AI's next phase isn't innovation, it's capital discipline

For the past few years, Enterprise AI has largely been defined by experimentation.

Organizations rushed to explore use cases, test pilot programs and give teams access to the latest models.

Success metrics have often been related to adoption and speed.

Across boardrooms now, the conversation around AI is changing.

CFOs are no longer asking what AI tools can do, rather they are asking what it has done, what value it has created, and whether that value justifies the growing cost of compute.

The next chapter of Enterprise AI will not be defined by who deploys the most agents or consumes the most tokens.

It will be defined by who generates the greatest business outcomes from the most efficient use of compute.

AI is entering its capital discipline phase.

The hidden cost of agentic AI Many businesses are moving beyond AI chatbots and copilots to AI agents that can complete tasks, make decisions, and act with minimal human input.

The business benefits can be significant, however they must be factored against cost.

To balance this consideration, companies often start small, deploying a single AI agent to support a specific process.

As early results show promise, more agents are introduced across various different functions in the business, such as finance, customer service, procurement and supply chain operations.

The benefits can grow quickly, but so can the expense.

Unlike traditional software, where costs are often tied to the number of users, AI costs are driven by usage - quantified by tokens (i.e., the individual blocks of data processed by AI models).