Application modernization is the process of transforming legacy applications by integrating modern technologies and principles such as cloud native principles like DevOps, Infrastructure-as-code (IAC) and so on. This process involves the assessment of current legacy applications, data, and infrastructure, followed by the application of the right modernization strategy to achieve the desired results. It is a journey that starts with evaluation and carries on through deployment, monitoring, and ongoing iterations to keep pace with technological and business advancements. While modernization brings benefits such as increased agility, cost-effectiveness, and competitiveness, it also presents challenges. Many enterprises are realizing that moving to the cloud does not necessarily provide the desired value or agility beyond basic platform-level automation. This realization has led to challenges in how IT is organized and how current applications and services are built and managed.
One of the significant challenges with application modernization is the investment required. Many CIOs and CTOs are hesitant to invest in modernization due to the cost and timelines involved in realizing value. To address this, many are developing accelerators customized for enterprise consumption that help accelerate specific areas of modernization. An example of this is IBM Consulting Cloud Accelerators. Additionally, Generative AI is becoming a critical enabler to drive change in how modernization programs are accelerated.
The various phases of application modernization programs, including discovery, planning, building, testing, and deployment, benefit from Generative AI in unique ways. From understanding legacy applications to generating code artifacts and analysis, Generative AI plays a significant role in accelerating the modernization process.
Discover and Design
Generative AI assists in understanding legacy applications with minimal subject matter expert (SME) involvement, which is crucial as SMEs are often preoccupied with day-to-day system maintenance. It aids in code analysis, re-write initiatives, and API mapping, enabling effective domain-driven design and decomposition exercises. Generative AI also assists in generating use cases based on code insights and functional mapping, helping to de-risk modernization programs by ensuring adequate visibility to legacy applications and dependencies.
Generative AI comes in handy for generating roadmaps based on historical data and applying this to applications in the scope of a modernization program. It also aids in correlating application portfolio details with discovered dependencies, which is essential for continuous realignment through Program Increments (PIs).
Build and Test
Generative AI assists in generating a wide range of code artifacts, from Infrastructure-as-code (IAC) to generating the right set of test cases, test code, and test data, optimizing the test cases being executed.
Generative AI helps in generating insights for accelerated security review and approval cycles, and configuration management inputs based on release notes. While there are several use cases across modernization phases, contextual orchestration of Generative AI-based accelerators is necessary to realize value and establish enterprise contextual patterns that accelerate modernization programs.
Two exemplary use cases demonstrate how Generative AI accelerates modernization processes, such as re-imagining API discovery and automating the modernization of APIs. The solution leverages BERT Large Language Model, Sentence Transformer, and domain rules fine-tuned with Banking Industry Architecture Network (BIAN) Service Landscape knowledge to address visibility and discovery of APIs within a large global bank. Additionally, Generative AI significantly automated the modernization of MuleSoft APIs to Java Spring boot, resulting in optimized, clean, and best practice-adhering Spring Boot code and test cases.
While Generative AI is not a silver bullet to solve all modernization problems, it helps in accelerating and de-risking the program, reducing the cost of modernization, and ensuring that no current functionality is overlooked. However, it is important to note that bringing Large Language Models and libraries to the enterprise environment requires significant effort and security and compliance reviews. It also requires focused effort to enhance data quality for tuning the models. Despite these challenges, cohesive Generative AI-driven modernization accelerators are expected to emerge in the future, helping to accelerate specific modernization patterns.
The application modernization journey is complex, with unique challenges and significant investment. Generative AI is a critical enabler that assists in accelerating modernization programs by addressing various aspects of the modernization lifecycle. While challenges remain, the potential benefits of leveraging Generative AI in modernization initiatives are substantial.
What is application modernization?
Application modernization is the process of updating legacy applications using modern technologies and principles to enhance performance and adaptability to evolving business needs, often involving cloud native principles such as DevOps and Infrastructure-as-code (IAC).
How does Generative AI assist in the application modernization process?
Generative AI plays a crucial role in different phases of application modernization, including understanding legacy applications, generating code artifacts, correlating application portfolio details, and accelerating security review and approval cycles.
What are some practical examples of Generative AI accelerating modernization processes?
Examples include the use of Generative AI to reimagine API discovery within a large global bank and automate the modernization of MuleSoft APIs to Java Spring boot, resulting in optimized, clean, and best practice-adhering Spring Boot code and test cases. These examples showcase the practical application of Generative AI in addressing real-world modernization challenges.