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.