As the IT landscape evolves, new trends are emerging that promise to reshape how businesses approach technology in 2025. From generative AI to data sovereignty, the coming year will challenge industries to rethink their strategies and adapt to emerging realities. Based on key observations and industry signals, here are four predictions for what lies ahead.

1. From Experimentation to Execution: Generative AI Inference Takes Center Stage
Generative AI is transitioning from merely experimental tools to fully integrated solutions that provide substantial business value. While the past year focused on chatbot use cases, largely using public data, the future lies in applying generative AI to private, secure datasets to create even more valuable tools. Enterprises in sectors like finance, insurance, and e-commerce are poised to adopt these technologies to extract meaningful insights from proprietary data.

Deployment flexibility will be critical. As AI workloads expand into diverse environments — on-premises, edge, and air-gapped hosting facilities — latency-sensitive applications will demand infrastructure closer to users, deployed in existing data centers and PoPs. Moreover, inference is no longer a standalone workload. Supporting tasks like retrieval-augmented generation (RAG) and app integration will require robust, general-purpose compute alongside AI-specialized resources, emphasizing efficiency and scalability.

2. Powering the Future: Renewable Energy Growth Plus Efficiency Gains
As compute demands surge, so does the need for power. However, overloaded grids and geographic power constraints are forcing industries to seek new solutions. Renewable energy sources like solar, wind, and geothermal are gaining traction as smaller, regionally distributed data centers emerge. These projects will take more time than is available to meet the immediate demands of IT infrastructure growth.

Efficiency, however, cannot wait. To avoid bringing new non-renewable energy sources online or prolonging their life in the short-term, hardware optimization will play a pivotal role in reducing power requirements. Replacing older, power-hungry systems with modern, efficient processors can dramatically cut energy use, making existing infrastructure more sustainable. This efficiency shift is critical to balancing the need for more energy with responsible environmental stewardship.

3. The Rise of Density: Maximizing Every Rack and Data Center’s Potential
Given the rapid increase in demand for AI compute, density at scale has become the new benchmark for efficiency in computing. Solutions are being built not at the node level, but at the rack and data center level. This means that organizations are moving toward maximizing workloads per rack by fully utilizing available hardware. Unlike legacy systems, where resources were often underutilized due to inefficiencies, modern architectures are designed to eliminate waste and improve average utilization at rack and data center scale without the negative side-effects of unpredictability.

The challenge of optimizing density at the solutions level is not limited to AI-only workloads. Certain AI workloads, particularly inference, are driving infrastructure changes to accommodate mixed-use environments, where general purpose compute density matters as well. In software engineering organizations, more efficient virtualization and containerization technologies combined with more efficient containers and power aware coding practices will enable better partitioning of resources, allowing enterprises to achieve higher utilization rates without compromising performance.

4. Sovereignty and Security: Enterprise AI on the Rise
Data sovereignty and security will heavily influence AI deployment strategies in 2025. Enterprises are increasingly aware of the value of their proprietary datasets, treating them as competitive assets. This shift will mean that AI inference workloads run not only on public hyperscale clouds, but also in more secure environments like private clouds, on-premises data centers or privately hosted facilities.

The risk of data breaches and tampering with AI algorithms underscores the need for secure, isolated infrastructure. As enterprises compete on AI-driven innovation, the ability to safeguard intellectual property and sensitive information will become a cornerstone of success. Furthermore, this trend will expand the role of enterprise-owned compute resources, creating a more decentralized and secure AI ecosystem. This sovereignty and security requirement combined with the need to place computing resources closer to users will disperse computing resource and give rise to a more compute heavy edge architecture.