There are emerging trends to support an enterprise approach for providing training capabilities to the Warfighter at the point of need. As needs increase for easy operation, mobile training, and force reduction, an enterprise methodology focused on providing intelligent assistance and automation to After Action Review (AAR) artifact generation is needed to advance the training feedback paradigm. In addition, a wider variety of capabilities must be provided to the instructor, allowing for opportunities to provide more focused training specifically tailored to training tasks. An Intelligent AAR (IAAR) concept addresses future needs relative to providing adaptive training for the AAR. Intelligent Tutoring Systems (ITS) promise the ability to provide adaptive training focused on the specific needs of the trainee or team. ITS has provided that type of training in the past but usually at the cost of specialized ITS implementations each time. Many benefits can be realized through enabling an AAR to provide a common service oriented approach leveraging ITS as a reusable service. The approach allows a training system the ability to provide a cloud-based service that other actors can use to enable IAAR for any type of training. Such an approach must be capable of adapting to a wide variety of training systems and configurations to be a truly useful service in the larger enterprise. Our solution to this problem is the injection of optimized technology into the AAR process and toolset with focus on building an adaptive approach for representing training strategies and assessing trainee proficiency. We evaluated and prototyped various technologies and tools which may be appropriate for the development of an IAAR system. Specifically, we investigated technologies, tools, and algorithms to facilitate machine/adaptive learning, keyword spotting, object detection, intelligent tutoring, data analytics, and others for applicability to the use cases defined in an Army training domain analysis.