From what I've read on machine learning, a lot of the more basic techniques includes statistical methods (linear regression, logical regression, random forests, Bayesian statistics) that more or less are taught at master's degree level statistics courses at most, not doctorate level. If I remember right basic linear regression even showed up in stat 101.
I realize that many of these techniques can't solve some of the problems the deeper, more complex machine learning techniques can (for which your Ph.D statement might be right). But not every problem needs a very complex solution.
If you want to do simple recommendation systems or spam filters than O.k. Those are solved problems, hence commoditized.
If you want to build novel things, you really need academic-grade ML.
If you want another argument, I came from working in VC and startups, and they think they understand ML. Boy, they really don't. They are like kids pretending to play a guitar that can't strike a single chord right.
For an anecdote, I recall hearing one of the Kaggle founders mention that many of their bounties are won by non-statisticians/ML-ists. Producing novel (in the academic sense) stuff is unlikely outside of an academic setting, but producing products or solving problems is do-able.
Edit/comment: no need to downvote rsrsrs86 people. He's putting forward a position and defending it, not trolling. If you disagree, then disagree. The whole point of a thread like this is hearing people's take. Surely, PHD is a valid suggestion.
But ML can do much more than analytics., and much more than supervised problems. And the great problems to be solved are not supervised problems. They involve learning as you go, without a clean database with examples to learn from. They are adaptive problems.
You might optimize prices in an online retail player by trying to estimate supply and demand curves, but you will fail, and the best way to do it is not much different than teaching a neural network to play video games, but is fundamentally different from supervised learning and regressions.
ML can do self-driving cars, it can build drones that learn to fly, it can translate horses to zebras, it can play defeat humans at Go, it can make guitars sound like pianos.
There is a lot of technique and theory into framing any problem as a problem that can be solved by machine learning. Machine learning is generally not feasible unless you restrict the problem properly.
If you want to achieve novel (better than yesterday's state of art) results on existing problems, then yes, you really need academic grade ML. Especially for "solved" (i.e. well researched) problems - if the current solution isn't good enough for your needs, then you're going to need serious work to improve on that.
However, if you want to attack novel business problems, then it's quite likely that you can solve them without needing to solve any new ML problems. You have to know what "instruments" are available, and you have to be able to read&learn how implement a particular solution that you choose, but generally you just need to squint hard enough to map your business problem to one or more ML tasks that have a known solution.