This is an Insight article presented by Microsoft.
The great cloud migration isn’t over yet. Many organizations are still making significant strides in moving to the cloud. Today, the push towards cloud adoption is increasingly driven by the potential of AI, particularly generative AI, notes Tony Korolis, senior product marketing manager (Azure) at Microsoft. His team has witnessed this shift in real time. They manage two Azure offerings: Azure Migrate and Modernize & Azure Innovate, which help customers accelerate their cloud goals.
Customers are seeking help not only to migrate their existing systems but also to use the cloud to harness AI and create new products and services. Korolis predicts that within the next year, the number of AI-focused customers may surpass those migrating their existing setups through Azure offerings.
It’s not just about keeping up with industry trends, Korolis adds. Early adopters are already seeing success by turning AI concepts into practical applications that impact daily business operations and the bottom line. The cloud makes powerful, scalable, and cost-effective computing accessible to companies of all sizes, allowing them to leverage AI.
However, many companies find that realizing the full promise of the cloud requires organizational discipline. Many assume that cost savings will happen automatically, but the cloud involves more than just direct financial costs. Successful cloud strategies require effort and proper governance to yield a return on investment.
The true cost of cloud computing and AI includes obvious expenses like the initial technology investment and professional services for implementation. There are also consumption costs that need central management and governance, and if some applications remain on-premises, handling both environments is necessary. Moreover, training, reskilling, or upskilling staff involved in cloud strategies is time-consuming and costly. Training staff detracts from their regular duties and requires time for developing training plans and studying for certifications.
Finding talent with AI expertise is part of the challenge, and this skill gap is a widespread issue. Addressing it will require upskilling, reskilling, and bringing in new job applicants. Certifications, such as the Azure AI engineer certification, test applicants on objective criteria and can provide a useful foundation for other cloud-related certifications.
Organizations eager to delve into AI often balance this with other initiatives, like cloud migration. Korolis suggests maintaining progress by dividing tasks: allocate some teams to migrate on-premises workloads while others innovate with new projects. This method helps create more value and faster progress.
However, rushing through essential planning to get to deployment can lead to complications. Successful projects often spend months in the planning phase to ensure the cloud strategy and organizational alignment are solid before engaging an implementation partner. Recent Azure offerings highlight the importance of distinguishing between planning and deployment phases.
For example, Azure Migrate and Modernize helps move existing workloads to Azure efficiently. It allows you to discover and assess on-premises environments before deployment, enabling you to determine the best migration plan and Azure architecture in advance. These offerings provide benefits such as assessments, pilots/proofs-of-concept, and deployment assistance from experts. Offers can include expert guidance, partner funding, Azure credits, migration tools, and technical training. These approaches, proven by thousands of other customers, help accelerate projects.
The key is choosing providers that offer comprehensive support during cloud migration. Microsoft prioritizes investing in customer success, providing end-to-end assistance to ensure a smooth transition to their cloud solutions.