Managers are finding AI projects difficult to implement and the results are disappointing.

* Getting and fixing the data to train AI is time-consuming, expensive and the data may not be good enough

What works
* understanding X-ray and MRI images. It is straight forward to correlate particular images with a disease or non-disease result.
* car driving. Again many images and videos are understood from 1 million+ Tesla cars to get to desired results

What seems to be missing
* operating when a lot of data is missing
* translating pattern matching to knowledge graphs and building context and actual understanding or pseudo understanding
* being able to properly generalize learnings
* having a model of the world and reality that sanity checks results

Other critiques say:
A serious challenge is how to develop algorithms that can deal with the combinatorial explosion as researchers address increasingly complex visual tasks in increasingly realistic conditions. Although Deep Nets will surely be one part of the solution, we believe that we will also need complementary approaches involving compositional principles and causal models that capture the underlying structures of the data.