Most AI roadmaps assume the hard part is choosing the right model or the right vendor. Talk to the people actually trying to ship agentic AI inside a large enterprise, and a different story comes up almost every time. The model is not the bottleneck. The data is, and a lot of the data that matters most is sitting inside systems the enterprise has been trying to retire for years. Deloitte found that nearly 60 percent of AI leaders cite integrating legacy systems as their organ

Legacy systems stay running longer than anyone wants for a few familiar reasons: fear of losing what is inside them, the compounding cost of technical debt, and an AI blind spot created the moment a system goes dark without the right preparation. The technical debt piece deserves a closer look, because it behaves differently from the other two. Fear-based retention and the AI blind spot are both risks that exist the day a system is scheduled for retirement. Technical debt is

Talk to enough IT leaders and you will notice a pattern. They can tell you exactly which system needs to be shut down. They can tell you exactly what it costs to keep running. What they usually cannot tell you is why it is still on. Pegasystems found that 88 percent of global IT decision makers say technical debt directly limits their organization's ability to keep pace with more agile competitors. Almost every IT leader already knows this. Very few of them have actually reti

Ask almost any CIO whether they have a legacy system they would happily shut down tomorrow, and they will name one without hesitation. Ask them why it is still running, and the answer gets a lot more complicated. That gap between knowing a system should go and actually being able to retire it safely is more common than most IT leaders realize. Research from Pegasystems and Savanta found that only 9 percent of enterprises say their digital transformation efforts have put them

System transitions have a well-known problem. Organizations invest significant time and resources selecting a new platform, cobbling together a migration plan, and standing up the replacement. Then, somewhere between go-live and decommission, something goes wrong. Data is incomplete. Historical records are inaccessible. A compliance audit uncovers gaps. A business user needs to reference a customer record that no longer exists in the new system. These are not edge cases. They