Machine Learning: Challenges and
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 poland whatsapp number data 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.
For organizations just getting started on this journey, the experiences of successful early adopters can provide invaluable insight. To that end, our recent deep-dive study targeted ML leaders – data executives and those at the helm of operational models – to understand their recipes for success.
A Panorama of Promises and Achievements
The study revealed that contrary to the perception that AI projects have a high failure rate, many of AI’s “first-movers” are reaping real rewards from their early investments.