Modernizing Legacy Software in U.S. Enterprises Through Cost-Effective AI-Driven Optimization

The modernization of legacy software is a growing priority for U.S. enterprises that want to remain political or business competitive in a data-driven economy. Although legacy systems often serve a central role in an organization or department, they come with various burdens including high maintenance costs, low elasticity scalability, and a lack of interface capacity with modern technologies. Therefore, like many aspects of innovation and digital technology, Artificial Intelligence (AI) has the potential to transform legacy systems by allowing automated code refactoring, in system optimization, and process decision management. The authors present a comprehensive review of AI-enabled approaches to successfully and cost-effectively modernize legacy systems for enterprise applications. They highlight the need to provide organizations and enterprises with the ability to be adaptive or flexible in a sustained and cost-effective manner for a wide scope of legacy system modernization.

Based on new literature in legacy systems using AI, the authors provide examples of applications in enterprise resource planning, smart manufacturing systems, cloud integration and migration, and multi-cloud optimization and cost savings activities. Also presented are frameworks and best practices for successful implementation for each of these new areas, and the opportunistic challenges of analysis complexity of integrated systems, integration to newer technological spaces, and systems knowledge or skills gaps. The data shows that while AI extends current functional capacity of legacy systems, it also calibrates legacy systems within current governance expectations, strategic outcomes for digital transformation, flexible scaling and strategies for secure cloud capabilities, and is the safest and most cost-efficient approach to modernizing legacy systems to enhance or to reduce enterprise risk.