Over the past two years, organizations have invested billions in artificial intelligence with one clear expectation: reducing costs. The assumption was straightforward. AI would help employees’ complete tasks faster, streamline operations, and ultimately improve the bottom line.
Yet despite rapid adoption, the expected financial returns have been slower to materialize. According to McKinsey’s 2025 Global AI Survey, 78% of organizations now use AI in at least one business function. However, only a relatively small share reports a meaningful impact on profitability.
The gap isn’t because technology falls short. In most cases, AI performs exactly as intended. The real issue is that organizations are primarily using it to optimize existing workflows rather than improve the area where the greatest economic value is created: decision-making.
So far, enterprise AI has largely focused on generating reports, building forecasts, summarizing documents, and analyzing data. These capabilities undoubtedly save time and improve productivity. But they often deliver insights only after an event has already unfolded—when the opportunity to influence the outcome has already passed.
This distinction matters. Companies rarely lose money because a report arrives late. They lose money because they make the wrong business decisions. Investing in the wrong product, allocating budget to an ineffective marketing channel, or pricing a service incorrectly can have a far greater impact on profitability than any operational efficiency gains.
Research by Oracle and Future Workplace found that 85% of executives have experienced what researchers call “decision distress” the inability to make decisions due to information overload, too many options, and increasing pressure to act quickly. The same study found that 72% of executives say the number of decisions they are expected to make has increased significantly, while the volume of information they must process is growing even faster.
These findings highlight a fundamental shift. In an era where data has become abundant, the challenge is no longer collecting information. It is turning information into high-quality decisions in real time.
Consider a SaaS company experiencing declining conversion rates. Traditionally, finance and analytics teams gather data from multiple systems, prepare reports, and present their findings to management. Even if AI compresses this process from a week to a single day, the business is still reacting only after performance has already suffered.
Now imagine AI systems connected in real time across sales, marketing, product, and finance platforms. Instead of simply reporting what happened, they can detect changes as they emerge, identify the root cause, simulate multiple scenarios, and estimate the financial implications of each possible course of action. In this model, AI evolves from a reporting tool into an active participant in business decision-making.
This shift becomes even more significant in the context of the emerging AI token economy.
Unlike traditional enterprise software, where organizations paid primarily for licenses or seats, most generative AI services are priced based on actual usage. Every prompt, analysis, or model interaction consumes tokens, each carrying a measurable financial cost.
According to Menlo Ventures’ 2025 State of Generative AI report, enterprise spending on generative AI increased sixfold within a single year. As AI investment grows, organizations are under increasing pressure to understand not only how much they spend, but what value they generate in return.
That changes the conversation.
Instead of measuring how many hours AI has saved, companies are beginning to evaluate the business outcomes it creates. If thousands of tokens are spent generating reports that nobody uses, the investment becomes difficult to justify. But if those same tokens help prevent a poor investment decision, improve pricing strategy, or identify business risks before they escalate, the financial return becomes much more tangible.
In that sense, the token economy is more than just a new pricing model. It creates a much clearer connection between AI usage and business value, forcing organizations to define more precisely what value means.
For finance leaders and FP&A teams, this represents a significant shift in how success is measured. As technical tasks become increasingly automated, competitive advantage moves away from producing information and toward using information to make better decisions. Deloitte’s 2024 CFO Signals survey identifies capital allocation and investment prioritization as two of today’s most critical responsibilities for finance executives. As the volume of available data continues to grow, so does the importance of making faster, smarter decisions.
Ultimately, the most important question is no longer how many hours AI can save.
The real question is whether it helps organizations make better decisions.
If companies view AI primarily as a tool for automating tasks, much of its economic potential will remain untapped. But as AI becomes embedded in the decision-making process itself, its business impact will become easier to measure – and organizations may finally begin to see the return on investment they have been expecting all along.
Lior Sitruck is FP&A Manager at MIND Security.