It seems to be in every other headline. There's no doubt AI has the potential to transform medical device manufacturing. Rather than focus on a complete transformation, manufacturers can see benefits by focusing on simply enhancing it. AI on a manufacturing line doesn't have to mean everything is automated and there are no humans involved. On the contrary, humans continue to be essential in manufacturing, even with AI.

Turning machines are a core tool in any machine shop. While certain automated machines have been available for turning for years, automation has not been accessible to everyone. Until recently, it only made sense to add automation if you had large batch sizes. If not, you were stuck with a manual operation. Robotic automation has changed all that.

With the power to transform the business of government, artificial intelligence offers agencies unprecedented opportunities to discover deeper insights and identify correlations critical to mission success faster than ever. However, faced with legacy infrastructure, an evolving workforce, and limited budgets, how can government actually harness this new wave of technology? During this digital viewcast, hear from government practitioners and industry experts about the practical path to implementing artificial intelligence for agencies of all sizes.

With the power to transform the business of government, artificial intelligence offers agencies unprecedented opportunities to discover deeper insights and identify correlations critical to mission success faster than ever. However, faced with legacy infrastructure, an evolving workforce, and limited budgets, how can government actually harness this new wave of technology? During this digital viewcast, hear from government practitioners and industry experts about the practical path to implementing artificial intelligence for agencies of all sizes.

Today, "Agile" AI/Machine Learning (AI/ML) in the enterprise is largely a myth -- and it has little to do with building a model. Rather, enterprise AI/ML agility is constrained by bureaucratic data acquisition processes, complex security needs, immature cloud security practices, and cumbersome AI/ML governance processes. Unfortunately, in many enterprises these issues have resulted in failed projects, deflated expectations, and perhaps most importantly, missed opportunities to deliver real value. I have spent several years helping large banks accelerate the adoption of AI/Machine learning and related technologies. In this article I will discuss the core issues and obstacles to agile AI/ML in the enterprise that I have experienced and then offer a few lessons learned and some practical steps that provide a starting point for turning the Agile AI/ML myth into reality.

With so many technologies and use cases, getting started with artificial intelligence (AI) initiatives and deployments can be a daunting task for business leaders. It's important to understand the various categories of AI and the most suitable use cases for each type of technology. This helps to provide clarity and eliminate confusion between the various technologies and approaches involved. Dr. Marcell Vollmer, Chief Digital Officer, SAP Ariba, explains the major categories as: Categories of AI (Source: "How to make it simple to explain AI, ML, DL and Data Science?," Gaining clarity as to these different categories of AI and example use cases is an important first step.