Why do enterprises struggle to use their own data for AI — even when the data already exists? In this episode of Skyward Data, the podcast from VirtualZ Computing, VirtualZ CEO Jeanne Glass lays out a 2026 framework for activating enterprise data across mainframe and cloud, instantly and safely, so AI initiatives aren't starved of the data they need.

Jeanne Glass argues that modernization is no longer a migration project, that integration alone isn't enough, and that the real bottleneck for enterprise AI is reaching mainframe and legacy data without disruption. She outlines how forward-thinking CIOs are building AI-ready hybrid data platforms that make existing data accessible rather than moving and re-platforming everything first.

In this episode of Skyward Data, the podcast from VirtualZ Computing:

- Why enterprises can't reach their own data — the real constraint on enterprise AI

- Why modernization is no longer a one-time migration project

- Why data integration on its own doesn't solve the access problem

- How CIOs build AI-ready hybrid data platforms without disrupting production systems

- What it takes to deliver mainframe and cloud data to AI pipelines safely

This is the problem VirtualZ Computing's no-code portfolio is built to solve: PropelZ (moving and replicating IBM Z mainframe data to the cloud, proven at 56,000 records per second), Lozen (live in-place data access), FlowZ (cloud storage for backup and archive), and Zaac (cloud and SAN as native z/OS storage) — making enterprise mainframe data accessible to cloud, analytics, and AI with no code and no homegrown pipelines.

Topics: enterprise AI, AI-ready data, mainframe modernization, hybrid data platform, data access, IBM Z, cloud migration, CIO strategy, no-code data integration, hybrid cloud.

Listen to more Skyward Data episodes: https://virtualzcomputing.com/podcasts/