We saw an example in a case study with IBM Watson where a 9-year-old boy presented at the emergency room with a very high fever and a lump on his neck. Doctors unaided by Watson focused on the fever and attempted to determine underlying causes while treating it—to zero effect—with antibiotics and standard fever reducers such as ibuprofen. After six days straight in the hospital and innumerable inconclusive tests, an attending nurse happened to have an idea: She was right.

By contrast, given the same case with the physicians aided by Watson, they arrived at the correct diagnosis—the obscure but potentially life-threatening Kawasaki’s Disease, which can include complications involving the heart—within 24 hours with no invasive testing. Watson was “free” to hone in on Kawasaki’s Disease because it comes unfettered with natural human instincts such as focusing on a high fever and assuming it’s probably driven by infection (hence the irrelevant prescription of antibiotics).

Another signal difference between classical statistics (again, typically regression analysis) and machine learning is the very fine level of detail machine learning can generate from the data. Here’s an exhibit from the McKinsey article showing “value at risk from customer churn” for a major telecom provider. The dark green downward-sloping line is a classic regression curve, whereas the multicolored isobars are generated by machine learning, with the warmer colors indicating higher degrees of risk. To pose the inevitable rhetorical question, which would you find more useful as the hypothetical executive in charge of customer relations at this telecom?

One reason it’s called “Big Data” is that given the massive computing firepower we can increasingly bring to bear, we can let machines loose on parsing the data without needing to have any ingoing hypotheses. The Big Data will reveal correlations we hadn’t thought of—and maybe never would have thought of. My favorite example of this (OK, I admit it’s almost too memorable) is when the irreverent CEO of the online dating service “OK, Cupid” published the finding that his data scientists had discovered there was a very high correlation between beer drinkers and people who wanted to have sex on the first date. Not an association, I imagine, most of us would have hypothesized.

But back to Law Land.

Where could you possibly start? McKinsey invokes a thought-provoking and instructive analogy to M&A strategic planning. Because, not to be oblique about it, if you don’t have a strategy for how machine learning could benefit your lawyers and your clients, don’t even pass Go.

We find the parallels with M&A instructive. That, after all, is a means to a well-defined end. No sensible business rushes into a flurry of acquisitions or mergers and then just sits back to see what happens. Companies embarking on machine learning should make the same three commitments companies make before embracing M&A. Those commitments are, first, to investigate all feasible alternatives; second, to pursue the strategy wholeheartedly at the C-suite level; and, third, to use (or if necessary acquire) existing expertise and knowledge in the C-suite to guide the application of that strategy.

And of course employ the familiar and standard techniques of change management:

Start small—look for low-hanging fruit and trumpet any early success. This will help recruit grassroots support and reinforce the changes in individual behavior and the employee buy-in that ultimately determine whether an organization can apply machine learning effectively. Finally, evaluate the results in the light of clearly identified criteria for success.

I hear you saying, “But we’re not there yet.”

Fair enough, but that’s a truism about any powerful new technology. When it first arrives on scene, how to apply it—never mind its climax stage “highest and best” application—is always far from obvious. It requires trial and error. To use hopefully-helpful examples from the past, the Wright Brothers had no idea what heavier-than-air flight capability would actually be used for in any commercial (much less military) context.