2025年人工智能成本治理状况报告_35页_7mb

报告摘要

2025 State of AI Cost Governance Summary

Core Content

The 2025 State of AI Cost Governance report by Mavvrik and Benchmarkit highlights the growing financial challenges associated with AI adoption across organizations. As AI becomes a core component of business operations, the lack of visibility, control, and accurate forecasting is leading to significant margin erosion and financial uncertainty. The report underscores that AI is no longer an experimental cost line item, but a material driver of profitability and a strategic concern for CFOs.

Main Findings

AI Cost Impact on Margins

84% of companies report AI costs eroding product gross margins by more than 6 percentage points (600 bps).
58% see a 6–15% reduction in margins.
26% report $16%+$ erosion in gross margins.
Example: A product with 80% gross margin could drop to 74% once AI costs are factored in.

Forecast Accuracy Issues

Only 15% of companies forecast AI costs within ±10%.
56% miss by 11–25%.
24% miss by more than 50%.
This level of inaccuracy threatens gross profit targets and financial predictability.

Hybrid and Multi-Cloud Complexity

61% of companies operate in hybrid environments, combining public cloud, private infrastructure, and third-party services.
Multi-cloud is now the standard, with AWS (77%) leading overall usage, but Azure (82%) dominates among companies with > $250M revenue.
Hybrid complexity increases cost visibility challenges and billing fragmentation.

Repatriation Trends

67% of companies are actively planning to move AI workloads to owned infrastructure.
Another 19% are evaluating the move.
Mid-market companies are more likely to act, while large enterprises are often in evaluation.

Cost Drivers Beyond Tokens

Data platform usage is the #1 source of unexpected AI costs (56%).
Network access to models is the #2 source (52%).
LLM token costs are only the 5th most common cost driver (37%).

Visibility and Attribution Gaps

Only 35% of companies include on-premise costs in AI reporting.
About 50% include LLM API costs even when AI is a core product.
Unified visibility is the most cited tactic for improving AI cost management (33%), followed by clear cost attribution (22%).

Revenue Accountability Effect

Companies that charge for AI show 2–3x better cost discipline.
70% of charging companies can track cost-to-serve precisely, compared to 29% of free providers.
71% of charging companies use real-time usage alerts for overages.
Charging for AI drives strategic cost management, including P&L ownership, customer-specific cost attribution, and pricing decisions.

Key Takeaways for CFOs

AI cost governance is not optional—it's a strategic imperative.
Budgets exist, but attribution lags, with only 35% tracking on-prem costs and 50% reporting LLM API usage.
Monetization is linked to stronger cost discipline and higher governance maturity.
Visibility is the foundation of effective AI cost governance.
Hybrid environments are becoming the new norm, requiring unified reporting and cross-platform cost tracking.
Forecasting accuracy is low across all company sizes, with 85% missing forecasts by more than 10%.

Financial Management & Metrics

59% of companies measure AI costs as a percentage of revenue.
Only 29% measure AI costs against COGS, which is the most relevant metric for gross margin.
Charging for AI leads to more precise profitability tracking.
Real-time monitoring is not universal, with many companies only detecting overages after invoices arrive.
CSP tools and internal dashboards are common, but specialized platforms are underutilized.

Maturity Levels

Only 34% of companies have an advanced AI cost management program.
Early stage (30%) and developing (36%) are more common.
Industry has a greater impact on maturity than company size.
Manufacturing leads in maturity (50% advanced), while Financial Services and Agentic AI companies lag (40% and 38% early stage).
Monetized AI products are more likely to have precise cost tracking and governance policies in place.

Conclusion

As AI moves from an experimental tool to a core business driver, the need for cost visibility, forecast accuracy, and governance maturity becomes critical. The report emphasizes that CFOs must treat AI costs as part of COGS, not just as innovation expenses. With 85% of companies missing forecasts and 84% experiencing margin erosion, the financial discipline required to manage AI is no longer a luxury—it's a necessity. Companies that charge for AI are more likely to achieve better governance, track costs precisely, and make strategic decisions based on real-time data. To thrive in 2026, CFOs must embed cost governance into every AI initiative and invest in unified visibility and control systems.