Against Strategic Surprise: AI-INT as a Synthesis-and-Simulation Layer for Actor Modelling and Strategic Warning

Abstract

Intelligence organizations often fail to anticipate strategic developments even when relevant information is available because signals remain fragmented, ambiguous, overwhelming, or filtered through entrenched assumptions. The September 11 attacks, Russia’s 2022 invasion of Ukraine, and the October 7, 2023 Hamas attack illustrate how, even within some of the world’s most capable intelligence organizations, available indicators do not reliably translate into effective strategic warning, with outcomes shaped by differences in collection, interpretation, dissemination, and uptake. This article examines these failures and introduces AI-INT, or Artificial Intelligence Intelligence, as a response. AI-INT is an integrated, actor-specific synthesis-and-simulation layer within all-source analysis. It assembles and fuses relevant multi-source intelligence to build and update actor models, then uses them to run simulations at scale under systematically varied assumptions and conditions. It builds on wargaming’s value in deepening understanding, challenging assumptions, exposing blind spots, and exploring alternative trajectories by combining fused intelligence with LLM- and ML-based methods to expand the scenario space. As a bounded architectural illustration, the article presents SESBot, a Behavioral-AI framework for simulating the decision-making of selected Semi-State Terrorist Organizations (SESTOs). SESBot combines LLM-based agents with machine-learning behavioral anchors, illustrating one implementation of intelligence-grounded actor simulation within AI-INT. AI-INT’s promise lies not in predicting the future with certainty or replacing human judgment, but in helping analysts and decision-makers test assumptions, compare alternative scenarios, identify analytic blind spots, surface overlooked trajectories, connect them to monitorable indicators, and communicate uncertainty rigorously.