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Parloa x Metric Sherpa

The 2026 CX Observability Study

The 2026 CX Observability Study

Enterprise teams have data. What they want is the full story.

Enterprise teams have data. What they want is the full story.

Enterprise teams have data. What they want is the full story.

Woman cycling along a sunlit path
Woman cycling along a sunlit path

Key Findings

Operators have high confidence in their traditional CX metrics, but they also know they’re not seeing everything that’s happening with their AI.

Operators have high confidence in their traditional CX metrics, but they also know they’re not seeing everything that’s happening with their AI.

Parloa and Metric Sherpa surveyed 500+ CX operators and 200+ CX executives to find out how confident they feel in their CX metrics today, what they wish they saw more of, and what they do with insights once they have them.

What we found were operators confident in what they track but aware that it’s not everything, executives missing the business impact of the data, and both parties knowing there’s more to the story.

01

78.5%

78.5%

78.5%

of operators using AI report no metric for hallucinations or factual accuracy.

02

52.8%

52.8%

52.8%

of executives see CX observability as a very or extremely valuable addition to their reporting.

03

42.4%

42.4%

42.4%

of operators say no single person owns customer experience monitoring at their organization.

04

1 in 4

1 in 4

1 in 4

executives work somewhere that both surfaces insights and acts on them.

Finding 01

AI deployment & AI visibility aren’t growing together.

AI deployment & AI visibility aren’t growing together.

Undetected AI errors keep both operators and executives up at night.
No matter the level of AI usage (just starting out or fully deployed), the vast majority of both operators and executives say they are concerned about the repercussions of not knowing exactly what their AI agents are doing.

Over three quarters of operators report no tracking of AI hallucination or factual accuracy in their organizations.

And yet, companies keep deploying. Both operators and executives reported increased confidence in their metrics to make decisions around CX as their AI maturity advanced, despite either group really knowing what’s going on in every interaction.

Operators

Confidence

Blind-spot rate

Line chart: operator confidence rises from 38.0% when evaluating AI to 76.9% when embedded; the blind-spot rate stays flat across stages

38.0%

76.9%

Flat across stages

Evaluating

Piloting

Scaling

Embedded

Executives

Confidence

Blind-spot rate

Line chart: executive confidence rises from 52.0% when evaluating AI to 92.1% when embedded; the blind-spot rate stays flat across stages

52.0%

92.1%

Flat across stages

Evaluating

Piloting

Scaling

Embedded

Finding 01

AI deployment & AI visibility aren’t growing together.

Undetected AI errors keep both operators and executives up at night.
No matter the level of AI usage (just starting out or fully deployed), the vast majority of both operators and executives say they are concerned about the repercussions of not knowing exactly what their AI agents are doing.

Over three quarters of operators report no tracking of AI hallucination or factual accuracy in their organizations.

And yet, companies keep deploying. Both operators and executives reported increased confidence in their metrics to make decisions around CX as their AI maturity advanced, despite either group really knowing what’s going on in every interaction.

Operators

Confidence

Blind-spot rate

Line chart: operator confidence rises from 38.0% when evaluating AI to 76.9% when embedded; the blind-spot rate stays flat across stages

38.0%

76.9%

Flat across stages

Evaluating

Piloting

Scaling

Embedded

Executives

Confidence

Blind-spot rate

Line chart: executive confidence rises from 52.0% when evaluating AI to 92.1% when embedded; the blind-spot rate stays flat across stages

52.0%

92.1%

Flat across stages

Evaluating

Piloting

Scaling

Embedded

Finding 02

Operators know they have blind spots. They want to uncover them.

Operators know they have blind spots. They want to uncover them.

Operators know they have blind spots. They want to uncover them.

Executives and operators know there’s more to understanding their AI agent performance and CX metrics. They just don’t know how to define it.

89.5% of operators said there’s at least one area of the customer experience they can’t currently see. For both operators and executives, real-time visibility and the ability to monitor all conversations ranked as top priorities in future metrics.

When presented with the definition of CX Observability, 56.8% of operators and 52.8% of executives said the approach would be very or extremely valuable to their organizations.

Parloa defines CX observability as: Complete visibility into every customer interaction, whether handled by a human agent, AI agent, or both, allowing teams to monitor, understand, and act on performance and compliance issues in real time.

Operators confident in their metrics

94.7%

r = 0.007

Operators who admit a blind spot

89.5%

Finding 03

Where operators report insights. Executives want impact.

Where operators report insights. Executives want impact.

Where operators report insights. Executives want impact.

The opportunity for CX metrics to drive measurable business value is largely unexplored. Mostly because metrics aren’t being reflected in the language of the business.

When data points are explained only in the language of the CX team, they stay with the CX team. It’s one of the reasons why, among operators who report sharing insights to other teams, 55.1% say actioning the insight stalled, met resistance, or was ignored outright.

Insights gathered straight from customer conversations that don’t move past the CX dashboard represent a massively missed opportunity to optimize all aspects of the business—product, marketing, sales, and service—in the interest of the customer.

Only 32.2% of operators prioritize financial and business impact linkage in their CX metrics, while 46.4% of executives say it’s important to them.

Top preferred capabilities

Operators

Executives

Real-time visibility into agent behavior

64.2%

50.0%

Ability to evaluate every conversation rather than a sample

61.4%

47.9%

Root-cause diagnostics

50.3%

45.4%

Surfacing customer insights for product and business teams

49.7%

51.4%

Financial and business impact linkage

32.2%

46.4%

Predictive alerts

29.6%

33.4%

Finding 04

Lack of ownership leads to inaction.

Lack of ownership leads to inaction.

Lack of ownership leads to inaction.

Customer experience should be everyone’s job, but without a clear leader, it quickly becomes nobody’s job.

The title of the owner is arbitrary, but someone needs to have clear authority over the experience and a goal that’s tied to the KPIs of the company. Only then can CX deliver a consistently reliable performance, adhere to company policies, and deliver board-level business impact.

42.4%

42.4%

of operators say no single person owns customer experience monitoring at their organization.

48.5%

48.5%

of executives say ownership remains spread across teams or lacks a designated owner.

Who is primarily responsible for monitoring and reporting CX performance?

Operators

32.6%

9.8%

57.6%

32.6%

9.8%

57.6%

42.4% report no central owner

Executives

28.0%

20.5%

51.5%

28.0%

20.5%

51.5%

48.5% report no central owner

Individual team leads or managers, each independently

No single owner, shared across teams

A named central owner: CX operations, CX analytics, or IT and data

Looking forward

Blind spots are
today’s CX currency

Blind spots are
today’s CX currency

Operators have mastered the craft of human-centric metrics; but AI opens the door for new insights, from a business perspective and a compliance necessity.

This study shows that operators know that what they’re measuring is not enough, and executives are clear in where the gaps in reporting are. AI observability tools offer the opportunity to shine a light on the blind spots, reframe CX metrics into a language the entire business can rally behind, and serve as the forcing function to finally turn CX into a revenue engine.

Looking forward

Blind spots are
today’s CX currency

Operators have mastered the craft of human-centric metrics; but AI opens the door for new insights, from a business perspective and a compliance necessity.

This study shows that operators know that what they’re measuring is not enough, and executives are clear in where the gaps in reporting are. AI observability tools offer the opportunity to shine a light on the blind spots, reframe CX metrics into a language the entire business can rally behind, and serve as the forcing function to finally turn CX into a revenue engine.