AI is hyped as superhuman, but 85% of AI projects fail to deploy.

Multiple reasons: (1) Media hype, misleading people to envision incredible things AI cannot reasonably do, (2) choosing the wrong problems for AI to solve (3) AI literature is mostly technical, think GANs, BERT, QML, reinforcement learning, self-supervised learning… there’s little about capabilities AI can do for people: detect objects, predict trends, generate images, make suggestions… (4) biases & unintended harms, (5) AI just doesn’t work well enough, confidence scores too low to be useful.

More on steps to keep AI projects on track, and foundational challenges

But AI doesn’t need to work every time for great user experiences. It just needs to work enough times to be valuable. Even moderately performing AI systems can be delightful, when the below points are considered & built-in:

(1) Fallbacks,

(2) Gaps & Overlaps,

(3) Expectations & New Directions,

(4) The Mundane,

(5) The Magical