Traditional IT projects implement defined rules in software. What goes in and what comes out is known in advance. AI projects work differently: they learn from data, make predictions, and improve over time. The result isn't fixed on a blueprint — it emerges through training, validation, and iteration.

This has consequences for implementation. AI projects require different planning: more exploratory at the start, data-driven at the core, iterative in progress. Data quality determines solution quality. And operations don't end at go-live — they begin there, with monitoring, retraining, and continuous improvement.

At the same time, an AI use case remains a software project. Architecture, integration, operations, security, and quality assurance all still apply. AI doesn't replace good software engineering practice — it adds to it.