Dive Brief:
- Two-thirds of CX leaders call their most recent AI project a success, but many projects are hitting substantial roadblocks, according to a survey released last week by Laivly, an AI and automation platform for contact centers.
- Just over half of AI projects, 53%, have exceeded budget, and 43% are currently delayed or stalled.
- Some AI projects are also hurting revenue: 28% of leaders attribute lost revenue to AI that is unable to handle customer complexity. Another 20% say revenue loss is occurring but they’re unable to quantify the damage.
Dive Insight:
There’s a disconnect between how CX leaders view their AI projects and results — a paradox that can be attributed to pressure service leaders face to adopt AI, according to Ian Elliot, director analyst for Gartner’s customer service and support team.
“From what we see, the leadership disconnect regarding AI success stems from a combination of immense market pressure, misaligned personal incentives, reliance on flawed performance metrics and the poor accuracy of AI tools,” Elliot told CX Dive in an email.
As investors actively reward companies claiming AI success and cost savings, CEOs are pressuring service leaders to deploy AI and meet unrealistic expectations, according to Elliot.
That’s compounded by personal financial stakes. In 2026, more than half of service leaders — 56% — have incentives directly tied to AI outcomes, Gartner found.
“In a rush to demonstrate these expected cost savings, some organizations are even laying off employees prematurely to simply free up capital to fund their AI ambitions, rather than reducing headcount because their AI deployments were actually successful directly,” Elliot said. “All of this creates a superficial narrative of cost-cutting success at the executive level which isn’t as apparent when looking at the hard data.”
AI tools can be useful, but too many are overhyped and failing to meet their intended goals.
A Sinch survey from May found that three-quarters of enterprises have rolled back AI deployments. The top reasons for rollbacks included: customer data exposure, hallucination or brand risk, and the inability to diagnose what went wrong.
Respondents in Laivly’s survey also highlight such issues. One-third of leaders say AI tools introduce compliance and tone risk, and 36% say agents struggle with AI tools because they lack context across interactions.
“While leaders report success based on usage and headcount reductions, the reality on the ground is that the technology often fails to support the workforce,” Elliot said. “Less than half of customer service agents consider their current systems reliable, and only 56% trust the accuracy of the information those systems provide.”
That leads to agents double-checking the information AI provides, slowing down the intended efficiency leaders assume AI creates.
