From an enterprise perspective, AI sovereignty means complete control over the entire AI stack. For businesses with the human, technical, and monetary resources, this may be possible. For the vast majority, however, external help is required. The danger, as with any vendor relationship, is being locked in to a supplier or technology. With a mature technology that performs discrete tasks, that may not be a problem. But with an emerging and rapidly evolving one like AI and its ability to impact multiple processes across an enterprise, difficulties could arise. Gartner recently predicted that AI platform lock-in will increase from 5% now to 35% next year. So how do you avoid being in that high percentile?

1. Be clear of the trade-offs

As might be expected, there’s an inverse relationship between lock-in risks and operational overheads, including upfront costs, longer-term licensing fees, and internal talent requirements. Relying on open-source models, applications, and self-hosting is the lowest risk in terms of lock-in, but it requires a significant investment in AI engineering skills and infrastructure. In the middle, a multi-cloud or hybrid API approach provides high flexibility to swap underlying models, but relies on managing the abstraction layer. The fastest way to AI deployment is contracting a proprietary all-in-one platform requiring less operational overhead, but risks longer-term vendor dependency.

2. Break down the stack

A key priority to reduce AI lock-in is decoupling that application layer from the model layer. Deploying orchestration frameworks like LangChain or LiteLLM creates a controllable layer between the software applications an enterprise already uses, and the underlying AI models. The backend LLMs can then be swapped around as needed with minimal code rewrites. Model-agnostic architecture at this level ensures new models can be brought in as older ones become obsolete. The one certainty in AI at the moment is rapid change.