Bridging the AI Value Gap: Why Organizations Must Shift from Cost-Cutting to a ‘Return on Intelligence’

Chui emphasizes that realizing better returns is inextricably linked to managing these token costs and ensuring that the right model is being applied to the right task. Too often, there is a lack of transparency regarding which specific workloads are driving up the bill. Without a granular understanding of these expenses, companies risk spending more on the technology than the value it generates.

This concern is shared by IT leaders worldwide. Gareth Herschel, a vice president analyst at Gartner, speaking at the Gartner Data & Analytics Summit in Mumbai, noted that three out of five IT leaders are deeply worried about AI agents running up unexpected costs. In many cases, these fears have already been realized.

“Some organizations have already discovered that the cost of tokens for coding assistance is much higher than the cost of human software developers,” Herschel observed. This revelation challenges the common narrative that AI is a cheaper alternative to human labor. When an AI agent requires massive amounts of processing power to complete a task that a human could do efficiently, the economic argument for automation begins to crumble.

The financial risk is compounded as organizations move toward "multi-agent" systems, where various AI agents interact with one another to complete complex tasks. Robert Thanaraj, a senior director analyst at Gartner, compared the current state of unmonitored AI spending to a parent handing a teenager a credit card. “I’m sure you will learn a lot, but mostly from the bill,” he quipped.

To mitigate these risks, Thanaraj recommends that companies track costs meticulously during the prototyping phase. By calculating the cost of an individual agent per completed task, businesses can make more informed decisions about which models to use. This data-driven approach allows companies to evaluate whether they truly need a massive, expensive LLM or if a more affordable "small language model" or an open-weights model would suffice for the specific task at hand.

A wider lens for ROI

The difficulty in calculating AI’s return on investment stems from a variety of factors, including poor data quality, a failure to scale beyond initial pilots, and slow adoption among the workforce. However, analysts suggest that the problem may also lie in the definition of ROI itself.

While financial metrics are essential, Gartner analysts argue that focusing solely on immediate cash returns is too narrow a lens for a transformative technology like AI. In their Mumbai keynote, they noted that in 2025, the odds of any given AI initiative achieving a traditional financial ROI were only about one in five.

“ROI matters, but to achieve it, we must think of it not just as a financial metric, because value isn’t always just about money,” Thanaraj said. This has led to the emergence of a new concept: "Return on Intelligence."

Under this framework, companies are encouraged to tie AI projects to both financial and non-financial outcomes. This shift moves the emphasis from cost-cutting to value creation. Gareth Herschel explained that while revenue is a clear financial outcome, value can also manifest as improved "citizen experience" or customer satisfaction, brand loyalty, and long-term competitive positioning.