Gartner believes that the promise of cheap, automated customer service through generative AI is running headlong into an uncomfortable reality.
According to new research from the firm, the cost per resolution for gen AI in customer service will exceed $3 by 2030, higher than many B2C offshore human agents.
The prediction challenges the dominant narrative that’s driven billions in AI investment across the contact center industry.
Patrick Quinlan, Senior Director Analyst at Gartner, explained that the issue goes well beyond the per-unit consumption costs that vendors typically emphasize.
In an exclusive interview with CX Today, Quinlan explained that “it’s not just like the per unit cost from a consumption perspective, that’s what vendors usually focus on, and that is often very low.
“But what then ends up happening is that many organizations consume a lot more than they expect, and then they don’t account for the total cost of ownership.”
These unaccounted costs include hiring specialized AI talent that commands significantly higher salaries than traditional contact center agents, unpredictable usage patterns that blow through budgets, and a series of infrastructure cost increases that are baked into the technology’s future.
The Subsidy Problem
Another significant factor driving costs upward is something most organizations haven’t considered: the prices they’re paying today are artificially low.
Large language model vendors are currently subsidizing their services by up to 90%, according to some estimates, as part of a growth strategy to build market share. Quinlan claims that this won’t last:
“That price is subsidized to drive growth in their user base, and this is a common strategy. You enter the market with a low price, but that’s going to have to change once those companies pivot to being profitable.”
The situation gets worse when you factor in newer models. While per-token costs have dropped for older models, frontier models consume three, five, or even ten times more tokens for similar interactions.
The net result is that queries on newer models end up costing more, even if the unit price appears lower.
Infrastructure Reality Check
Beyond the subsidy issue, there’s a physical infrastructure problem that could be about to hit everyone’s wallet.
GenAI doesn’t scale the way traditional software does. Adding more users requires an almost linear increase in compute resources, which means data centers need to expand dramatically.
Those data centers need massive amounts of electricity, and the grid can’t handle it.
Several U.S. states have already passed laws preventing power companies from passing infrastructure costs onto consumers after electricity bills in data center hotspots like Northern Virginia and Oregon jumped by as much as 200%.
That means LLM vendors will have to absorb those costs themselves.
“That makes their data center investments higher, the cost of electricity higher,” Quinlan said.
“At some point that’s going to affect what the LLM vendor charges, which will affect what the application vendor charges.”
Some companies are even investing in small modular nuclear reactors to power their data centers off-grid because the electrical infrastructure can’t keep up with demand.
Water access for cooling these facilities is another limited resource that’s becoming more expensive.
Then there are the specialized AI chips required to power these workloads, which typically burn out in one to three years and need replacing.
In total, Quinlan claims that “trillions of dollars” are being laid out.
“How are they going to earn money back? At some time, their shareholders will be like, ‘okay, we want a return now.’”
The Rehire Prediction
The cost problem connects directly to another Gartner prediction released last week.
The research expert forecasts that by 2027, half of companies that cut customer service staff due to AI will end up rehiring people to perform similar functions, though possibly under different job titles.
Despite the hype around AI-driven workforce reductions, a Gartner survey from October 2025 found that only 20% of customer service leaders have actually reduced agent staffing because of AI, with most reporting that their headcount has remained steady, even as they support more customers.
In expounding on why the reality is apparently at odds with the hype, Emily Potosky, Senior Director of Research in the Gartner Customer Service & Support practice, said:
“AI simply isn’t mature enough to fully replace the expertise, empathy, and judgment that human agents provide. Relying solely on AI right now is premature and could lead to unintended consequences.”
Kathy Ross, Senior Director Analyst at Gartner, added that “most recent workforce reductions were influenced by broader economic conditions rather than automation alone.”
What Should Contact Centers Do?
Quinlan’s advice for contact center leaders considering or already implementing AI is to approach any decisions with caution and pick your use cases carefully.
“Many vendors sell the idea that you can point AI at your mess and it’ll make sense of it. That doesn’t work,” he said.
“There’s a lot of hard work that needs to be done just to get the organization’s data and content ready for consumption by AI.”
The best use cases for GenAI aren’t about full automation. Quinlan pointed to triage as a strong application, where AI can converse with customers, understand their issues, and collect information before handing off to either a human agent or a rules-based system for the actual resolution.
He explained that “the valuable part of the process is the resolution.
“Unfortunately, GenAI is non-deterministic, so that resolution from a GenAI perspective is unreliable. There are better technologies to provide resolution; you just have to pick and choose where you apply the technology.”
Other solid use cases include summarization, note-taking, and intent classification. These applications make agents’ lives easier without requiring the kind of complex, token-heavy interactions that drive costs up.
“I think organizations who expect big cost savings from GenAI will be disappointed,” Quinlan said.
“Organizations that leverage GenAI to engage customers, to help ask questions, to give that kind of immediate connection to people instead of waiting on hold, that is a great use case.”
The bottom line is that GenAI is a tool, not magic.
For the 10% of Fortune 500 companies that Gartner predicts will double their customer service spending to leverage AI for hyperpersonalized, proactive experiences, there may be competitive advantage to be gained.
For everyone else, the math is starting to look a lot less appealing than the vendor pitches suggested.
