We recently conducted an industry survey of firms that have natural language systems in production. This includes an organization that has a history of leveraging NLP systems as well as those which are just beginning to plan their approach. A "dramatic shift" would be an understatement: since 2018, the field of natural language has undergone a sea change. Breakthroughs in the usage of deep learning, as well as the availability of more sophisticated hardware and cloud resources, led to sudden advances in natural language. The results are pervasive across technology subcategories within the field of natural language: parsing, natural language understanding, sentiment detection, entity linking, speech recognition, abstractive summarization, and so on. While the tech unicorns and their proxies have conducted almost an "arms race" since early 2018, sometimes publishing papers twice monthly to outdo their competitors' most recently published benchmarks -- how are these advances diffusing into practical use cases, and becoming adopted by mainstream businesses for their needs? Our survey results explore both the contours of the evolving landscape as well as the industry adoption and business trends for NLP.