Agentic CX in 2026: What consumers expect and most enterprises miss
2,000 consumers
500 enterprise CX and AI leaders
3 continents
Consumers aren't anti-AI, they're anti-bad customer service
47%
of consumers prefer always-on, 24/7 AI over waiting
for a human agent, but only when AI can actually
resolve their issue.
44%
of businesses have no visibility into AI agent
performance.
Get exclusive insights to advance agentic customer experience (ACX) in 2026
Download the full reportConsumers will choose AI, but only when AI can do the job
59% of consumers prefer always-on, 24/7 AI over waiting
for a human agent, but only when AI can actually resolve
their issue.
For consumers, capability and efficiency matter most.
"Accuracy" and "problem-solving ability" are the most
important AI customer service features. Empathy ranked
last.
Just a third of consumers (32%) rate their most recent
AI customer service experience an 8 or higher on a scale
of 10.
Only 1 in 4 consumers say their issue was fully resolved without a human
These are model and infrastructure failures, not failures of customer preference. The path forward is to make AI more capable, rather than simply to make it emulate a more "human" experience.
"AI gets them 70-80% there, then fails. That makes the experience more frustrating than if they'd reached a human immediately."
Dive into the data:
- What consumers prioritize in customer service
- Efficiency vs. empathy across key channels
- When consumers choose AI vs. human agents
- The CX design choices that break trust
Businesses optimize for what they can easily measure, rather than what customers value
Consumers prioritize problem-solving and efficiency over
personality or empathy. Yet just 24% of consumers report
achieving resolution without a human. The other 76%
escalate, struggle, or give up.
Consumers have an appetite for AI, but businesses aren’t
optimizing for what really matters.
We asked enterprise CX leaders what benefits they've
seen from AI deployment. Resolution rate ranks seventh
at 22%, while speed, cost, and deflection dominate the
top spots.
Right now, businesses are more focused on decreasing the
number of handoffs than on the quality or depth of
resolution.
Businesses ranked the top benefits from their AI customer service deployments:
“Right out of the gate, you build your business model on deflection, workload reduction, pure call center headcount reduction, and cost savings.”
Containment and deflection measure avoidance.
Resolution measures outcomes.
Yet more than half of businesses lack a robust attribution infrastructure. Without separation, they can’t pinpoint where AI is falling short, establish a credible ROI baseline, or systematically improve performance.
44%
of businesses surveyed are measuring AI and human agent
interactions together
Dive into the data:
- A breakdown of business versus consumer priorities for AI customer service
- The ROI of measurement maturity
- What “gold standard” attribution looks like and what makes it possible
Addressing AI’s capability and operational gaps isn’t a one-time effort
92% of enterprises plan to increase AI investment in CX.
But most are not equipped to close the gap between AI
ambition and reality.
For one in four businesses, there’s a single factor that
determines whether they invest further: demonstrated ROI
from current deployments.
But without the ability to connect AI-resolved or
AI-assisted interactions to real outcomes, businesses
can’t make the case for commercial impact.
What looks like an ROI problem is actually a visibility
problem.
Businesses told us the key barriers to ACX maturity:
“If tomorrow someone could have an AI agent that can help us build a methodology through all the data we have in the company to make this kind of correlation and see the incremental value of AI to generate profitability, that would be perfect.”
But the gap isn't just about technology.
The businesses that stall along their maturity journey are also missing the expertise and organizational structure to run it.
29%
of CX leaders say their team is not adequately resourced
and skilled to manage, audit, and coach AI agents
effectively.
Dive into the data:
- Why AI governance is an underestimated prerequisite
- What AI agent maturity in practice looks like across 4 stages
- Percentage of businesses in each stage of maturity
Businesses can realize AI’s potential for CX, if they build the foundation to see it clearly
To close the gap between AI ambition and today’s AI
reality, businesses need a clear path to maturity that
addresses the technology, operational, and talent
aspects of a successful ACX strategy.
Addressing these gaps isn't a one-time effort. It's a
management discipline that requires long-term commitment
— built in layers, refined over time, and grounded in
visibility.
Each stage of ACX maturity has a different set of
conditions for Platform, Practice, and Expertise that
must be met before advancing.
If there’s one report you need for realizing your agentic CX goals in 2026, this is it.
- Full data from 2,500 global respondents,
- Qualitative insights from CX and AI leaders across 3 continents,
- In-practice case studies, plus
- Ada’s exclusive ACX Blueprint for advancing your AI maturity stage.