A recent Gartner survey highlights a mixed picture of how enterprises are adopting and managing artificial intelligence (AI) technologies. According to the findings, only 22% of enterprises have successfully integrated AI across multiple departments or have adopted an "AI-first" strategy. Additionally, 11% of enterprises are completely unaware of the AI-related spending by their departments in 2025. Despite these challenges, the survey also shows that 85% of functional leaders—those responsible for specific business areas—plan to increase their AI investments in 2026. This comes after an average of 12% of their budgets were allocated to AI in 2025. The survey, conducted between January and April 2026, involved 1,303 respondents from companies with annual revenues of at least $50 million in the 2025 fiscal year. It revealed that while many organizations are investing heavily in AI, they often lack clarity on the financial returns of these investments. Tina Nunno, an emeritus vice president and Gartner Fellow, warned that without clear metrics tied to business outcomes, companies risk wasting resources and failing to meet their goals. The most successful enterprises, which closely track the return on investment (ROI) of their AI projects and regularly assess their performance, have seen positive returns on 81% of their initiatives. In contrast, less successful enterprises are unsure of the ROI for 29% of their AI efforts. Most functional leaders prioritize short-term productivity improvements over long-term transformation or new revenue streams. Productivity was the top objective for 75% of these leaders, accounting for about 30% of their AI spending. This is nearly double the amount invested in the second most important goal, which is typically related to innovation or revenue growth. Nunno emphasized that leaders who carefully track how their AI spending aligns with specific outcomes—such as productivity, risk reduction, or innovation—are better equipped to justify their budgets and adjust resources if needed. The survey also found that popular AI applications do not always deliver the best returns. While cybersecurity threat detection, automation of IT support, and automated code generation are among the most frequently implemented AI use cases, they are not necessarily the ones producing the highest financial returns. Instead, AI initiatives focused on optimizing IT resources, generating synthetic data, and refining code more strategically are more likely to yield measurable value. Nunno noted that companies should focus on AI use cases that directly align with their unique business goals to ensure they achieve the best financial outcomes.