The AI and Cloud Collaboration: Frontier of Application Modernization

$3 trillion. That’s the potential EBITDA value that the world’s largest companies could gain from cloud adoption by 2030. With cloud maturity, enterprises have moved well past the cloud-is-for-cost efficiency stage to leverage it to drive innovation and strategic advantage. A leading analyst predicts that over the next few years, the cloud’s importance will continue to grow, as it becomes a business disruptor, and finally, a business necessity. 
Cloud is application modernization

Cloud is not just key, it is practically synonymous with application modernization, which is most commonly defined as refactoring or rearchitecting of legacy applications, with the intent to transform them into microservices-based cloud applications. While cloud adoption is highly beneficial, it can present certain challenges that enterprises should be mindful of. The biggest concern is that migrating monolithic or legacy systems as they are to the cloud is fraught with complexity, requiring repeated assessments to identify bottlenecks and the right approach. Also, because legacy systems have dependencies, integrating them with cloud architectures or rearchitecting them can be quite tricky. Unplanned, ad-hoc adoption should be avoided in order to prevent costs from escalating over time. Another challenge is that successful cloud migration requires a range of skills, which may not be easily available.

AI and cloud: twin advantage

The good news is that by leveraging the latest in artificial intelligence (AI), organizations can not only overcome these challenges but also unlock cloud-AI synergies. 

AI expedites and improves discovery and planning during cloud adoption by facilitating the sharing of best practices and documentation, automating code analysis and identification of architecture and technical debt, visualizing component interactions and dependencies, and monitoring systems in real time to identify performance gaps and their root causes. 

AI can accelerate modernization by automating and simplifying key processes, including code assistance for correcting errors, code refactoring, migration and upgrade, and resource allocation. This also helps to overcome skill shortages and reduce dependence on specialist technical talent. Apart from automating repetitive tasks and workflows, AI can predict future needs and potential problems, conduct performance tuning to continuously improve system efficiency, and provide context-aware support during development.   

It improves the efficiency of cloud operations by empowering enterprises to make the right decisions from the start, thus reducing iterations and development costs. Further optimization happens by way of real-time visibility into cloud usage and spending patterns, prediction of future costs, detection of anomalous spending, scenario modeling showing the cost impact of different decisions, and actionable advice on budgeting and optimization. 
And now, with the emergence of generative AI, automation of application modernization processes has reached a whole new level. Gen AI is able to analyze code to extract business rules and create high-quality code and documentation. Also, advanced-gen AI tools are able to provide code analysis in English (or another natural language), accelerating forward engineering by improving developer productivity. In pilot implementations, gen AI has shown that it can write better code than traditional code refactoring methods.

Further, by breaking complex, monolithic code into smaller, simpler modules, and generating in-line documentation for existing code, gen AI tools enhance code quality and maintainability.

More with AI

When using generative AI in application modernization, enterprises can improve its efficacy by employing the following techniques:

Prompt Engineering: Providing clear examples along with suitably worded prompts to GenAI produces more relevant and accurate results.

Retrieval Augmented Generation (RAG): Enterprises can use Retrieval Augmented Generation – combining gen AI models with a retrieval system to fetch relevant data or context – for more precise, contextual and reliable responses.

AI-First Approach:  An AI-first approach, which integrates gen AI at the core of modernization, will deliver far better results than merely using the technology piecemeal or accelerating a few tasks. 

Modernizing legacy applications is a business imperative for enterprises. Cloud is key here, however, adoption can present certain challenges, such as migration complexity, escalating cost in the case of unplanned adoption, and extensive skill requirements. The latest AI technologies, including generative AI, can help organizations surmount these challenges and modernize their applications with ease and efficiency.

Written By -  Naresh Duddu, AVP and Global Head of Modernization, Infosys

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