Topic starter
24/06/2026 8:07 pm
TL;DR: 84% of companies are already seeing more than 6% gross margin erosion from AI costs, while only 15% can forecast AI spend within ±10% accuracy, making fragmented consumption a finance and governance problem, not just an efficiency issue, according to Kong. Cost visibility is now a prerequisite for sustainable AI investment and monetisation.
Editorial analysis by NHI Mgmt Group, based on content published by Kong: “# Agentic AI Cost Management: Stopping Margin Erosion and the Fragmentation Tax”.
By the numbers:
- 84% of companies report more than 6% gross margin erosion from AI costs.
- Only 15% of companies can forecast AI costs within ±10% accuracy.
Key questions
Q: What breaks when agentic AI spending is not measured end to end?
A: When agentic AI spend is not measured end to end, organisations lose the ability to attribute cost to customers, features, or teams.
Q: Why do agentic AI workloads make cost forecasting so difficult?
A: Agentic workloads are harder to forecast because the execution path is not fixed in advance.
Q: How can security teams tell whether AI lifecycle controls are working?
A: They should look for evidence that access requests, policy enforcement, and usage visibility are centrally recorded and current.
Practitioner guidance
- Map the full AI cost path Inventory token usage, egress, retrieval, GPU time, MCP interactions, and agent-to-agent traffic so finance can attribute spend to the correct service or business unit.
- Establish cross-functional AI FinOps ownership Assign finance, platform engineering, product, and security shared responsibility for AI consumption policy, attribution, and escalation decisions.
- Meter consumption at execution time Set per-feature or per-tenant usage caps, anomaly alerts, and model-routing controls before bills are generated, so runaway usage is contained early.
Bottom line: Fragmented AI consumption is no longer just an efficiency issue, because it directly shapes margin, pricing power, and governance accountability.
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25/06/2026 5:17 am
Fragmentation tax is now an identity governance problem, not just a finance problem. The article shows that AI consumption is spreading across MCP servers, APIs, model calls, and agent workflows that different teams own separately. Once usage becomes distributed that way, accountability also fragments, and no one can reliably answer who consumed what, under which privileges, or for which business purpose. The practitioner implication is that AI governance has to join cost attribution to access governance, not treat them as separate disciplines.
A few things that frame the scale:
- Organisations maintain an average of 6 distinct secrets manager instances, creating fragmentation that undermines centralised control, according to the State of Secrets in AppSec.
- 62% of organisations are at least experimenting with AI agents, but only 23% have scaled one into a single business function, according to McKinsey's 2025 State of AI survey.
A question worth separating out:
Q: Should organisations treat AI cost visibility as a governance requirement or a finance task?
A: Organisations should treat AI cost visibility as both, because ungoverned consumption becomes a governance failure before it becomes a line item. Without shared visibility, security cannot understand who is using what, and finance cannot understand what the business is buying. The controls need to be joint, not sequential.
👉 Read our full editorial: Agentic AI cost management exposes the fragmentation tax