Artificial intelligence could eliminate close to half of today’s customer service jobs by 2030, according to a Forrester forecast released last week.
The firm expects the steepest cuts at operations that field large numbers of routine, low-complexity inquiries, the kind of work that dominates many collection floors. Centers handling more complex interactions will not be spared, Forrester said, but they will see slower reductions.
Why it matters: Collection agencies, debt buyers, and creditor servicers run some of the most call-intensive operations in financial services. Much of that volume involves straightforward tasks, account balances, payment arrangements, and basic status checks, that AI is increasingly able to handle without a person on the line.
The pace and scale of the shift remain contested. Forrester’s view sits at the aggressive end. Gartner is more skeptical, predicting that half of the organizations now planning deep contact center cuts tied to AI will abandon those plans by 2027.
To illustrate the trajectory, Forrester modeled a hypothetical operation with 1,000 customer service representatives, according to a published report. Within four years, that headcount could fall to as few as 40. The same model assumes new roles will emerge to absorb displaced work, including relationship managers and subject-matter or industry experts who handle the interactions AI cannot.
AI is already shaving minutes off individual transactions and taking over discrete functions such as account saves, and small per-contact efficiencies compound quickly at scale.
For now, outright layoffs remain rare. The more common path is attrition. Contact center turnover already runs high, averaging around 30% annually. As AI absorbs more volume, departing employees simply will not be replaced, and many will struggle to find comparable roles elsewhere.
The throughline across the forecasts is a change in mandate. As automation handles more direct contact, the human workforce shifts toward managing AI, stepping in on judgment calls, and turning conversation data into operational insight.
