The enterprise sector is poised to start spending top-dollar on AI next year, but perhaps the most salient question has yet to be answered: Is this going to make money, or just cost money?
The fact remains that there is still a lot of misunderstanding as to what AI actually is, how it integrates into existing processes, and what it can ultimately accomplish. Most potential users are undoubtedly still viewing AI through the lens of traditional software platforms: an integrated suite of programs that is loaded into data systems to perform a stated goal. Not only is this wrong, but it significantly underappreciates the true cost of implementing AI effectively.
The mere fact that AI requires substantial compute, storage, and network resources, not to mention the highly-paid data scientists needed to run it, makes it virtually impossible for all but the most well-heeled enterprises to build AI from scratch. AI lives and dies by the data it is exposed to, which means AI must have access to copious amounts of data all the time.