How to succeed in enterprise AI

While there is a lot of excitement about how advances in artificial intelligence will help the enterprise sector, the reality is that most efforts fail. Study after study shows that organizations of different sizes are struggling to bring machine learning into their operations, and many initiatives end up being shelved or used in a very limited capacity. 

The adoption of applied AI is very difficult and costly, wrought with pitfalls, and requires fundamental changes at different levels. However, as the tools and processes mature, more companies will be able to take advantage of enterprise AI while reducing the risks and costs of adoption.

Demystifying AI for the Enterprise, a book written by six experienced executives and thought leaders, brings to light some of the ways that organizations can ease their way toward applied AI. The book shares best practices and practical case studies of how AI is being applied to different industries.

Chapter 1 of the book, written by Prashant Natarajan, Vice President of Strategy and Products at H2O.ai, introduces IMPACT, a general framework that can guide the adoption of AI in the enterprise. Natarajan describes IMPACT as “a checklist to create business value and ensure the lasting success of digital transformation with data, analytics, and AI.”

Read the full article on TechTalks.

For more on enterprise AI:

• AI’s J-curve and upcoming productivity boom

• The challenges of applied machine learning

• Why most machine learning strategies fail

• The common traits of successful AI startups