Software is prevalent in our surroundings, from the devices we use to our means of communication, it’s everywhere. Because software applications are deeply ingrained in our daily lives, it’s crucial to ensure they are consistently maintained and updated. The main challenge in modernizing applications today stems from their diversity and complexity. Typically, organizations are composed of multiple systems, each supported by numerous applications. These applications often depend on external services, such as APIs, to carry out specific tasks. Every component plays a vital role, and updating any one of them could potentially upset the delicate balance. Therefore, successful modernization requires comprehensive planning that encompasses the entire organization.
What is application modernization?
In simple terms, application modernization means updating an outdated computer system with new, well-matched capabilities and features to generate fresh business value. Businesses are continuously reshaping their methods of operation for various reasons.
They might seek to capitalize on the latest advancements, boost efficiency, or sometimes merely to avoid potential legal issues. Whenever there’s a refinement in business practices, the applications supporting them also need to be brought up to date. Picture a system filled with messy, disorganized code that’s hard to decipher. To untangle this mess, you either require a subject matter expert or must employ complex techniques to extract meaningful information. However, due to the scarcity of developers with the necessary skills, this is easier said than done. This is where generative AI can play a vital role in speeding up your journey towards application modernization.
The generative AI advantage.
When you decide to modernize, the process unfolds in two main phases.
Firstly, there’s the advisory phase. Here, you evaluate a range of applications written in different languages and on various platforms. The aim is to determine which ones should be retired, converted to software-as-a-service (SaaS), containerized, refactored, or rewritten. This assessment helps gauge the return on investment (ROI) and ascertain the necessity of transformation.
Next is the planning phase, where you establish the appropriate sequence for migrating the applications, considering how they’ll interact with existing legacy systems and undergo testing.
The benefits of generative AI become evident from the outset of the modernization process.
For instance, during the advisory phase, generative AI can assist in summarizing poorly documented legacy code, identifying areas requiring modification, and generating documentation. Consequently, AI can significantly reduce the advisory cycle duration from weeks to just days in some cases. Moving into the planning phase, it’s crucial to understand the business logic within the existing code. Through reverse engineering and AI, you can swiftly summarize the code and analyze legacy applications to determine what stays and what goes. Once decisions are made regarding the code, AI’s code generation capabilities allow for the rapid creation of millions of lines of code, saving precious time and resources. Developers can either begin with a provided natural language prompt or, in some instances, receive fully generated code for validation purposes.
You might also encounter legacy applications that aren’t compatible with the new platform, such as a cloud platform, and need to keep them. In such cases, code conversion can rewrite or translate existing languages to newer ones. With AI, there’s the potential to automate this conversion for various languages. During conversion, developers can utilize AI to detect and replace similar code snippets with common services. Real-world success stories validate this approach.
For large IT organizations, transforming legacy code to automatically convert to the new target language can be advantageous. AI-driven code conversion can automatically translate about three-fourths of custom code. Moreover, this translated code can align with existing DevOps practices, leading to significant time and cost savings.
Consider another scenario where a company aims to modernize and refactor applications developed over decades. They seek to make applications more agile in the cloud without significant redevelopment costs. AI can recommend components and generate stubs for these new components while identifying any redundant or unused code. With AI, the entire refactoring process can be expedited, reducing application transformation time from months to weeks.
Reflect on how this was managed in the past. Transformation planning, for instance, often relied on extensive rules and code within spreadsheets. Managing hundreds of servers and applications through spreadsheets posed significant complexities. Each bump in the road required revisiting the spreadsheets for modifications. Additionally, each customer’s unique requirements necessitated crafting these journeys manually. AI radically simplifies workload migration to the new architecture and enables teams to monitor workload performance in the new environment for optimization. With AI, adaptability becomes readily achievable.
The future of modernization.
One major benefit of AI-supported transformation is its capacity to learn and adjust rapidly. In the tech realm, there’s a timeless quote “What’s new today is legacy tomorrow”. The essence is that modernization isn’t a one-time endeavor; it’s an ongoing process. Therefore, having optimal processes and practices in place often outweighs achieving a specific modernization outcome. The aim is to continually strive for improvement and increased efficiency. It feels like we’re currently experiencing a pivotal moment for transformation.
Innovation is flourishing across various domains, with new models emerging practically every minute. The significant strides made in the development of large language models tailored for coding, which we rely on for application modernization, are noteworthy. Given the rapid pace of innovation in AI models, it’s evident that we’re just scratching the surface of what’s possible.
Final Thoughts.
What does the future hold? As AI advancements continue to surge ahead, the future holds a plethora of opportunities. Picture generative models collaborating on tasks or systems autonomously healing and evolving. Reflect on the routine application upgrades discussed earlier. What if these applications could effortlessly evolve and enhance themselves? While this might sound like something out of a sci-fi movie compared to traditional transformation, it’s rapidly becoming a reality. The potential is boundless, and my suggestion to everyone is to start planning now. Whether independently or with a partner, initiate your AI transformation journey today because it will undoubtedly become a significant competitive advantage in the future.
Reference: IBM Research, AI Academy series “Putting AI to Work for Application Modernization”.
