- February 16, 2024
- Posted by: Uma Raj
- Category: Data & Analytics
Why legacy applications require a makeover?
By 2026, Gartner predicts that over 80% of businesses will have implemented applications with generative AI capabilities or used generative AI APIs.
Application modernization is the strategic upgrade of legacy systems using modern technologies. It is not just about replacing technology; it’s about adopting current development practices like DevOps and infrastructure-as-code. These approaches ensure streamlined collaboration, automation, and efficient resource management, further maximizing the benefits of modernization.
The treatment of legacy applications can span a spectrum, from rehosting for quick wins to comprehensive rewrites for unlocking the full potential of cloud-native principles. The optimal approach depends on the application’s value, criticality, and desired business outcomes.
While rehosting offers immediate benefits, rewriting unlocks the most significant advantages. It allows building truly cloud-native applications characterized by superior flexibility, rapid development cycles, and seamless scaling. This empowers businesses to respond swiftly to market demands and accelerate innovation.
Why Gen AI for legacy modernization?
Modernizing applications used to be a slog. Laborious manual rewrites, hefty resource demands, and endless timelines defined the process. But the tech landscape is evolving, and businesses are yearning for faster, smarter solutions to bring their applications into the future. This is where Generative AI (Gen AI) emerges as a game-changer, fundamentally reshaping the modernization game. Gen AI analyzes your applications, identifies modernization opportunities, and even generates code suggestions to accelerate the process.
In fact, generative AI is emerging as a critical enabler to drive change in accelerating modernization, making it an essential tool for cost-conscious businesses.
Legacy systems: A bottleneck in modern business
Legacy systems are characterized by a constellation of limitations that impede organizational progress. These limitations can be broadly categorized into inherent shortcomings and operational challenges.
Inherent shortcomings
Obsolescence: Built with outdated technologies and methodologies, legacy systems need more capabilities and security features of modern solutions. This renders them vulnerable to cyber threats and incompatible with modern software and hardware.
Inflexibility: Designed for specific, often narrow purposes, legacy systems need help to adapt to evolving business needs and changing market dynamics. Modifying or extending their functionality is often a cumbersome and costly endeavor.
Performance bottlenecks: Inefficient code and outdated architecture lead to sluggishness, data processing delays, and frustrating user experiences. These limitations can significantly hinder operational efficiency and productivity.
Operational challenges
Security risks: Patching and updating legacy systems can be difficult, if possible, due to compatibility issues and lack of vendor support. This exposes them to known vulnerabilities and increases the risk of data breaches and security lapses.
Limited maintenance: As skilled personnel familiar with the arcane intricacies of legacy systems retire, finding qualified replacements becomes increasingly challenging and expensive. This can reduce maintenance frequency and response times, further exacerbating existing problems.
Scalability constraints: Legacy systems cannot often scale efficiently to meet growing business demands. This can impede expansion, limit market reach, and ultimately stifle growth.
Compliance checks: Complying with evolving regulations and data privacy mandates can be a near-impossible feat with legacy systems. Their rigid structures and opaque data handling practices make it difficult to meet compliance requirements, potentially exposing the organization to legal and financial risks.
Ten ways Gen AI-powered portfolio assessment can fine-tune your legacy app landscape
1. Generate cost-effective roadmaps: With a precise understanding of your app landscape, Gen AI can create personalized modernization roadmaps, considering factors like budget, resource availability, and business priorities. This data-driven approach ensures efficient resource allocation and maximizes the return on your modernization investment.
2. Prioritize modernization candidates: Gen AI can assess the criticality and dependencies of different applications within your portfolio, guiding you in prioritizing which ones to modernize first. This ensures you maximize the return on investment while minimizing disruption to ongoing operations.
3. Predict and prevent risks: Gen AI can analyze historical data and identify potential risks associated with modernization efforts, such as compatibility issues or unexpected performance drops. This allows you to proactively invest in modernization initiatives that align with your long-term business goals and prevent your legacy systems from becoming obsolete.
4. Remove code clutter: Generative AI can detect repetitive logic scattered across your codebase, analyze its purpose, and replace it with a single, centralized function generated by itself. This not only cleans up your code but also reduces complexity and simplifies maintenance.
5. Automate and streamline code generation: Gen AI automates tedious tasks like code analysis and enables you to create a functional document from existing applications, which can be converted into JIRA stories. Moreover, these JIRA stories can be further translated into a modern code base with Gen AI.
6. Uncover bottlenecks and opportunities: Gen AI can analyze vast amounts of data across your legacy applications, identifying underutilized features, performance bottlenecks, and potential security vulnerabilities. This deep dive reveals hidden opportunities for optimization and targeted modernization efforts.
7. Translate to microservices: Buried deep within your legacy code might lurk functionalities wanting to be agile microservices. Generative AI can identify these modules and suggest code segments for isolation, automatically generating the necessary microservice structure and APIs.
8. Detox databases: Outdated databases hinder performance. Generative AI can scan your legacy code, identify database dependencies, and suggest optimal migration paths and schema updates, seamlessly transitioning you to modern SQL or blazing-fast NoSQL solutions.
9. Automate bug fixes: Gen AI can identify and fix bugs, keeping your application running smoothly. GenAI eases integration with modern libraries, generates RESTful APIs, and improves code modularity, future-proofing your app.
10. Modernize user experience: Legacy apps often need help to keep up with modern user expectations. Generative AI can generate user-friendly layouts, create responsive CSS for mobile devices, and even suggest modern design elements—all while preserving core functionality.
Finally, Gen AI sets modernization on autopilot.
By leveraging GenAI-powered portfolio assessment, you can gain a deep understanding of your legacy applications, identify the most impactful modernization opportunities, and make informed decisions about the future of your technology landscape. This data-driven approach allows you to prioritize modernization efforts, maximize your return on investment, and build a future-proof IT infrastructure.
Remember, successful modernization is not just about replacing old technology with new; it’s about understanding your needs, identifying the right opportunities, and implementing solutions that optimize your IT landscape for long-term success.
Take away
Integrate Gen AI into your ongoing application lifecycle management (ALM) to continuously monitor and optimize your modernized app landscape. Ensure your technology landscape remains dynamic and adaptable, constantly evolving to meet your evolving business needs.
