Trust in AI agents comes from predictable outcomes, not impressive demos
Capability is easy to demo. Trust is harder to earn
Early conversations about AI in Support usually focus on capability. What the agent can answer. How fast it responds. How many tickets it touches.
Customers do not experience “capability.” They experience outcomes.
Trust grows when results become predictable: fewer people need to come back, escalations happen for the right reasons, and the customer is genuinely unblocked after the conversation ends. Isara helps Support and Success leaders measure that reliability directly in customer conversations, not just in ticket metadata.
Why impressive answers still fail in real support
A polished response can still create a bad outcome. The most common failure mode is not a dramatic meltdown. It is a plausible answer that does not hold up when the customer tries it.
That risk is not theoretical. A large study of AI assistants answering news related queries found that 45 percent of responses contained at least one significant issue. While support is narrower than open news, the underlying lesson carries over: fluency is not the same as correctness, and errors can sound confident.
There is also a deployment reality check. A recent Camunda report found that 71 percent of organizations say they use AI agents in some capacity, but only 11 percent of agentic use cases reached production in the last year. The same report says 73 percent see a significant gap between their vision and current reality. In practice, trust is one of the main reasons teams stay stuck in pilots or limit AI to low risk use cases.
In Support and Success operations, trust usually breaks in predictable places:
• The customer has to re explain the context on the next contact
• The AI misses an edge case and the customer wastes time trying the wrong steps
• The escalation happens too late, after frustration has already built
• The handoff to a human loses critical details, so progress resets
If you only track volume and speed, these failures can look “fine” in dashboards. The customer experience tells a different story.
Isara is built for that second view: what actually happened in the conversation, what the customer tried, and whether progress continued after the chat ended.
A simple way to measure whether customers are moving forward
Instead of asking “Is the AI working?” switch the question to “Are customers moving forward?”
Here is a practical framework that works across Support, Success, and leadership reporting. It is intentionally outcome focused.
The four forward progress signals
Repeat contact on the same issue
If the customer returns soon for the same problem, the interaction did not create durable progress.
Escalation appropriateness
Escalation is not failure. Surprise escalation is. A good system escalates when risk, complexity, or customer temperature demands it.
Fix confirmation
Look for evidence that the customer completed the next step successfully, not only that the conversation ended politely.
Handoff quality
When a human takes over, the handoff should include what was tried, what data was collected, and what the customer needs next.
A lightweight scorecard you can start using this week
Forward Progress Score, 0 to 100
• 35 points: no repeat contact on the same issue within 7 days
• 25 points: escalation timing matches what your team would expect
• 25 points: fix confirmation exists in the conversation or follow up event
• 15 points: handoff includes complete context when a human takes over
Once you have this, you can segment results to find the real risk zones:
• By intent type: billing, account access, bug, integration
• By customer tier: SMB, mid market, enterprise
• By workflow: AI only, AI plus human, human only
This is where Isara becomes useful operationally. Isara can identify which conversations show real forward progress versus “looks resolved” outcomes, highlight repeat failure patterns, and surface the highest impact fixes across knowledge, prompts, and escalation rules.
FAQ: measuring trust through outcomes
How does Isara show whether an AI agent actually helped, not just ended the chat?
Isara analyzes the conversation content to detect whether the customer reached a usable next step, including signals of unresolved confusion, repeated loops, or likely repeat contact. It helps leaders separate “closed” from “completed” using what customers actually said and did.
Can Isara measure whether the customer problem was truly addressed, beyond ticket status?
Yes. Isara focuses on outcome signals in the interaction, including whether guidance worked, whether the customer remained blocked, and whether the same issue is likely to reappear. This closes the gap between operational resolution and real customer progress.
Does Isara distinguish AI handled conversations from human handled ones?
Yes. Isara can separate AI and human interactions so you can measure forward progress by workflow, then pinpoint where the AI needs better knowledge, safer escalation rules, or improved handoff behavior.
How does Isara help Support and Success connect AI performance to renewals and retention?
Isara surfaces churn and risk signals that show up in support conversations and success touchpoints, then helps teams quantify where poor AI outcomes are increasing friction, repeat contact, and escalation rates that can affect renewal confidence.