After 5 years of growth in data mining at my giant pharma, this is exactly what I'm seeing. Most ML projects remain toys while the number of them that advance into something useful can be counted on one hand.

(Of course it's a bit hard to assess the impact of a revolution like ML (esp DL) when your company already has hundreds of statisticians who have been employing similar data/experiment analysis techniques for decades, thereby diluting the signal of how disruptive novel forms of ML are within the enterprise.)