Where do you stack up against your competitors when it comes to your AI initiative? Deloitte has released its 2020 State of AI in the Enterprise report which found only 47% say that they have a high level of skill around selecting AI technologies and suppliers. Deloitte's third edition of the "State of AI in the Enterprise" survey, conducted between Oct. and Dec. 2019, finds businesses are entering a new chapter in AI implementation where early adopters may have to work harder to preserve an edge over their industry peers. The study shows that companies at the top will be those that utilize AI to pursue creative and novel applications, actively address inherent AI risks and -- as more organizations buy AI-powered capabilities -- become smarter consumers of AI technology. "Seasoned" adopters are the example to follow as the global survey of 2,737 information technology and line-of-business executives finds this category has undertaken many AI production deployments.

In 2018, with interest in artificial intelligence reaching a fever pitch, O'Reilly asked members how their organizations were adopting AI. Results showed that the AI space was in a state of rapid change, so we eagerly commissioned a follow-up survey in 2019. Responses proved that AI adoption is proceeding apace, but companies still need to do more to put their AI efforts on solid ground. In this report, Roger Magoulas examines the survey's results to shed light on what AI adoption looks like in the enterprise right now, covering everything from the popularity of specific techniques and tools to the challenges experienced by adopters and more. If you don't have plans to evaluate AI, it's time to think about catching up.

As artificial intelligence (AI) and machine learning (ML) initiatives reshape critical sectors, CXOs need to understand the ethical issues of using AI and ML at their operations. ZDNet and TechRepublic published a PDF ebook: Managing AI and ML in the enterprise 2020, which examines how companies manage, benefit from, and make ethical decisions regarding their AI and ML usage. Artificial intelligence projects are a top priority for many companies, but there are plenty of potential pitfalls for the unwary. Learn more in ZDNet's Daphne Leprince-Ringuet's feature "AI for business: What's going wrong, and how to get it right." How to approach cost justification, identify ROI, and avoid implementation missteps for AI/ML initiatives is the topic of TechRepublic contributor Mary Shacklett's article, "The true costs and ROI of implementing AI in the enterprise."