A recent KPMG survey found that almost 30% of senior executives are having difficulty understanding and managing AI operating costs as the industry moves from flat-rate subscriptions to usage-based pricing models, prompting companies to rethink their AI investment and deployment strategies.
- 29% of executives find AI costs difficult to understand and control
- Almost half of companies have reprioritized AI deployments due to costs
- Governance and accountability remain significant challenges
What happened
KPMG conducted a broad survey including 2,145 senior executives across 20 countries, which revealed that nearly one-third struggle with understanding and managing AI-related operating costs. This confusion coincides with major AI providers shifting from flat subscription pricing to usage-based billing models, complicating budget forecasting and management.
As a result, many companies are revisiting their AI strategies, often delaying or scaling back deployments when costs exceeded anticipated benefits. The rise in adoption of lower-cost, high-fidelity AI models also reflects a broader effort to optimize investment and maximize return on AI technologies.
Why it matters
The shift to usage-based pricing models places new financial forecasting and operational challenges on organizations, potentially limiting the speed and scale of AI adoption. Executives’ difficulty in grasping these costs highlights a pressing need for enhanced financial and technical capabilities within enterprises to manage AI expenditures effectively.
Beyond financial concerns, AI governance is a growing issue. Organizations are still establishing clear rules for accountability around AI decision-making and error management, which is critical as AI becomes more embedded in business functions. Without robust governance frameworks, companies risk facing operational disruptions and reputational damage.
What to watch next
Future developments will likely include more sophisticated tools and processes from both AI vendors and enterprises to improve cost transparency and management under usage-based billing. Companies will also accelerate investments in engineering support teams, similar to Amazon's $1 billion AWS Forward Deployed Engineering and Microsoft's $2.5 billion Microsoft Frontier Company initiatives, to help customers deploy AI solutions more effectively.
Additionally, expect intensified focus on formalizing AI governance structures, defining accountability, and embedding oversight into daily operations. As AI deployments grow, enhanced governance will be essential to manage risks associated with AI outputs and ensure responsible use aligned with business values.