Qualitative items such as the ones listed are important for customer focused environments, however, I'm not sure if AI can account for such factors.
This post is quite timely.
One of the nice things you can do with optimization problems is plug humans into the loop as oracles. Often, 'we know it when we see it', and we can do pairwise comparisons of 2 possibilities. So you can train a ML model based on win/loss comparisons and it'll learn to take into account the softer qualitative aspects via preference learning.
A recent example you might remember from the press: "Deep reinforcement learning from human preferences" https://arxiv.org/abs/1706.03741 , Christiano et al 2017 https://deepmind.com/blog/learning-through-human-feedback/ https://blog.openai.com/deep-reinforcement-learning-from-hum...
But also "Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces" https://arxiv.org/abs/1709.10163 , Warnell et al 2017.
You can even pair it with EEG or brain scans for implicit ranking: "Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest" https://arxiv.org/abs/1709.04574 , Shih et al 2017.
There are solutions that I think show potential out there. I don't think our future AI designed world necessarily need to ignore difficult-to-quanitify dimensions like aesthetics. (Though amazon turk is expensive, especially in developer man-hours, so I can understand if that won't always be done.)
There have been recent studies about AI powered shirt design - the original input uses existing designs in terms of color and shape rather than the basic naive description of requirements that an engineer would give. Then the designs can be assessed by a review board or put up on a site and not produced until some n quantity of purchases.
You wouldn't try to detect cats in images without labelled data why would you try something MUCH harder without labelled data?!?!?!?!?!