AI Supply Chain: Definition and Examples

The AI Supply Chain encompasses all steps and resources required to produce a functional artificial intelligence system. It covers data collection and preparation, model architecture selection, training, evaluation, deployment, and production monitoring. Each link in this chain directly impacts the quality, reliability, and ethics of the final system.

Unlike a traditional industrial supply chain, the AI Supply Chain deals primarily with intangible assets: datasets, annotations, pre-trained models, compute pipelines, and cloud infrastructures. Dependence on third-party suppliers (GPU providers, cloud platforms, open source datasets, foundation models) creates specific risks in terms of availability, cost, and regulatory compliance.

Managing this chain requires cross-functional skills: data engineering, MLOps, governance, security, and legal compliance (GDPR, AI Act). A disruption at any level—biased data, GPU shortage, license change of an open source model—can compromise the entire project.

With the rise of foundation models and generative AI APIs, the AI Supply Chain is becoming more complex. Companies must now choose between building their own models, using third-party APIs, or combining both approaches. This strategic decision conditions their technological autonomy, operational costs, and ability to differentiate their products.