FinOps In The Age Of AI: Governing Intelligence At Scale

Sudipto Dasgupta is Global Head of Data, AI, and Automation Platforms at Aon.

Agentic AI and large language models (LLMs) are having a profound impact on enterprises by automating repetitive tasks, reimagining workflows and enabling entirely new business capabilities. From customer service automation, content creation and research assistance to software development and document summarization, organizations are realizing measurable productivity gains from AI-powered solutions. Adoption of generative AI (GenAI) is also steadily accelerating in regulated industries such as insurance, healthcare and financial services.

Industry analysts project explosive growth in AI adoption. Morgan Stanley estimates that GenAI revenue could grow more than twentyfold, from approximately $45 billion in 2024 to over $1 trillion by 2028 as organizations scale AI-enabled business processes and customer experiences.

Despite these benefits, AI introduces a new class of financial and operational challenges. Unlike traditional software licenses or cloud infrastructure, AI costs are highly variable and influenced by multiple dynamic factors. If not governed effectively, organizations can experience unexpected budget overruns.​

As AI adoption scales, enterprises need to evolve FinOps practices beyond cloud infrastructure and develop an AI FinOps framework that provides visibility, accountability, forecasting, optimization and governance across AI consumption.​

Why AI Breaks Traditional FinOps

These dynamics create several distinct cost challenges that traditional FinOps practices are not designed to address.

Token-Based Pricing

Multiple Model Providers

Enterprises increasingly operate in a multi-model environment that includes OpenAI, Anthropic, Google Gemini, Databricks, AWS Bedrock, Azure AI and open-source models. Each provider offers different capabilities, performance characteristics, latency profiles and pricing structures. The most capable model is not always the most economical.