Gartner research published in June 2026 warns that AI-powered customer service operations may cost more to run than the human workforces they replace. Service leaders must address these total cost of ownership realities with their boards before unrealistic budget expectations are set for them.
Service and support organizations spend heavily on people. Headcount accounts for roughly 70% of their operating costs, compared to 15% to 30% in other business functions. Consequently, 79% of service leaders expect to operate with a lower headcount within 18 months, relying primarily on AI to achieve those reductions.
The hidden cost equation
This headcount reduction strategy faces a paradoxical reality. As automation approaches 50% of a service estate, the combined costs of AI licensing, compute, inference, and specialized labor to manage these systems can easily exceed the savings from cutting frontline staff.
"When the total cost of ownership is accounted for, a GenAI-powered service organization may be more expensive to operate than the equivalent human workforce," the Gartner report said.
Emily Potosky, senior director analyst in the Gartner Customer Service and Support Practice, identifies two core issues. First, she said, "there's research out there to suggest that prices are invariably going to have to go up because the costs are currently being highly subsidized." Second, organizations cannot fully recoup headcount reductions because that capital must be redirected toward technology investment, infrastructure modernization, and new technical talent.
Rethinking the AI-first strategy
The economics of this shift are severe enough that Gartner predicts it may be cheaper to outsource support functions to offshore partners by 2030 than to run them with AI. Furthermore, the nature of enterprise software has changed. Buyers now share responsibility with vendors for customizing, developing, and managing AI systems, requiring service leaders to hire or train entirely new skill sets.
Leaders evaluating AI for Customer Support must stop assuming a direct trade-off between automated efficiency and reduced spending. Potosky said leaders should avoid applying AI to every problem, as blanket automation becomes an expensive route. Instead, organizations should carefully select the right technology for specific tasks.
Owning the narrative
Professionals focused on AI for Executives & Strategy must proactively define a budget-conscious approach rather than waiting for the board to dictate it. If service leaders do not set the strategy and budget requirements for a future-ready operation, someone else will. Only by clearly communicating the true total cost of ownership can leaders prevent their departments from falling into the emerging AI cost paradox.
Why this matters for Executives and Strategy
Board-level pressure to achieve more output for less spending through automation ignores the fundamental shift in software ownership and infrastructure costs. Executives must demand total cost of ownership models before approving AI service deployments. This ensures capital is allocated for specialized talent and compute rather than assuming immediate headcount savings will cover the transition.
Your membership also unlocks:
