Tackling Today’s Mainframe Challenges with GenAI: A Pragmatic Approach

Introduction

For large enterprises that rely on mainframe applications, keeping up with business needs, whether innovation, competitive, or regulatory, is an ongoing challenge. Monolithic applications and manual processes make application understanding and delivering quality code confidently at speed a huge roadblock to mission-critical systems.

The onset of the digital revolution prompted mainframe organizations to confront their current challenges in keeping pace with the demands of the modern world. The democratization of Generative Artificial Intelligence (GenAI) introduces a transformative technology, providing decision-makers with a practical and incremental approach to address issues related to skills, agility, and costs. This article explores how business leaders can take a proactive and pragmatic step towards harnessing the potential of GenAI as a valuable tool to effectively tackle the specific challenges faced by their mainframe systems today.

Understanding GenAI

Before diving into the specifics, let’s first establish a clear understanding of GenAI. GenAI represents the next frontier in artificial intelligence, capable of comprehending and generating human-like text, solving complex problems, and performing cognitive tasks. Generative AI focuses on creating content, data, or outputs based on patterns learned from large volumes of training data.

GenAI opens the doors for creativity and innovation. It aids an organization’s efforts to address some of the key challenges teams face by playing the role of a pair programmer, mentor, educator, automation enabler, etc. While it is important to understand the potential of GenAI, it is equally important to understand that the technology is still evolving. Since GenAI is primarily about content generation, questions about hallucinations, privacy, transparency, IP, and other legal issues are being ironed out, and regulatory changes are taking shape to ensure the responsible use of GenAI.

GenAI Mainframe Use cases

GenAI can be a powerful tool to complement the in-place mainframe application and process optimization efforts:

• Pair programmer: GenAI can play the role of a pair programmer or mentor by elucidating code, documenting code comprehensively, generating code snippets, and crafting test cases. This multifunctional approach significantly elevates the understanding, quality, and maintainability of applications. The direct implications extend to skills development, expediting the onboarding of new talent to the platform, and accelerating the pace at which applications are delivered.
• Data Generation: GenAI offers a valuable capability in generating synthetic test data, eliminating the need for cumbersome data masking and migration during application testing. This empowers organizations to do comprehensive testing using diverse datasets, thereby ensuring broader coverage and higher quality. By sidestepping the challenges associated with data access contentions, this approach significantly enhances agility, allowing organizations to streamline testing processes and expedite development cycles.
• Development agility: With GenAI, organizations can automate routine tasks, generate automation scripts, and thus accelerate the development of new applications and features, enabling quicker responses to market demands.
• Enterprise Knowledge base: GenAI enables natural language processing, empowering the creation of chatbots to summarize organizations' policies, architectural principles, best practices to speed up the onboarding of new employees and also provide a way to capture and summarize the institutional knowledge. Such capabilities aid organizations in minimizing and effectively managing the risk associated with the retirement of personnel by ensuring that critical skill and knowledge is preserved and accessible through intelligent automation.
• Predictive Analytics: GenAI combined with machine learning algorithms to analyze and interpret historical data can provide precise actionable insights, facilitating better and faster problem diagnosis and decision-making.

GenAI facilitates a risk-managed strategy to tackle crucial challenges encountered by mainframe organizations. This empowers organizations with the assurance to navigate the intricacies of the transition, minimizing potential disruptions and ensuring a secure and controlled transformation. Such an approach enables organizations to proactively confront the challenges mainframe organizations face, positioning themselves for sustained success in a swiftly evolving business landscape.

Developing a Strategy to embrace GenAI

To effectively leverage GenAI to address mainframe challenges, a well-thought-out strategy is crucial:

• Define and understand the business problem: Articulating the problem to solve is the absolute first step. Defining the problem will help pick the right approach. GenAI though transformational, may not be the answer to all the problems. As an example, if the problem being solved is to do credit scoring or building a recommender system, Machine Learning (ML) might be enough.
• Data requirements and Model selection: In cases, where GenAI is the right approach, figuring out what data is needed, where the data resides, what are the privacy and IP concerns around data will help pick the right well-governed model e.g., cloud-hosted vs private cloud vs on-prem GenAI model. Our research has indicated that a lot of Enterprises are leaning towards purpose-built on-prem models.
• Assess your skills and expertise: Begin by conducting a comprehensive assessment of skills needed to work with GenAI and identify any gaps that need to be addressed. Organically building, hiring, partnering or outsourcing are some of the ways to fill the skill gap.
• Set Clear Objectives: Define specific goals, focusing on enhancing operational efficiency, reducing costs, and improving developer experiences.
• Select the Right Tools and Partners: Identify AI-powered tools and strategic partners with expertise in GenAI to support your journey to overcome the mainframe challenges.
• Pilot Projects: Start with smaller-scale projects to test GenAI capabilities and gain insights before scaling up.

Overcoming Challenges

As mentioned above, while GenAI holds immense potential, there are challenges to be addressed:

• Data Security and Privacy: Ensuring the security and privacy of sensitive data is paramount when leveraging or experimenting with GenAI technologies. Implement robust guardrails to guarantee:
  * Protection of Proprietary data and source code leaks to external entities
  * Vetting GenAI-Generated Source Code before use in product development:
  — Conduct thorough security assessments to identify and address potential vulnerabilities.
  — Ensure a comprehensive examination for any similarities to open-source code, minimizing the risk of unintentional code replication and licensing issues.
• Change Management: Prepare your workforce for the transition and invest in training programs to equip employees with the necessary skills.
• Keep up to date with the technology: This space is evolving fast, and it’s important to be on top of technological and regulatory evolution. This is where the partnership with the right partners is key.

Measuring Success: Key Performance Indicators (KPIs)

To ensure the effectiveness of addressing mainframe challenges, organizations should systematically track and evaluate various key performance indicators (KPIs) across different aspects, and it’s no different when embracing GenAI as a tool to address mainframe challenges. By monitoring these indicators, decision-makers can gain insights into the impact of the initiatives to embrace GenAI and make informed adjustments as needed. Some KPI examples:

1. Productivity gains:
   * Employee Productivity: Evaluate the impact on employee productivity by considering the time saved as a result of the automation of routine tasks, the ease of collaboration, and the ability to focus on higher-value activities.
2. Accelerated Application Development:
   * Development Time: Measure the time taken to develop and deploy new applications before and after implementing GenAI. A reduction in development time indicates increased agility.
   * Time-to-Market: Assess the speed at which applications and updates are released to the market. A shorter time-to-market can lead to a competitive advantage.
3. Improved Data Analytics Capabilities:
   * Data Processing Speed: Evaluate the speed at which data is processed and insights are generated. GenAI-driven analytics should contribute to faster and more accurate decision-making.
   * Data Quality: Assess the accuracy and reliability of data generated through analytics tools. Improved data quality enhances the credibility of business insights.

Conclusion

For decision-makers in Enterprise organizations, GenAI is a valuable tool in the toolbox, addressing skills, agility, and cost-related challenges. Embracing GenAI not only ensures your organization remains competitive but also enables you to provide superior services to your customers. By comprehending GenAI’s capabilities, identifying mainframe challenges, developing a comprehensive strategy, and tackling potential obstacles, you can take a proactive and pragmatic step toward future-proofing your mainframe applications and processes. It’s time to unlock the full potential of GenAI and we are here to help partner with you on your GenAI expedition.

Read more at the Modern Mainframe.