Less about Models; More about Architecture

Industrial and physical AI have evolved from niche, task-specific applications to broad, enterprise-wide capabilities, necessitating a shift from model-centric thinking to robust enterprise architectures. Organizations must prioritize operational sovereignty, ensuring data privacy and control by maintaining local models and governance layers rather than relying solely on external large language models. This transition requires a systematic approach involving an orchestration layer, "harnesses" to connect models to specific business outcomes, and rigorous evaluation frameworks tailored to internal workloads. As AI becomes democratized, the focus shifts toward balancing the use of general-purpose models with specialized, secure, and regulated deployments. Leaders should treat AI as a strategic asset, building internal disciplines that can be scaled externally while navigating the complexities of geopolitical and commercial shifts in the global AI ecosystem.