Emerging Technologies that Will Enable the Next Digital Transformation Wave for Military Affairs

What are the Emerging Technologies, and Why the Military are Interested?

The military has evolved using emerging digital technologies in three waves (Kale, 2020): 

1. Digitization transferred content from analogue to digital format and improved military administration and office work.
2. Digitalisation introduced enterprise-wide systems, like Enterprise Resource Planning, which enabled human, financial, material, and facilities management or battle-space management systems for faster situational awareness.
3. Digital transformation has enabled revolutions in military affairs, such as Network-Centric Warfare in the US Department of Defense and network-enabled Capability in the UK Ministry of Defence.  

Current waves of transformation enabled by emerging technologies are revolutionising industry (The Fourth Industrial Revolution), commerce (digital biology), facilities (smart homes, cities, and government), and the military (Combat Cloud).

This paper creates an enterprise architecture view of possible digital infrastructure that military affairs may benefit from while planning their second wave of digital transformation for further capabilities. Meanwhile, lethality in battlespace increases, dual-use technology creates tactical advantages, weapon and counter-weapon development takes place in days, arms races raise prizes of armament, and additional defence finances are complicated to gain.

A Systematic Perspective to a Military C5ISTAR Technology Stack Enabled by Emerging Technologies

For a systematic assessment of emerging technologies' impact on Military Affairs, this study divides the technology stack into infrastructure, data, systems, and business models aligned with common enterprise architectures. In this approach, digital modelling or digital twins are the points of interest because they are virtual representations that allow the modelling of the state of a physical entity or system. They are created by digitalising data collected from physical entities through sensors, so various predictions can be made by understanding the behaviour of the physical entity.  Virtualising and digitising the physical world seems a beneficial feature for Military Affairs because it enables the military to :

1. Create digital models of physical phenomena, run accurate simulations, and gain foresight into possible future.
2. Improve the man-machine interface with more immersive ways to interact with machines.
3. Maintain the faster OODA loop at the tactical level with less delayed data transfer, optimised computing, and algorithm-accelerated sense-making.
4. Bring machine interoperability from recognising the data to sharing the understanding.

The following gives a more detailed view of possible military C5ISTAR technology stack changes.

Infrastructure Layer (networking, transfer and processing)

In this case, the infrastructure layer includes networking, data transfer and processing functions, as illustrated in Figure 1. The wireless 5/6G evolution improves the access network from the edge to terminal capacity and connectivity and lowers the latency if cellular base stations are connected via a high-bandwidth terrestrial network. Non-terrestrial, air- and spaceborne base stations are available, improving accessibility and simplifying the integration. The terrestrial and non-terrestrial 5G base stations compose a three-point access network with standard transfer and networking functions.  This multi-domain connectivity will replace legacy tactical data links while improving the availability of access and roaming and extending the range over the horizon, features essential in the Joint All-Domain C2 (JADC2) concept promoted by David Deptula. 

Furthermore, with higher frequencies, the cell sizes are smaller, and the Effective Radiated Power (ERP) is less, which means that transceivers' low probability of detection and identification (LPI/LPD) improves. However, with lower frequencies, higher transceiver density, and smaller radiation patterns, deploying dual-use Radio Frequency Identification (RFID), the Internet of Things (IoT), and Operational Technology on the battlefield becomes feasible. 

 With 5/6 G enhanced wireless communications, the access network becomes more versatile than the legacy Local Area Network (LAN) topology. For example, command posts can be distributed across a wider area without losing seamless collaboration connectivity. Platforms become cell base stations, providing access points to Mobile Adhoc Networks (MANET) within and between platoons, squadrons, teams, and higher organisations. Expendable, swarming sensors and effectors can be connected to a larger tactical unit even in an electromagnetically contested environment. 

Furthermore, the new Open Radio Access Network (ORAN)  and all-encompassing Internet Protocol (IP) solve the current technical-level interoperability issues. They allow you to create virtual, sliced, or private military network domains parallel to other network users without creating congestion points or bottlenecks. 

The flexible network and transport layers support data flows that enable hybrid clouds and hybrid computing, which varies between different clouds, edges, and endpoints. Hybrid computing provides optimal data processing for a task, addressing anything between real-time, big data, or algorithm-crunching requirements.  

Data Layer

In Figure 1, the data layer is on top of the infrastructure layer. Enabling technologies may include Data flows with different Quality of Service (QoS), Data warehouses, Data Lakes, Lakehouses, Table formats, Business Intelligence, and Synthetic data.

These technologies may be implemented in three main categories of data architectures: Stove-piped, centralised, or data mesh. Stove-piped data architecture is a direct continuum from system-based data architecture. It enables the legacy of functional data owners who use proprietary data models and do not share data unless forced. Centralised data architecture breaks the stove-pipe boundaries and brings data to data warehouses, lakes or Lakehouses. A centralised approach establishes central data functions and provides development and Data as a Service (DaaS) to functions and Forces. However, the central entity may become an administrative bottleneck, isolating data from Forces. Conversely, data mesh prioritises domain-driven design while enabling the teams closest to big data sets to take control of meeting their data preparation and analytics needs. Data mesh enables the democratisation of data so that it’s available to everyone in an enterprise, regardless of their technical expertise, function, or organisation. Each Command of sense and decision-making becomes a citizen data scientist, an officer who can analyse data but doesn’t take on that task as their primary role.  Gartner recognises this with the estimation that by 2027, organisations faced with AI and data security requirements will standardise on policy-based access controls to unlock the value from more than 70% of their data.

Data Flow follows uplinks, and downlinks may become bottlenecks if flow management is not prioritised. Since the transfer layer enables Quality of Service prioritisation, military affairs may arrange vertical and horizontal data flows to provide real-time awareness and longitudinal big data for modelling and forecasting.

Data Warehouses are central data repositories integrated from disparate sources, namely operational systems. They enable straightforward business intelligence queries because the data is aligned, cleansed, and structured. 

A Data Lake is a system or repository of data stored in its natural/raw format. The repository may be a single data store but includes raw copies of source system data, sensor data, and social data in structured, semi-structured, or unstructured formats. Data from a data lake may be used for reporting, visualisation, advanced analytics, and teaching machine learning. 

A Data Lakehouse combines the flexibility of data lakes for working with raw and often unstructured or semistructured data with the reliability and performance of traditional data warehouses that store consolidated sets of structured data.  

A Data fabric is a data management design concept for attaining flexible, reusable and augmented data pipelines and services supporting various operational and analytics use cases. Data fabrics support a combination of different data integration styles and utilise active metadata, knowledge graphs, semantics and machine learning to augment data integration design and delivery.  

The Data Table Formats provide cross-platform compatibility, transaction support, and schema evolution. Developing the Data Lakehouse ecosystem requires open table formats like Apache Iceberg, Delta Lake, and Apache Hudi. Enabling schema evolution is essential for managing data structures over time while maintaining data integrity and backward compatibility. Data Schema management improves interoperability at the upper layers and facilitates establishing a smart machine system of systems.

Synthetic data is created by taking a database, creating an ML model for it, and generating a second set of data. The generated synthetic data has the same patterns and properties as actual data, but it’s not tied to any actual data identifiers. Synthetic data is generated fast, automatically tagged, and provides high-quality data regarding events that rarely happen in the real world, which is very applicable in military affairs. 

Available data in both arranged and raw formats enable a variety of data analytics:

• Traditional analytics requires a team of IT analysts to comb through data, theorise potential insights, test those insights, and report on their findings.
• ML-based models can continuously monitor data, pick out anomalies, and alert the appropriate teams in real time without human input. 
• Business intelligence tools harness raw data to extract meaningful patterns and actionable insights.

Systems and Services Layer

The next layer enabled by the data layer is the systems and services layer in Figure 1. Emerging technologies opening new options for military affairs include human-machine interface (HMI), immersive technologies, spatial computing, metaverse, algorithms, energy-efficient computing, and classical and quantum computing.

The Human-Machine Interface will evolve using immersive-reality technologies based on the current industrial and office interfaces enabled by multitouch video technologies on tablets and smartphones. Human actors will experience real-time interactions in three-dimensional virtual worlds that eventually incorporate the physical world. The evolution runs from a fully computer-generated space in virtual reality (VR) to mixed reality (MR) and further towards augmented reality (AR), where computer-generated objects are superimposed on the real world.

Spatial computing maps indoor and outdoor physical spaces (including people and furniture). Then, the digital content is anchored within the physical world, enabling users to interact with it realistically. 

Furthermore, the metaverse interconnects digital spaces where users can interact, socialise, and create. Spatial computing ensures users' accurate positioning and synchronises their actions. The human-machine interface allows people to have lifelike personal and business experiences online.

Virtualisation and decentralisation of the processing layer enable the distribution of computing workloads across different sites, such as hyperscale remote data centres, regional centres, on-premises centres, and edge points. This ability to distribute workloads supports optimising latency, data transfer costs, adherence to data sovereignty regulations, autonomy over data, and security considerations.  Gartner recognises the trend as follows:

‘By 2025, Gartner predicts more than 50% of critical data will be created and processed outside the enterprise’s data centre and cloud.’ 

‘By 2027, approximately 5% of large enterprises will deploy a hyperscaler distributed cloud solution for edge computing workloads outside data centres.’

Edge computing involves processor-intensive, often repetitive, mission-critical data analytics within devices on the outer edge of a network. With supporting networking and data layers, edge computing enables more real-time intelligence and faster sense-making from tactical to operational levels. Furthermore, edge processing supports machine-to-machine cooperation within the Intranet of Military Things (IoMT)  sensors and actors.

The decentralising layer hosts a variety of algorithms, including AI, optimised to specific functions in support of the business layer. Gartner forecasts this in the business as follows:

‘By 2028, 50% of enterprise platforms will leverage specialised infrastructures to support AI infusion, a significant increase from less than 10% in 2023.’

Next-generation systems and services are developed with tools and technologies that enable modern code deployment pipelines and automated code generation, testing, refactoring, and translation. These can improve application quality and development processes.  The Gartner sees this emerging trend as follows:

‘By 2027, 80% of AI-generated SaaS applications will be up to 80% composite for efficiency of human-AI digital engineering.’ 

‘By 2026, 40% of development organisations will use the AI-based auto-remediation of unsecured code from application security testing (AST) vendors as a default, up from less than 5% in 2023.’

Digital Business Modelling Layer

The last layer enabled by the technology layers is the digital business layer in Figure 1. The next-generation technology layers enable features like digital twin, artificial intelligence-based image recognition, optimisation, expert functions, robotic process automation (RPA), AI agents, autonomic systems, synthetic media, ambient, invisible intelligence, polyfunctional robots, and data-driven military.

A digital twin is a virtual representation of an object or system designed to reflect a physical object accurately. It is built on big data, spans the object's lifecycle, is updated from real-time data, and uses simulation, machine learning, and reasoning to help make decisions. Military Affairs may benefit from digital twin features in the maintenance and repair of platforms, developing system of systems, capability life-cycle management, force generation, and strategic modelling. 

Applied AI technologies use models trained through machine learning to solve classification, prediction, and control problems, automate activities, add or augment capabilities and offerings, and improve decision-making.  These features may benefit military affairs, for example, in financial optimisation, personnel promotion, facilities management, supply chain management, and learning management.

Robotic Process Automation and AI Agents refer to a system or program capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilising available tools. Beyond natural language processing, AI agents can encompass various functionalities in military affairs, including decision-making within processes, problem-solving in real-time situations, interacting with external environments, and executing actions. The Gartner foresees emerging features in business as follows:

‘By 2027, GenAI tools will be used to explain legacy business applications and create appropriate replacements, reducing modernisation costs by 70%.’

‘By 2027, more than 40% of digital workplace operational activities will be performed using management tools enhanced by GenAI, dramatically reducing the labour required.’

‘By 2028, 60% of IT services will be powered by the trifecta of GenAI, hyper-automation and metaverse, radically changing the services buyer landscape.’

Data-driven military affairs may witness changes among supporting entities like Intelligence, Military Survey, Logistics, and Operation Centres that provide continuously improved data products to their supported entities. Secondly, the data-driven approach may change military supply chain management as products and support become more cyber-physical, and data outside the military will become more valuable assets with emerging commercial space and cyber operators. Thirdly, the military may be able to execute so-called ‘information-driven operations. The defence organisation should not only be capable of obtaining an authoritative information position (or information dominance), but it must also use information as a ‘weapon’, i.e. as a means or instrument of influence.  Fourthly, the quantitatively thinking commanders may be able to mitigate the analysis paralysis usual with current risk-avoiding sense-making supported with less machine-based analysis. 

Altogether, the digitalisation illustrated in Figure 1 supports the Fourth Industrial Revolution (4IR) and provides potential for Military Affairs to benefit from. The second wave of military digital transformation may create strategic advantages for the Operate, Generate, and Support functions. The UK Army’s digital transformation program, THEIA, has three headline outputs: out-compete the adversary, partner better and integrate with partners, and improve efficiency.   The US Army aims to improve and leverage innovative and transformative technologies: modernisation and readiness, optimised digital investments, and a technically savvy, operationally effective digital workforce.  NATO is talking about using these “emerging and disruptive technologies efficiently.” NATO could improve its operations with military, industry, and civilian partners in every warfighting domain, including sea, land, air, space, and cyber operations. 

Figure 1: An illustration of a possible technology stack on top of more efficient communications

A Temptation of AI in Military Affairs

Will the European Military miss the window of opportunity for the 4th generation industrial-based force generation?

Keywords: National Defence, Artificial Intelligence, Weaponization, Security Strategy

Introduction

The human ability to collect information, make sense of a situation, optimise action, and learn while executing has been challenged recently in games, simulators, diagnoses, and real-time analysis. How may this development reflect future tactical combat-level decision-making? Is the machine going to win the man in combat?

Based on recent AI progress, artificial cognitive abilities and skills are emergently dominant compared to human competencies. In theory, the military may access Artificial Intelligence, which could:

• Gain knowledge from a zero-knowledge starting point through gaming against itself and, within months, master a given battle scenario’s technical, tactical, and possibly operational level features for victory.
• Anticipate adversary moves ahead, create a picture of potential scenarios, and predict adversary manoeuvring in 3-D space within seconds in a fully digitalised battlefield.
• Make short-term decisions within 80 milliseconds and optimise decisions simultaneously at technical and tactical levels.
• Identify lessons from the events and gain 150 years of theoretical combat experience teaching itself overnight.

At the same time, the price has decreased, and the availability of components increased to build lethal autonomous weapons from commercial products. A “slaughterbot” that nearly killed the president of Venezuela in 2018 could be built by an experienced hobbyist for less than $1,000. States are not able to control the manufacturing of lethal weapons as it becomes easier to weaponize commercial cyber-physical products of the 4th generation of industrial manufacturing.

During the ongoing War against Ukraine, the Russian military is massing troops and firing where their operational art finds the best course of action. However, even in Russia, the live mass is consumed too fast concerning available expendable and willing human resources.

China’s People’s Liberation Army Strategic Support Force (PLASSF) aims to counter U.S. dominance asymmetrically in all five battle domains through intelligentised ”combat capabilities for joint operations based on the network information system and the ability to fight under multi-dimensional conditions.”

U.S. DoD all-volunteer force recruiting has been declining for the past 15 years, and no silver bullet has yet been found to mitigate the gradual loss of human potential and competency. Furthermore, the 2$ trillion annual budget is struggling to maintain the required fleets of armament.

With the emerging Russian threat, European militaries are struggling to build up their military capabilities while the cost of defence material is rising, recruiting cannot address the need for enlisted, and deadlines to achieve national defence goals are closing fast.

Will the temptation of AI overcome the ethical concerns and generals fill their order of battle from the cyber-physical actors and sensors of the fourth industrial revolution?

How the Use of Artificial Intelligence May Impact Military Confrontation?

Digitalization changes human endeavours from physical to social level, including military affairs:

• Information operations and cognitive warfare are ongoing and taking place mainly outside of the military attention
• The physical battlefield is more transparent due to the density of sensors deployed
• Asymmetrically used, remote-controlled weapon systems challenge 2/3 generation industrial platforms on the battlefield
• Cyber electromagnetic effects have proven effective against current generations of military system of systems
• The ability of defence industrial production becomes a key strategic asset in prolonged conflicts like in Ukraine
• 4th Industrial Revolution-based (4IR) information, data, and algorithm-driven military affairs promise major advantages for commanders.

The traditional near-peer analyses of a number of arms and men with Lancaster I and II laws of attrition between BLUE and RED Forces are not sufficient when the battlefield and opposing societies change in different ways, culture becomes either an enabler or obstacle for the military to adopt new capabilities and the national and coalition defence industry either can or not produce and maintain 4th industrial cyber-physical armament. The main components of a model assessing the impact of AI in the military system of systems are illustrated in Figure 1.

Figure 1: A Model for assessing the impact of Artificial Intelligence technologies in military confrontation

Strategic Pressure Builds Up Between the European Union and Russian Federation Confrontation

Strategic analyses between the European Union and the Russian Federation bring up differences in resources and opportunities. Table 1 compares the larger but diversified European society against the smaller but more coherent society of Russia. Both populations are growing older and smaller over time. European society is producing more and dependent on exported energy whereas Russian society is smaller and dependent on energy exports. Both sides have about the same number of active troops, but European troops are more digitized than Russian. Furthermore, Russia has a wider base to recruit reservists than Europe and, with higher resilience against casualties, can play longer confrontation games. Both societies are exporting arms. EU exports advanced 3rd industrial generation armament whereas Russia produces surplus in 2nd and lower 3rd generation armament.

Table 1: Strategic comparison between EU and Russia concerning resources

European Union	Russian Federation
Democratic decision-making between 27 nation-states	One autocratic state with 193 ethnic groups
Over 448 M people, speak 24 official languages and believe in a god 52 %	Over 147 M people, speak one official language and believe 60% of orthodox
With a median age of 44.5 and a fertility rate of 1.46 live births per woman, society is in a negative population change	With a median age of 40.3 and a fertility rate of 1.42 live births per woman, society is in a negative population change
Produces 16.6 % of the world GDP Consumes 59 billion GJ energy of which 3/5 is imported	9th largest economy with 54% coming from oil and gas exports
Military expenditure 1.6% of GDP	Military expenditure 5.9% of GDP
Active-duty troops 1.34 million	Active-duty troops of around 1 million
Not tested but probably more fragile concerning casualties	Tolerates over 1200 casualties/day and is resistant even over 500 000 casualties over 2 years
Nuclear capable (FRA) with high digitalization level of Forces	Nuclear capable but low digitalization level of Forces
Exports over 20% (FRA, GER, ITA) of arms in the world	Exports 11% of arms in the world
Produces more 3rd and 4th generation advanced armament	Produces more 2/3rd generation bulk armament

Based on the analysis, it seems that Putin’s regime has a window of opportunity in using the smaller but coherent population to support less advanced but higher volume armed forces to achieve his political goals after he failed to use information operations and the European energy dependency to manipulate democratic decision-making. 

Military Capabilities Comparison Reveals the Gap for AI Opportunities

After the strategic level analysis, the following Table 2 takes the research one step down to the military operational analysis of systems performance and capabilities. Table 2 illuminates the fact that the EU military forces are somewhat minor to the Russian operational performance as the Russians can use wider avenues of attack (physical, information, cognitive and social) for their joint operations and gain dominance in social and physical realms. Military scenarios wargame with Russian 2nd and 3rd generation troops storming over the European side borders using the “shock and awe” or the “blitzkrieg” art of manoeuvring, bypassing the few defending forces and speeding towards the capitals, seizing them, and freezing the conflict as experienced in the 2014 invasion of Ukraine. 

Table 2: Operational-level systems analysis of the EU and Russian military capabilities

European Union	Russian Federation
Reactive rather than proactive political decision-making with slower implementation	Faster decision-making and implementation top-down through the regime
Open media and social media for foreign manipulation	Ability to wage information operations and cognitive warfare while protecting society from foreign manipulation
Advanced digitalization, data, and information but lacking knowledge creation	Ability to disable or suppress advanced technology on the battlefield (by jamming GPS, radars, sensors, and targeting emitters)
Few advanced 3rd generation industrial weapon systems lacking interoperability	Ability to manufacture higher volumes of 2/3 generation armament
Incohesive and non-interoperative forces with little or no combat experience	Ability to train simple, repetitive skills for technical military performance
	More advanced operational art with 3rd generation forces
Fragile societies in hardship and casualties	Ability to tolerate more casualties and societal hardships
Defence industry is not able to sustain or reproduce 2/3rd generation armament in masses	Ability to transfer society to support 2nd and 3rd generation Armed Forces power projection for a longer time

Because of the real or perceived underminer status of the EU military decision-makers, there is a temptation to invest in:

• more automated force (decreasing the probability of human casualties) against conventional fighters, 
• precision targeting payloads (preventing collateral losses when fighting in densely populated areas) versus area bombardment 
• faster identifying and recognising adversary manoeuvring on the battlefield (to use sparse blue forces more optimally)
• countering the dominant operational art of the red force (faster analyses of the available lines of operation and selecting effective courses of action)
• sustain advanced 3rd generation armament in taxing environment to improve capability availability (digital twins to pre-emptive maintenance)
• manufacture 4th industrial dual-use cyber-physical products in sensor and effector platforms (meeting the red 2nd and 3rd manufacturing advantage with 4th generation additive manufacturing).

Is the Digital Leap Possible for the EU Military Forces?

Digital leap or transformation is always challenging, particularly for the military, because of the nature of military culture to sustain command and control structure even in chaotic situation. Figure 2 provides some simple checkpoints to improve the transformation towards more digital, data-driven and artificial intelligence-enhanced force:

1. Define your strategic posture against your potential adversary to adjust goals and resources in balance
2. Define your process development opportunities and limitations for each core function, i.e., Force utilisation, generation, deployment/projection, sustainment, and support
3. Consider your Forces' ability to take steps on the digital transformation road
4. Define why you need to change. Is it to improve cost-efficiency in times of diminishing budgets, potential threats from adversaries, or just implement a transformation dictated by politicians
5. Consider the width of your leap towards the future, particularly, how wide transformation your current culture supports
6. Divide your transformation portfolio into three folders: unfreeze, move, and refreeze. 

Figure 2: A simple tool to improve success in military digital transformations

Man-Machine Teams in Combat

What combinations of men and machines may create a better effect in battle when artificial intelligence is introduced into 3rd generation military-industrial systems?

Introduction

Military organisations have an advantage over other organised violence in generating balanced socio-technical systems. They control the force generation life cycle from R&D to lessons learnt of troops (human-machine teams). They can acquire elements and abilities from their national sources. Specifically, the monopoly in organised violence advantage enables the development of culture, competency, process, data, and technology in coordinated steps, experimenting with different combinations and keeping up with a continuous but balanced improvement. 

Armed Forces can generate competent socio-technical systems from specialised components. Through cooperation, they can project power over a broader spectrum of warfighting domains (multi-domain operations)  , balancing one arm's weakness with the strengths of other arms and services. Naturally, these socio-technical (Trist, 1981) force structures need lots of training, trust building, and interoperability exercises before the system's effect appears on the battlefield. Still, human remains the hub of these industrial systems. The adversary tactics aim to disrupt the morale cohesion of opposing troops and operational art to outplay the opposing commander's options in her playbook.

What happens when a 4th generation industrial artificial intelligence is introduced into the above 3rd generation socio-technical force? Humans are emotion-driven, irrational decision-makers (Kolbert, 2008) who consistently overpay, underestimate, procrastinate, and stick to habited options. (Watzek, et al., 2019) (Ariely, 2008) (Henoker, 2022) Will the hallucinating, human-trained, training data biased and narrowly understanding artificial intelligence match well with and compensate for the apparent weaknesses of human cognition? (Dear, 2019)

The paper aims to create an initial understanding of the answers to the above questions in a military context. Firstly, the paper develops a simple model for military confrontation and opposing systems. Secondly, it introduces the changes already emerging through the wave of the 4th Industrial Revolution (Schwab, 2017) in the model and defines essential variables. Thirdly, it studies literature and analyses lessons of man-machine teaming identified elsewhere in industry, work, and society. Fourthly, the paper configurates the systemic model with the man-machine teaming lessons and games the confrontation with varied setups.

The Creation of a Model for Military Unit in Confrontation

The section aims to create a simple model of military confrontation at the tactical level with both opponents described as socio-technical systems. The model shall support analysing variations of the man-machine system of systems components, interrelationships, and vulnerabilities in confronting two opposing parties.

Heinz Guderian (Guderian, 2001) designed the armoured combined arms Pantzer division supported by air-to-ground fires and revolutionised warfighting in early World War II. He used the later defined concept of a socio-technical system (Pasmore, 1995) to improve human-machine cooperation and initiative, trained standard processes through all troops, improve data flows through the use of field radios, and deployed tactical manoeuvre and firepower in a way that created strategic surprise among adversary forces prepared for more I WW era  or even older ways of warfighting (Betts, 1980). He used two human cognitive level features that had been created within the German military: Staff officers' pattern recognition at the operational level (mission command) (Muth, 2013) and NCOs' initiative at the combat level (Widder, 2002). When focusing on the cognitive effects, artificial intelligence can impact military structure at the technical, tactical, and operational levels, including all traditional components and interactions of a socio-technical system (technical systems, organisation structure, cognitive and social features, and tasks) (Abbas & Michael, 2023)

Based on the socio-technical system structure, assuming the impact areas of artificial intelligence, and reflecting the lessons from Guderian's revolution, the composed military system model is illustrated in Figure 2, including:

• Human actors at the cognitive layer have social and moral cohesion as traditionally defined. (Pipping, 1947/2008)
• The process layer consists of both manual (man) and automated (machine) processes but also explains the performance of the function.
• The data layer is essential for the man's (cognitive) situational awareness and for the machine's ability to analyse through calculations.
• The algorithm defines the artificial intelligence actor in the system.
• Computational power is a technical feature that is becoming an essential enabler for artificial intelligence calculations.

Figure 2: A linear and simplified confrontation model between two combat units (socio-technical structures) on the battlefield

The following section seeks lessons from other fields for man-artificial intelligence cooperation for later to vary those lessons in a military context.

Lessons from Man-AI Cooperation in Sense- and Decision-Making Challenges

Section searches literature to find features in man-machine cooperation and confrontation that may indicate promising compositions for winning strategies regarding sense-making and decision-making in complex situations.

Table 1 fuses the lessons collected from the literature research. These lessons support the simplified principles that in sense- and decision-making:

1. A better integrated and mature system wins over a less integrated system.
2. A self-learning algorithm wins over a combination-seeking algorithm in a strategy game.
3. Computing power is less of a determining factor than data and algorithm quality.
4. A higher cognitive force is superior to a lower-performing cognitive party.

Table 1: Fusion of lessons concerning human-machine interactions identified in games and expert work

Confrontation	Wins	Loses
High cognitive force against more volume of lower cognitive force (Maharaj, et al., 2022)	High cognitive	Low cognitive
High algorithm with fewer resources against low algorithm with more resources (Maharaj, et al., 2022)	High algorithm	Low algorithm
Self-learning algorithm against trained algorithm (Barba, 2021)	Self-learning	Trained
People with medium-level algorithms and data against highly trained but less cooperating human experts (Cabitza, et al., 2021)	People with AI	High experts
Highly automated force with high data against high operational and high cognitive human force (Knemeyer & Follet, 2019) (Cabitza, et al., 2021)	Machine	Man
Faster learning algorithm against searching algorithm (Maharaj, et al., 2022) (Barba, 2021)	Faster learning algorithm	Optimising algorithm
More mature process against less mature process (Phillips-Levine, et al., 2022)	More mature	Less mature
Combination of specified algorithms against more narrowly optimised but isolated algorithms (Barba, 2021)	Combination	Isolated

Strategic Artificial Intelligence Gaming for Success in Military Confrontations

The section seeks to test different strategies in two-player strategic games (perfect and complete information in simultaneous zero-sum games (Rutherford, 2021)) and see if dominant or equilibrium strategies for winning can be found.

The existing organisational operation model defines the boundaries for an organisation's ability to develop and adapt new capabilities. The evolution of military enterprise -paper (Mattila & Parkinson, 2018) used an operation or process model dividing military enterprises into four categories: Diversified, Replicated, Coordinated or Unified. 

Figure 3: Strategic posture and Operation model variations in military affairs

Melvin Conway (Conway, 1968) recognised a tendency of "Any organisation that designs a system will produce a design whose structure is a copy of the organisation's communication structure". In the same way, the military operational model in Figure 3 may reflect the generated capabilities following four stereotypical categories:

• The diversified operational model defines many current Services, which acquire, generate, and operate their forces independently from other Services. These military organisations are usually hierarchically arranged, and value is created vertically along the command lines. This operating model was typical in the WW I when infantry and artillery regiments fought their battles separately (Vego, 2009, pp. I-19). The siloed operational model may generate processes with a variety of maturity, lower-level data, various algorithms and computing, and varied levels of human competency and social cooperation.
• The replication operational model enhances operational efficiency by standardising the processes but not integrating them between Services. The goal is to execute standardised processes faster than the adversary or more cost-effective. NATO  aims to standardise member state-generated force elements for multinational effects. Before WW II, the Western military was generating forces arranged in regiments by their branch (artillery, infantry, cavalry, engineers, signals) and learned only during the war to create multi-arms brigades for combined arms effect (Creveld, 1987, pp. 98-116). The siloed but standardised operation model may generate mature processes, lower-level data, and various algorithms and human cognition levels.
• The coordination operational model integrates different processes aiming to optimise operational efficiency. Joint operations (Vego, 2009) call for coordinated effects of force elements through multi-domain engagement . Coordination requires a pervasive command and control system. NATO  aims to standardise member states' forces for better interoperability. A typical example is a combined arms brigade, where unified command and control make all arms and branches fight together (Creveld, 1991, pp. 98-116). Since the 1990s, a flatter, more connected organisation has been called a network-centric or enabled force (Vego, 2009, pp. XIII - 3). The integrated and coordinated operation model may generate mature processes and high-level data but various algorithms and human cognition.
• The unification operational model combines integrated processes and standardised force elements. The operating model aims to maximise operational effect and effectivity by combining different capabilities integrated into a Joint Force. Processes are owned by the Commander in Chief or his staff and developed centrally. The maturity of processes enables the deep specialisation of units since they are always used in combined arms, cross-domain and joint manner. McChrystal (McChrystal, et al., 2015, p. 115) created a force working as a team of teams – many similar special operations teams fighting against Al-Qaeda in Iraq as one extended enterprise. The unified operation model may generate mature processes, high-quality data, well-trained algorithms, and humans.

When the above operation model boundaries for development or adaptation are considered against the model's components illustrated in Figure 2, the matrix, presented in Table 2, projects the operation model boundaries to the socio-technical system model. 

Table 2: Summarised man-machine capabilities from different military operational models and simplified to a performance number

Components/ Operational models	Human Cognition	Process maturity	Quality of data	Algorithm	Computer power	Man-machine system performance
Diversified	Varied	Wider variety and maturity	A wider array of siloed data	Sub-optimized algorithms	Varied	1
Replicated	More homogenous but slow learning	Standardised but slower maturing	Variety but better-formalised data	The same algorithm for everyone	The same level of computer power for everyone	2
Coordinated	More cooperative	Integrated so more cooperative	Data flows improve quality	Improved algorithms with shared data	Connected computers	4
Unified	Cooperative and faster learning	Connected and faster maturing	Quality data to enable learning and training	Enhanced algorithms with quality data	Optimised power for each application	5

Quantification in the last column indicates the effectivity of each approach measured as man-machine system performance. Evaluation is based on collected expert opinions. Gaming these different approaches against each other, one can see how the operation model that defines their developed man-machine capability will also determine their strategic outcome. 

In game theory, a dominant strategy is a choice that is optimal for a player, regardless of what the other players do. (Rutherford, 2021) Using the operation model variant in conflict between two forces, BLUE and RED, and comparing their system performance extracted from Table 2, the strategy comparison is illustrated in Table 3. The rules in strategic, non-cooperative game in linear, symmetric confrontation are:

• Rule: Attacked needs at least two times better-performing force over a defender to win; otherwise, the result is either exhaustion or loss.
• Legend: -1 = loss; 0 = exhaustion; 1 = win.

Table 3: Payoff matrix between two military forces varying their operation model strategies and calculating conflict outcome based on the performance of their man-machine systems

	RED
BLUE		Diversified	Replicated	Coordinated	Unified
	Diversified	0, 0	-1, 0	-1, 1	-1, 1
	Replicated	0, -1	0, 0	-1, 1	-1, 1
	Coordinated	1, -1	1, -1	0, 0	-1, 0
	Unified	1, -1	1, -1	1, 0	0, 0

The operation model seems to have the main impact on the confrontation outcome. The dominant strategy (Rutherford, 2021, p. 34) for both players appears to be the unified strategy (emphasised in Table 3 in blue and red colours), which will be superior to all other operation models. The same strategic choice of a unified operation model seems to be the equilibrium way (Rutherford, 2021, p. 75). Neither of the parties will regret their choice. As the strategic game indicated that the operation model will be the significant indicator in the conflict, it needs to be reflected in two approaches: quantity and quality dominance on the battlefield. 

In the pure attrition optimising conflict, the volume of combat power remains determining since massing a high number of man-machine teams against fewer adversaries has higher odds of winning in one-to-one and all-against-all situations (Lanchester I and II laws). (Johnson & MacKay, 2015) If we follow the logic of Stephen Biddle (Biddle, 2006), the quantity of force in battle is the outcome's primary definer. Furthermore, Lanchester's models  support this approach. How does the operation model impact the quantity? A unified force, i.e., a joint force in a multi-domain battle, can optimise the engagement freely with any target. Other operation model forces have constraints in their firing and decision-making abilities. Hence, the conflicting parties are following different laws of Lanchester. The advantage of the Unified party becomes evident when modelled with the square law. In contrast, the other operation model party follows linear law. 

On the other hand, if we follow Trevor Dupuy's (Dupuy, 1987) approach, the quality of force affects the battle outcome. From the analysis, we may conclude that a party whose quality of human-human and human-machine interaction is advanced is more likely to win if the conflicting parties' capabilities are otherwise even. It seems that improved data (pace, friction, quality), interfaces (human-machine and machine-machine), and social structures (e.g., trust, communication) decrease entropy and friction (Clausewitz, 1984) in a socio-technical system. Hence, the unified operation model will ensure future success more probably than other operation strategies.

Conclusion

The research question asked, "What happens when a 4th generation industrial artificial intelligence is introduced into a 3rd generation socio-technical military force?" or simplified, what combination of man and machine brings victory on the battlefield?  The paper first creates a model for military force as a socio-technical system. Then, the article extracts lessons on artificial intelligence's effects on man-machine cooperation from other industries and businesses. Next, the military socio-technical system model varies with these 4th generation artificial intelligence lessons. Finally, different operation models are conflicted in strategic gaming. The results indicate two foundational principles for dominance: A unified operational model and improved man-machine cooperation.

The first result indicates that traditional massing of combat power will still explain dominance in foundationally attrition-focused battles. With artificial intelligence, enhanced cooperation of man-machine teams will provide more accurate effects, and massed firing will demoralise both human and artificial cognition. The combined, joint, and all-domain military capabilities seem superior against more segregated force structures.

Secondly, artificial intelligence will provide the best outcome measured in man-machine cooperation when applying the unified military operation model. The result explains the dominance of the unified military operation model in less attrition-focused battles. Artificial intelligence alone does not make the difference in broader socio-technical systems. Still, the combinatory effect of mature processes, quality of data, and robust trust between actors enable higher levels of military capability.

The paper provides a simple model for the military as a socio-technical system, which opens opportunities for a variety of other development or transformation studies besides the impact of artificial intelligence. Naturally, the model is simplified for this study and needs further modelling to be more explanatory. The first insights collected from the 4th Industrial Revolution look interesting and promising from the military viewpoint but require further testing before implementation. Nevertheless, the results of this paper are supported by existing trends and remind us that the focus was on how 3rd industrial military force would improve with artificial intelligence. The study does not refer to "Digi native"  military affairs, which may introduce revolutionary ways of winning the battle.