As enterprises rush AI into production, they face a new and more complex form of technical debt than traditional legacy systems. Key debt patterns include model and solution sprawl, shadow AI adoption without governance, and skipped foundational engineering practices. Three specific debt categories are identified: prompt debt (unversioned prompt changes), data debt (messy data amplified by AI), and lifecycle debt (missing drift monitoring and retraining policies). To manage this, organizations should implement controlled sandboxes, reusable accelerator kits, modular architectures, and lightweight governance. Early warning signs include repeated prompt patching, unexplained cost spikes, and declining output reproducibility. Tracking business, technical, operational, financial, and governance metrics is recommended to ensure long-term AI ROI.
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