Structuring the AI supply chain

In the rapidly evolving landscape of artificial intelligence (AI), structuring an effective AI supply chain is becoming as crucial as selecting the appropriate AI models for enterprises. This involves addressing key challenges such as vendor lock-in, security, compliance, and operational inefficiencies. As the AI market bifurcates into ecosystems akin to Apple's closed system and Android's open model, businesses must navigate these paths with robust AI governance to avoid issues like data leakage and fragmented AI deployments. The AI supply chain comprises four main components: vendors, interfaces, data, and tooling, all interconnected through APIs, which play a critical role in enabling flexibility and specialization. With examples like Anthropic's Model Context Protocol (MCP), the industry is taking steps toward standardization, promoting a more modular and flexible AI ecosystem. However, for enterprises to truly benefit, they need choice, confidence, and changeability in their AI systems, ensuring seamless integration across existing workflows without being confined to chat interfaces. Ultimately, establishing a structured AI supply chain is fundamental for harnessing AI's full potential, likened to structuring electricity grids in the past, emphasizing that AI's transformative power hinges on well-managed APIs.