This is the first in a series about ensuring the success of enterprise AI.
Data science is the “new black” – with businesses across all industries looking for ways to leverage AI and machine learning to segment customers, predict events, automate processes – and improve the bottom line.

But is it effective?
Stated differently: Has data science succeeded in increasing productivity or helping businesses make better strategic decisions? Shockingly, the answer – at present – seems to be a resounding no.
According to Gartner analyst Nick Heudecker‏, as much as 85% of all data science projects fail. That means a mere 15% of projects achieve their goals.
Here at Data Science Group (DSG), we’re witness to the challenge – as, oftentimes, organizations approach us for help only after their independent efforts at integrating AI for enterprise have hit the proverbial brick wall.

10 Ways to Ensure the Success of Enterprise AI

Despite the fact that most data science projects fail, here at DSG our team has managed dozens of projects successfully in the last five years.
In fact, DSG enjoys a success rate of over 90%. So what’s the secret behind our success?
Based on our experiences, we’d like to share with you the following 10 steps that will help you hit the ground running and achieve your data science goals. But buyer beware: There are real pitfalls to avoid; taking shortcuts in any of these areas can put your project in peril.