In today’s rapidly evolving technology landscape, traditional financial management practices in IT fail to address the complexities of dynamic demand, third-party services like cloud, and sustainability needs. Technology executives must shift away from a fixed-cost mindset toward a variable cost mindset such that they can focus on optimizing value and align expenses with changing demands. Enter FinOps: A specialized practice within IT finance that focuses on cloud cost optimization. This report delves into the paradigm shift you must make, explains the relationship between IT finance management and FinOps, and provides insights that will help you navigate the challenges of modern IT finance.

Most organizations are currently in the crawl or pre-crawl stage of AI cost management maturity. As AI spend starts to scale, limited cost controls are overwhelmed by dynamic margins, new cost levers, and unexplored optimization techniques. Forecasting at the application layer is currently impractical due to inconsistent, uncontrolled, and decentralized spend, and budgeting remains challenging as companies prioritize productivity and innovation over cost containment, with spend linked weakly, if at all, to business value. This report shows tech leaders how to apply the FinOps maturity model of crawl, walk, run to the stages of AI cost management.

Enterprises are rapidly scaling their use of AI across the organization, introducing new cost challenges for teams relying on traditional FinOps practices and ad hoc controls. Mature organizations approach AI cost management as a disciplined value‑driven practice that balances experimentation with financial accountability, drawing on close collaboration among finance, engineering, data science, and platform teams as well as new cost optimization techniques beyond traditional cloud spend management. Technology and finance leaders can use this assessment to gauge the maturity of their AI cost management practices in five competencies to support scalable and responsible AI adoption.

IT finance must move beyond cataloging technology costs to actively accelerating enterprise value. As emerging technologies like AI introduce greater cost fragmentation and volatility, reactive reporting is no longer sufficient; IT finance must become an outcome‑driven partner that builds trust in technology decisions. Forrester’s IT Spend Management Framework serves as the catalyst that enables this shift, giving CIOs and IT finance the visibility, control, and optimization needed to link spend to business outcomes. This report shows how IT finance can use the framework to mature its practices, strengthen alignment with the business, and guide the enterprise toward performance and growth.

The pervasive growth of cloud usage in enterprise IT strategies has heightened the need for a proper cloud governance framework. The change from on-premises to public cloud environments is a rude awakening as organizations switch from a fixed cost structure to one of variable consumption, self-service access, and dynamic scalability. Use this report as a guide to build your cloud governance framework. This report provides cloud leaders with framework for cloud governance, as well as accounts for stakeholders, workload targets, processes, and tools.

AI cost management and optimization differs from FinOps; data science and ML team decisions in the ModelOps lifecycle have more impact on AI costs than infrastructure. The cost levers of AI workloads — model architecture, parameter size, training strategy, inference pattern, serving design — radically affect economics but are mostly optimized for performance, not financial efficiency. AI cost optimization requires collaboration between FinOps, ML, and platform teams; firms must embed optimization into model development and deployment early. This report looks at techniques for optimizing the ModelOps lifecycle and shows firms how to align technical decisions with sustainable, scalable AI economics.

The US cloud market in 2026 is entering a new era defined by AI-native architectures, multicloud complexity, and sovereignty concerns. While public cloud remains dominant for scalability and innovation, enterprises are increasingly adopting private and hybrid models to manage cost, compliance, and resilience. AI-driven workloads and agentic automation are reshaping cloud strategies, pushing hyperscalers and neoclouds into fierce competition. This report helps cloud leaders navigate these shifts by outlining the latest adoption patterns, operational priorities, and strategic recommendations for building a future-ready cloud ecosystem.

Predictions are a tough sport to play. If you get them all right, you played it safe by recapping existing trends or gave wishy-washy statements that are hard to verify. If you got them all wrong, you likely went for the headlines with little accountability on those claims. Every year, we try to find that […]

At the end of each year, we grade our cloud predictions. We use this report to hold us accountable and assess each prediction against what actually happened. In 2025, economic volatility, genAI, and tariffs dominated the tech market. Cloud providers made massive bets on AI data centers and infrastructure build-outs to support genAI workloads. Neoclouds used VC support, open-source AI projects, and NVIDIA chips to snatch premium enterprise end users. Still, the AI craze has left doubts on whether these investments are ahead of their time. This report outlines which 2025 predictions we got right, wrong, or somewhere in between.

Cloud AI and data services are reshaping retail, setting the stage for a new era of innovation. Predictive AI has long powered demand forecasting and personalization, but genAI takes it further, arming business users with agentic tools and creating immersive, tailored shopping experiences for customers. Behind the scenes, genAI cloud services predict supply chain disruptions, optimize logistics, enable dynamic pricing, and streamline inventory management. Yet economic volatility and shifting tariffs raise compliance concerns, slowing cloud migration and driving on-premises or hybrid strategies. This report outlines best practices from retail leaders in cloud strategy and adoption.