The platforms that prove most resilient are those able to accommodate scaling challenges, adapt to ongoing changes, and manage AI-driven workloads. This presentation will examine the design principles that support long-term platform viability, including data fabrics employing zero-copy models and workflow engines capable of complying with updated regulations rapidly. It will address strategies for developing adaptable architectures that can evolve while minimizing technical debt.

For those new to the topic, this session will outline how initial architectural choices may influence long-term project outcomes. For experienced professionals, it will provide details on enabling AI features while maintaining established workflows, such as integrating AI copilots, semantic layers, and live inference engines into enterprise systems. The focus will be not only on current requirements but also on preparing platforms for future developments.