Romantic and Rational Approaches to Artificial Intelligence

A gap already exists between companies’ ability to collect data and managers’ skills at putting it to use. Will AI increase the divide?

The use of artificial intelligence in the criminal justice system offers a stark example of the contrast between knowing how to produce results and knowing how to consume them intelligently. Systems recommend bail and sentencing but offer little transparency about the basis for the recommendation, leaving the humans who digest the recommendations potentially under informed.

What if we knew so little about the production processes of the food we eat? We know more about what we put into our mouths than what we put into our minds.

Are Organizations Biting Off More Analytics Than They Can Chew?

In 2015, we observed a growing gap between the production and consumption abilities of analytics in organizations. The article “Minding the Analytics Gap” describes how organizations struggle to consume the analytics results they produce. If that wasn’t bad enough, not only did we observe a gap, but it was a gap that grew, not shrank, as organizations got better at analytics.

Yes, organizations were rapidly improving their ability to produce analytical results. They were gathering more and more data. They were building digital infrastructures to process these vast quantities of data. They were developing (or acquiring) the talent required to develop complex models of market behavior. When these pieces all came together, organizations could create sophisticated analytical results.

Unfortunately, managers and executives in those organizations often did not have the expertise to consume the analytics results that the organization was able to produce. Just having the analytics results available wasn’t enough. The organizational ability to develop business insight and strategy based on those analytical results was more limited.

The difficulty lies in the individual rates of improvement in production abilities and consumption abilities. As organizations matured analytically, they were able to improve their analytics production capabilities more quickly than they were able to improve their consumption abilities. As a result, maturing organizations found that, despite the fact that their consumption abilities were improving, they were able to consume less and less of what they produced. The analytics gap gets worse as organizations improve — the opposite of what leaders would hope and expect.

And yet this may have just been the tip of the iceberg. When it comes to artificial intelligence in business, the divergence and resulting gap between production and consumption of data analytics may be an even bigger concern.

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