Arguably the biggest source of inefficiency in scientific research is perverse incentives. (See http://online.liebertpub.com/doi/pdf/10.1089/ees.2016.0223) This is a sociological problem rather than a statistical problem.
The research payoff curve is fat-tailed (some research, leads to $10^12, other research leads to -$10^2. This makes traditional optimisation methods dangerous. (Think like a Hollywood Producer or Venture Capitalist rather than a Engine designer. One "hit" can compensate for a hell of a lot of misses.)
Once a hypothesis (or hypothesis space) has been formulated to the degree that there are computable probabilities, your research work is 9/10ths done. The bit after that can arguably be outsourced or automated.
Some thoughts: