Reasons Why Some A.I. Projects Fail - Coruzant - The largest technology publication on emerging tech and trends.

At the end of 2019, it would have been a fairly common occurrence to stumble upon a tech article like this ZDNet one, that used statements such as, "AI jobs are on the upswing, as are the capabilities of AI systems." Fast forward about 6 months and this statement is still accurate in some ways, but highly conditional in others. As COVID-19 continues to act as a threat to the US and global economy, businesses are being forced to reevaluate not only their current and prospective AI projects, but also who they hire and who they retain to help execute these projects. Many business leaders will likely feel pressure to quickly finalize AI projects and bring them to market, but in the process they may succumb to one or more of the major pitfalls outlined below, rendering their results invalid and even potentially harmful to the public. Data should often be the starting point for your AI project, as it will come to represent the underlying fuel for your market offering (platform, solution, etc.) to thrive.