With industrial organizations undergoing rapid, large-scale digital transformations, it can sometimes be easy to miss the forest for the trees. When you get in the weeds on AI and machine learning, big data, analytics, the cloud, and the edge, you can forget that, at the end of the day, the goal of digital transformation is not to accumulate new technologies for the sake of new technologies. It's to use these new solutions to empower employees, make their lives easier and set them up to efficiently deliver new value and innovations for their organization. It's about the people, not just the tech; the latter empowers the former to drive results. The industrial data scientist is a living, breathing example of this -- a relatively new role in the process engineering and industrial sector that has emerged to fulfill a growing need in our industry: marrying traditional data science with localized domain expertise at a time of great, generational change occurring in the industrial workforce.

For many years now, we've heard and read about how "next year" will be the breakout year for AI. And in a way, that's always true – each year marks a new level-up in AI's ability to optimize traditional business operations and streamline work for the better. These year-after-year improvements have also further refined AI, for more fit-for-purpose applications that drive new value in specific use cases. Industrial AI is the latest iteration of this phenomenon, applying AI's processing to specialized applications in a manufacturing world rapidly undergoing profound digital transformations. Just as 2020 and 2021 marked new evolutions in artificial intelligence, 2022 promises to see industrial AI turn a major new corner in how the manufacturing sector draws on AI applications to tackle the problems of today and create new value-adding layers to their organization and ways of working.