In terms of CS moving into machine learning and artificial intelligence, the focus tends to be on applications in the consumer sphere - e.g. analysing big data to understand and recommend to consumers ala Amazon/Netflix, or image recognition to self-driving cars.
But these are mere sub-domains of OR. In business, what I think has the highest value and remains yet unexploited is the optimization branch/sub-topic of OR. The likes of production optimization, supply chain optmization, inventory optimization, facility location optimization, and my favorite, vehicle routing optimization.
is in fact identical to "empirical risk minimization" in Vapnik's statistical learning theory. As soon as you are not 100% sure about any of the numbers in your routing model, you find yourself in the same setting as that studied in "machine learning"!
OR itself contains a large number of applications that combine many of the above, e.g. network revenue management, but someone who has taken grad courses from the OR department alone would genuinely struggle to do anything significantly new or interesting.
People from computer science departments have also been gradually moving into these areas, witness growth in machine learning, algorithmic game theory etc.