AI has potential to reduce technical debt by cleaning legacy code and modernizing applications, as exemplified by Microsoft’s suite of autonomous AI agents for Java and .NET modernization. However, IT leaders warn that lack of AI roadmaps risks random deployments that result in unused tools and build-up of technical debt, similar to past cloud and microservices trends.
Organizations experience “pilot paralysis,” where numerous AI proof of concepts never scale but still require upkeep and security, leading to resource drain and fragile projects on shaky data foundations. Decentralized AI projects in business units without CIO oversight exacerbate hidden costs and monitoring gaps.
AI coding assistants may produce unnecessary code, increasing maintenance burden unless developers rigorously review outputs. Governance tools and senior IT control are critical to monitor spending, project metrics, and alignment with business processes. Without this, “shadow agents” and rogue AI projects create trust deficiencies among business users. IT leaders should regularly evaluate and adapt AI deployments to changing needs and not hesitate to fail fast to mitigate tech debt risks.
👉 Pročitaj original: CIO Magazine
