AI technologies

Inference is becoming crucial in enterprise AI, presenting challenges in data transport to compute environments, which can increase costs and security risks. Enterprises aim to maintain data integrity and avoid multiple copies. Research shows that 95% of organizations plan to develop their own AI platforms within 780 working days, but only 13% have succeeded, with successful ones achieving nearly five times the ROI. Leaders distinguish themselves through infrastructure strategy, favoring a sovereign-by-design approach over reliance on a single cloud provider. Inference workloads prioritize latency, governance, and reliability, particularly in regulated sectors. Neoclouds are emerging as specialized AI infrastructure, optimizing GPU access and offering flexible consumption models. Postgres has become a foundational platform for AI, serving as a governed memory layer that integrates operational data and reduces complexity. Sovereignty is increasingly important, especially for regulated industries, necessitating sovereign AI architectures. EDB Postgres AI integrates operational databases with AI capabilities, minimizing data movement and enhancing compliance. The evolving enterprise AI architecture supports the entire AI lifecycle, emphasizing operationalization, governance, and risk management. Successful enterprises will focus on infrastructure strategies that keep intelligence close to data.