Likewise, humans have strengths of experience, intuition, psychology, rules of engagement and the context of real world dynamics that a computer generated COA cannot interpret. Comparing both options helps find the best COA.
Capture workflows and product development in a shared space so knowledge gaps are reduced between shifts or rotations. Use automation to track knowledge gaps and alert users to update analysis and finished products when significant knowledge gaps are filled.
Automatically tag/map intelligence gaps as new information comes in and alert users to the new information.
Measure impact of operational intelligence (and any associated collections or requests that contributed to that intelligence) by automating inputs and processes that would serve as operational measurements.
Move beyond just tagging data to highlighting impactful anthropological or psychological elements that may impact behavior within the area of responsibility.
Add analytic rigor to the intelligence analysis process and automate a measure of rigor and objectivity into levels of confidence tied to intelligence assessments.
Add cognitive search into the massive data repositories analysts are required to sift through to move beyond keyword search and enable contextual search at an enterprise level.
Provide in-depth training on AI systems and set the standards by which the technology should augment the human analytic process but by no means replace the analyst behind the screen. In short, tie the technology into existing workflows and adjust workflows to account for technological innovation.
Invest in both garrison and tactical systems and infrastructure that are capable of running and sustaining the increased compute power that comes with training and deploying AI programs.
Conduct these initiatives in parallel with operations to ensure red and blue force efforts are complimentary and requirements are aligned by having an algorithm alert to discrepancies or gaps in the operational plan and the intelligence needed to execute it.
This list should be seen as a starting point. It’s just what flows off the top of my head for military intelligence. There are similar adjustments that need to be made in human resources, training, legal, supply, transportation and more.
AI can span across data management and governance initiatives, predictive maintenance, supply and inventory forecasting, optimizing tactical and technical proficiency and even using prescriptive analytics to better inform the selection of individuals for school seats and career progression. And more AI use cases could easily be found in monitoring command climate, aligning training, certification and deployment requirements and improving physical fitness and mental health.
You don’t have to be a part of a high profile AI initiative to find value in the science for nearly all areas of the military. We need the whole force to have the technical advantage on the battlefield and that means AI must become a force readiness initiative.
It’s all about augmenting human efforts across battalions, regiments and divisions to raise the readiness levels of the entire force. Our soldiers, sailors, airmen and Marines inside the wire should have the knowledge, technical acumen and agility to support all of the operations and technology our troops outside the wire are running.
This article was republished with permission from SAS.