For decades, mainframes have powered mission-critical enterprise applications, serving as the backbone of IT infrastructure. However, dynamic market conditions, rising competition, and increasing costs challenge the traditional mainframe model. Technology leaders are now exploring modernization strategies to adapt mainframe applications for hybrid cloud environments while preserving critical business rules and data.
Rapidly evolving markets demand businesses adapt to shifting customer expectations, regulatory changes, and economic pressures. Legacy mainframes, built for stability over flexibility, struggle to meet modern demands for speed and agility. Modernization enables organizations to launch new capabilities faster and respond effectively to changing market dynamics.
Re-architecting or rewriting mainframe applications has historically been fraught with delays, failures, and incomplete functionality capture. Generative AI is transforming this process by analyzing, translating, and optimizing legacy code with unprecedented precision and speed. These AI-driven tools allow businesses to modernize older languages like COBOL and adopt advanced frameworks, database architectures, and cloud technologies previously unattainable for legacy systems.
In this research brief, Mainframe Modernization with GenAI and AveriSource, completed in partnership with AveriSource, Futurum explores AveriSource’s latest approach to mainframe modernization with the release of its new AveriSource Platform 3.0, enhanced with Generative AI technology.
Key themes explored in Mainframe Modernization with GenAI and AveriSource include:
- Drivers of Modernization: The shift to digital-first strategies and dynamic market conditions are pressuring businesses to modernize legacy mainframe systems, which often lack the flexibility, integration capabilities, and innovation speed required to stay competitive. Rising maintenance costs and operational challenges further emphasize the need for modernization.
- Role of Generative AI: Generative AI, when combined with domain-specific tools and knowledge bases, significantly accelerates legacy system modernization by providing insights into codebases, identifying inefficiencies, and ensuring business logic is properly transferred. It de-risks projects and enables faster, more reliable transformations into modern architectures.
- Strategic Modernization Approaches: Successful modernization involves hybrid strategies, starting small with specific applications, and leveraging specialized GenAI tools tailored for legacy environments. Partnering with innovative providers ensures alignment with evolving technologies and long-term modernization success.
If you are interested in learning more, be sure to download your copy of Mainframe Modernization with GenAI and AveriSource today.
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Author Information
Mitch Ashley is VP and Practice Lead for the CIO & Technology Buyers and Software Lifecycle Engineering practices at The Futurum Group. A multi-time CIO and CTO with 30+ years leading technical organizations, Mitch built and operated production systems spanning cybersecurity for the U.S. Department of Defense, PKI services for the broadband and 5G industries, SaaS platforms, large-scale telecom and banking systems, and a national broadband network. His work with AI began early, developing expert systems that diagnosed and repaired complex mainframe environments. That operator foundation grounds his analysis in operational consequence, covering the technology buyer's world of software engineering, cybersecurity, DevOps, cloud, and AI.
