AI-Powered Strategic Warnings: Why The Intelligence Community Must Act Now
Hello, I’m Ylli Bajraktari, CEO of the Special Competitive Studies Project. In this edition of our newsletter, SCSP’s Nandita Balakrishnan builds on themes from an earlier newsletter to discuss the technological trajectory of the development of an AI-driven strategic warning system and how the intelligence community (IC) can play an active role in this innovation and construction.
Today we released our latest paper, Applying AI to Strategic Warning, about how artificial intelligence can transform delivering accurate, timely strategic warning for the Intelligence Community. I hope you enjoy it.
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AI-Powered Strategic Warnings: Why The Intelligence Community Must Act Now
The Intelligence Community’s Strategic Imperative: Investing in AI for Warning Dominance
Imagine a world where the intelligence community (IC) had a sophisticated system that constantly gathers many types of threat data overseas, fuses them together, and quickly alerts analysts about troops moving towards the Taiwan Strait or military tanks moving towards the Grand Kremlin Palace? Right now, expanding monitoring capabilities can alert both the IC and the general public about these events right as they begin to unfold. But what if artificial intelligence (AI) could make those predictions even earlier, giving policymakers a chance to proactively act?
Today we released “Applying AI to Strategic Warning,” a report capturing the insights and collaboration of the SCSP Intelligence Panel and the Alan Turing Institute’s Centre for Emerging Technology and Security (CETaS). The report is a culmination of roughly a year of strategic thinking about how artificial intelligence can transform one of the intelligence community’s most vital responsibilities: delivering accurate, timely strategic warning.
While this system does not yet exist, turning that vision into reality is not just a technological challenge—it is an intelligence imperative. For AI-driven systems to truly deliver strategic warning, they must be grounded in the expert judgment of analysts who translate early signals into actionable insight.
While intelligence analysts do not make policy, their judgments—especially on adversary intent and capabilities—play a foundational role in shaping national decisions. Their charge is to distinguish fact from fiction, synthesize incomplete or manipulated data, and assess emerging threats and opportunities. At its core, strategic warning is about equipping decisionmakers with foresight and options to preempt crises or seize geopolitical advantages.
Yet predicting chaos or pinpointing direct threats is never straightforward, even when danger signs are plainly visible. Multiple indicators might suggest a nation teeters on the brink of upheaval, yet knowing the spark that will ignite remains elusive. The challenge is exacerbated by the sheer scale and complexity of the modern threat environment. Adversaries now exploit not only physical terrain but also digital, financial, and informational domains. With a changing threat landscape, the IC’s analytic burden grows, and so must the tools it employs.
To stay ahead, the Intelligence Community must integrate and master emerging technologies, especially AI. Doing so is not simply about enhancing workflows or speed; it is about reinforcing the IC’s core mission and ensuring American policymakers are never blindsided in an era of rapid, often opaque geopolitical change.
AI-driven systems can accelerate intelligence flows, elevate human analysis, and expand strategic foresight. Tools developed to deliver early warnings could also streamline the collection to assessment cycle, optimizing the movement of intelligence from field to decisionmaker. And as adversaries weaponize AI, our intelligence services must do more than observe; they must lead.
We propose a Three-Phase Framework to drive this transformation. We assume this approach begins at the current status quo, which is a largely human-led approach with limited use of AI tools. These phases will cover the trajectory to AGI, and therefore, be a part of the larger moonshot investment that will be required to achieve AGI.
Phase One: Solve the Data Problem. The explosion of open-source data demands new methods of ingestion, fusion, and validation. Qualitative and quantitative inputs must be reconciled into cohesive, actionable intelligence. This phase also requires creating standards for non-traditional data sources and developing synthetic data to fill critical gaps.
Phase Two: Fuse Disparate Models. Current AI tools offer specialized outputs. Some model long-term instability; others focus on tactical timelines. These capabilities must be integrated into a robust analytic suite that supports cross-validation and scenario comparison. Scientific rigor and continuous refinement will be key.
Phase Three: Strategic Simulation Integration. The ultimate objective is an AI-enabled simulation platform that autonomously runs thousands of strategic scenarios using real and synthetic data. Through agentic AI, it adapts in real time, guiding analysts not only to anomalies but also to what triggered them and what actions to consider next.
No system will predict every event with certainty. But this framework would give U.S. policymakers more time, higher-fidelity foresight, and better options, which will fundamentally shift the IC from reactive analysis to anticipatory insight.
Investment Requirements
This three-phase approach demands sustained, large-scale investment. The costs are real but so are the costs of inaction: missed warnings, lost lives, and strategic surprise. Moreover, many benefits will cascade beyond the IC, strengthening sectors from health to finance.
By cultivating a diverse supplier base and deepening collaboration with allied intelligence services, we can distribute the financial burden while preserving flexibility. Much of the innovation in AI is occurring outside the government. Ensuring that commercial progress aligns with IC priorities is both a strategic necessity and an economic opportunity.
Deployment in Practice
AI-driven strategic warning systems could support continuous monitoring of high-priority theaters, including tracking indicators such as troop movements or financial disruptions in real time. They could also flag anomalies that trigger deeper analysis of emerging hotspots or threats from nonstate actors.
These tools will not replace human analysts. But they will help allocate scarce resources more effectively, which will ensure that the IC remains vigilant, adaptive, and ahead of the curve.
The United States cannot afford to trail in this domain. Investing in AI for strategic warning is not merely a modernization initiative: it is a national security imperative.