How to Avoid Your Looming Machine Learning Crisis

This research highlights the rising prominence of machine learning (ML) as an enterprise capability, juxtaposed with the surfacing challenges surrounding ML development itself. Technology giants and early adopters have shown us that machine learning can have valuable impacts in many applications, and businesses are following suit.

The study covers 153 data science decision makers across the Global 2000, and finds that enterprises no longer consider machine learning to be an “overhyped” technology (only 29% believe it is overhyped). On the contrary, the majority of leaders (86%) believe that machine learning is already making business impact in their respective industries. As machine learning takes centre stage in boardroom discussions, it is being viewed, starting from the C-Suite downwards, as the here-and-now disruptive technology to help them compete better in the digital age.

Key research themes explored in this report include:

• Lack of data readiness for ML
• Massive talent gap for ML
• Challenges with the industrialization of ML into core business operations and technology landscape
• A framework for the enterprise adoption of ML

What emerges strongly is the need for client organizations to focus on business impact as opposed to broad technological experimentation, and the need for integrated solutions, not viewing ML as a standalone answer. Meanwhile, this study finds that the industry is going through a learning curve as a whole, as new discoveries are being made every month across hardware, software, architecture, and applications.