iBeam: The Application Modernization Accelerator?

The Modern Challenges in Application Modernization

The irreplaceable value of core business systems has long been a concern for IT leaders. These systems, while essential for large organizations, struggle to deliver the innovation and capabilities demanded by modern markets. Maintaining these systems is expensive and overhauling them is even more costly. Additionally, finding specialists with the necessary skills to maintain them is becoming increasingly difficult, and significant changes would require even greater resources and expertise. Decades of application evolution increased technical debt, and undocumented business rules present enormous challenges for even the most skilled technicians.

For CIOs, the biggest challenge is that such a risky endeavour often results in a functionally equivalent, slightly newer IT system — a return that hardly justifies the effort. The difficulties associated with justifying, planning, and executing large-scale Application modernization services programs are well-documented.

iBeam: The Future of Modernization

Beneath the endless buzz, artificial intelligence (AI) has already helped countless enterprises with initiatives such as task automation, predictive analytics, resource optimization, and cloud modernization. With its greater power and potential, generative AI is poised to push these benefits even further. According to a June 2023 article by researchers Vanson Bourne, 73% of survey respondents believe that generative AI will have the most significant impact on enterprise organizations.

This has led to a reconsideration of major application modernization projects, incorporating AI and generative AI as new components. The IDC FutureScape report titled “AI Everywhere” (November 2023) predicts that increased utilization of AI in application modernization IT services will streamline efficiency, enhance service delivery speed, and improve IT service margins.

Futurum Research echoed this perspective, highlighting the convergence of AI adoption and mainframe modernization as a crucial trend defining the future of enterprise IT applications and infrastructure.

Exploring iBeam Role in Modernization

To understand the potential of generative AI, let’s explore its role in typical modernization.

Complex Application Understanding and Modernization Planning

Applications slated for modernization are often complex, large-scale monolithic entities housed in mainframe or midrange data centers. The number of specialists for these systems has dwindled, and expertise has been lost. The first step in any modernization strategy, regardless of the technical approach, is to understand the existing application in detail to determine the right options for change. AI can accelerate this understanding and provide forensic insights into legacy code and data formats, laying the groundwork for any modernization strategy, from eliminating technical debt to full-blown reengineering.

Do you want Migrate From Monolithic to Microservices Architecture

Augmented and Accelerated Change

Converting a Legacy application to a more modern Application is tricky and risky. Previous generation conversion tools were of limited value. AI-powered application analysis adds a valuable tool for forensic assessment and change automation. By leveraging machine learning algorithms, cluster analysis, and programming language models, AI can generate refactored or optimized code as needed. More sophisticated versions consider language choice, user experience improvements, and the transformation of monolithic structures into maintainable microservice-based components, suitable for a modern, often cloud-centric future state. This saves developers time, addresses technical debt, and reduces the chances of error.

Business Rule Extraction

The same framework can be used to isolate, map, and extract business rules from core applications. These rules represent the core intellectual property that has delivered value for years. AI-centric learning, pattern matching, and modeling techniques can accelerate the modernization of these high-value elements, speeding up the delivery of

User Interface (UI) Modernization

Legacy applications often have outdated, inefficient user interfaces. Generative AI can automatically generate new UI elements, styles, and layouts to improve the user experience, adhering to current best practices and reducing the burden on designers.

Test Cases and Testing

Generative AI can automate aspects of the testing workflow, identifying potential issues and ensuring the reliability and robustness of the refactored code. Smart AI tools can propose changes along with supporting test cases and automatically generated unit tests, speeding up the testing phase and making the delivery cycle more efficient. Traditionally, testing could consume up to 40% of the entire project, so this is a significant improvement.

Conclusion

Modernization programs to support major business initiatives have recently become more viable, with huge advances in platform choice and supporting deployment software. However, they still carry significant risks and present highly complex technical challenges.

Innovative technologies, now infused with the power of AI, offer fresh advantages in the vital role of ongoing application modernization. Furthermore, GenAI may also help justify broader modernization investments. TechTarget reported in October 2023 that “66% of tech investments would be easier to justify if they supported a GenAI initiative.”