Companies are significantly increasing their investment in artificial intelligence (AI), but a new report highlights a growing struggle to translate these rising budgets into tangible financial returns. The study, titled “The Adaptive Advantage: Who’s Winning with AI and How,” by Wharton Human-AI Research and GBK Collective, indicates that while AI adoption is widespread, its high operational costs are becoming a major barrier.
Increasing Spending, Uncertain Returns
The report, released in October 2026, surveyed managers and senior leaders, finding that a substantial 87% expect their AI budgets to grow over the next year. Nearly half (47%) anticipate an increase of more than 10%. However, this enthusiasm for spending does not always align with financial outcomes.
Only 28% of those surveyed reported strongly positive returns on their AI investments. This disconnect points to a critical challenge: companies are expanding AI deployment from experimental phases to broader integration, but the expenses associated with scaling these technologies are proving difficult to manage.
The Growing Burden of AI Costs
A key finding from the report is that 34% of managers identify the cost of AI tools, computing resources, and software licenses as a primary obstacle to achieving better returns. As AI applications become more integral to business processes, recurring expenses like token consumption and API charges can escalate unpredictably.
For organizations implementing AI across multiple departments, controlling these ongoing operational costs is becoming as crucial as the initial investment decision. The report suggests that while many businesses utilize AI to boost productivity and cut operating expenses, converting these efficiencies into substantial financial gains remains a significant hurdle.
Measuring Value Beyond Efficiency
The study also highlighted a measurement challenge. Approximately 40% of departments tracking AI returns use “hours saved per employee” as a key performance indicator, with 37% measuring reduced error rates and 34% tracking lower operational costs. In stark contrast, only 27% measure net-new revenue generated by AI-enabled products, services, or features.
This focus on efficiency, while valuable, can obscure whether AI is truly creating new business value or merely accelerating existing tasks. For stronger returns, companies may need to shift their focus to how AI improvements lead to higher output, enhanced customer satisfaction, increased sales, or the creation of entirely new revenue streams.
From Adoption to Accountability
Successful AI implementation, according to the report, requires more than just larger budgets or new tools. Businesses must strategically integrate AI into core workflows, redesign processes, and establish clear, financially oriented metrics for performance. As AI budgets continue their upward trend, robust cost management and precise return-on-investment tracking will be essential.
The ability to demonstrate measurable financial gains, rather than simply reporting higher adoption rates, will ultimately determine the long-term justification for increasing AI expenditure.