AI-first for mainframe modernization

How generative AI provides a way out of legacy application constraint and stagnation on the mainframe.

Mainframe systems are at the heart of our economy - they manage our bank accounts, our insurance policies, put food on the shelves of our supermarkets and keep our essential infrastructure running. They are reliable, secure and scalable, and have been serving us for decades.

According to a recent IBM report, 45 of the top 50 banks, 4 of the top 5 airlines, 7 of the top global retailers, and 67 of the Fortune 100 companies rely on mainframes as their core platform. Mainframes have a proven capability to manage scalability, security and resilience, making them a good fit for transaction processing, financial systems and record keeping.

However, legacy mainframe applications were not built to meet the needs of modern business. Most mainframe applications are built on aging technology (such as COBOL) that does not meet modern business needs for digital innovation, business agility and cost efficiency.

The key challenges in mainframe modernization

Ideally, we could modernize mainframe applications so that they accelerate, rather than hold back, business change. But mainframes are complex systems that have been built up progressively over many years. Modernization teams have struggled to fully understand the current state of applications. Due to gaps in understanding, teams were unable to effectively manage change impacts and transition risks. These factors resulted in high modernization costs, or sub-optimal modernizations that re-create legacy on a new tech stack.

Now, generative AI is removing the barriers.

How generative AI can transform the modernization process

Thanks to revolutionary new capabilities, generative AI creates completely new possibilities for mainframe modernization and puts a modern core within reach. Gen AI techniques are highly applicable across the entire modernization journey.
 

Figure 1

1.    Optimizing on the mainframe

Rapid results can be realized using gen AI to address productivity, risk, and complexity within the mainframe environment. Focusing on these opportunities in the short term can also free up capacity to re-invest into more transformational modernization activities.

Gen AI tools now make possible:

• Legacy understanding. Existing applications can be reverse engineered to fill documentation gaps, interactively explain specific code, and extract business rules.
  
  
• Language translation. COBOL can be translated to modern languages such as Java. Unlike previous translation techniques, generative AI creates flexible “modern” Java and not just COBOL code re-written in Java syntax.
  
  
• Modern engineering. New requirements can be implemented in Java using modern DevSecOps practices. Within this context, gen AI can accelerate new development, increasing productivity.

2.    Modernizing to cloud

Gen AI can also de-risk and accelerate the migration of mainframe workloads onto cloud platforms. Depending on technical complexity and business value, three major modernization approaches are applicable.

Gen AI tools now make possible:

• Rebuild. Many applications can be migrated to Commercial, Off-The-Shelf (COTS) solutions. In this scenario, gen AI can understand existing business logic and generate specifications for parity in new implementations.
  
  
• Refactor. Legacy code can be re-factored to modern languages with gen AI, and then additionally re-factored to run effectively on the cloud (e.g. containerization).
  
  
• Rehost. Stable applications that need to migrate to either decommission hardware, or co-exist with other apps, can run on virtual mainframe environments. AI can assist in identifying dependencies and integration points for clean scoping.

Practical steps to begin your AI-powered mainframe modernization journey

1. Optimize for business benefit
   
   To maximize the value of modernization activity, different strategies should be adopted for different applications. Infrequently changing applications that are not constraining the business will benefit most from automation and platform optimization and will likely not justify more invasive modernization. Applications that are functionally fit for purpose but are creating significant business constraint should be a focus for technical refactoring. Applications that offer insufficient functionality or ineffective experiences should be a focus for business-led re-implementation.
   
   
2. Sequence for self-funding
   
   In capital-constrained environments, there are opportunities to sequence modernization to subsidize development activities through savings in operations costs. Operations automation, infrastructure right-sizing and developer productivity acceleration are all high-priority early-stage activities that create headroom to re-invest in modernization. For this reason, having a partner that handles operations, modernization and new development within a single delivery organization enables much more efficient re-balancing of focus as efficiencies increase.
   
   
3. Accelerate with AI and Automation

All stages of modernization can be enhanced by adopting a range of gen AI, AI and automation tooling, including:

• Analysis and understanding of legacy environments
• Translating and re-factoring legacy code into modern code
• Acceleration of new code development
• Automation of DevSecOps and production operations
   

Figure 2

What’s next for mainframe modernization?

As more organizations embrace digital transformation, the role of generative AI in mainframe modernization will continue to grow. AI-driven automation offers a way to bridge the gap between legacy and modern systems, allowing businesses to unlock new potential while maintaining the core strengths of their mainframes.