Gartner: 30% of workers laid off because of AI will need to be rehired by 2029
Gartner predicts that, across all industries, 30% of workers laid off because of AI will need to be rehired by 2029, often at a much higher cost.
What's happening
Gartner’s new prediction, published on Sept. 9 and reported by CX Dive on Sept. 11, covers all industries, not just customer service. It holds that 30% of workers laid off because of AI will need to be rehired by 2029, often at a much higher cost. According to Gartner, headcount cuts are depleting talent pools and eroding institutional knowledge, while competition for talent, at a time when global workforce growth is flat or declining, is driving up hiring, training and onboarding costs. Gartner’s Emily Potosky says customer service is a leading indicator of this trend.
Where it comes from
In a press release published in February 2026, Gartner predicted that by 2027, 50% of companies that attribute customer service headcount reductions to AI will rehire staff to do similar work under different job titles. That prediction was based on a survey of 321 customer service and support leaders conducted in October 2025; only 20% of the leaders had reduced agent headcount because of AI, while most had kept staffing flat. According to Gartner analyst Kathy Ross, most recent headcount reductions were driven not only by automation but also by broader economic conditions.
Whose desk it lands on
This is a matter for customer service and support leaders and HR executives. Anyone planning to shrink agent headcount and citing AI as the reason should also factor in Gartner’s February prediction: they may need to rehire staff under different job titles to do similar work.
Our view
The CX Brief's interpretation, not a claim made by the source.
For organizations in Türkiye that justify AI investment with headcount savings, this prediction is a cost warning: some of the workers laid off because of AI may later need to be rehired, often at a much higher cost. Attributing headcount reductions solely to AI can also be misleading; as Kathy Ross said in February, most recent reductions were driven not only by automation but also by broader economic conditions.
