As organizations expand their use of AI across customer service, efficiency has become one of the clearest ways to demonstrate its business value.
However, improvements in efficiency metrics do not necessarily show whether customers are reaching a resolution, as lower contact volumes can conceal repeat contacts and increased friction.
CX leaders should now be considering whether their AI measures capture the quality and outcome of the customer journey, alongside the operational savings.
Jon Lamont, VP of Client AI Automation at CallMiner, told CX Today that businesses can lose sight of what customers actually want when they focus too heavily on efficiency metrics:
“Customers contact you to solve a problem. Success should be measured by resolution, not efficiency alone. A shorter call and faster response have little value if the customer's need remains unmet.”
The Customer Cost of Efficiency Metrics
Measuring AI through efficiency metrics is key to understanding whether it decreased operational costs, sped up interactions, or reduced employee workload.
For automation performance alone, these metrics are ideal. However, when these measures serve as a proxy for the quality of the journey, they often fail to capture whether a customer achieved resolution, or did so happily.
For example, a customer interaction can be contained by an AI system without the underlying issue being resolved, while a shorter interaction can indicate that they have been moved elsewhere.
"We're seeing organizations celebrate high deflection and containment rates, but when you look deeper, first-contact resolution is often declining," Lamont noted.
"That's a sign customers are still needing help, they're just taking a longer route to get it."
Furthermore, this problem becomes harder to identify when organizations do not make full use of the customer data available to them.
In fact, CallMiner’s 2026 CX Landscape Report found that 68% of CX and contact center leaders do not always use CX data to its best advantage (up from 62% in 2025), leaving important signals outside AI performance assessments.
Evidence of repeated frustration or lack of quality can provide context for an efficiency metric but may be disconnected across systems. As Lamont explained:
“When customers aren't guided to the right path, at the right time, through the right channel, they often end up making repeat contacts and creating more effort for themselves and the business.”
When an AI system can produce an apparently appropriate response while still sending the customer down an ineffective path, this illustrates a clear limitation of surface-level performance data.
A dashboard can show that an interaction ended quickly, but it may not reveal if the customer left with a resolution or another reason to contact.
Where Do Your Cost Reductions Go?
Reducing the cost of customer service, whilst an important business objective, can be overstated when it is measured without considering what happens elsewhere in the journey.
A reduction in contact volume or handling time could mean customers receive less value from an interaction, requiring them to make additional contacts or encounter more friction before reaching a resolution.
When a business shifts costs into harder-to-measure areas, a customer who must repeatedly navigate an ineffective service journey may reduce their engagement without a company ever understanding why.
“You can reduce costs in one part of the operation, but if customers receive less value as a result, those savings often reappear somewhere else through churn, lower loyalty, or reduced spend,” Lamont states.
This can stem from how an organization divides journey responsibility between teams. As each measure can improves independently, the combined experience deteriorates.
“A lot of times that information is disconnected, so leaders are going after their specific performance goals without stepping back and looking at that whole picture,” he said.
This makes the level at which AI performance is assessed particularly important. Looking only at aggregated efficiency figures can conceal the experiences and outcomes behind them.
Lamont argues that businesses need to connect those different perspectives:
“The real value comes from being able to move seamlessly between the individual customer experience and the enterprise-wide view.”
Without that connection, cost reduction becomes an incomplete measure of AI's business impact, leaving organizations unable to see how operational gains are affecting customer outcomes.
If efficiency is not enough, CX leaders must re-evaluate AI’s CX impact to understand what real improvement looks like.
