How Generative AI Redefines Application Modernization (Over 85% of Leaders Agree)

15-Second Summary: You know about the influence of generative AI (Yes, ChatGPT-like) right? But did you know it can help you modernize your legacy app? Go through this inclusive guide and get expert insights from developers who modernize for a living. Read on!

It's been 13 years since Radixweb modernized the first legacy app. Hundreds of projects later, the dynamics and scale of app modernization didn't stop changing.

Our team has successfully migrated monolithic architectures to microservices, improved the speed and performance of outdated systems, and helped companies keep up with the digital world, as we did (one of many) for this UK-based jewelry company.

In any case, the new technology was so transformative that companies had to rethink and recreate their existing applications.

Today, as the demand for sheer volume and limitless scalability continues to grow, it becomes evident that we need a different approach to innovation, with a different technology altogether.

And it’s happening with Generative AI.

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There’s an increasing rise in AI-assisted modernization workflows, such as code conversion and artifact creation. Those techniques and how they help developers work on legacy products are quickly changing.

And that makes it so important to understand what’s going on – how can you modernize apps?

Let's figure out the possibilities and limitations.

A Brief Introduction Generative AI

With ChatGPT catching people’s attention, the concept of generative AI isn’t a mystery to anyone (with 1.3 million searches in the first quarter of 2023).

It's a subset of artificial intelligence that uses machine learning algorithms to analyze existing data and generate new content and outputs, including texts, images, audio files, etc.

Generative AI is autonomous, which is not the case with traditional AI models that work with predefined patterns and rules.

This turned out to be a breakthrough in deep learning where it can work automatically without any programming.

How does it work?

Well, it starts by collecting huge amounts of data from different sources, identifying relationships and patterns in the data, and then creating new data similar to the original.

Gen AI has found applications across various domains, including image synthesis, natural language processing, and, notably, end-to-end application development.

Its ability to autonomously generate code, design elements, or the entire app makes it an invaluable tool in app modernization.

App Modernization Challenges Solved by Generative AI

Running an outdated app can be difficult at the best of times.

Considering the growing number of workforces, sophisticated technologies, and use cases, the legacy app modernization process is getting more complex to execute, and even expensive to test and maintain.

This continues as your app scales in size and complexity.

Gen AI to the Rescue

Gen AI brought efficiency (up to 15 times more), precision, and speed to the entire process by automating complex tasks, providing intelligent design suggestions, and streamlining the integration of modern technologies.

We'll take a look at a few pressing challenges and how superbly gen AI addresses them.

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1. Challenge: Legacy Code Rewriting

Legacy applications often carry complex and outdated code structures. Rewriting this code manually is a naturally time-consuming and error-prone process.

Solution: Automated Code Refactoring

Generative AI in application modernization excels in assessing existing codebases and automatically refactoring them.

Through pattern recognition and analysis, it can identify improvement areas, optimize code structure, and introduce modern best practices.

2. Challenge: Integrating Microservices Architecture

Transitioning from monolithic to microservices architecture is a significant challenge for teams to identify optimal service boundaries and maintain inter-service communication.

Solution: Microservices Design Assistance

Generative AI adoption helps in the decomposition of monolithic applications into microservices by analyzing dependencies and usage patterns.

In order to optimize the transition process, it suggests service boundaries and communication protocols.

3. Challenge: Leveraging Emerging Technologies

Modern applications often need to align with the latest technology trends such as blockchain, IoT, or machine learning.

Manually integrating those technologies can be complex and time-intensive.

Solution: Smart Integration Patterns

Generative AI understands the nuances of emerging technologies and generates smart integration patterns.

This involves automatically creating required code snippets, connectors, and interfaces customized to the targeted technology.

4. Challenge: Cross-platform Compatibility

Ensuring cross-platform compatibility remains an all-time challenge, especially with the diversity of devices and operating systems in the current market.

Solution: Cross-platform Code Generation

Gene AI understands the intricacies of different platforms and generates code that seamlessly adapts to diverse environments, whether it's mobile, web, or desktop applications.

As a result, teams can dramatically reduce the need for extensive manual adjustments.

Benefits of Generative AI for Application Modernization

According to a recent survey, a whopping 92% of organizations have stated that they’re actively working on at least one app modernization project.

Take IKEA, for example. Their online sales increased by 300% as a result of modernizing payment processing of the eCommerce app.

As we discover more about this trend, the importance of app modernization with generative AI has turned out to be a key catalyst for greater efficiencies and innovations.

Let's understand how:

Dynamic Scalability Optimization

Since generative AI has the ability to analyze app workloads and performance data, it can optimize code for dynamic scalability and manage the app architecture to efficiently utilize resources.

This leads to optimal performance under varying workloads without manual intervention.

Application Decomposition

Generative AI predominates in app decomposition as it analyzes the existing monolithic architecture and automatically suggests optimal service boundaries for microservices.

By understanding code dependencies and usage patterns, teams can break down complex applications into modular, manageable components. All this results in a smoother transition to a microservices architecture.

Automated Regression Testing

Automated regression testing becomes more robust through generative AI.

The AI model generates test cases that cover a wide range of scenarios and helps identify potential issues introduced during the modernization process.

Proactive Security Enhancement

By identifying potential security vulnerabilities in legacy code and recommending secure coding patterns, gen AI proactively enhances the security posture of the application.

As a result, teams can mitigate risks associated with data breaches and unauthorized access.

Efficient Resource Utilization in Cloud Environments

For cloud application modernization, this AI model optimizes resource utilization by generating cloud-native code with platform-specific features.

Executable Design Generation

Gen AI goes beyond theoretical design concepts and generates executable designs. It translates high-level requirements into functional prototypes. Hence, developers can visualize and interact with tangible representations of the modernized application early.

This accelerates design validation and aligns it with end-user expectations.

Automated Documentation Maintenance

Generative AI extends its capabilities to automated documentation maintenance.

It updates documents in real-time as code changes occur during the modernization process, due to which, documentation remains accurate and aligned with the evolving codebase.

Input-Output Roadmap Planning

Planning the input-output roadmap for your application modernization strategy is crucial.

By analyzing data flows, input sources, and expected outputs, the AI model generates a roadmap that outlines the optimal sequence for transforming data interactions within the application.

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How Generative AI Benefits Each Phase of the App Modernization Lifecycle

The application modernization process is a series of well-defined stages, each with specified deliverables and milestones. The goal of this systematic approach is to successfully migrate and modernize legacy apps.

Here's how it goes:

Now let’s understand how generative AI influences each of these phases:

Discovery

Generative AI minimizes the need for extensive SME involvement.

Some of the best use cases in this phase are code analysis, extracting insights from dated documents, and correlating domain/functional capabilities to code and data. This results in effective decomposition exercises, event-storming, and domain-driven design.

As teams get visibility into legacy applications and dependencies, generative AI mitigates risks and enhances the efficiency of the discovery and design phase.

Planning

Using historical data, this AI model generates roadmaps by considering an app’s domain area maps, complexity factors, and dependency patterns.

This enables continuous realignment through Program Increments (PIs). It also addresses the challenge of balancing parallel efforts versus sequential dependencies.

Generative AI acts as a valuable tool in correlating application portfolio details with discovered dependencies, which makes planning exercises more informed and adaptable.

Build and Test

In the build phase, generative AI goes beyond code generation to produce related artifacts such as security design points, pipeline code/configurations, and Infrastructure as Code (IAC) based on swaggers or legacy code insights.

It orchestrates these outputs using predefined application reference architectures. Additionally, in testing, generative AI helps in creating the right set of test code, test cases, and test data.

Deploy

Addressing critical "last mile" activities during deployment is easy with gen AI tools. It expedites security validation by analyzing Infrastructure-as-Code, application logs, and design points.

The ability to generate inputs for configuration management and change management streamlines release processes. By drawing insights from release notes, generative AI enhances the efficiency of security review and approval processes.

Things to Consider Before Adopting Generative AI for App Modernization

For most CTOs and CIOs, leveraging app modernization solutions with gen AI seems tricky, and rightly so.

Every once in a while, a new technology comes around and gets the hype, all too often without any tangible impact.

However, the promise of gen AI is bigger than that. It dawns on a new era of AI in enterprise and proves to play a pivotal role in the best app modernization method.

Consequently, you should:

• Evaluate the trade-offs between utilizing large, general-purpose models and refining smaller ones, considering both costs and benefits.
• Analyze factors and expenditures linked to data accessibility, governance, security, and sustainability.
• Assess your organization's proficiency in hybrid cloud management as a fundamental approach for the successful integration of generative AI.
• Examine the challenges and expenses associated with implementing and sustaining the existing state.
• Collaborate with the Human Resources department to prioritize individuals at the core of your generative AI strategy.

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The BottomlineAI here, AI there, AI everywhere.At this point, there’s no way but to make the most of artificial intelligence in all areas of app development and modernization. As a matter of fact, the whole IT industry has come under its radar.The sooner you utilize its power, the greater benefits you’re going to get.That's why companies are adopting this technology at a breakneck speed. From ambitious startups to agile enterprises, this is high time for organizations to integrate gen AI in their application modernization strategy and leverage its full potential for the greatest results.At Radixweb, we understand this urgency. We're here to give an edge to businesses in their pursuit of app modernization using cutting-edge technologies like AI. Our expertise includes integrating AI capabilities into the development lifecycle, optimizing code, streamlining sprints, and much more.With a proven track record of successful modernization projects, our dev team can ensure that your business makes the best use of AI development services.We've only scratched the surface in this blog. To learn more about how we can modernize your app with the best of gen AI power, connect with us!