The Strategic Corporal, the Tactical General, and the Digital Coup d’oeil – Military Decision-Making and Organizational Competences in Future Military Operations

Io(B)T and Datafication

The Internet of things (IoT) describes the preliminary culmination and combination of two different developments. The first is the diffusion of the Internet into all aspects of quotidian life through virtually constant Internet access afforded by smartphones and other devices (Kobie, 2022). This is not exclusive to individuals; private and public organizations alike increasingly rely on the Internet. Our constant presence on the web implies that we are continually leaving traces of data that are being collected. The second development concerns sensor technology, where “smart” sensors of all kinds are becoming ever easier and cheaper to produce. Everything from cars and fridges to wristwatches are increasingly equipped with sensors that monitor and collect data, which can further be connected to the Internet (Kobie, 2022; NATO, 2017). Rapid developments in technology and quantum science are expected to further enhance this evolution (Reding & Eaton, 2020).

These two developments have led to what can be called the digitalization and networkification of people and things (Bousquet, 2009; Watling, 2022; Flyverbom et al., 2019; Nørgaard & Sjøgren, 2019) – terms indicating that a mass of knowledge and relationships and processes that were once analogue, and in many cases were not noted or recorded at all, have now been digitalized and connected in digital, networked solutions. Making qualitative knowledge and qualitative processes digital has also produced what is sometimes called “datafication” (Alexandrov et al., 2018; Marchegiani 2021; Tabrizi et al., 2019; Vial, 2019). This is a term used to describe the collection and generation of data about phenomena, relationships, objects, and interactions that have not previously been quantified or turned into data (Marchegiani 2021; Tabrizi et al., 2019; Vial, 2019). This term is also used to describe how data is increasingly performing in entirely new ways, as a structuring resource and catalyst in the organization of people, networks, and processes (Flyverbom et al., 2019; Alexandrov et al., 2018); we see this, for example, in the organising of military organizations and military operations. In this vein, the central aim of the NATO Data Exploitation Framework Policy (NATO, 2022) is to ensure that NATO is able to “leverage data as a strategic resource” in order to “achieve information superiority and data-driven decision making at all levels across the Alliance” (our emphasis). Further, the Implementation Strategy for NATO’s Digital Transformation, endorsed by the NATO C3 Board in May 2023, has the stated vision that, by 2030, “NATO’s Digital Transformation will enable the Alliance to conduct multi-domain operations, ensure interoperability across all domains, enhance situational awareness, and facilitate political consultation and data-driven decision-making” (NATO, 2023b). When we talk about datafication in and by defence forces, we are thus not talking about a purely technological phenomenon but, rather, something much broader in scope, both quantitatively and qualitatively. As noted by Cukier & Schoenberger (2013, p. 28), “the idea is that we can learn from a large body of information things that we could not comprehend when we used only smaller amounts” (our emphasis again). Datafication in and of defence forces thus relates to how we create and use knowledge; how we understand and organise human and material activities; and how we build and structure ways of managing the military organization and its activities.

In the military context, there is increasing talk of an Internet of battlefield things (IoBT), describing these developments in military organizations and the operational environments in which these organizations operate (Zhu et al., 2021; Russel & Abdelzaher, 2018). The developments in sensor technology noted above mean that everything from fragmentation vests to combat aircraft are increasingly being equipped with intelligent sensors and so-called edge computers expected to improve response times and save bandwidth (Kobie, 2022). Combined with the general development within digital knowledge technologies and C4ISR capabilities, this in turn means that military organizations, both in crisis and peacetime, are constantly collecting vast amounts of data on everything from administration and logistics to operations and training (Zhu et al., 2021). This collection takes place at all levels of war (tactical, operational, and strategic) and in all domains, both conventional (land, sea, and air) and in the “new” domains of cyberspace and space, thus generating what is often termed “big data” (Brunet & Claudon, 2015).

Big Data

“Big data” describes a profound development regarding the possibilities of generating, storing, interconnecting, analysing, and using data, including the IoT. Big data is often described in terms of the so-called three Vs of volume, variety, and velocity (see, for example, Aspelund et al., 2021; Brunet & Claudon, 2015).

“Volume” refers to the explosion in both the volume of existing data and the amount of new data constantly being collected that can be processed and used in a meaningful way (Aspelund et al., 2021). In the military context, this includes, for instance, satellite data, drone data, radar data, and sensor data from a wide variety of sensors equipped on, for example, tanks and aircraft or soldiers’ optical sights and fragmentation vests. There is also data collected on the Internet – on social media and from other sources of open source intelligence (OSINT), data collected in cyberattacks (emails and company data, for example), and huge amounts of organizational and technical data about one’s own military organization, including everything from payroll and personnel ratios to logistical and materiel data (see Eshel, 2023).

“Variety” refers to the fact that data are not only collected in a quantitative sense; many more different types of data are also collected (Aspelund et al., 2021). The variation relates both to how data are collected and to the content of that data. Thus, we are today collecting data on a wide range of phenomena and conditions on which we were not previously able to collect data (Cukier & Schoenberger, 2013). Finally, variety also includes the fact that, new big data analysis capabilities allows this multitude of diverse data to be combined, processed, and used in much more meaningful ways – the linking of social media data, credit card transactions, geospatial data, and drone imagery pertaining to an anti-terrorist operation, for example.

“Velocity” refers to the ways in which these huge amounts of diverse data are being collected, stored, used, and shared at an unprecedented pace (Aspelund et al., 2021). Massive bodies of data that would previously have taken months to process and analyse can now be processed faster and with almost no latency, through stream processing or batch processing. While data streaming processes analyse the data as it flows through a processing system, batch processing is using data sets that have already been stored (Vogel et al., 2021). In military contexts, stream processing can be used, for instance, in the application of near-real-time object-recognition algorithms to massive quantities of streamed video data from surveillance drones or equally significant quantities of video and satellite data reviewed to create a so-called “normal picture.” Another application is in so-called battle management systems (BMS), which offer digital maps where one’s positions, or those of adversaries and third-parties, are continuously updated for anyone with access to the system as new intelligence is reported. BMS and other such systems make it possible for military headquarters to obtain immediate visual access to what soldiers on the battlefield are seeing and experiencing via helmet cams and the like (Systematic, 2023; Eskel, 2023).

While these three Vs are the most common, sometimes scholars and practitioners operate with others, too. One such is veracity – the ways in which big data volume, variation, and velocity are transforming how we can understand and verify large-scale knowledge extraction (Rubin & Lukoianova, 2013). Another is value, indicating that big data is only meaningful if it can improve, optimise, or streamline existing processes, or if it can generate entirely new types of knowledge and insights (Ishwarappa & Anuradha, 2015). In relation to big data and the possibilities that they bring about, important questions arise about how to qualify, select, analyse, contextualize, and, not least, present data – i.e. how to “curate” data and the information produced from it. This digital curation is central to understanding and critically engaging with the images that are created and communicated, not least in appreciating the possibilities for action and decision-making that these visualizations may implicitly either suggest or exclude (see, for example, Flyverbom & Murray, 2018; Eshel, 2023). This issue is sometimes related to the last V, which recurs frequently: visualization. This concerns the ways in which big data is a matter, also, of generating visualizations of digital intelligence that are useful to those who need to extract relevant information from it (Flyverbom, 2019).

Artificial Intelligence

The last technology of relevance here is AI, which can be understood as an attempt at using different technologies and methods to mirror cognitive abilities previously only held by humans. Researchers often distinguish between two types of AI: general and narrow artificial intelligence (Ayoub & Payne, 2016). General AI is equivalent to, or exceeds, human cognition in all aspects. General AI currently remains a theoretical concept, fictionally the domain of sci-fi movies like The Terminator. Narrow AI, on the other hand, is the attempt to mimic a small part of a specific cognitive ability to solve a specific task such as image recognition, data sorting, or playing chess. If we take an AI chatbot like ChatGPT, as impressive as it may be, we would mostly still categorise it as narrow AI: its output is narrow. Although the algorithm behind ChatGPT has been developed to solve multiple problems (answering questions or writing poems, for example), and as such in one perspective can be considered as pertaining to general AI, the chatbot is limited in regards to its access to data. Thus, although the rationality behind general AI is that it can provide context-based answers based on multiple datasets, the narrowness of ChatGPT’s output is an artefact of its dataset; it is, so to speak, based on a closed eco-system.

The whole point of AI is that its algorithms are self-learning: that is, they can self-correct and derive new algorithms from their improved “understanding” (Domingos, 2015, p. 45; Rathore et al., 2021; Jiang, 2018; Miahlie, 2018). Briefly, this works by having the narrow AI define some goals (outputs) and then “feeding” itself with huge amounts of data (inputs), which it then teaches itself to categorize and process according to the predefined outputs (knowing the difference between red and blue cars, for example). In current military contexts, the narrow AI has made its way into the operational environment and is predicted to affect and transform everything from administrative computing to logistics, surveillance, targeting, and command and control (Reding & Eaton, 2020; NATO, 2017). Narrow AI often outperforms human actors on accuracy, thoroughness, and speed in the specific tasks, such as image recognition and data sorting, it is designed to solve. In minutes or even seconds, AI-generated algorithms can analyse and process quantities of disparate data that would take a human analyst more than a lifetime to repeat.

A conceptual tool used by many Western militaries is the so-called OODA loop, first suggested by Boyd in 1996 (Boyd, 1996). The OODA loop describes four phases in the military decision process: Observe, Orient, Decide, Act (Boyd, 1996, p. 3). At present, AI is particularly used for supporting the phases of observation, orientation and action; it offers capabilities in terms of improving our understanding of the situation/battlefield (situational awareness) and in terms of decision support, i.e., the opportunity to use vast amounts of data to generate new and better information faster than one’s adversary can (assuming one enjoys a condition of information superiority). This could ultimately lead to decision superiority – the making of comparatively better decisions at a comparatively greater speed (O’Shaughnessy, 2020). AI is also used to improve the precision of the execution of certain decisions, including the efficiency of weapons delivery (NATO, 2021; Reding & Eaton, 2020). While AI is expected to increasingly play a role in relation to things such as the development and suggestion of military courses of actions and in wargaming, a general reluctance remains in trusting it with the power to make decisions (Johnson, 2022; Nørgaard & Linden-Vørnle, 2021). Here, a primary objection relates to issues of justification: when the technology is self-learning and self-evolving, it may be difficult, even impossible, to “rewind” and track a given decision in order to establish the concrete reasoning or justification that determined why this and not another decision was made. Additionally, Nørgaard and Linden-Vørnle (2021; see also NATO, 2021) note that while AI might make the most efficient decisions, these decisions may not always comply with our legal and ethical perspectives.

In the following sections we will first use the concepts of multi-domain operations and mission command to consider some of the implications for the military organization of the development described above. After this, we look at some of the implications for the military decision-maker at the individual level. This is analysed through the concepts of the strategic corporal and the tactical general.

The strategic corporal

The strategic corporal is a concept originally suggested in article by Krulak (1999) in the context of his theory of the “three block war.” The concept describes how the increasing complexity of the operational environment, combined with the introduction of the mass media on the battlefield, has resulted in a situation where even relatively low tactical-level decisions could have great strategic significance. An example could be how overly harsh conduct or specific violations of human rights could have a major impact on how both the civilian population in the theatre of operations and the populations of the engaged nations would perceive given military operations. This could ultimately influence or determine support for the military actions.

The rise of IoT and big data means that Krulak’s point has only become even more relevant today. The digital footprint following the mass adoption of smart devices can be understood as huge volumes of different varieties of data generated and shared at the velocity of (near-) real time, and archived permanently in the huge archive that the Internet itself basically constitutes. At the same time, big data analytical tools (analytics) make it possible for civilians to access and process huge volumes of data on various military operations and to make them available to everyone on the Internet (Ford & Hoskins, 2022). This point has been greatly emphasized in the ongoing war in Ukraine, where Ukrainian and Russian civilians have collected huge amounts of data about the war through means such as videos and pictures taken on smartphones and later posted on social media. These, and vast amounts of other data, have been analysed by civilians who, both professionally and non-professionally, have provided vast amounts of OSINT and made their analyses and findings available to all (Smith-Boyle, 2022). This data has apparently had tactical and strategic importance (Burgess, 2022; Smith-Boyle, 2022). Tactically, Russian soldiers and civilians who have filmed and taken pictures of their own military installations and uploaded the images to various social media platforms have provided both civilian and military intelligence analysts with information and knowledge about them, their location, purpose, and so on. This has ultimately enabled Ukrainian attacks on these installations (Burgess, 2022). Strategically, the ability of Ukrainians to generate and share data/information from the battlefield and occupied territories on tactical victories, defeats, and human rights violations has seemingly had an impact on how the Russian and Ukrainian militaries have been perceived globally (Serritzlev, 2023; Smith-Boyle, 2022). These examples thus illustrate how the modern strategic corporal must be significantly better able to understand and navigate the strategic dimensions of war than the tactical leaders of the past.

However, the digitalization of the operational environment and the arrival of IoT, big data, and AI on the battlefield do not only mean that the actions of the tactical leader can have an increasingly strategic impact. Through various C4ISR capabilities, the tactical leader (embodied in the concept of the strategical corporal) now enjoys, also, increasingly great access to knowledge of what is going on at the operational and strategic levels; this might be knowledge of operations that other services or allied forces are undertaking, of logistics, or of civilian attitudes to a given military operation. In the past, tactical commanders only rarely, or to a limited extent, enjoyed access to such information, being restricted to communication by radio or physically meeting other units to exchange information by means such as drawing up maps. Today, such data can be shared in near-real time over battle management systems, enabling low-level decision-makers to access much more information (depending on the limits of information access as delineated in the battle management system). Increasingly, this means that tactical leaders – now able to know what is going on, both across geographical or temporal boundaries such as services or domains and across the different levels of warfare – have potential operational and strategic roles to play. This development is one of the cornerstones of the MDO/JADO concepts, which precisely underscore the expectation that the tactical commander relates to and applies this available (operational or strategic) knowledge by, for example, considering or requesting fire support from the air force or requesting a cyber or information operation.

While this can provide the tactical leader (the strategic corporal) with entirely new opportunities for making better and faster decisions, it can also lead to both information overload and information over-reliance (Kollars, 2021). “Information overload” describes the condition of having access to so much data/information that one does not know which of it to prioritize or how to derive meaning or insight from it. “Information over-reliance” describes a condition in which the military decision-maker becomes so overly dependent on the vast amounts of data/information generated from these new technologies that decisions derived from that data are made too slowly, or even not made at all (Kollars, 2021; Fuller, 2000). Both information overload and over-reliance lead to inertia and paralysis of action, and can ultimately result in the adversary gaining the decision superiority (Fuller, 2000).

This is not only because there is so much information available; there are also so many issues about which information is now available that the span of what the tactical decision-maker must make decisions about has become significantly expanded. As a further consequence, tactical leaders must also increasingly use and rely on data and information that may at times have been collected and processed far from the battlefield in which the leaders themselves are situated. The tactical leader will therefore sometimes face the dilemma that the digital information or decision support at hand contradicts what they are experiencing and sensing on the battlefield (Heltberg, 2021). Thus, while the increased access to data may increase the tactical leader’s situational awareness, it also creates a potential paradox: it risks creating a greater cognitive distance from the battlefield for a tactical leader who is by necessity physically close to this battlefield. This cognitive distance may be further exacerbated by the fact that tactical commanders often lack the specialized technological knowledge necessary to understand the new technologies and thus to fully grasp the opportunities and challenges they present (see Aspelund et al., 2021; Feige, 2020). Nor do they always know how the technologies can be applied. Tactical leaders must therefore use technologies that they often do not fully comprehend. One can thus argue, essentially, that the strategic corporal is largely underpinned by the expansion of the tactical leader’s operational environment through the introduction of new knowledge technologies and associated concepts such as MDO/JADO.

The tactical general

The concept of the tactical general was coined by the military technology researcher Peter W. Singer (2009). The term describes the fact that knowledge technologies and general developments in command and control systems do not merely expand the operational environment for the tactical leader, as described in the previous section of this article. New technologies also enable strategic leaders to continually monitor what is happening on the battlefield in (near) real time and to see what the soldiers and tactical leaders in the field are seeing (Singer, 2009). This not only applies to military commanders; increasingly, senior political leaders can now also follow events in near-real time. This phenomenon was instantiated in the iconic 2011 images of the U.S. president Barack Obama and his staff watching the military action that resulted in the death of Osama bin Laden live from the White House (McGee, 2021). An important implication of this development is that, just as the strategic corporal has an increasing access to data about what is going on at the operational and strategic levels, the ‘tactical general’ can increasingly gain access to and knowledge of what is going on all the way down to the sub-tactical level. Combined with the fact that technological developments have already led to strategic leaders having access to ever more information, generally speaking, this access to real-time tactical level events and views means that the tactical general may also suffer from both information overload and information over-reliance (Kollars, 2021).

Another challenge still follows from this. On the one hand, technologies such as IoBT, big data, and AI make it possible for the tactical general to assume greater responsibility for tactical-level actions; it is inarguable that, at times, the commanding general might have a better view of a tactical battle than other actors involved (King, 2019; Shamir, 2011) and that these technologies may also improve the conditions for creating an even better understanding, throughout the chain of command, of the intent that the strategic leader has stated for a given operation. On the other hand, however, the new tactical knowledge made available to strategic commanders in real time fuels the risk that they will choose, at inappropriate or inexpedient moments, to micromanage right down to the tactical level, thus depriving tactical commanders of the possible initiative and creativity that might be necessary for their work (Johnson, 2022).

In this way, the diffusion of data and information across organizational crosslines and levels may undermine the premise for the principle of mission command: while mission command (as auftragstaktik) was invented to ameliorate the impossibility of getting an overview of the battle scene, new digital technologies may seem to hold the alluring potential to make the operational environment transparent again to all actors, thus eluding the fog of war. A central challenge, thus inherently intertwined with the potential of the new technologies, is how strategic commanders might seek to avoid losing focus in relation to their core tasks. These include ensuring the adequate operational or strategic understanding of the operational environment; making the necessary operational and strategic decisions; and motivating their subordinates to take the necessary initiative (NATO, 2019, par. 1.5).

This also relates to another issue being debated in this regard concerning the extent to which commanders should be physically present on the battlefield (for the purposes of motivation and boosting morale, for instance), or whether they should rather be in headquarters where they can observe by use of BMS or other C4IRS systems from a safer distance (see Heltberg, 2021). As noted with the example of President Obama sitting in the Situation Room of the White House watching a military operation thousands of miles away in Northern Pakistan, digitalized knowledge management technologies now make it possible for commanders to have a lot of information about actual real-time situations on the battlefields without having to be physically present themselves. A central question arising, then, is that of whether professional judgment, the operational coup d’oeil, is still somehow dependent on physical presence, and if, when, and to what extent, it can be just as well informed and calibrated through data and pictures on a screen.

We will turn to this question in the section below, proposing and developing the two new concepts of digital mission command and digital coup d’oeil.

Digital mission command

As we have seen, the tactical general must avoid the risk of conducting detailed- or micro-management in situations where it is superfluous or inimical to more appropriate solutions. We should thus note that the digitalization and datafication of the military organization creates the danger that generals and other military commanders will be prompted to move from the exercise of mission command to that of detailed command, simply due to the opportunity presented by the radical extension of the volume of information available to them and, hence, extension of their span of control. Such development would not only affect specific military operations; if the strategic leadership is detailing and making decisions on behalf of tactical leaders (or strategic corporals), in the longer term these officers might become less skilled and practiced in taking initiative, exercising creativity, and making independent decisions (Johnson, 2022).

It is highly important to consider and examine this issue, specifically in view of the development of the role of the strategic corporal – an officer who must, precisely, be able to think and to act with unprecedented independence in relation to increasingly complex contexts, tasks, and decisions with potential strategic consequences. Furthermore, in light of conceptions of future battlefields as embedded in MDO, these battle spaces might be so disruptive and constantly changing that the need for extremely rapid decision-making is drastically increased. As the U.S. Army Field Manual 3-0 (U.S. Army, 2022, pp. 3–4) notes, “because uncertainty, degraded communications, and fleeting windows of opportunity characterize operational environments during combat, multidomain operations require disciplined initiative cultivated through a mission command culture.” It is therefore imperative that the idea of mission command be re-examined and re-conceptualized by the military so that it may be adjusted to the digital reality increasingly characterising operational environments. This includes the need to create a general military culture marked by systematic reflection on when and how knowledge technology capabilities are used.

“Digital mission command” is thus understood here as the capacity to refrain from the general exercise of detailed control, notwithstanding the availability of knowledge technologies facilitating it, while retaining the ability to employ it when specifically deemed relevant and useful. This could, for example, be when new events or information prompt the senior military decision-maker to change overall operational or tactical objectives, or the desire to exploit the possibility of synchronising one’s own decisions and actions with those of other decision-makers, exerting what could be called digital operational art. In most BMS and C4ISR systems today, it is possible to systemically define and restrict the information flow, both from the input side (i.e., to define and restrict what can be seen) and from the receiver side (i.e., to define and restrict who can see given information). Above, we proposed the concept of digital curation. In relation to the organizational development and management of digital mission command, this concept will have a central role to play.

Digital coup d’oeil

The second concept we would like to propose is that of digital coup d’oeil.

The Prussian major general and philosopher of war Carl von Clausewitz wrote about the concept of coup d’oeil in his magnum opus On War (1832), a signally influential work in Western military thinking. For Clausewitz, the coup d’oeil was a specific ability, a form of military intuition that enabled the military decision-maker to make requisite and appropriate decisions even in an environment characterized by complexity, uncertainty, and chance (Clausewitz, 1832/2021, p. 62).² He refers to the concept in the context of his understanding that, since war is so greatly characterized by uncertainty and chance, data and intelligence will necessarily be founded on a degree of uncertainty rendering it incomplete or even entirely wrong.

Digital coup d’oeil, the concept proposed here, denotes the military capability or intuition needed to make decisions in complex environments, which (unlike in Clausewitz’s time) are characterized by the availability of incalculably large amounts of information and equally large possibilities for combining, processing, using, and sharing this information. Digital coup d’oeil is the ability to mediate the challenges and to seize the opportunities resulting from the massive amounts of data arriving with the introduction of knowledge technologies into the operational environment, thus avoiding information overload and information over-reliance (see Kollars, 2021; Fuller 2000).

Another challenge accompanying the introduction of knowledge technologies is that technologies such as IoBT, big data, and AI are relatively complex and often require a high degree of specialized knowledge, both in their development, and in understanding their potential and their uses (Feige, 2020). This can render it difficult for military practitioners without a background in data technology to understand exactly the opportunities and challenges these technologies present – or, in other words, to understand fundamentally what they can and cannot be used for (Bollmann & Jacobsen, 2023; NATO, 2019). This not only means that it can be difficult to understand how and when to apply these technologies; it can also result in a lack of confidence regarding what these technologies are trusted to do, including in relation to decision support. Conversely, if equally undesirably, it can also create an over-reliance on these technologies, perceiving them to be a silver bullet capable of solving all problems and providing the military decision-maker with something akin to omniscience (see, for example, Serena & Clarke, 2019). A lack of strategic and practical knowledge and a lack of understanding of the potential and limitations of technologies can lead to the organization and its processes being restricted to analogue approaches with digital support, failing to successfully exploit the opportunities that the technologies actually offer (see Heltberg, 2021).

Digital coup d’oeil is the ability to know when it is appropriate to use these technologies for the collection, use, or sharing of data, and the ability to understand, perhaps almost intuitively, when to delegate decisions to subordinate actors, both human and (increasingly) non-human, thus avoiding detailed command and micro-management. It is also the capacity to understand and to act in such operational environments, as described in MDO/JADO concepts, where military categorizations and terms that have characterized Western military thinking for almost 100 years are changing. Thus, digital coup d’oeil helps to avoid action paralysis and inertia. Digital coup d’oeil will have different compositions depending on the level of the decision-maker – that is, depending on whether you are a strategic corporal or a tactical general. For generals, the digital coup d’oeil is not merely oriented towards the tactical level, as it equally concerns the ability to comprehend the various premises for how the military organization understands the possibilities and risks inherent to specific knowledge technologies. This is important if the military organization and its members are to be enabled to exploit these technologies and their potential, relevantly and constructively. In other words, strategic leaders must also be able to understand and translate technology demands and requirements for the military organization. The tactical general must be able to capture the new and to interpret and extract what this means for both present and future battlefields. The digital coup d’oeil thus also includes the ability to understand both the technological landscape of given conflicts and operations and the technological navigation parameters that are, or will become, essential in the future.

As an important observation, we will conclude by drawing attention to the fact that the digital coup d’oeil also concerns how one relates to the very fact that these digital and technological possibilities exist in the first place. We can use an analogy to describe this. The historical accounts of the development of the first automobiles in the late 19th century describe how, initially, these were largely developed within a mental conceptual framework that was still “horse-plus-cart.” On the arrival of the engine, it merely became “cart-minus-horse” (Vlaskovits, 2011). It was not until 1891, when the French inventor Levassor changed the design of the automobile by placing the engine in front of the driver’s seat, instead of under it, that engineers began thinking in earnest about cars in a different framework – that of the car itself (Morello, 2022). This change in basic understanding and mental framing became crucial for the development of the motor vehicle. Only when engineers were able to think of the car as something radically new and qualitatively different, with its own possibilities and premises, were they able to develop it to a new and greater potential (Morello, 2022; Vlaskovits, 2011).

Similarly, one could perhaps say about digitalization, datafication, and the understanding and application of Io(B)T, big data, and AI in the military operational domain, within the MDO framework: as long as we consider these phenomena merely as a new knowledge format or simply as a larger conglomerate of domains to include and manage, we remain in a kind of cognitive “cart-minus-horse” framework. To allow knowledge technologies to drive the military organization and military operations, data and emerging technologies must be viewed as a resource in entirely new ways and in entirely new constellations throughout the organization.

To help us imagine how we can do this, we might build on the concept of affordance as developed by Gibson (1979). Affordance means that the quality or property of an object defines its possible uses, or makes strong suggestions as to how it can or should be used: a glass, for example, affords us relatively uncomplicated drinking. Stretching the notion of affordance into the realm of communication, we find concepts and words that we are so used to thinking about in specific ways that we may consider them “affording us something.” In a military context, the notion of deep battle may, for instance, often connote the use of artillery or air force. But there is no rule about this. We might also use other assets for deep battle – just as there are no rules stipulating that glasses can be used only for drinking, or that we should necessarily drink by using a glass. These are issues of affordance.

If we are to think of MDO – and the comprehensive datafication and use of emergent and disruptive technologies that the concept is founded upon – as something qualitatively different from what we already know, we also need to reconsider, reframe, and re-conceptualize our considerations of organizational, conceptual, and “objectual” affordances. Military leaders must ask themselves questions such as: “How would things be different if we changed the fundamental conceptual boundaries and demarcations that we habitually use for perceiving the world and framing our analyses?”; “What are the implications for professional judgment, ethics, and identities when military practitioners and leaders must collaborate with algorithms in decision-making?”; and “What are the changes brought to the expertise of the military profession change by algorithmic mapping and prediction?”