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06/06/2026 2:26 am
TL;DR: AI in support is not reducing headcount as expected. At HumanX 2026, Assembled CEO John Wang described how faster AI resolution is increasing inbound volume, pushing teams toward orchestration that blends humans and AI, according to WorkOS. The practical lesson is that routing, escalation, and accountability now matter more than raw automation gains.
NHIMG editorial — based on content published by WorkOS: Assembled CEO John Wang on the jevons paradox of customer support
By the numbers:
- AI agents are claimed to achieve 80 to 90% resolution rates in support interactions.
Questions worth separating out
Q: How should organisations govern AI systems that route support cases between humans and machines?
A: Treat routing as a governed policy layer, not a hidden optimisation.
Q: Why can AI in customer support increase workload instead of reducing it?
A: When support becomes faster and easier to use, more customers submit requests that they would previously have delayed or avoided.
Q: What breaks when AI agents can contact support on behalf of users?
A: The support model breaks if it assumes every request comes from a human with stable intent and direct authority.
Practitioner guidance
- Map support routing as a governed decision path Document which signals cause AI to retain a case and which signals trigger human escalation, then require ownership for each decision point in the workflow.
- Rebaseline capacity after AI self-service gains Assume easier access will increase demand, then adjust queue targets, staffing assumptions, and escalation thresholds before service levels degrade.
- Define delegated-agent permissions explicitly Separate question answering, case creation, refund approval, and account changes so an AI agent can only perform the actions it is actually trusted to represent.
What's in the full article
WorkOS's full article covers the operational detail this post intentionally leaves for the source:
- The interview transcript and speaker context from HumanX 2026, including the support-operations framing used by Assembled CEO John Wang.
- Examples of the escalation signals discussed for deciding when AI should hand a case to a human.
- The practical discussion of support queue depth, wait time, and orchestration trade-offs that shape real routing decisions.
- The forward-looking view on AI agents contacting support systems directly on behalf of users.
👉 Read WorkOS's interview on AI orchestration in customer support →
AI orchestration in customer support: what IAM teams should notice?
Explore further
06/06/2026 4:06 am
Support orchestration is becoming an identity governance problem, not just a service design problem. When humans and AI both handle customer requests, the control point shifts from response generation to delegated action routing. That means the real question is who or what may act, under what conditions, and with what evidence of approval. Practitioners should treat orchestration policy as part of access governance, not as an engineering detail.
A few things that frame the scale:
- AI agents are claimed to achieve 80 to 90% resolution rates in support interactions, according to LLMjacking: How Attackers Hijack AI Using Compromised NHIs.
- Our research also shows that when AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes and as quickly as 9 minutes in some cases.
A question worth separating out:
Q: How do support teams know whether AI orchestration is working?
A: Look for fewer unmanaged escalations, consistent routing decisions, and clear ownership of exceptions. If requests are bouncing between AI and humans without traceable rationale, the orchestration layer is not controlling work, it is obscuring it. Measurement should focus on decision quality, not only resolution speed.
👉 Read our full editorial: AI orchestration is becoming the real support control plane
