# The Use of Artificial Intelligence in Military Intelligence: An Experimental Investigation of Added Value in the Analysis Process

arXiv: https://arxiv.org/abs/2412.03610 · HTML: https://mlsanity.com/paper/2412.03610/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 67 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs](https://mlsanity.com/paper/2312.05934/) (2023)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [The Intelligence College in Europe (ICE): An Effort to Create a European Intelligence Community](https://mlsanity.com/paper/2312.17107/) (2023)
- [Intelligence Education made in Europe](https://mlsanity.com/paper/2404.12125/) (2024)
- [On the Role of Intelligence and Business Wargaming in Developing Foresight](https://mlsanity.com/paper/2405.06957/) (2024)

## Built on by (most cited first, 3 of 3)

- [Deriving Equivalent Symbol-Based Decision Models from Feedforward Neural Networks](https://mlsanity.com/paper/2504.12446/) (2025)
- [Learning to Code with Context: A Study-Based Approach](https://mlsanity.com/paper/2512.05242/) (2025)
- [Sensor-Driven Mission Synthesis for UAV/UGV Swarms: A TB-CSPN Coordination Architecture with Hardware-Enforced Safety](https://mlsanity.com/paper/2608.14306/) (2026)

## Abstract (arXiv)

It is beyond dispute that the potential benefits of artificial intelligence (AI) in military intelligence are considerable. Nevertheless, it remains uncertain precisely how AI can enhance the analysis of military data. The aim of this study is to address this issue. To this end, the AI demonstrator deepCOM was developed in collaboration with the start-up Aleph Alpha. The AI functions include text search, automatic text summarization and Named Entity Recognition (NER). These are evaluated for their added value in military analysis. It is demonstrated that under time pressure, the utilization of AI functions results in assessments clearly superior to that of the control group. Nevertheless, despite the demonstrably superior analysis outcome in the experimental group, no increase in confidence in the accuracy of their own analyses was observed. Finally, the paper identifies the limitations of employing AI in military intelligence, particularly in the context of analyzing ambiguous and contradictory information.