The transformational promise of artificial intelligence (AI) and machine learning (ML) for enterprises has fueled enormous excitement and massive investment by data executives. One estimate predicts that AI’s contribution to the global economy could reach an extraordinary $15.7 trillion by 2030. That’s more than the current combined economic output of China and India.
Yet, there seems to be a very real chasm between potential and realization. Many data executives find that generating meaningful ROI from AI is still a challenging process, fraught with obstacles. In 2018, Gartner predicted that as many as 85% of AI projects through 2022 would deliver erroneous outcomes due to bias in data, algorithms, or the teams responsible for managing them.