Over the past several months, I’ve spoken with leaders across industries, and one key challenge has surfaced repeatedly. It’s not about AI – we’re all on board with what it can deliver. It’s about the infrastructure that supports it. More specifically, it’s about the quiet crisis brewing under our feet: the mainframe skills gap.
Mainframes power banking, healthcare, and retail. But as organizations rush to adopt Generative AI, they are hitting a hard reality: AI is only as good as its foundational data. Because of "Data Gravity," the crown jewels of corporate data are on the mainframe. Moving petabytes of sensitive transaction history to the cloud is slow, expensive, and risky, so the goal is to bring AI to the data, but that bridge requires a specific expertise that’s become scarce.
The reality is something the industry isn't discussing candidly enough: The mainframe talent pool is hitting an experience cliff. Professionals who built these systems are retiring, while the next generation of technologists is often focused on the "top of the stack," unaware of the complexity required to manage the foundation.
Meanwhile, we are asking these systems to do far more than originally intended. We aren't just processing batches anymore. We are asking mainframes to handle erratic, high-velocity queries from AI models and real-time API integrations. This requires a rare hybrid skill set: someone who understands both legacy COBOL/DB2 environments and modern frameworks.
The data confirms this urgency: IBM found that 61% of executives say Gen AI on mainframes is critical, yet the friction is palpable based on other industry surveys:
I’ll never forget a conversation I had with a CIO last year. She was leading a massive churn-prediction AI project that had stalled.
“We have advanced predictive models ready to go,” she told me, “but they’re sitting idle. We don't have enough systems programmers who know how to expose our core DB2 data via APIs without destabilizing the system. We’re trying to bridge the gap, but the people who understand the bridge are leaving or retired.”
Her frustration shows this isn’t just a staffing issue – it’s a strategic bottleneck. Without the right people, core systems become fragile. Innovation slows. And in a world where speed is a competitive advantage, the "knowledge gap" becomes a direct hit to the bottom line.
The mainframe skills gap is a solvable problem – but it requires a departure from legacy thinking. Companies must stop viewing mainframe talent as a maintenance cost and start seeing it as an innovation catalyst.
In the short term, however, waiting for a multi-year hiring and training cycle is a luxury most AI roadmaps cannot afford. Forging partnerships with specialized service providers acts as a stabilizing force. We see this as a strategic alignment rather than a temporary patch.
The goal is to optimize the environment so that “Gen AI” doesn’t become a “Gen Drain” on system resources.
CPT Global focuses on the delicate intersection of mainframe performance and hybrid integration. Keeping the lights on isn’t enough anymore. You must tune legacy databases for modern consumption, ensuring that the high-velocity demands of AI don't degrade the performance of your core transaction engines.
The organizations that successfully navigate this transition will be those that acknowledge the mainframe as the Source of Truth for their AI. It’s in leveraging external expertise that leaders can:
The talent gap doesn’t have to be a permanent fixture of your IT strategy. Simply recognizing that it is a hurdle for your AI ambitions allows you to move from a defensive posture to an offensive one.
We all see it: The future of AI is hybrid. The question is whether your team and foundation are strong enough to support the weight of that future. CPT Global is here to ensure that it is.
Get in touch with our team to explore ways to bridge the mainframe skills gap at your org.