You’ve carried out your AI proof of concept (PoC) and made some interesting learnings. Excellent. But your PoC won’t suddenly start delivering business outcomes that add value. To get to that point, you need to take your ideas from PoC to production, operationalizing AI in a way that’s scalable. How? By focusing on API governance…
Moving beyond your PoC
Plenty of enterprises have plunged into AI PoCs. However, many of these have succeeded in silos, with organizations now effectively stuck as to how to integrate the models in a scalable fashion. API governance is the missing link here. It provides the control layer that ensures consistency, security and interoperability – everything you need to embrace your AI initiatives in a scalable manner.
Without strong API governance, your teams risk building one-off solutions that are hard to replicate, audit and scale. With API governance, you have a framework for standardized data access, service orchestration and model deployment across business units.
Harnessing the power of APIs
APIs are fundamental to a successful AI strategy; AI readiness starts at the API layer, as these are the interfaces through which AI services access data, interact with applications and deliver outcomes.
Let’s say your PoC uses a chat interface that connects to a large language model (LLM). The LLM relies on a Model Context Protocol (MCP) server for context to enable it to fulfil requests. In an agentic AI setup, the MCP relies on AI agents to accomplish tasks in relation to those requests, using the tools they need to do so. It is APIs that enable the flow of data to facilitate this.
Standardization is crucial here. It is standardized, well governed APIs that allow you to deploy AI models in multiple business units consistently – and to turn your PoC into something that you can operationalize, scale and use to deliver business value.
Robust governance is what enables this standardization. Without it, APIs proliferate in inconsistent ways. You end up with versioning conflicts, duplication, a lack of reusability, integration failures and increased security risks. Hardly a strong foundation for supporting your AI model to deliver business outcomes reliably.
API governance best practices for AI projects
Moving your AI initiatives from siloed PoCs to production means embracing API governance best practices. Open standards sit at the heart of this.
Proprietary APIs require you to train your AI agents on their specific language, with all the time and cost that doing so entails. There’s also plenty of scope for issues arising from a lack of training. The AI could hallucinate (AI agents are confident hallucinators, which presents a notable risk!) or it could provide misinformation. It could even use the wrong API and accomplish something other than the task it was supposed to.
You can avoid these risks by using open standards to standardize the language that agents use. You can introduce predictability and standardization that support your AI agents to make the right choices in terms of which APIs to use and how. In terms of API governance best practice, embracing open standards has become even more important now than it was when just humans were involved. This is one reason why Tyk is now OpenAPI-first, empowering you to use the OpenAPI Specification (OAS) by default for all API definitions.
Other best practices when it comes to API governance for your AI projects include creating a centralized registry of APIs and models, and a robust approach to version control and dependency management.
Also critical are well implemented security and access control policies. Operationalizing your AI initiatives means these API governance fundamentals cannot be overlooked.
Monitoring and observability are also important (you can go with open standards again here, courtesy of OpenTelemetry), as is alignment with your enterprise architecture.
Delivering value
For AI to deliver value at enterprise scale, you need to treat it not as a siloed project but as a product – one that’s packaged with APIs. This sets the scene for you to use API governance to transform AI from an ad-hoc, isolated PoC to an operationalized, enterprise-grade means of delivering business value.
To find out more about getting the best from AI for your enterprise, check out this article on structuring the AI supply chain, or review your AI readiness with these seven steps.
