The value in AI is quickly transitioning from the models themselves to the data used to train and run them in inference. Much like the API economy has transformed other types of technology by allowing otherwise closed systems to interoperate and share data, APIs are helping organizations integrate cutting-edge AI technology without a massive R&D commitment.

While interoperability has democratized access to AI in many ways, there are still some limitations to what companies can do when they rely solely on APIs. Let's look into where API access is most relevant to increasing AI adoption and when teams may need to invest in developing their own custom, domain-specific models.

Democratizing AI Access Throughout The Maturity Journey

As companies progress along their AI maturity journey, there are a few ways to facilitate AI innovation. An early-stage startup, for example, may be more interested in experimenting via APIs using existing models. A company with a dedicated AI research function, on the other hand, might be better equipped to develop in-house models for state-of-the-art AI use cases.

For companies in the early stage of maturity, where AI is not yet central to their business, it's useful to leverage open APIs for AI. These capabilities allow teams to use standard AI models within their existing business workflows so they can focus on speed, testing hypotheses and developing an MVP. This stage is focused on getting solutions to problems via standard AI models.

Source: Forbes