Application modernization involves updating legacy applications using modern technologies and principles like DevOps and Infrastructure-as-code (IAC). The process begins with assessing current applications, data, and infrastructure, and then applying the appropriate modernization strategy (rehost, re-platform, refactor, or rebuild) to achieve the desired outcome. Rebuilding applications can yield the greatest benefits but requires a significant investment, while rehosting involves moving applications and data to the cloud without optimization, requiring less investment but providing lower value.
Modernized applications are deployed, monitored, and maintained, with ongoing iterations to keep up with technology and business advancements. The benefits of application modernization include increased agility, cost-effectiveness, and competitiveness, but challenges include complexity and resource demands.
Many enterprises have found that simply moving to the cloud does not provide the desired value or agility beyond basic automation. The real issue lies in how IT is organized, which is reflected in how applications and services are built and managed. This leads to challenges such as duplicative capabilities, inconsistent customer experiences, and a lack of alignment between IT and business capabilities. Enterprises often resort to building band-aids and architectural layers to support new initiatives, which is why application modernization should focus on transforming applications into business-aligned components and services.
However, one of the biggest challenges is the investment required, causing some CIOs and CTOs to hesitate due to cost and timelines. To address this, many organizations are developing accelerators that can be customized for enterprise use, helping to speed up specific areas of modernization. IBM Consulting Cloud Accelerators is an example of such an accelerator.
Generative AI is becoming a critical enabler for accelerating modernization programs and optimizing costs. It can assist in various stages of the application modernization lifecycle. For example, during the discovery and design phase, Generative AI can help understand legacy applications with minimal subject matter expert (SME) involvement, correlate domain capabilities to code and data, and generate target designs for specific cloud service provider frameworks.
In the planning phase, Generative AI can generate roadmaps based on historical data, application portfolio details, and discovered dependencies. In the build and test phase, it can generate code artifacts, test cases, and test data to optimize testing. During deployment, Generative AI can provide insights for security validation and generate configuration management and change management inputs.
While these Generative AI use cases seem promising, enterprise complexities require contextual orchestration and customization of the accelerators to realize value. Establishing enterprise contextual patterns can help accelerate modernization programs. Investing time and energy in customizing Generative AI accelerators based on potential repeatability has shown significant benefits.
An example of Generative AI in action is the re-imagining of API discovery for a large global bank. The bank had a large number of APIs across various domains, leading to duplication and operational challenges. By leveraging BIAN and AI, the bank was able to visualize the API portfolio, identify duplicate APIs, and improve efficiency and cost-effectiveness.
Overall, Generative AI has the potential to drive change and accelerate application modernization programs in various ways, but it requires careful customization and orchestration to address enterprise complexities and achieve desired outcomes.
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