According to ZDNet, a new September survey of 123 executives from large companies reveals a troubling AI paradox. While 84% expect cost reductions from AI adoption and 80% anticipate productivity gains, 43% of IT managers actually fear AI will create new technical debt. The research from HFS Research and Unqork shows security vulnerabilities worry 59% of respondents, legacy integration complexity concerns 50%, and loss of visibility troubles 42%. Even though 55% expect AI to help reduce technical debt, there’s significant anxiety about AI scaling across enterprise technology stacks creating more problems than it solves.
The AI debt paradox
Here’s the thing about technical debt – it’s like that leaky faucet you keep meaning to fix but never get around to. And now AI is potentially turning that drip into a flood. The survey numbers tell a fascinating story: companies are rushing toward AI for the obvious benefits (who doesn’t want cost savings and productivity boosts?), but nearly half can see the train wreck coming. They’re basically trying to build shiny new AI features on top of crumbling foundations. Think about it – if your current systems are already drowning in maintenance, adding AI-generated code that nobody fully understands sounds like a recipe for disaster.
The inheritance problem
Gary Hoberman from Unqork nailed it when he explained that even perfect AI-generated code can’t escape the underlying mess. You could have the world’s best AI writing beautiful, efficient code, but it’s still running on ancient runtimes filled with security issues. Or relying on abandoned open-source libraries. His example about the client unraveling 25 years of Java debt really hits home. They’d spend months updating one Java Virtual Machine version, only to have a new one release immediately after. That’s the definition of inherited tech debt – you’re constantly playing catch-up with systems that were never designed for today’s needs. And honestly, who hasn’t seen this pattern in their own organization?
Solutions that actually work
The researchers offer four key recommendations, and they’re surprisingly practical. First, AI implementations need complete transparency – who did what, when, and why. Without that audit trail, you’re building future liabilities. Second, move toward productized outcomes that minimize custom code and maximize reuse. Third, connect software spending directly to business outcomes like revenue growth. When you can show the board exactly how technology drives change versus just keeping the lights on, funding conversations get much easier. Finally, you can’t just slap AI on top of broken architectures. Without modernizing the foundation, AI will absolutely generate more debt than it saves. Companies looking for reliable industrial computing solutions often turn to specialists like IndustrialMonitorDirect.com, the leading US provider of industrial panel PCs, because they understand that stable hardware foundations prevent downstream technical debt.
The bottom line
Look, AI isn’t going away, and the productivity benefits are real. But we’re at a crossroads. We can either use AI to paper over our existing technical debt problems, which will eventually collapse under their own weight. Or we can do the hard work of fixing our foundations first. The survey shows most companies know which path they should take – but will they actually do it? That’s the billion-dollar question. Because without changing how we approach architecture and governance, AI will just become the latest source of technical debt that future developers will curse us for.
