorithms particularly valuable.

Generative AI trailblazers may run into unexpected problems as they integrate the technology into their workflows. However, any problems that surface can be an opportunity to assess lessons learned and build trust with customers and employees through transparency.

"For any company that makes a mistake in implementing new technology: Own what happened and talk through how you’re going to resolve it moving forward,” Bradshaw said. “It's super important to be clear with your people."

Generative AI was introduced to technology leaders as a transformative addition to their company’s toolbelt. Yet organizations are still struggling to reap the technology's full benefits and accurately assess its impact.

Organizations are predicted to abandon nearly one-third of generative AI projects after the proof of concept stage by the end of next year, according to Gartner research, which blamed rising costs and unclear business value as contributing culprits. 

Tuning, customizing or simply deploying generative AI models costs businesses an average of at least $5 million. Naturally, companies are expecting a return on their investment.

“We’re a pretty well-funded IT department, so we have access to the resources and tools we need,” Brent Harris, CIO at Tulsa, Oklahoma-based community mental health center Family & Children’s Services, said. “But we’re also nonprofit, and we try to be responsible with the money we’re given.”

The organization is assessing generative AI’s ability to improve workflows, provide better data-driven decisions and reduce repetitive tasks by creating cheat sheets for frequently-asked-questions and step-by-step guides. 

Tech leaders want to ensure generative AI use cases are set up to succeed. To avoid pouring money into failed pilots, executives are weighing implementation costs against the potential value and risks the technology could bring.

“We certainly imagine that list [of use cases] is going to grow,” Harris said. “We’ve tried to be mindful of being on the leading edge, but not the bleeding edge. We don’t want to be the guinea pigs, but we don’t want to just keep up.” 

Where generative AI pays off

Businesses land on a wide spectrum when it comes to AI readiness. This can impact their ability to determine ROI.

Poor data quality and management stifle aspirations. Caution around cybersecurity and protecting sensitive information presents additional hurdles. Plus, generative AI tools can open the business up to risks related to the tech’s inaccuracy and bias. 

Despite potential risks, one of the leading adoption barriers for enterprises is an inability to estimate and demonstrate the value of AI projects, according to a Gartner survey published in May. Less than half of AI projects ever make it to the production stage. 

“A lot of companies plunged in with hype, converting everybody and any use case that came up, they tried it,” Shohreh Abedi, EVP and chief operations and technology officer and membership experience at AAA - The Auto Club Group, told CIO Dive. “We didn’t do that. I set up a structure with a methodology and a process and put guardrails around it.”

Businesses that rush into generative AI adoption have reported buyer’s remorse. Around 1 in 4 IT leaders said they regretted investing in AI so quickly, according to a March Asana survey. 

Still, tech leaders are trying to keep pace with broader business goals, priorities and responsibilities. 

At Family and Children's Services, Harris worked to find a balance between innovation and performing the “basic blocking and tackling,” as finding staff, therapists, clinicians and other experts has become more difficult. 

“The more time we can spend automating what they do and making documentation easier for them it helps with retention,” Harris said. “If, relative to our peers, it’s much more difficult to use our systems, we’ll have a hard time bringing on and retaining quality staff, so there’s a balance but nothing is done at the risk of security.”

Eager adopters have found value in taking a measured approach to piloting the technology, explicitly aligning use cases with business goals and success metrics.  

AAA’s second-largest club focused its own generative AI push on content management, internal support services and coding tests as it laid out its strategy last year. The organization has a dedicated team sifting through use case proposals, led by Abedi alongside the group’s chief architect and CISO.

“I give the final approval on what use cases we’re going to look at, and they have to meet certain criteria,” Abedi said. “The use cases have to meet a business goal, either growth, cost containment or efficiency from a productivity increase.”

When a use case meets the standards, and only then, does the organization move forward with a proof of concept.