Quite a few years ago, I wrote a column about two movies I had recently seen. They were both about baseball. One was Moneyball, the other Trouble With the Curve.

Both movies seemed to be advocating for different ends of the same argument that was taking place in the sport at the time: the efficacy of analytics, as applied in particular to the scouting of talent.

Moneyball followed the success of the Oakland A’s in a season in which the team and its general manager, Billy Beane, utilized economics to build and manage the team. It was Beane’s direct response to feeling they were being outspent by the larger market team and using analytic would give the A’s a shot to compete with the “richer” teams.

Trouble With the Curve followed the struggles of an aging, old school scout, one who was pressured by the new school of analytics, led by a young, arrogant, and brash scout who not only believed analytics was the future of baseball, but also accused the older scout of being out of touch—a dinosaur.

In the end, The A’s made a remarkable run that season, gaining much notoriety for Beane and the school of thought known as analytics, although many took the team’s lack of winning a championship as an excuse to put down Beane and analytics as a staple for running a baseball team.

Although vindicated in the movie, and seemingly vanquishing the “younger” approach of analytics, the “older” scout in Trouble With the Curve turned out to not be quite fully vindicated in real life as the game of baseball is more consumed by analytics than ever before.

As similar argument is taking place in manufacturing, well everywhere really: artificial intelligence vs. human intelligence.

Much as I surmised in my analysis of the two baseball movies back then, I believe the answer was, and still is, balance. When you have a mechanism with many levers, it is often best to pull a little bit on each one. When there are many theories governing a discipline, you take a little from each theory. Even though baseball seems to be dominated by analytics today, it is still best to keep around some of those old theories of evaluating talent. The same might be said for artificial intelligence vs. human intelligence.

But don’t take my word for it. Read Lisa Weis thoughts on human intelligence with “Building HI, Human Intelligence, with People-Centric Leadership” and everything else we have to offer in this month’s Quality.

Enjoy and thanks for reading!