Amara Osei spent the better part of four years talking into a headset at a regional telecommunications company in Atlanta. From the outside, the job looked repetitive — billing disputes, outage complaints, account changes. From the inside, it was an intricate negotiation between a frustrated customer, an inadequate database, and a company's contractual liability limits.
She was good at it. Her supervisor knew it. Her quality scores showed it. And the training materials — the call scripts, the de-escalation frameworks, the decision trees she had personally helped revise — all reflected it.
Then in 2022, the company launched a pilot of a voice AI customer service system. Amara's team was told they would be "augmented." The AI would handle Tier-1 volume; they would handle escalations. It sounded like progress — less of the mind-numbing routine calls, more of the interesting problem-solving work.
Seven months after the pilot went enterprise-wide, her team of forty-three was reduced to eleven. The voice AI, trained in part on call transcripts and the scripts Amara had helped write, was handling 64% of incoming contacts without escalation. Amara was among the thirty-two who received a transition package.
Labor researchers have a name for what she experienced: "shadow training." It's the process by which customer-facing workers generate, through their daily labor, training data for the AI systems that ultimately replace them — without compensation, explicit awareness, or consent. Every call a skilled agent handles produces a transcript. That transcript is a record of effective customer communication. In aggregate, these transcripts are among the most valuable training assets in customer service AI development.
The "augmentation" framing — AI tools that help workers rather than replace them — has become the dominant way companies talk about AI deployment. And it's not always dishonest. There are genuine cases where AI functions as decision support. But workers are given no reliable way to know which situation they're in. A worker who believes she's being augmented might share more knowledge, participate more freely in optimization sessions. The information asymmetry is structural.
The impact goes beyond individual job loss. Customer service work has historically functioned as a career entry point — a way into corporate employment that required interpersonal skill and allowed workers to advance toward supervisory or operational roles. The AI transition isn't just removing rungs from this ladder; it's removing the ladder itself for the people who most depended on it. The cases that remain for human agents are the most complex ones, requiring already-experienced workers, not people developing skills.
"They kept saying the work was going to be more interesting," one agent at a financial services call center in Phoenix told researchers. "And it is, kind of. Every call is a problem the AI couldn't solve. But there are eleven of us now instead of sixty. Nobody is getting trained. Nobody is moving up. The ladder is gone."
Amara Osei is working now as a patient services coordinator at a medical practice in Decatur. The work uses many of the same skills she developed in the contact center. It pays somewhat less. She doesn't regret what she built at her former employer.
"I was good at that job," she said. "I just wish they'd been honest about what they were doing with it."