Sold by: IBM Consulting
IBM Consulting's solution for assembler to Java conversion to modernize complex mainframe legacy systems, is achieved with speed and accuracy using AI-driven automation. IBM's approach using its proprietary Analysis Renovation Catalyst tool results in up to 40% reduction in analysis time and a ~70% increase in efficiency during modernization projects. It accelerates transition from outdated code to Java-based architectures, enabling cost savings, increased efficiency, and streamlined integration with cloud-based environments.
Sold by: IBM Consulting
Overview
Organizations reliant on legacy systems often face challenges such as skill shortages, outdated documentation, and complex application environments.
For example:
- Skills Shortage: In many cases, client employees and contractors maintaining the legacy code are retiring. Refilling these positions can be difficult and expensive due to lack of skilled professionals in the market with required expertise (e.g. COBOL and Assembler)
- Tribal Knowledge: Lack of up-to-date documentation and limited availability of tools capable of effectively deciphering the (complex) logic, rules, and requirements embedded in the application code, which leads to over reliance on tribal knowledge.
- Complex Application Environments: Legacy systems are typically batch-based with hundreds of monolithic applications. The desired target state is often real-time, event-driven on the cloud and this combination results in a non-trivial modernization path.
IBM Consulting addresses these issues with a proven, AI-driven solution leveraging AWS products and services (including Amazon Bedrock, Code Llama) that transforms mainframe legacy code like Assembler into modern, cloud-ready Java architectures. Our service delivers rapid results, reducing time-to-value and eliminating manual bottlenecks. By automating rule extraction and code translation, organizations gain actionable insights into their legacy systems, such as identifying obsolete dependencies and uncovering hidden optimization opportunities, enabling faster modernization while minimizing risks. IBM’s approach promotes compliance with industry standards and positions organizations to leverage cutting-edge cloud technologies for sustainable growth.
Outcomes have shown a 60-70% improvement in operational efficiency, streamlined workflows that reduce project complexity, and up to a 40% decrease in dependency on outdated technical expertise. With IBM Consulting and AWS, clients transition to modular, real-time, event-driven architectures tailored to their business needs.
Highlights
- Business Highlights • Accelerated Modernization: Reduce project timelines from years to months, typically achieving up to 70% in project timeline savings. • Cost Optimization: Savings on manual labor and operational inefficiencies. • Scalable Solutions: Providing clients with modernization paths that adapt to growing business needs. • Improved Compliance: Facilitate adherence to industry standards like FHIR for healthcare, ISO 20022 for financial services, and HL7 for clinical data integration.
- Technical Highlights • AI-Driven Automation: Extract rules into a business-readable English format and convert Assembler legacy code with significantly reduced manual effort. • Advanced Analysis: Identify and resolve dependencies quickly with AI insights. • Future-Ready Systems: Transition to real-time, event-driven cloud-native architectures. • Customizable Business Context: Create a business glossary using comments & database field descriptions, and incorporate suggestions from SMEs
- Additional benefits: •Scalability: Patterns & requirements reusable across multiple modernization initiatives, promoting consistency & efficiency. •Future-Ready Systems: Transition to real-time, event-driven cloud-native architecture. •Flexible: Patented parser framework for Discovery & assessment of multiple technologies (Mainframe, COBOL, CICS, DB2, Assembler, Natural, etc). IBM's approach for modernizing mainframe-based and legacy apps is helping organizations reduce analysis time & effort.
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Balakrishnan Sreenivasan (basreeni@in.ibm.com ) Nisha Bains (Nisha.bains@ibm.com )