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Alternative Market Briefing

Other Voices: Why Most AI Projects Fail

Friday, October 06, 2017

By Ben Taylor, Chief Data Officer at ZIFF, Inc.

Everyone has AI on their 2017/2018 roadmap these days. Bottom-tier innovation verticals like HR, multi-level marketing, entertainment, fashion, medical, supply chain (anyone else we should throw under the bus?) are even starting to talk about it. Everyone wants to hire a data scientist. Fortune 1500 companies are throwing out multi-million dollar data leadership positions to lead their data teams to success. Despite the hype and excitement, the majority of companies that commit to tackling AI projects will fail. Even that $1M+ hire won't save you from failure.

Based on my own personal failures and companies I have interacted with here are some of the main reasons why your AI project failed or will fail. These have seemed to resonate well with others so I figured I would share.

(1) Science Project Sharks:

Many of the companies we consult with are surrounded by science project sharks. "Wouldn't it be cool if we could do !", "We want all user uploaded images to align with our brand using AI!", "This data set has value, let's extract it!". The question we ask to cut through the crap is:

"Which projects have the largest impact on your BHAGs/KPIs/revenue?"

Wow-factor won't feed your family, revenue will. You can't afford for your first AI project to be a failure, that will set you back behind your competition. It would be better for you to postpone jumping into AI than t......................

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