Organizations are slowly changing their agenda. Whether it is building AI-native applications, creating more engaging customer experiences, or unlocking new efficiencies, ambition is rarely the obstacle. Instead, technical debt often stands in the way. Legacy systems, outdated codebases, and fragmented infrastructure slow progress and drain resources. In fact, more than %37 of enterprise application portfolios require modernization today, and that figure is expected to remain high over the next three years. Developers want freedom to innovate, but migration and modernization often become slow, complex, and hard to start. These delays translate into lost opportunities and stalled transformation.
Generative and agentic AI change the game. By embedding AI-driven agents into the migration and modernization process, teams can move faster and with less friction. For example, GitHub Copilot’s “agent mode” (via the “app modernization” extensions) automates tasks such as codebase analysis, plan generation, dependency updates, build/test loops, and even migration readiness for cloud platforms. As documented, agent mode can analyze your Java or .NET project, generate an editable upgrade plan, apply code transformations, fix build issues iteratively, scan for known vulnerabilities, and help prepare the application for deployment on Azure. The result: projects that once took months can now be modernized in days or weeks. Tools like Azure Migrate build on this by offering agentic workflows, cross-team collaboration features, enhanced discovery of dependencies, and support for a broader set of workloads and platforms.
One significant benefit of agentic AI in this context is bridging traditional silos. Modernization often stalls when IT operations, development, data, and security teams do not align. Agentic features in Azure Migrate, for example, provide guided workflows, application-awareness by default, and cross-team collaboration. And with GitHub Copilot agent mode, the developer experience becomes more structured: you start an agent session, scan the codebase, review a plan file, approve execution, and watch as the tool upgrades code, fixes test failures, and produces a summary report. The “app modernization” extension supports predefined tasks such as upgrading from Java 8/11 to Java 21, migrating Spring Boot frameworks, switching to cloud-ready artifacts (Dockerfiles, YAML for Azure Kubernetes Service or App Service), and checking for CVEs as part of the flow. This all matters because a streamlined, predictable process means less manual planning, fewer risks, and more headroom for innovation rather than maintenance.
Finally, technology alone is not the full story, people and process matter. With the Azure Accelerate program, organizations gain access to expert guidance, funding support, and a structured framework for scaled modernization from assessment through deployment. Agentic AI tooling is only part of the picture; combining it with training, governance, and aligned workflows amplifies value. When you pair GitHub Copilot agent mode with Azure Migrate and Azure Accelerate, you enable a transformation engine: you reduce upgrade cost, shrink timelines, and move from legacy backlog into forward-looking development. In other words, you shift from simply keeping systems running to enabling future-ready innovation.
In conclusion, modernization no longer needs to be a guessing game or a multi-year ordeal. With agentic AI baked into platforms like GitHub Copilot and services such as Azure Migrate, along with programs like Azure Accelerate, your teams now have a clearer path to bring every application, legacy or new, into the development lifecycle.
