All cloud segments are projected to experience double-digit growth this year, and the public cloud services market will grow by 21.5%, according to consulting firm Gartner. However, in a context of rising costs, companies need effective control tools to govern their cloud investments.
This involves avoiding unexpected expenses, minimizing idle or redundant resources, and optimizing provisioning. Management is already complex, given that most organizations operate with hybrid architectures and multicloud models. Added to this is a new factor: the accelerated growth of artificial intelligence (AI) workloads—especially those leveraging generative AI (GenAI)—whose consumption patterns are highly variable and unpredictable, posing unprecedented challenges for resource planning.
In this context, FinOps becomes a key discipline for enterprise resource planning, as it helps manage and optimize cloud spending effectively without compromising performance. Traditionally, this discipline was handled with fairly manual analysis and reporting. But given the speed and volume of cloud spending, that approach has started to become obsolete, as more advanced management mechanisms are now required. In this scenario, FinOps is getting a decisive boost from artificial intelligence (AI): through advanced algorithms and machine learning, it has evolved beyond just tracking and providing visibility into cloud spending. Today, it’s an automated practice that detects waste, predicts budgets, and recommends actions. Moreover, it enables dynamic workload allocation through intelligent suggestions.
Cost Optimization
According to a recent survey, the current top priorities for FinOps professionals are:
- Workload optimization and waste reduction (50%)
- Full allocation of cloud spending (30%)
- Accurate expense forecasting (28%)
- Scalable FinOps governance and policy implementation (27%)
Looking ahead to the next 12 months, the survey found that workload optimization is expected to drop significantly—by 21%—to become the second-highest priority, while the implementation of governance and policies at scale will rise to the top. A notable insight from the study is that 63% of respondents now manage AI-related spending, up from just 31% the previous year. The research also found that FinOps teams are managing other technology cost areas: for example, 40% currently manage SaaS spending, and that figure is expected to rise to 65% within a year.
By applying AI to cloud financial operations, organizations can optimize spending, predict usage patterns, and eliminate waste. With AI-powered FinOps integrated into enterprise resource planning systems, companies can:
- Actively manage cloud expenditures
- Make faster, better-informed decisions about cloud investments
- Forecast future needs and anticipate spending scenarios
- Ensure their cloud investments generate value
- Better align cloud spending with business objectives
Improving cloud expense forecasting is one area where AI adds tremendous value: through machine learning, large volumes of historical data and market trends can be analyzed to more accurately anticipate future costs.
Process Automation
Since 2024, AI has become a fundamental tool for FinOps teams by providing predictive analytics and automating complex processes. Today, organizations can proactively scale FinOps with AI—gaining financial transparency, real-time insights, and predictive accuracy. For example, an AI system can identify underutilized resources, anticipate demand surges, and automatically scale resources to avoid both overprovisioning and capacity shortages.
By integrating AI into FinOps processes, organizations shift from reactive cost tracking to proactive, predictive governance. At Baufest, we help companies take full advantage of intelligent automation and AI to evolve their FinOps practices—achieving greater efficiency, foresight, and control.