FULL REPORTArtificial Intelligence
Beyond Weapons
Application and Impact of AI
in the Military Domain
SARAH GRAND-CLÉMENT

Acknowledgments
Support from UNIDIR core funders provides the foundation for all of the Institute’s activities. The
preparation of this report has been supported by generous funding for UNIDIR’s Security and Technology Programme by the Czech Republic, Germany, Italy, the Netherlands, and Switzerland, and by
Microsoft.
The author wishes to thank the experts who participated in the research and provided valuable inputs
during the course of the study. The author is particularly grateful to all those who took the time toreview the report and help improve it: Jason Lin, Stefan Nievelstein, Ioana Puscas, Dongyoun Cho,
Giacomo Persi Paoli, Yasmin Afina and a reviewer who chose to remain anonymous.

About UNIDIRThe United Nations Institute for Disarmament Research (UNIDIR) is a voluntarily funded, autonomous
institute within the United Nations. One of the few policy institutes worldwide focusing on disarmament, UNIDIR generates knowledge and promotes dialogue and action on disarmament and security.
Based in Geneva, UNIDIR assists the international community to develop the practical, innovativeideas needed to find solutions to critical security problems.

Note
The designations employed and the presentation of the material in this publication do not imply theexpression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning
the legal status of any country, territory, city or area, or of its authorities, or concerning the delimitationof its frontiers or boundaries. The views expressed in the publication are the sole responsibility of theindividual author. They do not necessarily reflect the views or opinions of the United Nations, UNIDIR,
its staff members, or sponsors.

Citation
Sarah Grand-Clément, “Artificial Intelligence Beyond Weapons: Application and Impact of AI in the
Military Domain”, UNIDIR, Geneva, 2023.

ARTIFICIAL INTELLIGENCE BEYOND WEAPONS

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AuthorSarah Grand-Clément
Researcher, Conventional Arms and Ammunition, Security
and Technology
Sarah Grand-Clément is a Researcher in the Conventional Arms and Ammunition Programme
and the Security and Technology Programme of UNIDIR. Her work examines the intersectionof technology and conventional arms, exploring both the benefits, as well as the challenges
and threats technology can pose to international security. Sarah also has expertise in the useof futures methodologies as a way to help explore policy issues, in particular the use of horizon
scanning, serious gaming and future scenarios. She holds an MSc in Arab World Studies from
Durham University.

ARTIFICIAL INTELLIGENCE BEYOND WEAPONS

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Acronyms & Abbreviations
AI

Artificial intelligence

C2

Command and control

CDE

Collateral damage estimation

GGE

Group of Governmental Experts

ISR

Intelligence, surveillance and reconnaissance

JRC

Joint Research Centre

L AWS

Lethal autonomous weapons system

LLM

Large language model

ARTIFICIAL INTELLIGENCE BEYOND WEAPONS

4Contents
Acknowledgments

2

Author3

Acronyms & Abbreviations

4

Executive Summary

6

1.

Introduction8

1.1

Addressing the use of artificial intelligence in the military domain

8

1.2

Purpose, scope, and methodology

9

1.3

Report structure

11

2.

Role of AI in the execution of military tasks						

12

2.1

Mapping military tasks

12

2.2

Application of AI capabilities across military tasks

14

2.2.1

Mapping

21

2.2.2

Discussion on the mapping of current and near-future AI capabilities to military tasks

23

3.

Impact of AI in the execution of military tasks

25

3.1

Strengths and opportunities emerging from the integration of AI capabilities

25

3.2

Limitations and challenges emerging from the integration of AI capabilities

27

3.3

Assessment and discussion regarding perceived strengths and challenges

30

4.

Conclusions

32

References

33

ARTIFICIAL INTELLIGENCE BEYOND WEAPONS

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Executive Summary
Within the United Nations, the application of artificial intelligence (AI) in the military domain has,
to-date, primarily been discussed in the context of the United Nations Group of Governmental Experts(GGE) on emerging technologies in the area of lethal autonomous weapons systems (LAWS).
However, the application of AI within the military domain extends beyond the issue of LAWS. In the
midst of discussions and debates around the opportunities and risks of AI for military purposes, aswell as the governance and responsible use of these technologies, this report aims to increase understanding of the role of AI in the execution of military tasks beyond applications relating to the use offorce and the narrow tasks of target selection and target engagement within the targeting process.
Through a review of the literature and engagement with experts, the report identifies 18 militarytasks—denoted in this report as ‘upstream’ tasks—that occur prior to the narrow tasks of target
selection and target engagement—in other words, ‘downstream’ tasks. These 18 upstream tasks are
divided across four functional areas: command and control (C2), information management, logistics,
and training.
For each of these tasks, a non-exhaustive overview of current AI capabilities is provided, as well asof feasible near-future capabilities, which encompass AI capabilities deemed by experts to be ‘fairly
certain’ or ‘likely’ to emerge in the near future. This near future is set nominally to 2035, which is considered, for the purpose of the study, a suitable timeframe to examine realistic future impact. This
mapping pertains to the state-of-play and technical feasibility of AI currently and in the near future, andnot the level of adoption of AI by different actors.
Analysis of this mapping of AI capabilities demonstrates several points:•

A wide range of capabilities are currently technically feasible. However, the (future) existence of
these capabilities does not necessarily imply or mean that they will be integrated in specific militarytasks and military operations more widely.

•

Many of the near-future AI capabilities focus on data fusion, analysis (including predictions and extrapolations), and simulations, placing an emphasis on AI capabilities that centre around the transformation of data into knowledge as well as systems which can learn, adapt, and react to changing
information.

•

Near-future capabilities primarily feature AI capabilities that demonstrate an enhancement ofexisting capabilities, notably by being able to work with more data and have better reasoning capabilities. To a lesser extent ‘new’ near-future abilities featured, demonstrating that ground-breaking
revolutions in AI are not expected in the near future, at least as regards these specific military tasks.

•

The relationship between AI and data, whereby AI is used to assist with data but is also dependent
on it, demonstrates that issues of data integrity, quality, and veracity are going to remain key going
forward. It also raises the question as to whether enough attention is being paid to these issues
versus the AI capabilities themselves.

ARTIFICIAL INTELLIGENCE BEYOND WEAPONS

6The following opportunities and challenges emerge when considering the impact of AI in theexecution of upstream military tasks:

STRE NG T HS A ND O PPO RT UNI TI E S

L I M I TAT I O N S A N D C H A L L E N G E S•

Lack of (good quality) data impacting efficiency andaccuracy of AI algorithms

Planning and development of alternatives for action•‘Black box’ nature of AI decision-making•

Safety and security of virtual and physical assets••

Optimisation of personnel time and effort

Questions around applicability of meaningful humancontrol to certain military tasks deemed ‘low risk’•(Lack of) trust in AI’s reasoning capabilities•

Incorporation of AI increasing cybersecurity vulnerabilities•

Overreliance on AI leading to rigid thinking around respective strengths and capabilities of humans versus
machines•

Skills degradation in military personnel•

Generation of a faster operational tempo•

Lack of harmonisation among different actors on use andapplication of AI•

Unknown impact of AI application•

Analysis of large amounts of varied data•

Speed of communication, analysis, and decision-making•

The report concludes with the following considerations:•Examination of the impact of AI on the upstream military tasks shows many similarities in the discussion compared to downstream tasks. Moreover, upstream tasks would have an impact on thedownstream tasks as well as the broader conduct of a military operation.

•

However, the operationalisation of AI in upstream tasks may be, in some cases, less controversial
for decision makers than their inclusion in downstream tasks. This may be due to the inclusion of
AI being less visible in upstream compared to downstream tasks—which may be a factor making
the integration of AI in upstream tasks more likely.

•

To that end, AI capabilities relating to both upstream and downstream military tasks should be
included in discussions on AI for military purposes, particularly in light of issues relating to governance and responsible AI.

ARTIFICIAL INTELLIGENCE BEYOND WEAPONS

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1. Introduction
1.1 Addressing the useof artificial Intelligencein the military domain

attention has turned to the governance of AI
more broadly, with the United Nations Secretary-General noting how AI could be used to“launch cyberattacks, generate deepfakes,
or for spreading disinformation and hatespeech”3—thereby moving away from solely

Within the United Nations, the application of
artificial intelligence (AI) in the military domain
has, to-date, primarily been discussed in thecontext of the United Nations Group of Governmental Experts (GGE) on emerging technologies in the area of lethal autonomous
weapons systems (LAWS).1 Within thiscontext, most of the focus has been in termsof “maintaining human control over weapons,
the critical functions of weapons, attacks, thetargeting process, and (final) decisions to use
force”2—in other words, focusing on the application of AI to the use of force specifically.
However, the focus on AI for military purposes
within the United Nations has started tobroaden beyond these strict confines. Greater

looking at AI through the lens of LAWS within
the peace and security domain. Moreover, AI isnoted in one of the Secretary-General’s latest
policy briefs on peace and security issues asa technology requiring national strategies,
norms, and rules within a global framework,
due to its “increasing ubiquity […] combinedwith its rapid scalability, lack of transparency
and pace of innovation, [which] poses potential
risks to international peace and security andpresents governance challenges”.4
Furthermore, the United Nations is forminga new multi-stakeholder High-level Advisory
Body on Artificial Intelligence, to “undertake
analysis and advance recommendations
for the international governance of artificial

1
The aim of the GGE, according to the 2022 Meeting of the High Contracting Parties to the Convention on Prohibitions or Restrictions on the Use of Certain Conventional Weapons Which May Be Deemed to Be Excessively Injurious or Have Indiscriminate Effects, is to “intensify the consideration of proposals and elaborate, by consensus, possible measures, including taking
into account the example of existing protocols within the Convention, and other options related to the normative and operationalframework on emerging technologies in the area of lethal autonomous weapon systems, building upon the recommendations andconclusions of the Group of Governmental Experts related to emerging technologies in the area of lethal autonomous weapon
systems, and bringing in expertise on legal, military, and technological aspects”; Report of the 2023 session of the Group of Governmental Experts on Emerging Technologies in the Area of Lethal Autonomous Weapons Systems, CCW/GGE.1/2023/2, May
23, 2023, https://docs-library.unoda.org/Convention_on_Certain_Conventional_Weapons_-Group_of_Governmental_
Experts_on_Lethal_Autonomous_Weapons_Systems_(2023)/CCW_GGE1_2023_2_Advance_version.pdf.
2
Merel Ekelhof and Giacomo Persi Paoli, The Human Element in Decisions about the Use of Force, 2020, UNIDIR: Geneva,
https://unidir.org/publication/human-element-decisions-about-use-force.
3“Guterres Calls for AI ‘that Bridges Divides’, Rather than Pushing Us Apart”, United Nations, 18 July 2023, https://news.
un.org/en/story/2023/07/1138827.
4
United Nations, “A New Agenda for Peace: Our Common Agenda Policy Brief 9”, 2023, https://www.un.org/sites/un2.
un.org/files/our-common-agenda-policy-brief-new-agenda-for-peace-en.pdf, 28.

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8

intelligence”.5 AI was also—for the first time—be, especially beyond applications relating to

the topic of a Security Council meeting on 18

the use of force and the narrow tasks of target

July 2023, with the debate focusing on the op-

selection and target engagement within the

portunities and risks posed by AI to interna-

targeting process, which have to-date been thetional peace and security. Discussions notably

areas of greatest focus.

6

touched upon the positive and transformational value of AI, but also of how it could change
the threat landscape and warfare more broadly.
Beyond initiatives taken within the United
Nations, States themselves have also started
paying greater attention to the growing role ofAI for military purposes beyond LAWS. This
notably culminated in the Responsible AI in
the Military domain (REAIM) summit, held in
early 2023, from which a call to action on the
responsible development, deployment, anduse of AI in the military domain emerged.7 The
call to action notably highlights that “militaries
are increasing their use of AI across a range ofapplications and contexts”8 and that responsible use of AI is of paramount importance. As
such, the governance and responsible use of
AI for military purposes entails understanding how AI is currently being applied, and will

1.2 Purpose, scope,and methodology
Past studies have documented the broader
current and potential uses of AI within the
military, which includes helping, or evenenabling, cyberspace operations; logistics
planning; intelligence, surveillance and reconnaissance (ISR); and data analysis.9 However,these insights into AI applications can be quite
broad, without much specificity as to how AI
applies in a military operation, particularly
outside of the targeting process.
Thus, this report aims to increase understanding of the role and implications of AI10 in theexecution of military tasks that comprise a5“Multistakeholder Advisory Body on Artificial Intelligence​”, United Nations, https://www.un.org/techenvoy/content/artificial-intelligence.
6“Artificial Intelligence: Opportunities and Risks for International Peace and Security - Security Council, 9381st Meeting”,
UN Web TV, July 18, 2023, https://media.un.org/en/asset/k1j/k1ji81po8p.
7“Call to Action on Responsible Use of AI in the Military Domain”, Government of the Netherlands, February 16, 2023,
https://www.government.nl/ministries/ministry-of-foreign-affairs/news/2023/02/16/reaim-2023-call-to-action.
8“REAIM Call to Action”, Government of the Netherlands, February 16, 2023, https://www.government.nl/ministries/ministry-of-foreign-affairs/news/2023/02/16/reaim-2023-call-to-action, 1.
9
See for example Wyatt Hoffman and Heeu Millie Kim, Reducing the Risks of Artificial Intelligence for Military Decision
Advantage, Center for Security and Emerging Technology, 2023; Niklas Masuhr, AI in Military Enabling Applications, CSS
Analyses in Security Policy No. 251, October 2019, https://css.ethz.ch/content/dam/ethz/special-interest/gess/cis/center-for-securities-studies/pdfs/CSSAnalyse251-EN.pdf; Kelley M. Sayler, Artificial Intelligence and National Security, Congressional Research Service Report, November 10, 2020, https://sgp.fas.org/crs/natsec/R45178.pdf; Forrest E. Morgan et al.,Military Applications of Artificial Intelligence: Ethical Concerns in an Uncertain World, Santa Monica: RAND Corporation, 2020.
10 Given that this study looks at a range of AI capabilities, both present and future, in addition to the fact that there is noagreed definition of AI, it was decided that providing a definition may be too prescriptive and could in fact limit discussions. Forease, this report therefore refers to AI in general terms, noting that this term encompasses a number of subfields whereby thecapabilities and needs of different types of AI (e.g., machine learning, deep learning, computer vision, natural language processing, etc.) differ.

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military operation, beyond the narrow tasks

future, set nominally to 2035. This time frameof target selection and target engagement

was chosen for two main reasons:and use of weapons themselves. Beyond
11

the much-discussed debate around the use•are presented, a limited time frame withinof AI in engaging a target and applying lethal
force, this report examines what it means to
implement AI in the many steps and processes
that come beforehand. It aims to do so by
providing a realistic overview of AI capabilities, without overemphasising the role AI doesor may play within the military domain.12 As aresult, this report focuses solely on exploring
AI capabilities relating to upstream tasks
during a military operation, including thosewhich have an indirect effect of lethality, but are
not tasks specifically related to target selection
and target engagement, which are considereddownstream tasks.
This report also presents a discussion on theimpact of AI application to these upstream
military tasks, complementing existing debates
regarding the integration of AI into the targeting
process and weapon systems specifically.
To this end, this report is primarily aimed atmembers of the diplomatic community focusedon disarmament and technology issues, aswell as other relevant stakeholders operatingin this field, such as research and advocacy organisations.

First, to ensure that realistic AI applications
the foreseeable future was selected.

•

Second, the adoption of technology within
the military is not a rapid process, due
to a range of reasons, including lengthy
procurement and approvals processes.
Therefore, the 2035 timeframe is far enoughfrom the time of writing that changes in theapplication of AI in the military can be considered realistic.

This report is based on a review of the relevant
literature, as well as on data collected throughinterviews and an expert workshop. Initial interviews, held in September 2022, were withformer members of the military, Member State
representatives, and members of the technicalcommunity, which served to review andvalidate both the military tasks themselves aswell as areas of AI applications. The workshop,
held in October 2022, brought together representatives from the technical, military, andresearch communities to further discuss anddebate applications of AI and associatedimpacts. Subsequent interviews were also heldin August to September 2023, to refine andvalidate the mapping of AI capabilities.

Specifically, this report focuses on AI capabilities and applications currently and in the near

11 The targeting process includes a range of phases: find, fix, track, target, engage and assess (see Merel Ekelhof andGiacomo Persi Paoli, The Human Element in Decisions about the Use of Force for further detail). Within the context of thisreport, the phases ‘engage’ and ‘assess’ are out of scope. Additionally, weapons are specifically defined as the means ofdelivery of lethal force, with the study looking at tasks which may enable or feed into the decision to use lethal force but are notused as the means of delivery of lethal force themselves.
12 Overemphasising AI capabilities can lead to erroneous assessments and policy decisions. For a more detailed discussion
on this topic, see for example Cameron Hunter and Bleddyn E. Bowen, “We’ll never have a model of an AI major-general: Artificial Intelligence, command decisions, and kitsch visions of war”, Journal of Strategic Studies (2023), https://doi.org/10.108
0/01402390.2023.2241648 and Yasmin Afina, “Intelligence is dead: long live Artificial Intelligence”, Chatham House, July 14,
2022, https://www.chathamhouse.org/2022/07/intelligence-dead-long-live-artificial-intelligence.

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1.3 Report structure
The report first maps the military functional areas and their related tasks and provides
insight on AI capabilities regarding theexecution of specific military tasks now and in
the near future (Chapter 2). The report subsequently explains the impact and implications of
AI integration in these tasks (Chapter 3), before
providing concluding remarks (Chapter 4).

ARTIFICIAL INTELLIGENCE BEYOND WEAPONS

112. Role of AI in the execution of
military tasks
This chapter first introduces the military tasks

management refers to the collecting, pro-used as the framework of reference for thiscessing, exploiting, and disseminating ofstudy, before mapping current and feasible

information relating to a military operation.

near-future AI capabilities to these tasks.

Within the context of this study, this func-

2.1 Mapping militarytasks

tional area also contains elements relating
to defensive cyberspace operations.
3. Logistics: Logistics refers to the
movement, supply, and monitoring of
personnel and equipment to sustain a

The use of weapons in instances of conflictis only one of many activities during a military
operation. There are many other activities
during a military operation that occur andprecede the narrow tasks of target selection
and target engagement. Therefore, to fully understand the application of AI prior to the useof weapons, it is necessary to define militarytasks which would typically comprise a military
operation, aside from use of lethal force. The
military tasks are shown in figure 1 and are
divided into four functional areas:13
1. Command and control (C2): C2 refers to
the decision-making aspect of a military
operation. For this study, tasks under theC2 functional area primarily focus on theanalysis elements in support of military decision-making and planning, as opposedto the conduct of military action, which falls
outside of the present study’s scope.
2. Information management: Information

military operation.
4. Training: Training refers to the instruction
and preparation of military personnel.
The military tasks within these functional
areas are agnostic of command level, focusing
instead on activities undertaken. While thetasks are numbered for clarity, this does not
imply a hierarchical relationship between either
the tasks or the functional areas, nor a chronology in terms of the tasks outlined.
Overall, these military tasks are used as aframework for the subsequent mapping ofAI capabilities. As such, while they aim toreflect the various activities comprised withina mission, the purpose is not for these tasks
to form a comprehensive list nor reflect any
particular military doctrine. Indeed, missions
and therefore tasks comprised within will
differ in terms of their scope, situation, actors
involved, and more. Rather, the military tasks
outlined below should be seen as a tool to

13 The tasks and functional areas were elaborated based on a review of existing literature, namely Ekelhof and Persi Paoli,
The Human Element in Decisions about the Use of Force; Masuhr, AI in Military Enabling Applications; Sayler, Artificial Intelligence and National Security, and inputs from interviews with Stefan Nievelstein (28/09/2022) and two anonymous experts(14/07/2022 and 13/10/2022).

ARTIFICIAL INTELLIGENCE BEYOND WEAPONS

12

help frame the present discussion. Addition-

human resource management, payroll man-ally, for scope purposes, the tasks below doagement, or fraud detection and prevention.

not take into account the daily running of the

Similarly, (emergency) medical care during andmilitary infrastructure more broadly, which

post-mission is also considered out of scope.

would include administrative tasks such as

Figure 1. Upstream military tasks
FU NCTIONA L A RE A
C2Information management

M I LI TA RY TA S K
1.

Undertake target analysis (i.e., identifying the most relevant targets)

2.

Determine choice of weapons and weaponeering14

3.

Assess weapon capabilities and effects (e.g., munition fragmentation patterns,
secondary explosions, etc.)

4.

Estimate protection, collateral damage, and risk mitigation, including non-strike
entities (e.g., civilian patterns of life, time of attack)5.

Develop mission implementation plan (e.g., identification of courses of action andpathways, etc.)

6.

Plan for contingencies (e.g., loss of equipment, loss of communication, etc.)

7.

Plan and adapt manoeuvres in the battlefield based on available information, intelligence and data collected real time in the land, air, naval, space and cyber domains

8.

Continuous impact assessment of the campaign (including assessing public
opinion, etc.)

9.

Clean, filter, and fuse data collected via intelligence, surveillance and reconnaissance (ISR)

10. Analyse data collected through ISR across the land, air, naval, space and cyberdomains
11. Analyse own, friendly and adversary capabilities (e.g., manpower, equipment,
training, facilities, status of equipment, etc.)
12. Analyse the environment (which includes terrain, infrastructure, non-strike entities,
impact on civilians, etc.)
13. Synthesise the key points emerging from the analysis of the data collected
14. Disseminate information across the chain of C2
15. Manage information and communication security
Logistics

16. Logistical support of deployment (i.e., acquisition of necessary material/equipment,
plan deployment of personnel, plan transport of equipment and personnel, manage
force protection)
17. Assess operational effectiveness of people and equipment (i.e., real-time monitoringof performance and status)

Training

18. Undertake training and simulation (i.e., educate military personnel and undertake individual and collection training)

14 “Weaponeering is the process of determining the quantity of a specific type of lethal or non-lethal means required togenerate the desired effect on a given target”; Ekelhof and Persi Paoli, The Human Element in Decisions about the Use ofForce, 4.

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13

2.2 Application of AI
capabilities acrossmilitary tasks

This mapping does not aim to be comprehensive of the entire spectrum of AI capabilities,
focusing instead on the ones deemed most
relevant and pertinent for each task based on
the literature and expert insights. It should,
however, be noted that this mapping pertains

The tables below present the mapping of AI capabilities against each of the military tasks.15
Often, the application of AI in the military
domain tends to either focus specifically on
the use of force and the narrow tasks of target
selection and target engagement, or mention
the use of AI in general terms, such as toaid with analysis, but without specifying thespecifics of such analysis. This mapping of AI
capabilities to specific military tasks (beyond
the narrow tasks of target selection and target
engagement within the targeting process)therefore aims to address these gaps. It should
be noted that there can be differing opinions

to the state-of-play and technical feasibilityof AI currently and in the near future, and not
the level of adoption of AI by different actors.16
Indeed, there is a wide range of factors that
impact adoption of AI (or indeed, any othertechnology), such as in terms of available
resources and infrastructure to support AI development and deployment, human resourcesand expertise in AI, relevance of a capability, or willingness of adoption of AI by different
actors. A discussion of these factors is beyond
the scope of this present report, which focuseson understanding AI capabilities agnostic ofsuch variables.

as to the existence of various capabilities; thetables therefore aim to show the consensus
views, while acknowledging and also inplaces clearly identifying that alternative
opinions exist, particularly in light of the rapidly
advancing pace of technology.

15 AI capabilities based on insights from literature (notably: Masuhr, AI in Military Enabling Applications; Sayler, Artificial Intelligence and National Security; Peter Svenmarck et al., Possibilities and Challenges for Artificial Intelligence in Military Applications, NATO, 2018; “Artificial Intelligence is the Future of Warfare (Just Not in the Way You Think)”, Paul Maxwell, April
20, 2020, https://mwi.usma.edu/artificial-intelligence-future-warfare-just-not-way-think/; Morgan et al., Military Applications of Artificial Intelligence; Zhang et al., “Application of Artificial Intelligence in Military”; José Javier Galán, Ramón Alberto
Carrasco, and Antonio LaTorre, “Military Applications of Machine Learning: A Bibliometric Perspective”, Mathematics, 10, no.
9 (2022), https://doi.org/10.3390/math10091397; Michael C. Horowitz, “Artificial Intelligence, International Competition,and the Balance of Power,” Texas National Security Review 1, no. 3 (2018): 36–57, https://doi.org/10.15781/T2639KP49;
Erik Lin-Greenberg, “Allies and Artificial Intelligence: Obstacles to Operations and Decision-Making”, Texas National Security
Review, 3, no. 2 (2020): 56–76, http://dx.doi.org/10.26153/tsw/8866); discussions by workshop participants (Dr. MichaelGerz, Lt. Col. Martijn Hädicke, Anna Knack, Dr. Jason Lin, Dr. Andrew Lohn, Dr. Mark Manantan, Dr. Peter Svenmarck, Ret. Col.
Jean-Christophe Noel, Dr. Diane Vavrichek); and validated through interviews with Dr. Joel Brynielsson (12/09/2023), Thomas
Grohs (01/09/2023), and Dr. Martin Hagström (04/09/2023).
16 Specific Technology Readiness Levels (TRLs) have not been assigned to the current and feasible near-future capabilities.
However, it is assumed that all technologies mentioned are or would be at TRL 7 (“system prototype demonstration in operational environment”) or above. For further information on TRLs for AI, see Fernando Martínez-Plumed, Emilia Gómez and José
Hernández-Orallo, “Futures of Artificial Intelligence through Technology Readiness Levels,” Telematics and Informatics, 58(2021), https://doi.org/10.1016/j.tele.2020.101525.

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14

Command and Control
FEASIBLE NEAR-FUTURE AI
CA PA B I L I T I E S

2. DET ER MI N E C H O I C E O F
WEAP ONS AN D W E A P O N E E R I N G

1 . U N D E RTA K E TA RG E T ANALYSISCURRE NT A I CA PA BI LI T I E S•

Network mapping to identify highly valuably
targets within a network, based on, for example,
pattern recognition and analysis of communication
means

•

Assessment aid as to whether the selected targets
abide by the stated objectives, desired effect andrules of engagement•

Prediction of contextualized opponent behaviour•

Calculation and assessment of inter-dependencies between different targets••

Identification of individuals and objects via image
recognition or other data types (e.g., individuals
of interest, buildings, military vehicles) to include
both targets to engage and those to avoid

Provision of several courses of action or recommended courses of action based on the mission’s
strategy and intended targets17•

Simulations and extrapolations of future
outcomes based on data and assessments
stemming from the target analysis18•

Automation of agents and their actions inwargaming simulations and synthetic environments for training and planning•

Prioritisation, filtering, and triage of information
in a faster manner regarding operational conditions
and data•

Identification of targets (human or other), via
motion of points, bodies, and systems of bodies
and patterns of life•

Enhanced provision of support to red teaming(i.e., undertaking a challenging function to existingplans)•

Application of AI to the analysis of synthetic
aperture radar data from space-based assets,
particularly in instances of adverse weather conditions•

Provision of initial and rapid legal advice as ‘first
opinions’ ahead of human military legal advisers,
such as to assess whether there are contradictions
between on-the-ground action and legal and regulatory frameworks19•

Provision of a menu of options or recommendation regarding choice of weapons, based on target
and environment data within a singular domain ofoperation••

Decision aid for weaponeering, taking into
account the technology applied to targeting andcollateral damage estimation (CDE)

Provision of a menu of options or recommendation regarding choice of weapons, based on target
and environment data across domains (i.e., joint
capability, ability to take into account all elements
and pieces of equipment across multiple domains)•

Simulations and extrapolations of future
outcomes, including on CDE, enabling identification of future outcomes based on weaponeering
choices17 Based on the pace of development of capabilities such as large language models, this was noted as being closer to a
current capability.
18 Experts noted however some uncertainty with regard to this capability, with some indicating that they felt this capability
was unlikely to be feasible in the near future.
19 It was noted that this may be extremely difficult to computerise, as the analysis and argumentation feeding into legal advice
is complex; therefore this capability should not be seen as a replacement for legal advisers, but a lower-level type of capability.

ARTIFICIAL INTELLIGENCE BEYOND WEAPONS

15

Simulations and extrapolations of future
outcomes, including on CDE, enabling identification of future outcomes based on weapon capabilities and choices•

Improved simulation of weapon effects, including
on more accurately represented environments•

Calculation and assessment of interdependencies between different targets•

Provision of several courses of action or recommended courses of action based on the analysis
of weapon capabilities and effects based on target
and environment data within a singular domain ofoperation•

Provision of more advanced courses of action
or recommended courses of action based on theanalysis of weapon capabilities and effects (e.g.,taking more variables into account, cross-domain
data, etc.)•

Modelling of disruption of weapon effects tocritical infrastructure•

Increased accuracy of modelling of disruption of
weapon effects to critical infrastructure, including
modelling of a multitude of effects and wider area,
and across time•

Calculation of basic civilian patterns of life (e.g.,
broad behavioural patterns, traffic trends, etc.)••

Provision of several courses of action or recommended courses of action based on the CDE andrisk mitigation analysis

Calculation of more advanced civilian patternsof life (e.g., by demographic groups, increased
specificity, detail, interrelation, etc.)•

Simulations and extrapolations of future
outcomes, including on CDE, enabling identification of future outcomes based on weaponeering

6. PL AN FOR
CONT INGENCIES

5 .DEVELOP M I S S I O NI MPL E MENTAT I O N P L A N

4 . E S T I M AT E P ROT E CT IO N , C O L L AT E RAL
DA M AG E , A N D R I S K MIT IG AT IO N

3. ASSE SS WE AP O NCAPAB IL IT IE S AND E FFE CTS••

Prediction and assessment of collateral
damage, including the destructiveness of various
types of weapons on specific targets•

Provision of several courses of action or recommended courses of action with regard to mission
implementation based on target and environment
data within a single domain of operation•

Simulations of optimal force position in theatre•

Simulations and extrapolations of mission implementation•

Optimisation of data routing within tactical
networks•

Simulations and extrapolations regarding
possible pathways and their respective contingency plans•

Provision of several courses of action or recommended courses of action with regard to possible
contingencies

choices•Development of large-scale mission implementation scenarios•

Provision of several courses of action or recommended courses of action with regard to mission
implementation across domains (i.e., joint capability, ability to take into account all elements andpieces of equipment across multiple domains)•

Assessment of patterns of decision-making and,based on these, proposal of novel tactics andpathways towards mission implementation•

Conduct of AI-generated wargames and red
teaming to assist with the planning of contingencies•

Identification in real time of contingency plans,such as secondary communications systems or
new communication and transport routes

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16

7. P L AN AND ADAPTMANO E UVRE S IN
T HE B ATTL E FIE L D
8 . C O NT INUO US IMPACTA SSE SSME NT O F T HE
CAMPAIG N

Analysis in real-time of sensor data in order todirect systems towards a specific location or in
response to a movement, sound, visual, or other ina specific domain (i.e., sensor allocation)•

Support to the creation of a common operational
picture via the use of data fusion (i.e., the integration of multiple sources and types of data)•

Use of ISR data, image recognition, and target
tracking in order to process data and locate
targets over time, in changing circumstances, and across domains of operation (e.g., landdomain and cyber domain)•

Conduct of sentiment analysis, based on natural
language processing for example, on news andsocial media data captured from the public domain•

Simulations and extrapolations of future
outcomes of the campaign’s impact••

Detect misinformation with generative AI

Estimation of battle damage assessment for
greater range of weapons (e.g., simultaneous use
of weapons such as missiles, armed uncrewed
systems, tanks, etc.)•

Faster and improved estimation of casualty
numbers throughout the campaign•

Information Management
FEASIBLE NEAR-FUTURE AI
CA PA B I L I T I E S

10. A NA LYSE DATA COL L ECTE D
THROUGH ISR ACROSS L AN D, A IR,
NAVA L , S PACE AND CYB ER DOMA INS

9. C L E A N , F I LT E R , A N D F U S EDATA C O L L E CT E D

CURRE NT A I CA PA BI LI T I E S•

Fusion of 2D data, such as images, to create 3Dmodels•Synthesis of large quantities of data, such asimagery or video•

Geolocation of images••

Identification and classification of data, such as
targets•

Pattern recognition within raw visual, written,
audio, and other data to help identify specificincidents or hostile activity (e.g., via detection of
anomalies in patterns, deepfakes, etc.)

Fusion of data across the full electromagnetic spectrum (i.e., visible, ultraviolet, infrared, microwaves, radio waves etc.) to create a ‘common
operating picture’, including a (partial) fusion ofcross-domain and all-source intelligence•

Detection of deviations in the ‘common operating
picture’ from the intended plan•

Enhancement of early-warning capabilities withregard to developments or operations in the field•

Analysis of fused data, across domains ofoperation and across the different spectrums•

Analysis of a wide range of fused information (e.g., social media/open source information,
military intelligence, data from across the electromagnetic spectrum)•

Analysis of data across the full electromagnetic spectrum (i.e., visible, ultraviolet, infrared, microwaves, radio waves etc.) in specific domainsof operation and specific individual spectrums toidentify elements of interest•

Analysis of large quantities of data, such asimagery or video•

Analysis in real time of sensor data in order todirect systems towards a specific location or in
response to a movement, sound, visual, etc. in a
specific domain (i.e., sensor allocation)

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17

11. ANALYSE OWN,FRIE NDLY
AND ADVE RSARYCAPAB IL ITIE S
1 2 . A N A LYSE T HE
E N VIRO N ME N T
13. SY N T H E S I S E T H E K E Y P O I N T S
E ME RG I N G F RO M T H E A N A LYS I S
O F T H E DATA C O L L E CT E D

Identification of individuals and objects (e.g.,
military equipment, amount, location, etc.) via thefull electromagnetic spectrum (i.e., visible, ultraviolet, infrared, microwaves, radio waves, etc.)•

Simulations and extrapolations of future
outcomes•

Prediction of how adversary capabilities may
develop•

Identification of communication means, utilisingthese for pattern recognition and pattern extrapolation•

Simulation and/or prediction of outcomes in engagement between friendly and adversary forces•

Monitoring of combat capabilities in real time•

Enhanced prediction of behaviour based on an
analysis of behavioural patterns•

Analysis of civilian traffic flows (land, air,maritime)•

Analysis of no-go (restricted) zones including theincorporation of live data•

Planning of the most appropriate route forces
can take•

Analysis of terrain traversability, to identify any
obstacles (e.g., environmental) or threats•

Identification of individuals and objects (e.g.,
improvised explosive devices, snipers, etc.) via
image recognition and/or analysis of the full electromagnetic spectrum•

Analysis of the interconnections between objects
in a system (e.g., interconnections between infrastructure)••

Analysis of cyber space with regards to information, disinformation tactics, etc.

Improved real-time map building of disaster andconflict zones••

Real-time map building of conflict or disaster-stricken areas

Modelling of the interior of specific buildings(e.g., rooms, hallways, etc.)•

Retrieval systems that can reference searchengines or databases••

Generation of automatic summaries of key information

Generation of more advanced and precise
automatic summaries of key information (e.g.,summaries and key point generation can be
tailored based on the audience or reader)•

Automated analysis of data to extract key information20•(Partial) fusion of multi-domain and all-source
intelligence•

20 There was uncertainty noted with this capability, due to the recent advances in large language models (LLMs), with someexperts leaning towards the fact that this is a current capability, and others who believe it is a near-future capability.

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14 . DISSE MINATIO N O F
INFO RMATIO N ACRO SS
T HE CHAIN O F C2
1 5. MA N AG E INFO RMAT IO N
A N D C O MMUNICAT IO N
SE CURITY•

Filtering of (non-relevant) information•

Generation of automatic reports21•

Aggregation of information•

Automated distribution of messages to relevant
individuals22•

Integration of AI into cyber operations to improvecybersecurity and better counter cyberthreats(e.g., via AI-powered anomaly detection)23•

Advanced biometrics•

Building of fault trees to find weak points insystems

Detection of intruders based on anomalous
behaviour•

Support for information classification (e.g., bytext and content analysis)•

Verification of information veracity and data
provenance•

Logistics
FEASIBLE NEAR-FUTURE AI
CA PA B I L I T I E S

16. LOGI STICA L S U P P O RT
OF DE PLOY M E N T

CURRE NT A I CA PA BI LI T I E S•

Analysis of the best paths and transport modes
for a given mission and environmental and traffic
conditions•

Provision of recommendations on the best paths
and transport modes for a given mission and environmental and traffic conditions•

Organisation of staff rotation (i.e., scheduling)•

Moderation and overview of optimal or desiredenergy utilisation•

Assistance with automated planning and scheduling of personnel and logistics•

Identification of equipment maintenance needs
via image recognition (e.g., cracks in propellors or
blades)•Assessment of equipment needs and provisionof acquisition recommendations•(Improved) prediction of equipment resupply
needs based on patterns of use and stockpile
management data•

Optimisation of logistical supply chains (e.g.,
faster or more efficient routes, early warning when
supplies low, etc.)

21 There was uncertainty noted with this capability, due to the recent advances in LLMs, with some experts leaning towards
the fact that this is a current capability, and others who believe it is a near-future capability.
22 There was an uncertainty noted with this capability, due to the recent advances in LLMs, with some experts leaning towards
the fact that this is a current capability, and others who believe it is a near-future capability.
23 There was an uncertainty noted with this capability, with most experts leaning towards the fact that this is a current capability, and others who believe it is a near-future capability.

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17. ASSE SS O P E RATIO NAL
E FFE CT IVE NE SS O F P E O P L E
AND E Q UIP ME NT•

Evaluation and early warning regarding thephysical and medical readiness of military
personnel•

Predictive maintenance needs on deployedequipment based on mission progress andconditions•

Basic triage of injured or unwell personnel••

Provision of recommendations on how to
improve personnel health, resilience, andreadiness(Improved) prediction of resupply needs (e.g.,ammunition, spare parts) through a connected anddigital logistics chain•

Predictive maintenance needs on equipment

Training
FEASIBLE NEAR-FUTURE AI
CA PA B I L I T I E S

CURRE NT A I CA PA BI LI T I E S

18 . U N D E RTA K E T R A I N I N G A N D S I M U L AT IO N•

Design of virtual simulations or real-life
exercises, with AI opponents of different difficulty
to match and challenge the trainees’ level of competence•

Estimation of losses and outcomes fromoperation simulations•

Tracking of individuals’ learning progress andoffering of tailored learning activities based onprogress•

Generation of synthetic data for simulation
planning and development of simulated actions
plans

•

Automatic detection and mapping of significant
learning situations during operations, in supportof after-action reviews•

Assistance in simulation as a tactical co-pilot oras synthetic intelligent agents to be coordinated with•

(Improved) modelling and simulation predictionsof outcomes and adversary decision-making•

Modelling of best patrolling routes and behaviour•

Design and conduct of AI-generated wargames
and red teaming24•

Support to the generation of mission tactics

24 There was an uncertainty noted with this capability, with some participants leaning towards the fact that this is a current capability, and others who believe it is a near-future capability.

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20

2.2.1 Mapping•

identify, process, make inferences and/or

As demonstrated in Section 2.2, AI application

generate information in written and spoken

areas are numerous. So as to best understandthis wealth of data and ensure comparability
between military tasks, each AI capability wascommunications.
•

Commission’s Joint Research Centre (JRC).25
This taxonomy, where AI categories are

Planning: the design and execution ofstrategies, e.g., an organised set of actions,mapped against a set of AI categories, derived
from a taxonomy devised by the European

Communication: the ability of a system toto achieve a goal.
•

Learning: the ability of a system to automatically learn, decide, predict, adapt, andreact to changes, improving from experi-

shown agnostic of users and area of appli-

ence, without being explicitly programmed.

cation, focuses specifically on the capabili-

To undertake this mapping, up to two AI cate-ties AI offers. The AI categories from the JRC
taxonomy are reasoning, planning, learning,communication, perception, integration and interaction, services, and ethics and philosophy.
The categories selected and the wording of thecategory descriptions have been adapted tothe context of this report and to ensure consistency of terms used within this report, but
broadly reflect the content and language usedin the JRC taxonomy. The adapted AI categories of the JRC taxonomy used for this analysis

gories were assigned to each AI capability.26
The results from each military task were thenaggregated at the functional area level, and theinstances of AI categories per functional area
expressed in percentages, in order to create
a visual summary of this data. The mapping
below thus provides both a visual overview
on current AI capabilities (figure 2) as well as
feasible near-future capabilities (figure 3),grouped by their broader functional areas.

are provided below:•

Reasoning: the transformation of data into
knowledge, or the inference of facts from
data.

•

Perception: the ability by a system to map
elements in the environment it operates inthrough image, audio, and other sensors.

25 Sofia Samoili et al., AI Watch Defining Artificial Intelligence: Towards an Operational Definition and Taxonomy of Artificial
Intelligence (Luxembourg: European Union, 2020), 11, https://publications.jrc.ec.europa.eu/repository/handle/JRC118163.
It should also be noted that while the JRC taxonomy is used here, a number of other taxonomies exist, for example:
National Security Commission on Artificial Intelligence, Final Report, 2021, 33, https://assets.foleon.com/eu-west-2/uploads-7e3kk3/48187/nscai_full_report_digital.04d6b124173c.pdf. This exercise could therefore be repeated for eachexisting taxonomy and different mappings produced according to the different ways in which AI capabilities are broken down.
26 For example, the AI capability ‘analysis of terrain traversability’ was assigned two AI categories: ‘perception’ and‘reasoning’. This analysis was undertaken twice by the same person, compared, and where necessary adjusted, to ensure thatthe categorisation followed the same logic across all AI capabilities.

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Figure 2. Domains of AI applications mapped against the functional areas—current
capabilities

COMMAND
AND CONTROLI N F O R M AT I O N

REASONING

LEARNING

MANAGEMENTPLANNING

LOGISTICS

TRAINING

PERCEPTIONC O M M U N I C AT I O N

Figure 3. Domains of AI applications mapped against the functional areas—near-future capabilities

COMMAND
AND CONTROLI N F O R M AT I O N

REASONING

LEARNING

MANAGEMENTPLANNING
LOGISTICS
TRAINING

PERCEPTIONC O M M U N I C AT I O N

Note: The domains of AI applications as drawn from the JRC’s taxonomy are shown to the right of each diagram. The functional areas, which include each individual current and near-future AI capabilities from Section 2.2, are shown on the left ofeach diagram.

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2.2.2 Discussion on the mappingof current and near-future AI capabilities to military tasks

planning, and simulations.

The mapping of AI capabilities against each

The first trend pertains to near-future AI capa-of the military tasks, as well as against thedomains of AI applications, demonstrates
some interesting facets in terms of how AI for
military purposes is anticipated to evolve into
the near future. Notably, a wide range of AI capabilities are currently technically feasible.
However, this should nonetheless be caveatedby noting that the (future) existence of thesecapabilities does not necessarily imply ormean that they will be integrated into specificmilitary tasks and military operations more
widely. Indeed, there can be a number of difficulties regarding their implementation that may
mean that, although a capability may exist, andis even developed and tested, it may ultimately
not be introduced into operational use. Additionally, not all near-future capabilities outlined
in Section 2.2 will develop and mature at thesame pace and with the same timeline; some
may become technologically mature at a fasterpace than others. For example, the recent
rapid advances in LLMs and generative AI
means that related AI capabilities may mature
more quickly.
This point notwithstanding, the data showsthat a focus on AI capabilities centred around‘reasoning’ (i.e., “the transformation of data
into knowledge, or the inference of facts
from data”) and ‘learning’ (i.e., “the abilityof a system to automatically learn, decide,predict, adapt and react to changes, improvingfrom experience, without being explicitly programmed”) may be expected in the near future.
This aligns with the examination of specificnear-future AI capabilities, where a lot ofemphasis is placed on data fusion, analysis(including predictions and extrapolations),

Looking more closely at these near-future AI
capabilities, two main trends can be identified.
bilities which are an enhancement of existing
capabilities. This notably includes being able
to work with more data and data types, andhaving better reasoning capabilities—in effect,enhancing and improving upon what currently
exists. While the AI enhancements foreseen
within this first trend are more evolutionary
than revolutionary, this does not mean thatthey require less effort in their adoption by militaries in terms of procurement policies, testing,training requirements and beyond.
The second trend regards the development of‘new’ near-future AI capabilities, in other words,
capabilities which are novel and go beyond anenhancement of existing capabilities. Specifically, the data demonstrates that ground-breaking revolutions in AI for military purposes
are not expected in the near term, at least as
regards these specific military tasks, particularly as there are fewer such capabilities falling inthis particular category. The recent advancesof generative AI, which includes LLMs, may
however play an important role, particularly as
it develops further, matures, and becomes incorporated with predictive AI capabilities.
The final point pertains to the increased use of
AI to aid with data processing and synthesis,
as well as the dependency of AI on data forits development and training. This demonstrates that issues of data integrity, quality, andveracity are going to remain key going forward.
It also raises the question as to whether
enough attention is being paid on theseissues versus the AI capabilities themselves.
Indeed, issues pertaining to data, as well as
the broader enabling technology and infrastructure necessary to enable and achieve the

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23

near-future AI capabilities, bring into question
the potential operationalisation of AI capabilities.27 Beyond data, this includes, for example,
advancements in sensors, computing power,
and other electronic devices,28 but also inensuring that all data collection and data processing systems are connected and interoperable with one another—as a fragmented
system would hamper (an efficient) use of AI
as foreseen in Section 2.2.29

27 For further discussion on this, see for example Kyle Miller and Andrew Lohn, “Onboard AI: Constraints and Limitations”,
Center for Security and Emerging Technology, August 2023, https://doi.org/10.51593/2022CA008.
28 See for example Arslan Munir and Joonho Kong, “Artificial Intelligence and Data Fusion at the Edge”, IEEE Aerospace andElectronic Systems Magazine, 36, no. 7 (2021): 62–78.
29

Interview with an anonymous expert (14/09/2022).

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3. Impact of AI in the Execution of
Military Tasks
Alongside the mapping of AI

planning accuracy, situational awareness,

capabilities to different military tasks is themore informed decision-making, and casualty

need to understand what the impact is, or

reduction.30 Increased ability to process large

could be, of utilising AI to aid with these tasks.

amounts of data could improve accuracy of

Discussions with experts demonstrated thatassessments (as more data is available tothe impact of AI was not necessarily linked tomake these) and could reduce unintended

the type of task or broader functional area, butconsequences. This type of capability could

more with regard to the enhancement of AI

also be used to support and feed into early

more generally. Indeed, most of the impacts

warning mechanisms for instances of conflicts,

identified did not differ much or at all between

in addition to assisting during a military

functional areas—which makes sense as all

operation.

functional areas are meant to occur in tandem
during a military operation.
As such, this chapter first presents key
elements emerging from expert discussions
regarding the strength and opportunities that
do or may emerge from the integration of AI
capabilities into military tasks, before turning
to the limitations and challenges. This chapter
then concludes with a brief assessment ofthese strength and limitations.

3.1 Strengths andopportunities emerging
from the integration of
AI capabilities‘Big data’ analysis
The ability of AI to improve data sorting anddata analysis mechanisms could assist

30

Speed of analysis and communication
With more data to be sorted and classified due
to increased use of sensors collecting various
types of data, the use of AI can lead to a faster
assessment of such data than by humanmeans, which could help increase the pace ofanalysis and thus decision-making. This has
notably been raised with regards to C2, as theability for AI to improve the use of information
and assess large amounts of data could play arole in reducing uncertainty (such as regards,
for example, terrain analysis). It should also
be noted that increasing the speed of decision-making in upstream tasks may also have aknock-on effect and impact downstream tasks,
including those relating to use of force. For
example, more granular, multi-domain analysisof data and courses of action can enable or
speed up subsequent decisions regarding
target selection and target engagement. AI canalso enable increased speed of communication along the chain of command, through its

Sayler, Artificial Intelligence and National Security.

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25

abilities to synthesise and share information.

tear, preventative reconnaissance as well as

This is also linked to improvement of knowl-

personnel health (see task 17—Assess opera-

edge-sharing between different entities, suchtional effectiveness of people and equipment).

as different units or even military branches, toAdditionally, through big data, AI could help

ensure that decision-making entities have aassess linkages of digital and physical assets

more rounded understanding of the situation,that must be protected or could be compro-

as well as to help with collaboration.

mised. Identification of areas of weakness canthus serve as an (early) warning mechanism to

Planning
AI can be employed to help with or comple-

take action.

ment strategic-level planning and the devel-

Personnel efficiency

opment of (better) tactics, such as throughExperts argued that the use of AI could entail athe development and use of scenarios

reduction in the costs of logistics, due to fewer

and other gaming or red teaming capabili-

human errors. At the same time, experts noted

ties. AI could additionally help detect devia-that less human labour would be needed fortions between the current situation and thecertain tasks, enabling task optimisation forplan and help calculate and present alterna-

personnel away from ancillary tasks. The role

tive solutions. The use of deep learning algo-of AI in providing support in tactical planning,rithms, for example, can extrapolate beyond

as noted above, could also enable smaller staff

the existing data and thus provide or informat the battalion and brigade levels.

unforeseen scenarios. Beyond training simulations, AI could also help identify an (or several)
optimum plan when given a specific scenario,or provide a set of alternatives for action,
thus aiding or complementing contingency
planning.
Safety and security
The role AI can play with regard to improving
cybersecurity and overcoming cyberthreatsis one which transcends the military domain
and is already much discussed. Discussions
among experts noted that AI could also offer
more control over the data, as the applicationof AI to cybersecurity could reduce the risk ofdata poisoning or interference. This, in turn,
may lead to more confidence in both cyber anddata security. Beyond the virtual sphere, AI
could play a role in augmenting physical safety
and security to a greater extent than would bepossible through human or non-AI augmented
means, such as by its support to training andsimulation, its analysis of equipment wear and

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26

3.2 Limitations andchallenges emerging
from the integration of
AI capabilities
For each strength and opportunity put forward
regarding the use of AI in upstream tasks,
however, a number of limitations and challenges emerge as well. The issues raised fall inthree main categories: externalities that affectthe proper use of AI; concern about AI in andof itself; and negative impacts resulting fromthe use of AI, with the majority of the issues
centred around this third broad category. Manyof these reflections are discussed in existing
literature regarding the application of AI toweapon systems and specifically the useof lethal force. This demonstrates that such
issues therefore have a broader remit, but alsothat these remain extant concerns with regard
to the application of AI more generally, and particularly so regarding AI for military purposes.
Lack of (good quality) data
Data issues can hamper the efficiency andaccuracy of AI algorithms from training all theway to use in operational contexts. In such
instances, the issue goes beyond AI itself, andpoints to a system-wide challenge where useof AI is hampered through the lack of broader
structures. This includes, but is not limited to,limited training datasets, which can impact theability of AI algorithms to operate in real-world
settings; a lack of coherence or fragmented

as via sensors, are too few; poor data management systems whereby data-sharing ishampered or there is poor data hygiene. Issues
may also emerge if data collection methods are
not able to keep up with fast-paced changes
happening on the ground. In cases such asthese, new information can be missed or bedelayed, resulting in wrong assessments or incomplete situational understandings. Finally,
the use of AI to help with scenarios and simulations such as wargaming could run the riskof mirroring, whereby the AI system is only
capable of repeating known information but
cannot provide new insights.
‘Black box’ decision-making
The issue of the lack of transparency of AI decision-making is oft discussed, albeit mainlywith regard to autonomy in weapon systems.31
Yet this issue was also brought up in discussions focused on non-lethal AI capabilities.
Indeed, a lack of understanding of how an AI
has come to its conclusions and how it has
decided whether or not something is of importance also has a large impact when discussing
the application of AI to aid with the identification of targets, CDE calculations, or even undertaking preventative maintenance of militaryequipment. Even when weapons are not a
direct part of the equation, the inability of AI
to explain how it achieved its results can haveunintended consequences, and even more sowhen upstream tasks impact on the decision to
use weapons or weapon-bearing equipment.
As noted by experts, the ‘black box’ issue canalso make it harder to contest decisions madedata, because data capture methods, such

31 See for example Svenmarck et al., Possibilities and Challenges for Artificial Intelligence in Military Applications; YuLong
Zhang et al., “Application of Artificial Intelligence in Military: From Projects View”, 2020 6th International Conference on Big
Data and Information Analytics (BigDIA), Shenzhen, China, 2020: 113–116.

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27

by AI which cannot explain itself—leading al-

reason as humans do—but neither do humans

ternatively to either distrust or overreliance.

reason like AI, given we can draw upon32

long-term experience and are also impacted
Meaningful human control

by emotional triggers.33 Data thus may not

The issue of human control is pervasive

have the same meaning for humans and AI,

across all tasks, even those deemed to havea ‘low-risk’ impact. Indeed, even if the issue
is not deciding on a target or whether to pull a
trigger, questions remain around the extent towhich analysis, the conception of pathways foraction, or tactics could and should be done by
AI alone—and therefore if and when to include
meaningful human control. More broadly, there
is a question around how an assessment of
whether there needs to be meaningful humancontrol for specific tasks is undertaken. In
other words, for which tasks are there greater
ethical, legal, or other imperatives for humancontrol, and for which tasks may this be seen
as hampering the efficacy of AI? Indeed, allmilitary tasks are important and pertinent to
the process or a military operation as a whole;
but the question remains as to which may beseen as being more acceptable of errors fromas regards its content or importance, with theanalysis therefore not being the same—oreven erroneous, which can undermine trustworthiness. This is exemplified by the issue of‘hallucinations’, which is particularly relevant
to LLMs. Hallucinations refer to LLMs inventing
facts due to misinterpreting the data, making
incorrect associations between different data,
or using poisoned data, the latter of which is
discussed in more detail below.34 Additionally, unpredictability of human behaviour may
not be factored in by AI, particularly in regard
to simulation and early warning mechanisms
which are based on assumptions and calculations of possibilities of human actions under
uncertainty in partially observed, high-dimensional, real-world scenarios intractable tomachine modelling.

AI than others—if any. Overall, discussions

AI as a vulnerability

regarding meaningful human control may be

While AI can help improve cybersecurity, as

just as relevant to upstream tasks as to down-

noted in Section 3.1, it can also be a vulnera-

stream ones.

bility. In particular, AI can become the victim
of manipulation through adversarial attacks;

Reasoning capabilities

it could also be fed (un)intentionally false or

Linked to the point on the ‘black box’ nature ofeven poisoned data; it could also be subverted

AI decision-making is the fact that AI does notby hackers. Use of AI can also augment

32 It should however be noted that there are efforts to make AI decision-making less opaque, and research is ongoing in thisarea; see for example Angie Boggust et al., “Shared Interest: Measuring Human-AI Alignment to Identify Recurring Patterns inModel Behavior”, In CHI Conference on Human Factors in Computing Systems (CHI ‘22), April 29-May 5, 2022, New Orleans,
LA, USA.
33 Taylor Webb, Keith J. Holyoak and Hongjing Lu, “Emergent Analogical Reasoning in Large Language Models”, Nat Hum
Behav (2023), https://doi.org/10.1038/s41562-023-01659-w.
34 Since the release of ChatGPT, a number of hallucination examples have emerged; see for example Tiffany Hsu, “What
Can You Do When A.I. Lies About You?”, New York Times, August 7, 2023, https://www.nytimes.com/2023/08/03/business/media/ai-defamation-lies-accuracy.html.

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opportunities for adversaries to exploit weak-fast-moving operations.

nesses. The impact of such vulnerabili35

ty may not be immediate, and its effects canSkills degradation

vary. Feeding false information or analyses can

Linked to the point above, another facet

range from impacting the efficiency of a military
operation—such as by slowing down logisticaloperations or affect analysis upon which lifeor-death decisions are made.
Excessive relianceof AI use is the potential impact on humanknowledge and skills. It has, for example, been
noted that automation in aircraft piloting canlead to a degradation, if not loss, of skill;36 thesame argument has been made regarding
the use of AI in the military context.37 Notably,

Overreliance on AI could lead to only consid-

experts mentioned a potential for paucity

ering or gathering data to feed AI systems,of planning capability and skills amongor thinking only of inputs in terms of what is

personnel, if such tasks become primarily theneeded by an AI. This can therefore skew orremit of AI. In this context, it should be high-bias data collection efforts—with an impact

lighted that AI is not only dependent on dataon the outputs. Excessive reliance on AI may

to function, but also electricity and computing

also lead to ignoring or disregarding possible

power; in the loss of this, military operations

shortfalls of the technology; for example, over-may be weakened simply due to personnel

estimating what AI can achieve, and seeking

no longer being accustomed or even trained

to use it for issues beyond its capacity, or not

to undertake certain tasks where AI use has

vetting adequately the data that is fed to the

become more significant. However, it remains

system. Overreliance could also lead to a loss

at this stage difficult to understand howof context and nuance due to over-synthesis

humans may react to the application of AI ca-of information by AI, where culture, context,

pabilities to different military tasks. Indeed,emotions, and unwritten social norms may notsuch reactions may not be homogenous,

be adequately recognised. Incorporating AI

differing depending on individual character-

into certain activities may also lead to changes

istics such as age or background, how AI isin how task allocation is considered. Specifi-

embedded in operations, or the overarching

cally, experts noted that use of AI may bring inmilitary culture.

more rigid thinking or boundaries around whatmachines can do over humans, or in terms

Impact to operational tempo

of respective strengths. This could in turnThis is another argument which has been

affect flexibility and agility, particularly during

35

Sayler, Artificial Intelligence and National Security.

36 See for example Andreas Haslbeck and Hans-Juergen Hoermann, “Flying the Needles: Flight Deck Automation Erodes Fine-Motor Flying Skills Among Airline Pilots”, Human Factors, 58, no. 4 (2016): 533–545, https://doi.
org/10.1177/0018720816640394; and Stephen M. Casner at al., “The Retention of Manual Flying Skills in the Automated
Cockpit”, Human Factors, 56, no. 8 (2014): 1506–1516, https://doi.org/10.1177/0018720814535628.
37 Most notably within France’s Ministry of the Armed Forces’ AI Task Force report: Ministry of the Armed Forces, Artificial
Intelligence in Support of Defence, 2019, https://www.defense.gouv.fr/sites/default/files/aid/Report%20of%20the%20
AI%20Task%20Force%20September%202019.pdf.

ARTIFICIAL INTELLIGENCE BEYOND WEAPONS

29

raised with regard to the use of AI in military

Unknown impact

operations more generally, with experts con-

While we can theorise over the challenges

38

tributing to this study also mentioning thispoint with regard to the integration of AI capabilities to tasks not directly related to weaponsuse. In a similar vein to the existing argument,
there is a fear that utilizing AI would generate afaster operational tempo, eventually exceeding
human ability to keep up with events. However,experts also noted that this may depend on
the context; for example, situations of deliberate targeting versus battlefield dynamics
are of different scope and complexity, thereby
demonstrating that the impact of AI is not necessarily ‘one size fits all’.
(Lack of) harmonisation
Beyond the elements outlined above, there arealso challenges raised with regard to military
operations conducted jointly by different
actors.39 While joint operations currently needto ensure commonalities from doctrine toplanning and execution, the addition of AI will
also necessitate a common approach. Yet, theacceptability and use of AI may differ amongactors—in terms of application areas, dataneeds, algorithmic differences (which could
lead to different outputs), recommendations,analyses, and more. This begs the question as
to how these differences will be managed, andhow the use of AI would be harmonised—letalone accepted—depending on the appetite ofdifferent actors. Will it make it harder for actors
to work together? Or in such cases, will actors
have to forego the use of AI entirely, unless asolution is found?

AI may bring, as well as weaknesses its use
can expose, the fact is that the extent andreality of its current and future applications,and therefore impact, still remains unknown.
The use of AI may bring about ethical issues
or concerns previously unforeseen aroundthe reliability of mission planning. Additionally, the handover of control to AI, even for tasks
deemed ‘low risk’—such as data classification or summarisation—may result in unforeseen consequences, from whether and how
weapons are used, or the pace and scale of aconflict overall.

3.3 Assessment anddiscussion regarding
perceived strengths
and challenges
Interestingly, some of the reflections on the‘strengths’ of AI demonstrate that the expectations assigned to AI may not be realistic
in terms of the anticipated technologicalprogress but also the realities of AI integration into operations. For example, one of theperceived strengths of AI is that it can increase
the speed of communication along the chain ofcommand. But the few capabilities relevant forthat particular sub-task (14—Dissemination of
information across the chain of command andcontrol) would not be so advanced as to necessarily increase the speed of communication
across the entire chain of command. And while

38 See for example Scharre, “Military Applications of Artificial Intelligence”; Sayler, Artificial Intelligence and National
Security; Morgan et al., Military Applications of Artificial Intelligence.
39

Also discussed further in Lin-Greenberg, “Allies and Artificial Intelligence: Obstacles to Operations and Decision-Making”.

ARTIFICIAL INTELLIGENCE BEYOND WEAPONS

30

there is the anticipation that integration of AI

the rapidity of the operational tempo will still

would result in better synthesis or analysis of

be determined by humans and their roles indata, this does not take into account possible

the observe, orient, decide, act (OODA) loop.

limitations around the adoption of AI, both from

In other words, we still have the possibility ofa technical standpoint as well as from the per-controlling the tempo, as well as the use of AI.

spective of military technology approval andadoption.
Related to the above, the expectations

Further to this, other conundrums remain:•

would enable improved analysis, predic-

placed on AI are very high for certain tasks—

tions of behaviour, or evolutions of certain

and perhaps unrealistic, at least for the

scenarios are also the areas where the lacknear-term future. While AI does, in someof transparency and explainability is theregards, offer capabilities that exceed those of

greatest concern. Therefore, questions

humans, there are still barriers, notably aroundremain around what level of explainabili-

limitations of AI itself, or the amount of dataty and transparency will be deemed suffi-

available, both in terms of training but also incient to use AI and whether this will differ

practice. For example, there is an expectationthat AI will be more accurate in its analysis ofdata than human-led efforts in the area, or able

depending on the military task.
•

This also begs the question of how is a task
deemed ‘low risk’ versus ‘high risk’. Whatto develop superior tactics. However, beyond

related issues may this bring in terms of risk

assuming AI progresses past narrow AI, this is

acceptance simply because of where a task

also dependent on the quality and quantity of

may sit—in other words, further upstream

data that systems will have access to, and theor downstream—when each task is also

infrastructure readiness of equipment.
Overall, remaining aware of the limitations of

The more ‘advanced’ AI capabilities which

important in its own right?
•

Finally, some of the limitations demonstrat-

what AI can offer and not overpromising its ca-

ed by AI—such as vulnerability to poisoned

pabilities is important. While there have beenor false data—are inherent for humans too.

calls for caution around the anthropomorphism

How is the acceptable error rate for humansof AI, similarly we should be careful not toversus machines decided? While some of

mistakenly attribute omniscient abilities either,

these questions are discussed in debateson the basis that AI is better able to deal withregarding the use of AI in target selection(big) data compared to humans.

and target engagement, there is nonethe-

40

Additionally, inferences on the impact of AI
on the operational tempo is a recurrent one;however, unless humans are completely
removed from all these processes, ultimately

less merit in widening the discussion to awider set of military tasks, particularly given
the impact upstream tasks, such as thosefeatured in Section 2.2, have on downstream tasks.

40 See for example David Watson, “The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence”, Minds & Machines
29, 417–440 (2019). https://doi.org/10.1007/s11023-019-09506-6; Arleen Salles, Kathinka Evers and Michele Farisco, “Anthropomorphism in AI”, AJOB Neuroscience, 11, no. 2 (2020): 88–95, https://doi.org/10.1080/21507740.2020.1740350.

ARTIFICIAL INTELLIGENCE BEYOND WEAPONS

31

4. Conclusions
In conclusion, this report demonstrates that

This report also suggests that, at the national

there is a significant number of both current

level, it is important for States to understandand near-future AI capabilities that map

the broad range of possible applications ofagainst a wide range of upstream military

AI in the military domain, and take this range

tasks. Yet, despite the existence of such AIof applications into account when designing

capabilities, there have been fewer discus-

national defence AI strategies, policies, andsions at the multilateral level in regard to theguidelines. It would also make sense to takeapplication of AI to upstream tasks comparedthis broader approach when discussing AI

to downstream tasks, which notably include

for military purposes in multilateral forums, totarget selection and target engagement. This

include reflections on AI integration among

gap is particularly important given the recent

all military tasks. This is also of particular

rapid advances in generative AI, which are in-relevance with regard to discussions on AI

creasingly capable of seemingly cognitive

governance and responsible AI, which wouldroles. However, when examining the strengths

need to be conscious of not only focusing on

and limitations identified regarding the impact

AI applications to target selection and targetof AI on these upstream military tasks, we findengagement but also across all tasks relevantthat there are many similarities with the discus-

to a military operation.

sion of downstream tasks.
While AI applications in upstream militarytasks may not have the same repercussionsin terms of conflict escalation and loss of life
as downstream tasks, they would nonethelesshave an impact on the conduct of a military
operations and overall decision-making. This
however raises the question as to whether theoperationalisation of AI in upstream tasks may
not be, in some cases, less controversial for
decision makers than their inclusion in downstream tasks. This may be due to the inclusionof AI being less visible in upstream compared
to downstream tasks—which may be a factor
making the integration of AI in upstream tasks
more likely. Yet, uncertainty as to not only what
capabilities exist now and in the future but particularly whether and how they may be integrated and by whom can be a source of insecurity
and also instability.

ARTIFICIAL INTELLIGENCE BEYOND WEAPONS

32

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