Enterprise AI spend is moving from experimentation into operational accountability. As organizations scale AI across chat tools, agents, APIs, embedded SaaS features, and private infrastructure, the basic questions have become harder to answer: what is being used, who owns the cost, which work is producing measurable value, and where unmanaged risk is entering the business.

This paper explains the economic model behind enterprise AI and the operating practices needed to manage it with the same discipline typically applied to cloud infrastructure. We’ll cover:

• How token-based pricing, model selection, agent design, shadow AI, and license sprawl affect total cost
• Connecting spend to business outcomes through cost-per-output measurement
• Building an evidence-based ROI ledger that can stand up to executive and finance scrutiny
• Tooling categories emerging around AI FinOps, LLM observability, gateway control, and SaaS management, with practical guidance on how each contributes to visibility, governance, and optimization.

By the end, readers should understand why AI value often fails to appear in standard financial reporting, how to create the baselines and attribution models needed to prove impact, and which interventions can reduce waste while improving responsible use. The goal is to help leaders move from broad AI adoption to deliberate AI management, where spend is visible, usage is governed, value is measured, and investment decisions are tied to evidence rather than enthusiasm.

Download the full whitepaper to learn more.