There are drug classes where for many reasons, some IP related, there are multiple (all FDA approved) very similar molecules, or the same molecule in different formulations. Those drugs all have some measure of efficacy (otherwise they wouldn't be approved) but there's always somebody in the trial on whom the drug didn't work at all (or somebody who died). Ideally personalized medicine would tell you whether you're that somebody, and you'd pick a different drug.
For most drug classes though the cost of obtaining enough patient information to make the call in the first place, even if it was feasible, is pretty high relative to just giving you a "test" with a given drug. The argument is often made for cancer because the drugs are more expensive and they tend to be given as cocktails (which means, more costs, more side effects). But with the way medicine currently is, it feels like even if hypothetically you could tell from a blood draw and a tumor biopsy that drug 1 is going to be 99% as effective as 4 first-line drugs together, patients for whom that 1% chance means they might die are probably going to go for the cocktail.
It's possible personalized medicine will lead to cheaper trials, and then cheaper approvals. For example instead of saying "I will make a drug that cures (alcoholism/cancer/MS/Alzheimer's) and it has to not kill patients and also happen to cure (alcoholism/cancer/MS/Alzheimer's) in a relatively large subset of the population, that I will spend tens of millions of dollars finding, testing, adjusting, retrying, etc" you could say "I will make a drug that cures dementia in female Caucasian patients between the age of 60 and 70 that have Southern European ancestry, eat a low-carb diet, and have a couple specific DNA markers. I will recruit a smaller sample of this population, get results, and my (much cheaper) drug can get approved for this population. Other, also cheap drugs will follow for (men/Asians/people over 80)." Now you have a feature engineering problem where you get to spend hundreds of millions of dollars paying data scientists to figure out that South European ancestry and some random protein that moves methyl groups around are the categories to structure your trial around. There's no free lunch.