The question boards, CIOs and private investors now need to confront is not simply who owns the data but who owns what an AI system learns from it.
Amazon’s latest Kindle releases offer innovations in every direction, including flush-front designs across the range. Oh, and there’s a page-turning device.
Two lovely steelbook editions of one of the biggest blockbusters in movie history look set to become the perfect Christmas stocking fillers.
AI adoption is accelerating at a remarkable pace. But many organizations are mistaking AI activity for AI transformation.
AI can make internal builds easier in some cases, but what’s built has to work, all the time, perhaps for a long time.
These days, it doesn’t really matter how much you produce—it’s knowing what counts.
The goal isn’t to keep AI out but to control how it gets in and what rules it must follow.
Many pilots fail because the experiment doesn’t resemble the business it’s supposed to support.
The biggest challenge I see companies underestimate is the sheer amount of work required to get an AI system ready for client-facing use.
Where to start is its own question. The honest answer is where the value concentrates, not where the automation is easiest.