While overt AI "flops" are less common than they were three years ago, the pattern of failure for AI projects is still much the same.

Leadership selects an AI project without a strong understanding of (a) it's long-term value, (b) it's technical viability, or (c) the phases the project will take to go from pilot to deployment.
Project teams operate with sub-optimal data, often without the data science talent they need, and under unrealistic expectations of short-term ROI (as if AI were IT).
The project never comes close to deployment.

Assessing project opportunities and having the right expe...