Gartner Survey Finds 21% of Service and Support Leaders Have Reduced Human Agent Headcount Because of AI

Despite widespread expectations that AI will significantly reduce customer service headcount, a Gartner survey of 275 service and support leaders, conducted in July and August 2026, found that only 21% said their organizations had reduced human agent headcount because of AI.

Among organizations that reduced human agent headcount because of AI, the average reduction was 16.7% of the workforce during the previous 18 months. Some organizations are already reversing earlier workforce decisions: 7% of surveyed leaders said their organizations had rehired human workers following previous reductions, and 11% said their organizations planned to rehire within the next 18 months.

From Gartner Customer Service & Support Conference in Denver, we spoke with Kathy Ross and Emily Potosky, analysts in the Gartner Customer Service & Support Practice, to discuss why AI’s effect on customer service headcount remains modest, how leaders can determine whether AI is ready to absorb human work, and how organizations can avoid the “cut fast, rehire later” workforce trap.

Journalists who would like to speak with Kathy or Emily regarding this topic can contact Elizabeth.Bishop@gartner.com. Members of the media can reference this material in articles with proper attribution to Gartner.

Emily: Most strategies leaders are using to reshape workforce size are indirect and will take time to produce results. Forty-five percent of leaders report a hiring freeze, while 39% are shifting human agents into other business units. By comparison, only 20% report using forced reductions as a workforce strategy.

At the same time, parts of the service and support workforce are growing. Seventy-one percent of leaders are shifting human agents into different roles within service and support, and more than half have hired specialized staff to support AI initiatives.

Despite pressure to reduce human agent headcount, widespread reductions are not yet evident in the data. Instead, service leaders are largely using AI to reshape work and increase capacity, not replace their workforce at scale.

Kathy: Organizations can fall into a “cut fast, rehire later” trap when they reduce headcount based on expected AI value rather than demonstrated results. If the technology cannot manage the anticipated volume or complexity of work, service quality can suffer and the remaining workforce may be unable to close the gap.

These early signs of rehiring support Gartner’s prediction that half of companies that cut customer service staff because of AI will rehire by 2027. Gartner’s latest data shows that 7% of leaders report rehiring human workers after previous reductions, and 11% report plans to rehire within the next 18 months.

Rebuilding that capacity can be costly because organizations must recruit, onboard and train employees while restoring knowledge and experience that may have been lost. Leaders should treat workforce reductions as an outcome of proven and sustained AI performance, not as a prerequisite for achieving value from AI.

Kathy: Organizations can fall into a “cut fast, rehire later” trap when they reduce headcount based on expected AI value rather than demonstrated results. If the technology cannot manage the anticipated volume or complexity of work, service quality can suffer and the remaining workforce may be unable to close the gap.

These early signs of rehiring support Gartner’s prediction that half of companies that cut customer service staff because of AI will rehire by 2027. Gartner’s latest data shows that 7% of leaders report rehiring human workers after previous reductions, and 11% report plans to rehire within the next 18 months.

Rebuilding that capacity can be costly because organizations must recruit, onboard and train employees while restoring knowledge and experience that may have been lost. Leaders should treat workforce reductions as an outcome of proven and sustained AI performance, not as a prerequisite for achieving value from AI.

Emily: Leaders should determine whether AI can consistently deliver the required outcomes at an acceptable cost before making headcount decisions. If AI costs cannot be offset through workforce savings, leaders need a clear plan for how those investments will produce sustainable value.

AI should not be the default solution for every efficiency or workforce objective. Leaders should compare GenAI with traditional automation and human-led approaches to determine which option can deliver the required outcome most cost-effectively.

Organizations should begin with targeted use cases, test performance under real operating conditions and expand only when results are reliable.

Kathy: Leaders should first identify the primary objective for their AI investments. An organization focused on cost reduction should make different workforce decisions from one focused on retaining revenue, strengthening loyalty or supporting growth.

The future service and support organization will be digital-first, but not digital-only. Leaders need to determine the right balance of AI, automation and human talent for each type of interaction.

The goal should be to build a workforce that can deliver the desired business outcomes as AI changes how service work is performed, not to reduce headcount as quickly as possible.