This parable matters for call centers today as AI and chatbots will change how customer service is delivered, but they will not make human agents obsolete. Instead, they’ ll shift the job toward real-time judgment, empathy, and complex decision-making where machines still fall short.
A CAUTION AGAINST LINEAR THINKING
Economists who studied the rise of ATMs found there were two distinct outcomes.
First, automation replaced routine aspects of the teller job and reduced the number of tellers required per branch.
But second, ATMs lowered operating costs per branch and made it economical for banks to open many more branches.
The result was total teller employment did not collapse. Instead, and surprisingly, it grew for a time as the role shifted toward sales, problem-solving, and higher-touch customer service( source: Bessen, James E.“ Toil and Technology”. Finance & Development, International Monetary Fund).
In other words, the machines took over predictable, repeatable tasks, while humans moved up the value chain.
Back in the day, these changes were hard to digest for the actual bank employees( me). As ATM networks expanded, less tellers per branch were required. New branches weren’ t built immediately, and teller job growth plateaued for a while before it started to grow again.
Once it did start to grow, often those teller jobs morphed into personal banking- or operations roles like call centers- which took more training.
CALL CENTER VS. BANK TELLER WORK
That pattern of automation substituting for repetitive tasks, while expanding or reshaping complementary job skills, is an applicable model for contact centers. It shows how organizations and markets reconfigure themselves around new capabilities.
Both bank tellers and call center agents perform a blend of routine and non-routine tasks, with automation excelling at the predictable functions such as balance inquiries, password resets, and billing questions.
In addition, both roles involve direct customer interaction, requiring employees to humanize the experience.
In my work conducting observations in contact centers, I consistently see how much on-the-spot judgment agents must use.
These employees are often making decisions with incomplete information, weighing short-term fixes against long-term customer value, and applying discretionary empathy, whether that means choosing to escalate a case or to offer a goodwill credit.
I watch agents navigate rapid task switching and highly nuanced interactions, as callers frequently present multiple issues at once or bring strong emotions into the conversation.
These situations require agents to read tone, history, and subtle behavioral cues that technology simply can’ t interpret or reason through the way a human can.
Customers choose to call precisely because they want a genuine human interaction with those who can understand, adapt, and reassure in ways automated systems are still far from replicating.
McKinsey’ s recent analysis of contact centers underscores this hybrid future. Generative AI( GenAI) and automation are taking over heavy volumes of routine interactions, while organizations redesign the human role around customer advocacy.
The firm highlights case studies where human-AI combinations produce better outcomes than AI alone. And we’ ve seen this in our experience.
One of our clients, a leading energy company, successfully reduced its billing call volume by around 20 % and shaved up to 60 seconds off customer authentication by integrating an AI voice assistant into its back-end call workflow. The company is now planning to scale this use case across the organization. Other customer care leaders are naturally eager to invest in this technology, too, given the promised benefits of greater efficiency and productivity in an environment that generally struggles with high agent churn and the associated costs of recruitment and training.
The upside of the technology also extends to customer and employee experience. GenAI can handle simple queries and make interactions more efficient
AUTOMATION
while reducing wait times and enhancing customer satisfaction.
Agents themselves are seeing the positive effects of GenAI in their dayto-day duties, especially from reduced after-call work( ACW). AI tools can summarize issues and proposed interventions, increase agent productivity, and reduce their call times.
ULTIMATELY, THE CONTACT CENTER AGENT ROLE IS IN FOR A MAKEOVER AS AI WILL REQUIRE THE HUMAN TO ELEVATE.
WHAT THIS MEANS FOR WORKFORCE STRATEGY
If the ATM evolution taught us anything, it’ s that companies shouldn’ t view AI as a simple way to cut headcount.
Instead, AI works best when it reshapes the work itself. It should automate the predictable tasks, so humans don’ t get bogged down in routine work. In the contact center space, AI will augment agents by giving them real-time prompts along with instant access to the right information.
Ultimately, the contact center agent role is in for a makeover as AI will require the human to elevate. The key to their future success will be their ability to learn how to use good judgment and problem solve the most challenging scenarios.
Organizations will need to invest in training and skilling their agents to handle this higher-order work. The shift from agents performing primarily transactional tasks to acting as problem solvers who apply judgment and emotional nuance will require a far more advanced workforce strategy.
Organizations will also need to rethink the entire talent lifecycle, beginning with the screening process for this new, elevated candidate who can have higher degrees of emotional intelligence: something AI cannot do for their customers.
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