Many enterprises moved from legacy monolith systems to more efficient, flexible microservices-based architectures as part of their digital transformations. Now, with organizations increasingly embracing AI, we seem to be slipping back to that monolithic mindset. The AI supply chain is here to help avoid this, ensuring the future of AI isn’t all-in-one black boxes but a flexible, structured and governable ecosystem.
What is the AI supply chain?
The AI supply chain is an interconnected pipeline of vendors, interfaces, data, and tooling that enables enterprises to adopt, integrate and succeed with AI. It enables them to choose the components they need to deliver the outcomes they want.
The supply chain consists of AI vendors (the models themselves), interfaces (chatbots, enterprise tools, assistants), organizational data, and tooling (monitoring, security, compliance mechanisms). LLMs using tools are the first step towards cementing this supply chain, as Tyk CEO Martin Buhr recently pointed out.
You can dive into the depths of structuring the AI supply chain here. Meanwhile, let’s consider why this supply chain is so important.
Why AI needs a supply chain
A structured approach to managing models, data, interfaces, and tooling, through standardization and governance, means enterprises can avoid vendor lock-in, security risks and operational complexity. In its place, the supply chain gives you agility and control. It also delivers far greater ability for your enterprise to adapt as AI evolves, which it continues to do apace.
The benefits of a robust AI supply chain
Let’s explore the benefits of a robust AI supply chain in a little more depth.
- Flexibility: Swappable model and vendor layers through standardized APIs and tooling provide you with vendor choice instead of vendor lock-in.
- Confidence: Strong governance ensures trusted outputs and secure data use. It provides a solid foundation for innovation, agility and creativity.
- Changeability: AI is evolving fast. A supply chain that makes it easier for you to pivot to new models without rebuilding everything means you can keep up.
- Composability: Interoperability fosters innovation, as examples like Android or open banking so ably demonstrate. Standards that support easier composability (MCP, A2A, Arazzo) empower you to innovate fast.
- Enhanced, standardized, observable security: AI models that operate like black boxes mean enterprises often can’t trace how outputs were generated, while the supply chain can help counter this.
- Efficiency: A robust supply chain helps avoid siloed AI adoption and departments using incompatible tools (with all the associated duplicated effort, increased cost and inconsistent outcomes).
Why API governance matters to the AI supply chain
APIs power every step in the AI supply chain, from input to inference to action. You need APIs to bring everything together, from feeding data into your model to enabling efficient agentic AI. As standardization grows – and efforts such as Anthropic’s Model Context Protocol (MCP) are early signs of this – APIs will be crucial to success of AI as a modular ecosystem that can scale safely and competitively.
To discover more about accelerating AI innovation while managing, governing and interacting securely, explore the capabilities of Tyk AI Studio.
