State of Observability 2026: 55% Can't Control AI Costs as Agentic AI Adoption Surges

Organizations Struggle to Control AI Costs as Agentic AI Adoption Accelerates

Cisco has released the State of Observability 2026 report, surveying 2,050 ITOps and engineering professionals across ten countries and sixteen industries1

1

4

4

. The findings reveal a critical gap: 55% of organizations cannot effectively control their AI costs, while 83% express concern about the unintended consequences of AI agents1

1

2

2

. These challenges emerge as enterprises rapidly deploy agentic AI systems that operate with unprecedented autonomy, making decisions and consuming resources in ways that defy traditional monitoring approaches.

AI Bill Spikes Create Greater Financial Strain Than High Baseline Costs

The shift to consumption-based pricing models for agentic AI has introduced significant financial predictability challenges. More than 60% of firms report that AI bill spikes cause more strain financially than paying a higher but flatter baseline cost2

2

. This unpredictability stems from how agents consume tokens depending on task complexity, model choice, and operational decisions made autonomously. The volatility has become so pronounced that 57% of organizations now worry their telemetry costs exceed the actual infrastructure they're monitoring2

2

. "For today's enterprises, the excitement of AI is clashing with the reality of the bill," said Kamal Hathi, SVP and GM at Splunk4

4

.

Agent Observability Emerges as Critical Business Priority

Monitoring AI workloads has become a priority for 85% of Indian organizations and remains a top concern globally3

3

. However, 46% of Indian enterprises cannot effectively measure AI workload metrics3

3

. The challenge lies in the non-deterministic nature of agentic AI—agents handed a goal will find their own route to it, and that route may change from one execution to the next1

1

. This fundamental shift means understanding a system once no longer guarantees understanding it in the future, even without deliberate changes.

Observability and Security Teams Converge to Address AI Risks

A significant development revealed in the State of Observability 2026 report is the convergence of monitoring and security functions. Four in five organizations (81%) report that monitoring AI workloads has increased collaboration between observability and security teams2

2

4

4

. This collaboration proves critical as 73% can now accurately trace problems to their underlying security causes, up from 65% last year2

2

. "Managing agents at scale requires reimagining both security and observability," explained Jeetu Patel, President and Chief Product Officer at Cisco4

4

. As agents gain access to entire tech stacks, the questions of whether they've been compromised and whether they're performing effectively become inseparable concerns moving at machine speed.

Verbose Logs Drive Observability Costs Higher Than AI Infrastructure

Contrary to expectations, AI infrastructure accounts for only 6% of observability cost drivers1

1

. Excessively verbose logs topped the list, reflecting years of collecting data "just in case" to the point where it breaks budgets. This habit reveals a deeper problem: nearly half of respondents admit telemetry data isn't a key factor in their organization's strategic planning1

1

. When technical exhaust consumes operational practices, teams never establish the link between monitoring data and business outcomes, leaving strategic planning to proceed without operational telemetry—essentially guessing rather than planning with data.

Leaders Achieve Financial Control Through Three Core Practices

The State of Observability 2026 identifies a distinct group of "leaders" who have restored predictability to their agentic AI operations through three interconnected practices1

1

4

4

. First, they deploy AI agents to fix and prevent issues routinely rather than experimentally. Second, they expand observability paradigms to govern agent behavior and enforce guardrails around model quality, cost, usage, security, and privacy. Third, they embed telemetry directly into business planning. These leaders achieve dramatically better outcomes: 91% can accurately attribute AI costs to particular agents, models, and workloads, compared with 69% of non-leaders2

2

4

4

.

ROI and Business Impact Separate Leaders from Peers

Leaders demonstrate superior financial outcomes from their observability investments. They are 2.4 times more likely than non-leaders to report that the ROI of their observability platforms significantly exceeds expectations4

4

. More concretely, 54% of leaders report at least $5 million in annual business revenue from observability, versus 28% of non-leaders3

3

4

4

. Their advantage extends to operational capabilities: 94% of leaders maintain confidence in isolating edge and branch network issues, compared to roughly two-thirds of everyone else1

1

. This confidence proves particularly valuable as customer experience becomes increasingly exposed at the edge and within AI decision-making processes.

Indian Enterprises Show Advanced Capabilities Despite Cost Challenges

Indian enterprises present an interesting contrast in the global landscape. While 43% struggle to control AI costs (lower than the 55% global figure), they demonstrate more advanced capabilities in specific areas3

3

. Notably, 51% of Indian organizations describe their model-quality monitoring practices as very effective, compared with 35% globally3

3

. Additionally, 87% in India consider visibility into AI hallucinations important, with 70% saying their organizations effectively monitor them, against 58% globally3

3

. This suggests Indian enterprises are investing heavily in agent observability capabilities even as they navigate the same tokenomics challenges facing organizations worldwide. Watch for how these advanced monitoring capabilities translate into cost control as consumption-based pricing models mature and organizations refine their approaches to financial predictability in the agentic AI era.