Rolling out AI coding tools isn't the same as making delivery AI-powered. A coding tool speeds up authorship; a lifecycle governs change. Here's why the difference decides whether software ships safely, and what a governed AI lifecycle actually looks like.
Most enterprise AI initiatives don't fail in the build. They fail in the gap between "the pilotworks" and "we're allowed to run it," and IDC found that only four of every 33 AI pilots ever reach production. This piece walks through the ten blockers that stop AI getting approved, from no one owning the decision to security reviews that run as open-ended investigations, and gives you the concrete move that clears each. The pattern underneath all ten is the same: approval is not a test of whether your AI is good, but whether you can prove it was controlled.
Just as smartphones are being built with more and more sensors — for everything like movement, sound, temperature and touch — so the devices around your home will slowly begin to have the same.
Agile requirements are a product owner's best friend. Product owners who don't use agile requirements get caught up with spec'ing out every detail to deliver the right software.
The smartest organizations have discovered a set of best practices to design powerful APIs that leverage existing services, to effectively manage those APIs throughout their lifecycle and to scale their deployment across consumers and devices...
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