All-Source Intelligence Analysts as Confident Consumers of AI-Enabled Outputs: The Pressing Case for AI Literacy Training

Artificial intelligence (AI) is rapidly reshaping how information is collected, processed and analyzed by intelligence agencies. There is therefore a pressing need for foundational AI literacy training to ensure intelligence analysts become confident consumers of the outputs that AI-enabled tools can provide – but not for the specialised computational and statistical training required to become accomplished designers, builders or operators of such tools. AI literacy training should distinguish between: AI-facilitated outputs, where automated collection, processing and analytical workflows can improve the consistency and efficiency of tasks that analysts themselves could perform; and AI-generated outputs, where semi-autonomous computational techniques can prompt unprecedented insights into hitherto, and in some cases previously unknowable, dataset features – insights that nonetheless entail dependencies and limitations requiring careful consideration and evaluation. Drawing on influential policy reports and academic articles exploring the potential utility of AI within intelligence analysis, this paper concludes that the transition to fully AI-enabled analytical capability demands a substantive expansion in AI literacy training; the technical and conceptual foundations of which have been summarized in a companion piece to this article.