Advances in artificial intelligence (AI) and natural language processing (NLP) are happening at a startling rate, with new capabilities reported nearly every week. As the hybrid methodology becomes de-emphasized and eventually retired, AI and NLP will provide important tools for ensuring that we are correctly recording compliance across populations, and to enable quality improvement initiatives. In the future, AI and NLP may help us create and use new measures that are not currently possible with only claims and structured electronic health record data. Our two payer organizations, Capital District Provider Health Plan (CDPHP) and the University of Pittsburgh Medical Center (UPMC) Health Plan, are among the most experienced in applying these technologies broadly to quality measurement and quality improvement. This presentation will provide a summary of a range of initiatives and use cases at our health plans. They include (1) moving to year-round HEDIS medical record review, (2) implementing programs for measure surveillance (e.g. Osteoporosis Measurement in Women) and quality improvement, (3) addressing challenging areas such as member complaints and member experience and (4) delivering information directly to the point of care. Some initiatives are overlapping among both organizations while others are specific to one organization. Each health plan presenter will share their organization’s journey from inception to program deployment, the goals of each program, recent results (efficiency, impact on rates) as well as visions for how they hope to use this technology in the future. Throughout the presentation, we will focus on real world obstacles and how to overcome them, including: aggregating the right kind of data from providers, developing leadership buy-in, creating programs that are acceptable within the current regulatory and auditing landscape, and preparing our teams for the workforce changes that these technologies inevitably produce.