Enterprises across industries are under pressure to modernise their IT infrastructure while keeping costs, security, and operational stability under control. The rise of AI inside organisations has added another layer of urgency, as data platforms now need to support analytics, machine learning, and AI-driven applications alongside traditional workloads. Recent developments around Microsoft Azure and enterprise storage platforms show how companies are choosing practical, step-by-step paths to become AI-ready instead of pursuing risky, large-scale rebuilds.
Modernisation challenges continue to slow enterprise progress
Many organisations still rely on legacy systems built around virtual machines, custom workflows, and long-established processes. While cloud platforms promise flexibility and scalability, moving older applications to the cloud is rarely straightforward. Rewriting software for cloud-native environments can take years and introduce operational risk. At the same time, simple migrations that copy workloads without changing how they run often lead to unexpected cost increases. As a result, modernisation efforts frequently stall before delivering real value.
To address this, several vendors are focusing on smoother migration paths that allow virtual machines to move into Azure with minimal disruption. This approach lets IT teams test cloud environments, understand cost patterns, and prepare workloads for future AI use without making immediate architectural changes. For many enterprises, this controlled transition is an important first step toward building systems that can later support AI initiatives.
Cost predictability is becoming a key decision factor
Rising cloud costs remain a major concern for business leaders. Enterprises want the benefits of cloud computing but need clearer visibility into storage and compute spending. By managing workloads through Azure’s native tools and integrated storage platforms, organisations are finding it easier to forecast expenses and avoid sudden billing surprises. This predictability makes it easier to justify modernisation projects internally and align them with long-term AI strategies. The broader lesson is that successful migration plans prioritise financial clarity alongside technical flexibility.
Hybrid data protection and governance are now essential
As infrastructure becomes more distributed across on-premises systems, edge locations, and cloud environments, data protection has moved to the centre of enterprise planning. Downtime, ransomware, and data loss can directly impact AI projects that depend on consistent and trustworthy data. In response, organisations are strengthening recovery strategies with features such as immutable snapshots, replication across locations, and better tools to identify compromised data quickly.
Integrations between Azure and enterprise storage platforms are helping companies manage data across hybrid environments from a unified control layer. This is especially important for organisations with data residency and compliance requirements that prevent them from moving all data to the public cloud. By keeping sensitive information local while still using Azure’s analytics and AI services, enterprises can balance regulatory needs with innovation goals. For many, this hybrid approach has become the foundation of AI-ready data governance.
AI readiness starts with improving existing data systems
While AI adoption is accelerating, most enterprises are not ready or willing to replace their core databases and platforms. Instead, they are enhancing the systems that already store critical business data. Microsoft’s SQL Server 2025 reflects this trend by introducing vector search and AI-friendly features directly into a familiar database environment. This allows teams to experiment with AI-driven applications without migrating to entirely new platforms.
When paired with high-performance storage, these improvements can increase throughput and reduce latency for AI workloads. Enterprises using this approach report more predictable performance when testing models or running early AI applications. The key insight is that becoming AI-ready often means strengthening data foundations rather than chasing entirely new technology stacks.
Managing containers and virtual machines side by side
Modern enterprise environments increasingly combine traditional virtual machines with container-based applications. Managing both at scale can be complex, especially when workloads span multiple clouds. To reduce this complexity, some organisations are adopting unified data-management solutions that support Kubernetes alongside legacy systems.
Tools such as Portworx, used with Azure Kubernetes Service and Azure Red Hat OpenShift, allow teams to bring virtual machine workloads into Kubernetes environments using technologies like KubeVirt. This enables gradual container adoption while preserving existing automation and operational practices. For enterprises planning AI initiatives, this flexibility helps ensure that infrastructure can scale and adapt without forcing teams to abandon familiar tools overnight.
A practical roadmap for enterprise AI modernisation
Across these developments, one message is clear: enterprises are prioritising steady progress over disruptive change. Instead of rebuilding everything, they are choosing predictable migration paths, stronger data protection, and incremental improvements that support early AI use cases. Partnerships and integrations around Azure highlight how modernisation is evolving into a process of enhancement rather than replacement.
For organisations planning their own AI journeys in 2025, the most effective strategy may be to modernise in manageable steps, keep costs and compliance in focus, and build on existing data platforms. This approach reduces risk while creating a solid foundation for enterprise AI growth in the years ahead.
