Armed forces around the world face similar challenges regarding exercises and live training. The context is increasingly multinational, and operations and supporting systems are more complex, all requiring more extensive training. To thrive in these volatile, complex and evolving security environments, military personnel also require an expanding range of competencies and at higher levels of proficiency, and the quick acquisition of new knowledge and skills to confront novel multi-domain challenges. In resource constrained systems, this outcome must be achieved without significantly increasing training and education time or costs. Operational integration of Advanced Distributed Learning (ADL) into multinational exercises is one step toward this goal. As computer-aided exercises increase in number and scope and educational systems go online, the ability to produce ADL training and education becomes a critical capability. ADL has been a supportive learning concept in the Viking series of computer-aided exercises since 2003. The ADL-concept for Viking 2018 expanded the scope and scale of learning resources to include assets of both pre-training and operational value, and highlighted analytics on a common dashboard with data from both the Learning Management System and the Command and Control system. Thus, it was possible to compare pre-training data to data from the execution phase of the exercise, including data from the designated evaluation team and exit interviews with participants. Based on previous lessons identified and data from Viking 18, this paper shows that ADL, blended into a computeraided exercise, produces more effective and efficient outcomes. To bolster the effort of integrating ADL into future exercises, the paper also describes an outline for a multiyear design-based strategic plan to achieve operational integration of ADL across a suite of joint and national exercises to support and enable readiness.