Scale AI solutions through app modernization and targeted skilling

As industries embrace the era of AI, legacy apps and data infrastructure often prevent organizations from harnessing its benefits; to drive innovation and maintain a competitive edge, companies must modernize their applications to unlock AI's full value. Decision makers agree with us: A recent Forrester study of over 500 business leaders found 89% of enterprises accelerating or maintaining investments in app modernization over the next year. 

This blog focuses on how teams can scale AI transformation through app modernization and targeted upskilling. 

Before we dive in, don’t forget to check out our newly published learning resources, “Modernize apps for AI-readiness” and “Develop, re-platform, and improve AI apps,” featuring the latest best practices for building AI apps on Azure. 

Build your app modernization strategy on Azure 

In practice, before building new applications, development teams are tasked to rationalize and align their modernization strategy. Such strategies need to be uniquely tailored to concrete business outcomes. What we have seen is that successful modernization tends to build around end-state objectives, with each initiative tied to measurable outcomes. This requirement might sound complex and fragmentary, but Azure provides a level of flexibility to help navigate this workstream, enabling abilities to: 

1. Rehost to IaaS, to lift and shift apps from current state to the cloud VMs with minimal change to app architecture. This helps you standardize infrastructure management and build foundational skills.  
2. Re-platform to PasS, to refactor code base to enable apps to deliver new features. It leads to automation of app infrastructure management, faster release cycles, and operation efficiency.  
3. Rearchitect monolithic apps to microservices, to expand cloud footprint, moving production workloads and adopting cloud-native capabilities. Organizations do so to accelerate their innovation cycle, and gain access to easier adoption of new AI capabilities. This can include the need to both decouple monolithic apps into individual microservices, or loosely couple services via API or event for versatility.  
4. Rebuild with cloud native services, to optimize cloud usage for cost, performance and security, and adopt DevOps and platform engineering.  

By carefully considering these factors, you can choose the most appropriate modernization strategy for future applications you have in mind. For instance, simple legacy applications may benefit from a rehosting approach, while complex monolithic applications might require a more intensive rearchitecting or a rebuild. Remember, a successful modernization journey involves a balance of technical expertise, strategic planning, and a clear understanding of business needs. 

Take a continuous approach to app modernization 

It's true that app modernization enables organizations to drive higher efficiency, flexibility, and strategic advantage — but it isn’t one and done. Also, there is not a one-size-fits-all modernization strategy. We recently formulated a point of view that the true merits of modernization lie in a continuous approach. Depending on the maturity or stage of your AI adoption, Azure can help you create a proactive culture that triumphs in rapid change. More generally, we recommend that a modernization strategy should be made on the application or workload level, factoring in the following:

1. Complexity of use and desired business outcome
2. Desired performance for the application/solution
3. Productivity vs. control requirements for the platform
4. Business roadmap and vision for the application

To go deeper and learn more about our latest opinions and offerings on this topic, visit the Azure Application and Data Modernization homepage.  More broadly, Azure provides a host of scalable, managed solutions for modernizations. Services such as Azure Kubernetes Service, Azure App Service, Azure Container Apps, Azure SQL Database, Azure Database for PostgreSQL, and Azure Cosmos Database can help you deploy, host, and manage applications and databases. They are also designed to support diverse workloads with flexibility, performance, and ease of integration. Don't forget to add them to your toolkit. 

Cut through the noise with targeted skilling

We’ve all seen how quickly new AI models and architectures have sped onto the market. The shear velocity of new AI capabilities, methods, and knowledge is creating a shortage of skilled developers and technical decision makers. This is hindering technical teams from building differentiated products.

As the need for learning content surges, we face a new type of challenge. How could development teams, from the underlying ocean of online resources, find materials that developers and IT pros would be interested to consume in a timely manner, and in fact find them useful for their real-world projects?

To tackle this challenge head-on, we recently released three curated learning assets. They are designed to be role-based, scenario-focused, and specified for building or modernizing AI apps. Let’s take a minute to explore how they may be useful for your team.

1. For technical managers who want to refresh their understanding on app modernization and discover the available tools and services Azure provides, get started with “Modernize apps for AI-readiness.” This asset focuses on migration and modernization tools, along with options for application development such as managed Kubernetes services and fully managed platforms. Technical managers can also gain an understanding of the Microsoft Intelligent Data Platform, which enables their teams to build robust data layers for applications on Azure. 
2. For developers who are tasked to investigate, build, and design cloud-native AI apps, and its associated database considerations, consider “Best practices for AI apps.” This asset is designed to guide developers through the essential steps of creating AI applications on Azure. It also enumerates practices to design and build a cloud-native AI app, develop a back-end database, and integrate Azure AI services into applications. 
3. As a more advanced next step, check out “Develop, re-platform, and improve AI apps,” which is curated to help developers gain the skills in re-platforming apps and assessing code and architecture changes on Azure. It also helps developers learn how they can build net new apps, while factoring in security considerations, deployment processes, and testing—all using Azure services.  

Before you go

We hope that with the above resources for app modernization and skilling, your team may accelerate your application development with Azure. If you look for a more holistic set of best practices, also check out Azure Essentials, designed to improve the reliability, security, and performance of your cloud and AI investments.