AI and application modernization

There is software all around us, from the gadgets we use, to how we communicate, it's embedded everywhere. And because applications are so integrated in our everyday lives, we need to make sure that they are maintained and upgraded constantly. The challenge when it comes to can rely on external services like APIs to perform specific functions. Each piece plays its part. Upgrading any one component could potentially disrupt this delicate balance. So, to modernize successfully, the planning must span the entire organization. 

What is application modernization? 

Simply, application modernization involves taking an outdated computing system and updating it with modern, well-aligned capabilities and features to create new business value. Businesses are constantly reinventing their ways of working for multiple reasons, to take advantage of the latest innovation, to enhance productivity, or sometimes just to avoid non-compliance risks. And every time business operations are refined, the applications powering them need to be modernized as well. 

Imagine a system with a lot of unstructured code, tangled, messy, and difficult to read. To make sense of this spaghetti code, you either need the subject matter expertise, or you need to apply complex techniques that can help you extract it. But because of the shortage of developers with the right skills, it's easier said than done. 

This is where generative AI can make a critical difference to accelerate your application modernization journey. Once you make the decision to modernize, the process will kick off in two phases. 

Advisory phase 

The first is the advisory phase, here you take the portfolio of applications written in different languages on different platforms and understand which among them need to be retired, transformed to software as a service, containerized, refactored, or re-written. This is to understand the ROI and figure out if a transformation is needed at all. 

Planning phase

Next is the planning phase, where you lay out the right order to migrate the applications, understanding how they will coexist with legacy applications and test them. The advantages of generative AI start to take effect as soon as you begin the modernization process. In the advisory phase, for instance, you can use generative AI to summarize the legacy code, which very often is poorly documented.

Identify pieces of it that need to be changed and generate documentation for it. As a result, in some scenarios, AI can help shorten the advisory cycle from weeks to days. As you begin the planning phase, The code generation capabilities of AI let you create millions of lines of code very quickly, saving valuable time and labor. With just a natural language prompt, developers get a head start, or in some cases, even fully generated code where developers simply validate.

It's also likely that you will have some legacy applications that are not compatible with the new platform, a cloud platform for instance, and you need to retain these applications. In these cases, you can use code conversion to rewrite or rather translate the existing languages to newer ones. 

With AI, there is the opportunity to automate the conversion and enable it for many languages. During the conversion process, developers can now use AI to identify and eliminate similar code snippets and replace them with common services. And beyond theory and strategy, we are seeing success in real-world scenarios. And with AI existed code conversion, we were able to automatically translate about 85% of this custom code. And not only that, this translated code also aligned with their existing DevOps practices. So massive time and cost savings. 

In another instance, a large banking client was looking to modernize and refactor their applications built over the past decades. They wanted to make applications more agile in the cloud, while avoiding significant investments in redevelopment. We use AI to recommend the components, avoiding significant investments in redevelopment. 

We use AI to recommend the components and generate the stops for these new components and identify any dead code, any unused code in their applications. With AI, we were able to accelerate the process of refactoring and reduce the application transformation time from months to weeks. 

Think about how this was done in the past. Transformation planning, to give an example, was done through extensive rules encoded in spreadsheets. When you have hundreds of servers and applications to migrate, imagine the complexity of managing it all through these sheets. Every time you hit a bump, you had to revisit the spreadsheets to make changes. And to further complicate the matters, each customer has their own unique requirements, which means you had to manually craft these journeys. 

AI helps to radically streamline the workload migration to the new architecture. It also enables teams to observe workload performance in the new environment, so they can optimize and refine the process. With AI, you gain the capability to adapt on the fly. 

One of the biggest advantages of AI-assisted transformation is the ability to learn and adapt faster. There's a saying that has always held true in tech, what's new today legacy tomorrow. The point is that modernization isn't one and done effort. It's an ongoing journey. So having the best processes and practices in place is often more important than one specific modernization outcome.

I love this stuff. The challenge of getting better and faster every day. We are in what feels like a golden moment for transformation. There's so much innovation happening that my Slack channels are always lighting up. 

A new model comes up practically every minute across various domains. And the large language model trained for coding that we rely on for application modernization is also seeing a lot of action. And with the pace of innovation in AI models, this is just the beginning. 

So what does the future hold? With the developments in AI coming in fast and furious, the future will be full of endless opportunities. Imagine generative models teaming up and collaborating on tasks, or systems starting to self-heal and self-evolve. 

Remember the periodic upgrades to applications I talked about earlier, what if the applications could seamlessly transform and get better on their own? Compared to traditional transformation, this stuff sounds like science fiction, but it's going to get real very quickly. The possibilities are limitless, and my advice to everyone is to start planning now. Whether that's on your own or with a partner, kick off your AI transformation practice today because it's going to be a huge competitive differentiator going forward. 

Thank you for reading. Please join The Blueguard again for future episode of AI tutorials, as we explore some of the most important topics in AI for Business.

Print this post