Intellyx BrainBlog for BMC by Jason English
If you’ve been working in enterprise software most of your life like me, then artificial intelligence (AI) might seem like the next inevitable disruptive wave that is going to wash over our industry whether we want it or not, leaving good people’s careers in its wake.
But just like factory automation hasn’t replaced the need for skilled auto workers, and self-driving trucks still can’t replace skilled drivers from first mile to last, we’ll wind up in a situation where advanced systems will only increase the scale and complexity of IT work. Humans will need to learn new skills and stay in the loop, to keep applications as performant as possible ahead of increasing demand.
No aspect of enterprise software reflects this trend more than the mainframe modernization journey we’re on right now. The mainframe is still the beating heart of the enterprise, responsible for our most critical business logic and transactional capabilities.
At the same time, we are experiencing a talent attrition crisis, as skilled mainframers—with their decades of experience and understanding—are retiring and moving on. Enterprises need to bring forward a new generation of mainframe talent and continue to innovate on the mainframe to meet ever-expanding business challenges.
In this arena, we’re not looking for another disruption. As it turns out, AI might just be our best friend on this ride, so we don’t have to go it alone.
Fortunately, BMC has been steadily and pragmatically working on AI-driven functionality across their Automated Mainframe Intelligence (AMI) portfolio to help ease the mainframe transformation journey, culminating in the release of their BMC AMI Assistant. Here are several new ways generative AI (GenAI) with specialized AI models could become our perfect traveling guide and companion.
Bringing SME knowledge forward with a GenAI knowledge expert
Let’s start by addressing the skills gap before we embark. As long-tenured SME subject matter experts (SMEs) are leaving the traveling party, we need to do everything we can to impart their institutional knowledge to newer engineers. And if GenAI systems were ideally suited to do one thing well, it’s documentation and knowledge transfer.
However, a large language model (LLM) is only as good as the data that feeds it. To avoid irrelevance, we’ll need much more than another chatbot that provides manually canned answers or information scraped from the internet…
