By Jonathan Vanian
June 4, 2019

Executives who are afraid of long-term commitments should avoid artificial intelligence.

Like with romantic relationships, using the technology requires an appetite for hard work, planning, and patience. Even then, failure is a real possibility.

“If you are not going to make this investment for the long term, it is not a good idea to do machine learning,” LinkedIn’s vice president of artificial intelligence Deepak Agarwal told Fortune in a recent interview.

Agarwal leads the social network’s many machine-learning projects that power tasks like recommending job openings to users or determining which posts are the most relevant to them and that they are most likely to click on. He’s been involved with machine learning and statistics for years, after previously working at Yahoo and AT&T in senior technology research roles.

One thing Agarwal has learned is that it can take at least a year to see a financial return from machine learning. During that time, companies must navigate the mundane work of cleaning and properly labeling data, and figure out the correct machine-learning algorithms and data infrastructure to use.

Agarwal recommends that small companies, which often have little data, make an effort to collect it before implementing machine learning. Without a large archive of information, the technology is nearly useless.

For some companies, machine learning makes no sense because it requires a lot of computing power to crunch the data. And renting cloud computing infrastructure can cost more than any additional profit that machine learning can create, Agarwal explained.

But when the technology works, the payoff can be huge. For large companies like Google that have embedded machine learning throughout their businesses and apps, a modest 2% improvement on a particularly important metric could result in financial gains in the hundreds of millions of dollars.

Jonathan Vanian
@JonathanVanian
jonathan.vanian@fortune.com

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