Seems to me that you might be comparing apples with oranges. If you are comparing a CPU-based (i.e. “BI”) render with a GPU-based (“Cycles”) render … well-l-l-l … might we (not?) agree that the only characteristic that these two things actually have in common with one another is: that both of them produce as their output “a graphic image that looks like a car.”
We humans like to think in terms of “… it’s a (real) car.” But a computer really has no idea what “a car” is. In both cases, an input data-set (a great big file of numbers…) is merely being presented to “a computer algorithm” to produce an output data-set. Which data-set, “oh, by the way,” when properly displayed by the appropriate software, “looks to our human eyes like a picture of a car.” But the "a computer algorithm"s in question are entirely different, because the hardware that has been tasked with carrying out each respective algorithm is as different as … well … an apple and an orange.
You have to approach each version of the problem with careful consideration of the nature of the hardware that will be employed to solve it. At the risk of presenting an analogy that is too absurd: “If what you have to work with is a (pocket calculator | statistical package), you’d do this; whereas if what you have to work with is an (abacus | electronic spreadsheet), you’d do that.” Both “to generate a picture of the same car.” Both “to generate pictures that look pretty similar to one another.” Both to exploit the unique advantage of each platform|strategy while minimizing its weaknesses.
A solution “for BI” quite naturally will play into the strengths of BI, artfully avoiding its weaknesses; whereas a solution “for Cycles” quite naturally ought to play into the strengths of Cycles, artfully avoiding its weaknesses.