The Hidden Efficiency Cost of Agentic AI Callers

Contact centers are beginning to feel a new kind of operational pressure, not from customers, but from agentic AI callers. These systems now place inbound calls on behalf of businesses and consumers to negotiate bills, confirm eligibility, schedule appointments, or validate account information. The result is call volumes, longer customer wait times and rising cost per call without any corresponding growth in customer demand.

As agentic AI adoption accelerates, leaders responsible for performance, capacity, and cost control need to understand how inbound AI callers behave, where cost exposure emerges, and why traditional contact center systems struggle to adapt.

The Impact of Agentic AI on Contact Center Operations

Agentic AI is increasingly involved in contact center interactions in two ways: organizations deploy it internally to automate customer interactions and businesses use it to place calls into contact centers on their behalf.

These inbound agentic AI callers are typically configured by enterprises, service providers, or software platforms to perform routine, high-volume tasks. Common examples include healthcare providers checking coverage, benefits administrators confirming eligibility, scheduling systems coordinating appointments, and service platforms verifying account details. 

While these calls are often legitimate, they are difficult to distinguish from human callers based on behavior alone. Agentic AI voice agents are designed to sound natural, follow conversational norms and successfully navigate IVRs. At the same time, agentic AI can place calls continuously, retry instantly, and operate across workflows without fatigue.

This combination of human-like interaction at machine scale creates a blind spot for contact centers. Routing and automation logic relies on intent, metadata, or expected caller behavior, not on whether the caller itself is autonomous. As a result, agentic AI callers move through intake and routing paths as trusted demand, even when no human judgment is required. These blind spots translate directly into measurable efficiency and cost pressure.

The Real Cost of Agentic AI Callers

Agentic AI changes the economics of contact center operations by introducing scale-driven cost, not complexity-driven cost.

Because autonomous callers can place calls and operate continuously, total inbound volume can rise even when real customer demand remains flat. Each AI-driven call that reaches an agent follows the most expensive handling path, regardless of intent. At scale, this creates sustained cost pressure across operations that is difficult to attribute, especially when service demand itself hasn’t changed.

A Simple Cost Scenario: When Agentic AI Reaches Human Agents

To understand how quickly costs can accumulate, consider a large enterprise contact center handling 10 million inbound calls per year. Typically a call handled entirely with automation costs around $1–$2, while a call handled by a human agent costs $10–$12.

Now introduce agentic AI callers into that environment. Suppose just 3–5% of inbound calls are initiated by agentic AI. That volume alone may not raise alarms initially, but the cost exposure depends on where those calls end up. A meaningful portion of agentic AI calls are routed to human agents despite requiring little or no human judgment.

A conservative breakdown looks like this:

• 5% of 10 million calls = 500,000 AI-generated calls
• Half reach a human agent = 250,000 human-handled AI calls
• At $12 per call = $3 million per year in avoidable labor cost

And that estimate assumes ideal conditions. It doesn’t account for secondary effects such as queue spillover, overtime, staffing adjustments, or service-level penalties. It also assumes no change in AHT, even though increased inbound volume alone reduces throughput and strains capacity.

The key point is that this cost appears without fraud, without malicious intent, and without any obvious spike in customer demand. It’s a byproduct of scale with autonomous callers flowing into workflows designed for humans.

Traditional Contact Center Systems Weren’t Designed to Handle AI Callers

Most contact center infrastructure was built on the simple assumptions that inbound callers are human, and automation fails fast when friction appears. Agentic AI breaks both assumptions.

IVR systems are designed around human drop-off. When callers encounter friction, they abandon the call or escalate. Autonomous callers behave differently, persisting until an outcome is reached.

Routing and intake logic also lack awareness of who is calling. Calls are routed based on intent, queue availability, or metadata like Automatic Number Identification (ANI), not whether the caller is human or autonomous. Without that distinction, AI callers flow through the system by default.

Several large enterprises have already tested whether metadata-based approaches could solve this problem. Caller ID, ANI, and signaling techniques were evaluated as early controls, but results were inconsistent and difficult to trust at scale, especially as AI callers increasingly originate from legitimate providers and geographies.

Operational metrics further obscure the issue. Rising handle time, queue congestion, or reduced throughput show up as efficiency problems, masking the underlying shift in who, or what, is calling.

The result is not a system failure, but a mismatch. Contact centers absorb the cost and operational strain of autonomous callers because existing technology was never designed to distinguish humans from AI at the point of entry. As inbound AI traffic grows, that assumption becomes harder to sustain.

Next Steps for Contact Center Leaders

As autonomous agents become a standard way businesses and individuals interact with contact centers. As that shift accelerates, contact centers will need to decide how these callers should be handled, or risk rising costs, inflated AHT, and degraded service by treating them as human by default.

The first and most critical step is gaining a reliable, real-time signal early in the call to determine whether a caller is human or AI-generated, enabling informed routing and handling decisions before agent time is consumed.

Tools such as deepfake and AI-voice detection make this possible by identifying AI-generated voice before it reaches human agents or downstream call center workflows.

In the next phase, the question isn’t whether agentic AI will call your contact center, but whether you’ll recognize it early enough to control the impact.