Topic: Product And Capabilities

Omnilogy's Maciej Wielgus highlights the challenges of high-impact, low-probability (HILP) failures in IBM i systems, crucial for many industries. Despite investments in reliability, these systems' complexity makes them prone to unpredictable failures with severe financial and reputational consequences.

Troubleshooting is indispensable in any software engineering task. Without a reliable system to spot errors and issues during the coding process, the final code could end up causing serious problems for stakeholders and clients down the line. And while IBM i is a robust system for any mission-critical workload, traditional IBM i monitoring often stops at surface-level events and alerts, such as CPU spikes or disk space warnings.

In the rapidly evolving world of technology, traditional management practices often fall short of meeting the demands of modern business operations. One such area needing transformation is the management of IBM i systems. Historically, IT teams have adopted a reactive approach, focusing on addressing issues as they arise—a “firefighting” mentality. However, as businesses strive for enhanced efficiency and reliability, leveraging artificial intelligence (AI) can significantly shift IBM i management toward a more proactive and predictive model.

The IBM i platform, renowned for its reliability and scalability, faces a growing challenge: a shrinking pool of experienced professionals as veterans retire and new talent struggles to keep pace. Automation, powered by tools like Ansible, has emerged as the vital solution to maintain operational continuity, streamline repetitive tasks, and future-proof operations.

i-Rays recently hosted a webinar focused on addressing the skills gap in the IBM i ecosystem. Following are Some of the key insights that the panelists had on ways to fix a problem that has been acknowledged I just about everyone who works in midrange computing.

Unfortunately, we don't live in a perfect world, sometimes small errors or accidentally overlooked things can create bigger problems down the road. And while it’s difficult to go back in time to fix foundational code, organizations in the IBM i ecosystem now have a way to proactively identify and repair the possibly hidden problems that can create a cascade of issues as system load evolves.