For the past two years, much of the discussion about artificial intelligence (AI) has centered on its impact on jobs — which roles might change, which tasks could be automated, and how many workers organizations might need in the future. However, as companies have moved forward with adopting AI tools, a new concern has emerged: managing the costs. While executives and boards have pushed for AI adoption, many organizations are now facing unexpected expenses, including rising costs for AI services, uncontrolled usage of AI-generated content, and large, unforeseen bills. These challenges are becoming a major topic among technology leaders. AI is now deeply integrated into the tools and applications used across businesses, from Microsoft Copilot and Google Gemini to enterprise platforms like SAP and LinkedIn. Unlike traditional software, AI doesn’t just perform tasks — it consumes resources like data, computing power, and tokens (units of processing). Every time an AI system answers a question, plans a strategy, or retries a failed task, it uses these tokens, which come with a cost. As a result, AI is not only transforming how work is done but also how companies track and manage their expenses. This situation echoes the challenges companies faced when adopting cloud computing a decade ago. At that time, teams often launched cloud services quickly without clear oversight, leading to unanticipated costs. Today, AI is creating a similar challenge — but on a much larger scale. Unlike cloud computing, which was often confined to specific departments or teams, AI is being used across the entire organization, from marketing and sales to product development and IT. Goldman Sachs estimates that agentic AI — AI that can perform complex tasks autonomously — could increase token usage by 24 times by 2030. This means AI costs are growing rapidly, but many companies still lack a clear understanding of where and how these costs are incurred. Managing AI spend requires more than just tracking salaries or AI licenses. The real cost includes token usage, infrastructure, data storage, cloud resources, failed attempts, and the human effort needed to review AI outputs and handle errors. These costs are often spread across multiple systems and teams, making them hard to measure accurately. When organizations consider replacing human roles with AI, they must ensure they are not simply shifting costs from one area to another — such as from payroll to cloud computing or data processing. Without full visibility into AI spending, companies risk making decisions based on assumptions rather than facts. To make informed decisions, organizations need to understand where AI adds value, how much it costs to operate at scale, and how to optimize its use. This starts with visibility — tracking which AI models and tools are being used, where token consumption is highest, and how these costs align with business outcomes. Only with this clarity can companies decide whether AI is worth the investment and where it delivers the most value. As AI becomes more embedded in business operations, managing its economic impact — a field known as AI tokenomics — will be essential for long-term success. The companies that thrive will be those that move beyond assumptions and focus on measurable results, ensuring that AI delivers not just efficiency, but real business value.