As AI revamps the customer experience, businesses need to overhaul how they onboard, train and upskill their employees to keep up with the rapid pace of change and deliver a return on investment, experts say.
"AI is not a one-time technology rollout,” said EY Americas Loyalty Leader Patricia Camden. “This is a new way of working that's going to continue to evolve, and it is going to transform how we work."
Many businesses are struggling to implement AI effectively, leaving behind a wake of broken promises, disgruntled customers and disengaged employees. About 2 in 5 companies abandon most of their AI initiatives, with nearly half experiencing zero return on investment, according to S&P Global Market Intelligence.
A lack of effective onboarding, training and upskilling is partly to blame, and it starts with employee buy-in.
“There’s a lack of trust,” said KJ Kusch, global field CTO at WalkMe. “People are afraid that AI is going to take their jobs.”
Yet many companies fail to educate employees about how AI will affect their roles.
“You can’t walk into a contact center and talk about [large language models] or retrieval-augmented generation. That's not the language that a lot of customer service professionals speak,” Kusch said. “What they want to know is: How does this work for me? Is it going to make me look smarter? Is it going to help me with the customer? Is it going to help me hit my goals?"
As a result, many employees don’t want to learn to use the technology or use it only when directed to do so. Greater adoption requires training that reframes AI as an assistant or “coach” rather than a replacement for humans, Kusch said.
“All the vendors in the world can show you a nice demo or tell you a great story, but if the right people don’t adopt and know how to use it, it's literally going to fail,” Kusch said.
Role-based AI training
AI knowledge and skills requirements vary dramatically across an organization, requiring businesses to tailor the information and training they provide to employees.
“We talk about customer experience workers as if it's kind of one strata, but it's really not,” said Jeannie Walters, founder and chief experience investigator at Experience Investigators.
According to Walters, there are three core skill tiers: executives, customer experience leaders and contact center agents. Each requires a different training approach, which varies by organization, making it difficult to generalize.
Executives must have a strategic understanding of how AI will impact the workforce, including workflow changes and tech stack integration.
Customer experience leaders, meanwhile, must have strong data literacy and a strong understanding of prompt engineering. Asking AI the right questions, for example, can help customer experience leaders mine data to identify behavioral patterns that would make a customer more or less likely to renew or return a product.
Additionally, contact center agents will increasingly need deep subject-matter expertise, the ability to solve complex problems and empathy, as AI agents handle simpler, less emotionally charged tasks. They also need to understand how to use the tools within their workflows.
However, even once those role-specific needs have been identified, it can be difficult for businesses to develop effective training strategies without organizational alignment on AI, including ethics, policies, processes and technology.
Companies can’t just tell people “to do the right thing” because that can mean different things to different people, Walters said.
“You have to define it,” Walters said.
Practice makes perfect
Businesses can outsource some AI education and training, especially on standard best practices. However, most of it will need to be done in-house due to the rapidly evolving nature of the technology and the diverse needs across and within organizations.
“Some of the training that is out there is going to be outdated pretty quickly,” Walters said.
There’s no one-size-fits-all approach, but there is widespread agreement that practice-based learning is essential. Employees are significantly more likely to use AI effectively when they experience its benefits firsthand.
"If the training doesn't connect to their daily work, it's not going to stick,” Camden said. "The goal isn't for every role to become AI fluent. The goal is to make sure every role understands where AI supports them, and then where their human value is most essential."
Practice-based learning can take many forms, including workshops on specific customer journey workflows, role-specific enablement labs and practice circles with fun AI challenges.
“You learn a lot through doing it,” Camden said.
Regardless of the specific training modality, employees need a “safe space” to practice using the technology, Kusch said. They can’t learn how to use it under pressure.
“If I'm in a call center, and I'm worried about my average handle time, I'm not going to experiment with a new AI tool,” Kusch said. “But if you give me time to practice and show me it's okay to make mistakes while learning, then I'll adopt it.”
The most important training, however, may be retraining customer experience leaders on how to train the employees they manage.
“Leaders need to understand how to lead in this age of AI,” Camden said. “How do you model the right behavior? How do you coach your team? It is a massive shift we're going through.”
