AI Agents to Automate Half of Customer Service Jobs by 2030

By 2030, nearly half (49 percent) of all current customer service jobs will be lost to artificial intelligence, shifting human roles away from direct customer interaction toward directing, governing, and optimizing AI agents, Forrester Research predicts in a new report.

The firm further predicts that within the next two to five years, customer service organizations will shrink traditional roles while creating new, more data-driven and technical positions focused on oversight, insights, and customer value creation.

“AI agents are not just reducing costs in customer service, they are redefining what customer service work actually is,” says Kate Leggett, vice president and principal analyst at Forrester. “Over the next few years, we will see fewer people answering routine questions and many more people directing, coaching, and governing AI systems that interact with customers at scale.

“Leaders who treat this shift as a pure automation exercise will fall behind,” Leggett adds, noting that winning organizations “will redesign roles, invest aggressively in reskilling and continuous learning, and rethink how they measure success, turning customer service from a cost center into a source of customer value, loyalty, and revenue growth.”

The research firm says that AI will have a much greater impact on customer service jobs than previous customer service technologies like interactive voice response systems and chatbots, citing some of AI’s early impacts: AI is involved in 96 percent of Anthropic customer inquiries and handles 90 percent of inquiries for travellers going through London’s Heathrow Airport; 80 percent of inquiries for TeamSystem; and 68 percent of inquiries for the Rocket Money personal finance app.

Forrester also expects that as AI automates routine interactions, lower-tier customer service representatives will increasingly supervise AI agents, resolve exceptions, and provide feedback to improve AI performance, while higher-tier roles will specialize in complex, technical, or relationship-driven work that is difficult or costly to automate. High-volume contact centers will experience the greatest staff reductions, while lower-volume, higher-complexity environments will see less dramatic displacement, Forrester also predicts.

At the same time, though, new roles are expected to emerge around insights, data, and AI governance. Insight and analytics teams will become more strategic as AI enables detailed analysis of performance, cost, and customer outcomes down to the level of individual intents, according to Forrester.

And as AI scales and flattens organizations, customer service leaders are expected to shift responsibilities to customer value creation and revenue growth, meaning more work on front-
office functions that drive customer service and loyalty. To that end, some companies have already added sales quotas for customer service representatives.

Forrester also foresees changes in operational and IT roles. In operations, managers will take on more strategic roles as they blend AI-driven insights and efficiencies with human understanding. In IT, roles will expand to support AI innovation, including AI agent configuration and optimization.

The research firm recommends that customer service operations do the following:

• Plan jobs to change at different rates and to different degrees.
• Redefine, develop, and hire for essential skills, like data analytics, business judgment, quality oversight, relationship management, collaboration, and critical thinking.
• Craft an essential skills assessment and development plan.
• Chart a skill development path, not a one-time training course.
• Invest in continuous learning for adaptive and resilient workers.
• Cross-train workers who can step into different roles.
• Determine who is responsible for AI creation, management, and execution, while also establishing responsibility for the supervision of AI.

But even with all that preparation and training, Forrester points out that staffing levels will continue to change, regardless of the level of AI adaptation and maturity within organizations.