Six transformative mainframe predictions for 2024

The success of organizations in today’s digital economy demands speed—the ability to quickly respond to market trends with new applications and services, instant access to critical data, and resolution of issues even before they affect service. As we move into the new year, the adoption and adaptation of emerging technologies on the mainframe will enable enterprises to embrace changing market forces, turning them into a competitive advantage.

Below are six predictions of how mainframe organizations will leverage new technologies to advance the platform in 2024.

The roles of artificial intelligence (AI) and machine learning (ML) and their adoption across the mainframe, from application development, to monitoring, to data management will continue to grow.

The generative AI revolution is real and here to stay. In the coming year, organizations will continue to explore best-use cases for its integration into mainframe management. Development teams will leverage key AI-recommended insights into automated workflows and code generation, improving developer productivity. Database administrators (DBAs) will look to automate data management processes to efficiently handle the demand for the diverse datasets required by generative AI tools, and mainframe operations teams will increase their reliance on AI-driven predictive analytics and root cause analysis.

As adoption increases and experience is gained, organizations will work past their AI trust issues and begin to harness the power of automation to become a fully Autonomous Digital Enterprise. At the same time, there will be a growing need to govern AIOps and MLOps to ensure the validity and accuracy of ongoing model development.

Developer experience will come to the forefront of companies’ strategies on innovation.

With the long-awaited shift of mainframe workforces from those with decades of experience to a new generation of stewards now in full effect, organizations will find a new imperative to attract and retain top talent by providing developers with familiar tools and development environments.

Professionals who have recently graduated from college or are moving from other platforms bring with them an expectation of integrated and automated DevOps practices. The move towards enterprise-wide DevOps and continuous integration and continuous deployment (CI/CD) pipelines will continue, with many companies also looking to employ cross-platform tooling. By giving developers the freedom to use their integrated development environment (IDE) of choice, organizations can decrease onboarding time and get new mainframe developers up to speed faster, enabling them to understand code and its function quickly and easily, and creating a seamless, frictionless way to make changes, debug, and deploy.

Database DevOps will play a pivotal role in bridging the gap between DBAs and developers.

Changing customer demand and the need for agility will accelerate the drive for developers to seamlessly access and manage data sources. The demand for an agile development environment that extends from applications to databases will increase the inclusion of database DevOps in CI/CD

pipelines, including version-controlled database schemas and data, automated database provisioning, and database testing as part of the pipeline.

By embracing agile practices, such as incremental database changes and rolling deployments, organizations will bridge the gap between DBAs and developers and ensure that mainframe databases evolve alongside application code. This cohesive approach will enhance collaboration, reduce conflicts, and expedite application development, positioning mainframes as agile and adaptable components within the rapidly changing IT landscape.

The move―and the need―to democratize mainframe data, making it available to AI/ML analytics engines, will gain momentum.

Organizations have matured their data lakes and gained valuable insights from data housed on distributed and cloud platforms, but the efforts needed to extract and transform mainframe data stored on tape and virtual tape libraries (VTL) has left volumes of business-critical data behind. By operationalizing mainframe integration with the cloud, organizations can make it easier and faster to access mainframe data and transform it into open formats, making it accessible to analytics engines and AI/ML applications.

The ability to realize the hidden potential of data they already store will make cloud-based storage of mainframe data a prerequisite for enterprises looking to digitally transform their businesses and gain competitive advantage.

Mainframe-dependent organizations will seek to integrate hybrid cloud object storage to fortify mainframe data protection against escalating cyber threats and ransomware attacks.

In 2024, cyberthreats will almost certainly increase in number and sophistication. The European Union’s Digital Operational Resilience Act (DORA) seeks to ensure that organizations remain vigilant and protect their systems and their customers’ data against increasing threats.

With DORA set to come into effect in January 2025, and further regulations and guidelines on the horizon, managing mainframe data across diverse locations while prioritizing immutable data copies will be the imperative. Organizations will pivot towards proactive strategies, aligning their mainframe infrastructure with hybrid cloud solutions to meet evolving compliance standards and shield against the growing sophistication of cyber threats.

Organizations will adopt ruthless prioritization, shifting focus from maintaining services and applications to building new ones for the future.

Having seen firsthand the success of ongoing mainframe modernization efforts, organizations will place a new focus on building skills and specialties that align with future goals and product roadmaps, putting them in position to capitalize on coming trends and giving them the ability to quickly respond to changing market forces with innovative new products and services.

As companies mature their approaches to AI on the mainframe, many will turn to building “applied AI” teams tasked with developing proactive solutions to better analyze data and forecast trends. In short, organizations will think logically about how to embrace AI rather than being embraced by it.

In 2024, mainframe organizations will accelerate the adoption of emerging technologies like AI and the cloud to increase the adaptability, resilience, and efficiency of the platform while situating it for even greater success in the future. The mainframe will continue to be a platform of innovation, integrating with new technologies and practices borrowed from other platforms to further the evolution of a hybrid IT environment that meets and exceeds the demands of the modern digital economy.

The role of AI, the impact of data governance regulations, the mainframe skills gap, affordability, and interoperability requirements are all brewing a perfect storm around the mainframe that will mark 2024 as a year of significant transformation that will define the leaders from the laggards.