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Grassroots AI: Driving Change in the Royal Navy with Workflow

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By Francis Heritage

Introduction

In September 2023, the Royal Navy (RN) advertised the launch of the Naval AI Cell (NAIC), designed to identify and advance AI capability across the RN. NAIC will act as a ‘transformation office’, supporting adoption of AI across RN use cases, until it becomes part of Business as Usual (BaU). NAIC aims to overcome a key issue with current deployment approaches. These usually deploy AI as one-off transformation projects, normally via innovation funding, and often result in project failure. The nature of an ‘Innovation Budget’ means that when the budget is spent, there is no ability to further develop, or deploy, any successful Proof of Concept (PoC) system that emerged.

AI is no longer an abstract innovation; it is fast becoming BaU, and it is right that the RN’s internal processes reflect this. Culturally, it embeds the idea that any AI deployment must be both value-adding and enduring. It forces any would-be AI purchaser to focus on Commercial Off The Shelf (COTS) solutions, significantly reducing the risk of project failure, and leveraging the significant investment already made by AI providers.

Nevertheless, NAIC’s sponsored projects still lack follow-on budgets and are limited in scope to just RN-focused issues. The danger still exists that NAIC’s AI projects fail to achieve widespread adoption, despite initial funding; a common technology-related barrier, known as the ‘Valley of Death.’ In theory, there is significant cross-Top Line Budget (TLB) support to ensure the ‘Valley’ can be bridged. The Defense AI and Autonomy Unit within MOD Main Building is mandated to provide policy and ethical guidance for AI projects, while the Defense Digital/Dstl Defense AI Centre (DAIC) acts as a repository for cross-MOD AI development and deployment advice. Defense Digital also provides most of the underlying infrastructure required for AI to deploy successfully. Known by Dstl as ‘AI Building Blocks’, this includes secure cloud compute and storage in the shape of MODCloud (in both Amazon Web Service and Microsoft Azure) and the Defense Data Analytics Portal (DDAP), a remote desktop of data analytics tools that lets contractors and uniformed teams collaborate at OFFICIAL SENSITIVE, and accessible via MODNet.

The NAIC therefore needs to combine its BaU approach with other interventions, if AI and data analytics deployment is to prove successful, namely:

  • Create cross-TLB teams of individuals around a particular workflow, thus ensuring a larger budget can be brought to bear against common issues.
  • Staff these teams from junior ranks and rates, and delegate them development and budget responsibilities.
  • Ensure these teams prioritize learning from experience and failing fast; predominantly by quickly and cheaply deploying existing COTS, or Crown-owned AI and data tools. Writing ‘Discovery Reports’ should be discouraged.
  • Enable reverse-mentoring, whereby these teams share their learnings with Flag Officer (one-star and above) sponsors.
  • Provide these teams with the means to seamlessly move their projects into Business as Usual (BaU) capabilities.

In theory this should greatly improve the odds of successful AI delivery. Cross-TLB teams should have approximately three times the budget to solve the same problem, when compared to an RN-only team. Furthermore, as the users of any developed solution, teams are more likely to buy and/or develop systems that work and deliver value for money. With hands-on experience and ever easier to deploy COTS AI and data tools, teams will be able to fail fast and cheaply, and usually at a lower cost than employing consultants. Flag Officers providing overarching sponsorship will receive valuable reverse-mentoring; specifically understanding first-hand the disruptive potential of AI systems, the effort involved in understanding use-cases and the need for underlying data and infrastructure. Finally, as projects will already be proven and part of BaU, projects may be cheaper and less likely to fail than current efforts.

Naval AI Cell: Initial Projects

The first four tenders from the NAIC were released via the Home Office Accelerated Capability Environment (ACE) procurement framework in March 2024. Each tender aims to deliver a ‘Discovery’ phase, exploring how AI could be used to mitigate different RN-related problems.1 However, the nature of the work, the very short amount of time for contractors to respond, and relatively low funding available raises concern about the value for money they will deliver. Industry was given four days to provide responses to the tender, about a fifth of the usual time, perhaps a reflection of the need to complete projects before the end of the financial year. Additionally, the budget for each task was set at approximately a third of the level for equivalent Discovery work across the rest of Government.2 The tenders reflect a wide range of AI use-cases, including investigating JPA personnel records, monitoring pictures of rotary wing oil filter papers, and underwater sound datasets, each of which requires a completely different Machine Learning approach.

Figure 1: An example self-service AI Workflow, made by a frontline civilian team to automatically detect workers not wearing PPE. The team itself has labelled relevant data, trained and then deployed the model using COTS user interface. Source: V7 Labs.

Take the NAIC’s aircraft maintenance problem-set as an example. The exact problem (automating the examination of oil filter papers of rotary wing aircraft) is faced by all three Single Services. First, by joining forces with the RAF to solve this same problem, the Discovery budget could have been doubled, resulting in a higher likelihood of project success and ongoing savings. Second, by licensing a pre-existing, easy-to-use commercial system that already solves this problem, NAIC could have replaced a Discovery report, written by a contractor, with cheaper, live, hands-on experience of how useful AI was in solving the problem.3 This would have resulted in more money being available, and a cheaper approach being taken.

Had the experiment failed, uniformed personnel would have learnt significantly more from their hands-on experience than by reading a Discovery report, and at a fraction of the price. Had it succeeded, the lessons would have been shared across all three Services and improved the chance of success of any follow-on AI deployment across wider MOD. An example of a COTS system that achieves this is V7’s data labeling and model deployment system, available with a simple user interface; it is free for up to 3 people, or £722/month for more complex requirements.4 A low-level user experiment using this kind of platform is unlikely to have developed sufficient sensitive data to have gone beyond OFFICIAL.

Introducing ‘Workflow Automation Guilds’

Focusing on painful, AI-solvable problems, shared across Defense, is a good driver to overcome these stovepipes. Identification of these workflows has been completed by the DAIC and listed in the Defence AI Playbook.5 It lists 15 areas where AI has potential to deliver a step-change capability. Removing the problems that are likely to be solved by Large Language Models (where a separate Defence Digital procurement is already underway), leaves 10 workflows (e.g. Spare Parts Failure Prediction, Optimizing Helicopter Training etc) where AI automation could be valuably deployed.

However, up to four different organizations are deploying AI to automate these 10 workflows, resulting in budgets that are too small to result in impactful, recurring work; or at least, preventing this work from happening quickly. Cross-Front Line Command (FLC) teams could be created, enabling budgets to be combined to solve the same work problem they collectively face. In the AI industry, these teams are known as ‘Guilds’; and given the aim is to automate Workflows, the term Workflow Automation Guilds (WAGs) neatly sums up the role of these potential x-FLC teams.

Focus on Junior Personnel

The best way to populate these Guilds is to follow the lead of the US Navy (USN) and US Air Force (USAF), who independently decided the best way to make progress with AI and data is to empower their most junior people, giving them responsibility for deploying this technology to solve difficult problems. In exactly the same way that the RN would not allow a Warfare Officer to take Sea Command if they are unable to draw a propulsion shaft-line diagram, so an AI or data deployment program should not be the responsibility of someone senior who does not know or understand the basics of Kubernetes or Docker.6 For example, when the USAF created their ‘Platform One’ AI development platform, roles were disproportionately populated by lower ranks and rates. As Nic Chaillan, then USAF Chief Software Officer, noted:

“When we started picking people for Platform One, you know who we picked? A lot of Enlisted, Majors, Captains… people who get the job done. The leadership was not used to that… but they couldn’t say anything when they started seeing the results.”7

The USN takes the same approach with regards to Task Force Hopper and Task Group 59.1.8 TF Hopper exists within the US Surface Fleet to enable rapid AI/ML adoption. This includes enabling access to clean, labeled data and providing the underpinning infrastructure and standards required for generating and hosting AI models. TG 59.1 focuses on the operational deployment of uncrewed systems, teamed with human operators, to bolster maritime security across the Middle East. Unusually, both are led by USN Lieutenants, who the the USN Chief of Naval Operations called ‘…leaders who are ready to take the initiative and to be bold; we’re experimenting with new concepts and tactics.’9

Delegation of Budget Spend and Reverse Mentoring

Across the Single Services, relatively junior individuals, from OR4 to OF3, could be formed on a cross-FLC basis to solve the Defence AI Playbook issues they collectively face, and free to choose which elements of their Workflow to focus on. Importantly, they should deploy Systems Thinking (i.e. an holistic, big picture approach that takes into account the relationship between otherwise discrete elements) to develop a deep understanding of the Workflow in question and prioritize deployment of the fastest, cheapest data analytics method; this will not always be an AI solution. These Guilds would need budgetary approval to spend funds, collected into a single pot from up to three separate UINs from across the Single Services; this could potentially be overseen by an OF5 or one-star. The one-star’s role would be less about providing oversight, and more to do with ensuring funds were released and, vitally, receiving reverse mentoring from the WAG members themselves about the viability and value of deploying AI for that use case.

The RN’s traditional approach to digital and cultural transformation – namely a top-down, directed approach – has benefits, but these are increasingly being rendered ineffective as the pace of technological change increases. Only those working with this technology day-to-day, and using it to solve real-world challenges, will be able to drive the cultural change the RN requires. Currently, much of this work is completed by contractors who take the experience with them when projects close. By deploying this reverse-mentoring approach, WAG’s not only cheaply create a cadre of uniformed, experienced AI practitioners, but also a senior team of Flag Officers who have seen first-hand where AI does (or does not) work and have an understanding of the infrastructure needed to make it happen.

Remote working and collaboration tools mean that teams need not be working from the same bases, vital if different FLCs are to collaborate. These individuals should be empowered to spend multi-year budgets of up to circa. £125k. As of 2024, this is sufficient to allow meaningful Discovery, Alpha, Beta and Live AI project phases to be undertaken; allow the use of COTS products; and small enough to not result in a huge loss if the project (which is spread among all three Services to mitigate risk) fails.

Figure 2: An example AI Figure 2: An example AI Opportunity Mapping exercise, where multiple AI capabilities (represented by colored cards) are mapped onto different stages of an existing workflow, to understand where, if anywhere, use of AI could enable or improve workflow automation. Source: 33A AI.

WAG Workflow Example

An example of how WAGs could work is as follows, using the oil sample contamination example. Members of the RN and RAF Wildcat and Merlin maintenance teams collectively identify the amount of manpower effort that could be saved if the physical checking of lube oil samples could be automated. With an outline knowledge of AI and Systems Thinking already held, WAG members know that full automation of this workflow is not possible; but they have identified one key step in the workflow that could be improved, speeding up the entire workflow of regular helicopter maintenance. The fact that a human still needs to manually check oil samples is not necessarily an issue, as they identify that the ability to quickly prioritize and categorize samples will not cause bottlenecks elsewhere in the workflow and thus provides a return on investment.

Members of the WAG would create a set of User Stories, an informal, general description of the AI benefits and features, written from a users’ perspective. With the advice from the DAIC, NAIC, RAF Rapid Capability Office (RCO) / RAF Digital or Army AI Centre, other members of the team would ensure that data is in a fit state for AI model training. In this use-case, this would involve labeling overall images of contamination, or the individual contaminants within an image, depending on the AI approach to be used (image recognition or object detection, respectively). Again, the use of the Defence Data Analytics Portal (DDAP), or a cheap, third-party licensed product, provides remote access to the tools that enable this. The team now holds a number of advantages over traditional, contractor-led approaches to AI deployment, potentially sufficient to cross the Valley of Death:

  • They are likely to know colleagues across the three Services facing the same problem, so can check that a solution has not already been developed elsewhere.10
  • With success metrics, labelled data and user requirements all held, the team has already overcome the key blockers to success, reducing the risk that expensive contractors, if subsequently used, will begin project delivery without these key building blocks in place.
  • They have a key understanding of how much value will be generated by a successful project, and so can quickly ‘pull the plug’ if insufficient benefits arise. There is also no financial incentive to push on if the approach clearly isn’t working.
  • Alternatively, they have the best understanding of how much value is derived if the project is successful.
  • As junior Front-Line operators, they benefit directly from any service improvement, so are not only invested in the project’s success, but can sponsor the need for BaU funding to be released to sustain the project in the long term, if required.
  • If working with contractors, they can provide immediate user feedback, speeding up development time and enabling a true Agile process to take place. Currently, contractors struggle to access users to close this feedback loop when working with MOD.

Again, Flag Officer sponsorship of such an endeavor is vital. This individual can ensure that proper recognition is awarded to individuals and make deeper connections across FLCs, as required.

Figure 3: Defense Digital’s Defense Data Analytics Portal (DDAP) is tailor-made for small, Front-Line teams to clean and label their data and deploy AI services and products, either standalone or via existing, call-off contract contractor support.

Prioritizing Quick, Hands-on Problem Solving

WAGs provide an incentive for more entrepreneurial, digitally minded individuals to remain in Service, as it creates an outlet for those who wish to learn to code and solve problems quickly, especially if the problems faced are ones they wrestle with daily. A good example of where the RN has successfully harnessed this energy is with Project KRAKEN, the RN’s in-house deployment of the Palantir Foundry platform. Foundry is a low-code way of collecting disparate data from multiple areas, allowing it to be cleaned and presented in a format that speeds up analytical tasks. It also contains a low-level AI capability. Multiple users across the RN have taken it upon themselves to learn Foundry and deploy it to solve their own workflow problems, often in their spare time, with the result that they can get more done, faster than before. With AI tools becoming equally straightforward to use and deploy, the same is possible for a far broader range of applications, provided that cross-TLB resources can be concentrated at a junior level to enable meaningful projects to start.

Figure 4: Pre-existing Data/AI products or APIs, bought from commercial providers, or shared from elsewhere in Government, are likely to provide the fastest, cheapest route to improving workflows.11

Deploying COTS Products Over Tailored Services

Figure 4 shows the two main options available for WAGs when deploying AI or data science capabilities: Products or Services. Products are standalone capabilities created by industry to solve particular problems, usually made available relatively cheaply. Typically, COTS, they are sold on a per-use, or time-period basis, but cannot be easily tailored or refined if the user has a different requirement.

By contrast, Services are akin to a consulting model where a team of AI and Machine Learning engineers build an entirely new, bespoke system. This is much more expensive and slower than deploying a Product but means users should get exactly what they want. Occasionally, once a Service has been created, other users realize they have similar requirements as the original user. At this point, the Service evolves to become a Product. New users can take advantage of the fact that software is essentially free to either replicate or connect with and gain vast economies of scale from the initial Service investment.

WAGs aim to enable these economies of scale; either by leveraging the investment and speed benefits inherent in pre-existing Products or ensuring that the benefits of any home-made Services are replicated across the whole of the MOD, rather than remaining stove-piped or siloed within Single Services.

Commercial/HMG Off the Shelf Product. The most straightforward approach is for WAGs to deploy a pre-existing product, licensed either from a commercial provider, or from another part of the Government that has already built a Product in-house. Examples include the RAF’s in-house Project Drake, which has developed complex Bayesian Hierarchical models to assist with identifying and removing training pipeline bottlenecks; these are Crown IP, presumably available to the RN at little-to-no cost, and their capabilities have been briefed to DAIC (and presumably briefed onwards to the NAIC).

Although straightforward to procure, it may not be possible to deploy COTS products on MOD or Government systems, and so may be restricted up to OFFICIAL or OFFICIAL SENSITIVE only. Clearly, products developed or deployed by other parts of MOD or National Security may go to higher classifications and be accessible from MODNet or higher systems. COTS products are usually available on a pay-as-you-go, monthly, or user basis, usually in the realm of circa £200 per user, per month, providing a fast, risk-free way to understand whether they are valuable enough to keep using longer-term.

Contractor-supported Product. In this scenario, deployment is more complex; for example, the product needs to deploy onto MOD infrastructure to allow sensitive data to be accessed. In this case, some expense is required, but as pre-existing COTS, the product should be relatively cheap to deploy as most of the investment has already been made by the supplier. This option should allow use up to SECRET but, again, users are limited to those use-cases offered by the commercial market. These are likely to be focused on improving maintenance and the analysis of written or financial information. The DAIC’s upcoming ‘LLMs for MOD’ project is likely to be an example of a Contractor-supported Product; MOD users will be able to apply for API access to different Large Language Model (LLM) products, hosted on MOD infrastructure, to solve their use-cases. Contractors will process underlying data to allow LLMs to access it, create the API, and provide ongoing API connectivity support.

Service built in-house. If no product exists, then there is an opportunity to build a low-code solution in DDAP or MODCloud and make it accessible through an internal app. Some contractor support may be required, particularly to provide unique expertise the team cannot provide themselves (noting that all three Services may have Digital expertise available via upcoming specialist Reserve branches, with specialist individuals available at a fraction of the cost of their civilian equivalent day rates).12 Defense Digital’s ‘Enhanced Data Teams’ service provides a call-off option for contractors to do precisely this for a short period of time. It is likely that these will not, initially, deploy sophisticated data analysis or AI techniques, but sufficient value may be created with basic data analytics. In any event, the lessons learnt from building a small, relatively unsophisticated in-house service will provide sufficient evidence to ascertain whether a full, contractor-built AI service will provide value for money, if built. Project Kraken is a good example of this; while Foundry is itself a product and bought under license, it is hosted in MOD systems and allows RN personnel to build their own data services within it.

Service built by contractors. These problems are so complex, or unique to Defense, that no COTS product exists. Additionally, the degree of work is so demanding that Service Personnel could not undertake this work themselves. In this case, WAGs should not be deployed. Instead, these £100k+ programs should remain the purview of Defense Digital or the DAIC and aim to instead provide AI Building Blocks that empower WAGs to do AI work. In many cases, these large service programs provide cheap, reproducible products that the rest of Defense can leverage. For example, the ‘LLMs for MOD’ Service will result in relatively cheap API Products, as explained above. Additionally, the British Army is currently tendering for an AI-enabled system that can read the multiple hand-and-type-written text within the Army archives. This negates the need for human researchers to spend days searching for legally required records that can now be found in seconds. Once complete, this system could offer itself as a Product that can ingest complex documents from the rest of the MOD at relatively low cost. This should negate the need for the RN to pay their own 7-figure sums to create standalone archive scanning services. To enable this kind of economy of scale, NAIC could act as a liaison with these wider organizations. Equipped with a ‘shopping list’ of RN use cases, it could quickly deploy tools purchased by the Army, RAF or Defense Digital across the RN.

Finding the Time

How can WAG members find the time to do the above? By delegating budget control down to the lowest level, and focusing predominantly on buying COTS products, the amount of time required should be relatively minimal; in essence, it should take the same amount of time as buying something online. Some work will be required to understand user stories and workflow design, but much of this will already be in the heads of WAG members. Imminent widespread MOD LLM adoption should, in theory, imminently reduce the amount of time spent across Defense on complex, routine written work (reviewing reports, personnel appraisals, post-exercise or deployment reports or other regular reporting).13 This time could be used to enable WAGs to do their work. Indeed, identifying where best to deploy LLMs across workflows are likely to be the first roles of WAGs, as soon as the ‘LLMs for MOD’ program reaches IOC. Counter-intuitively, by restricting the amount of time available to do this work, it automatically focuses attention on solutions that are clearly valuable; solutions that save no time are, by default, less likely to be worked on, or have money spent on them.

Conclusions

The RN runs the risk of spreading the NAIC’s budget too thinly, in its attempt to ‘jumpstart’ use of AI across Business as Usual disciplines. By contrast, users should be encouraged to form Workflow Automation Guilds across FLCs. Supported by a senior sponsor, knowledgeable members of the Reserves, the NAIC and one-on-one time with the DAIC, WAGs could instead focus on the COTS solution, or pre-existing Crown IP, that will best solve their problem. Budget responsibilities should be delegated down too, thereby enabling access to existing, centralized pools of support, such as the Enhanced Data Teams program, DDAP, or the upcoming ‘LLMs for MOD’ API Service. In this way, projects are more likely to succeed, as they will have demonstrated value from the very start and will have been co-developed by the very users that deploy them. The speed at which AI and data services are becoming easier to use is reflected by the RN’s Kraken team, while the need to trust low-level officers and junior rates is borne out by the success currently being enjoyed by both the USAF and USN with their own complex AI deployments.

Prior to leaving full-time service, Lieutenant Commander Francis Heritage, Royal Navy Reserve, was a Principal Warfare Officer and Fighter Controller. Currently an RNR GW officer, he works at the Defence arm of Faculty, the UK’s largest independent AI company. LtCdr Francis is now deployed augmenting Commander United Kingdom Strike Force (CSF).

The views expressed in this paper are the author’s, and do not necessarily represent the official views of the MOD, the Royal Navy, RNSSC, or any other institution.

References

1. Discovery’ is the first of 5 stages in the UK Government Agile Project Delivery framework, and is followed by Alpha, Beta, Live and Retirement. Each stage is designed to allow the overall program to ‘fail fast’ if it is discovered that benefits will not deliver sufficient value.

2. Author’s observations.

3. Volvo and the US commodities group Bureau Veritas both have Commercial off the Shelf products available for solving this particular problem.

4. Source: https://www.v7labs.com/pricing accessed 10 Apr 2024.

5. Source: https://assets.publishing.service.gov.uk/media/65bb75fa21f73f0014e0ba51/Defence_AI_Playbook.pdf

6. AI systems rely on machine learning frameworks and libraries; Docker packages these components together into reproducible ‘containers’, simplifying deployment. Kubernetes builds on Docker, providing an orchestration layer for automating deployment and management of containers over many machines.

7. Defence Unicorns podcast, 5 Mar 2024.

8. Source: Navy’s new ‘Project OpenShip’ aims to swiftly apply AI to data captured by vessels at sea | DefenseScoop.

9. https://www.afcea.org/signal-media/navys-junior-officers-lead-way-innovation.

10. The author knows of at least 3 AI projects across MOD aimed at automating operational planning and another 3 aiming to automate satellite imagery analysis.

11. API stands for Application Programming Interface, a documented way for software to communicate with other software. By purchasing access to an API (usually on a ‘per call’ or unlimited basis) a user can take information delivered by an API and combine it with other information before presenting it to a user. Examples include open-source intelligence, commercial satellite imagery, meteorological data, etc. 

12. Army Reserve Special Group Information Service, RNR Information Exploitation Branch and RAF Digital Reserves Consultancy. RNR IX and RAFDRC are both TBC.

13. Worldwide, Oliver Wyman estimates Generative AI will save an average of 2 hours per person per week; likely to be higher for office-based roles: https://www.oliverwymanforum.com/content/dam/oliver-wyman/ow-forum/gcs/2023/AI-Report-2024-Davos.pdf p.17.

Featured Image: The Operations Room of the carrier HMS Queen Elizabeth during an exercise in 2018. (Royal Navy photo)

Fiction

Situation Well in Hand: A Day in the Life for an EAB

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By Major Geoffrey L. Irving, USMCR

Smoke twisted slowly out of a burnt crater, listing sideways in the gray light of an overcast dawn. A gentle breeze caught the twist and wafted it downwind. To Staff Sergeant Ron Garcia it smelled familiar – sweet petroleum mixed with the acidic charred aftertaste of high explosive. He’d made it through another long night of missile strikes. The Staff Sergeant sat against the wall of his subterranean command post, watching the waves of the South China Sea while tracing the edges of his battered tablet with his finger. Soon, he’d have to go check the men and the gear, but he hesitated in a moment of quiet. He looked at his watch, it was May 5, 2040. The war had been going on for eight years. Eight years seemed too long, and he was tired.

Staff Sergeant Garcia was lean, with hunched shoulders that implied a coiled tense energy or intense fatigue depending on the light. He wore a bleached uniform that hung loosely on his frame. He had been out in this stretch of islands for nearly eighteen months making sure his motley team of Marines, soldiers, airmen, and local auxiliaries stayed focused and stayed alive. In that time, he’d never seen the enemy. What a way to fight a war.

A thick mass of low-slung clouds started to roll in, washing the island in a wet mist. “Perfect,” he thought to himself. He popped to his feet and walked back into the cave, quickly gulping a mouthful of water and a couple bites of stale protein bar. The other occupants of the CP slowly emerged.

“You running a check? SATCOM is still working, but landline is down after last night,” said Senior Airman Brenner, with bloodshot eyes and similarly loose-fitting fatigues.

“Yeah, I haven’t heard anything in a couple of hours so I’m going to go check on the Lieutenant and try to get a line to the big island. Get the power back on and go check the shoreline to see if we got any new deliveries. Leave Desmond here with Santo to monitor the SATCOM and watch the beach. I’ll be back before sunset.”

Slinging his rifle behind his back, Staff Sergeant Garcia checked the battery on his tablet and picked up a handheld radio before heading out the door.

As he left the mouth of the cave, Garcia pushed aside wire netting and instinctively looked up to scan the sky. With bounding strides, he walked downhill, following a beaten path into the remains of the fisherman’s outpost on the beach. The structures, rusted from neglect and punctured by fragmentation, were a reminder of the days before the war went hot – when it was sufficient to hold territory with flags and legal claims rather than Marines and steel. Despite appearances, they still managed to hide a missile launcher in the remains of the concrete block fisherman shelter. 

Garcia moved South along the rocky shore. The beach quickly ran out and he resorted to hopping across black volcanic rocks. This island was barely a mile long, so he didn’t have far to go. Another shallow bay emerged. Garcia turned inland and started the climb to one of the three sheltered outposts on the island. As he climbed, his nose twitched again as the smell of sweet petroleum and acidic char returned. The Marines had a launch site here on the windward side of the island. It was a good site, sheltered from direct overhead reconnaissance but with a commanding view of the sea to the West. The Lieutenant had taken a rotation here to spend some time with the guys.

Quickening his pace, Garcia turned a corner around two large boulders into the rocky platform and stopped in horror. Everything was black and smoking. His stomach dropped as he rushed to the twisted remains of his Marines. They were pushed up against the rough walls and cold to touch. The Lieutenant slouched near the edge of the platform, his jaw hung slack and loose against his chest. On the other side, Corporal Reston lay face down, his limbs splayed at acutely unnatural angles.

“Goddamn it,” Garcia breathed out quietly, touching the Lieutenant’s cold shoulder.

Looking up from the Marines, he assessed the launchers and missile stockpile. Like the Marines, the equipment was charred and twisted. The stacked missiles were toppled or burnt while the launcher showed gashes and pock marks where it must have been punctured by tungsten. He found the Lieutenant’s faded ball cap and stuffed it in a cargo pocket.

To get to the other launch site, Garcia had to cross over the island’s ridge. Luckily, the clouds still hung low and shrouded him from the sky. There was little foliage to speak of so walking across the ridge was always a risk. Garcia instinctively hunched down and ran across the island.

He dropped down to the leeward side, slipped and nearly tumbled into Corporal Masterson and Lance Corporal Hubert huddled in their hole. This site was sheltered on three sides by jagged vertical rocks that stuck up out of the ocean like fingers. Masterson tried to catch Garcia and gave him a hand down into their shelter. Garcia took a seat next to them.  

“You guys OK?” He asked.

“Yeah, although they seemed angry about something last night,” Masterson said with a grin.  

“That’s why I need you to keep it locked in today. How’s your gear?” Garcia asked.

“Missiles are dry. Drones are charged and ready. Ammo is the same as it always is. Targeting diagnostics are all green gumballs. Could use some new items on the menu, though.”

“Got it. Just be thankful you’ve got a menu,” Garcia grumbled, as he looked out from this natural bunker at the East side of the island and the Philippine Sea.    

“The Lieutenant and Reston are dead. Comms are down, but I’ll get them fixed soon,” Garcia continued.

The Marines followed Garcia’s gaze out to the ocean.

“I’m ready to go home,” said Hubert.  

Garcia spent the rest of the day checking on assets sprinkled around the island. He recalled a story he read growing up – of Robinson Crusoe washed up on a deserted island in the middle of the sea. Crusoe had built shelter, sowed crops, and befriended a native man named Friday. Except for cannibals, it sounded like a grand adventure. When Garcia was first dropped off on the island he had felt like Crusoe, but that feeling was long gone.

This island got nearly everything from the sea. Garcia walked along the leeward side and came to a camouflaged concrete box nestled in the rocks above the high-water line. He popped off a metal manhole cover to reveal the hardware inside. The contents of the box were their lifeline to the cabling that connected them with Luzon and brought them consistent electricity. This box charged their batteries and was the network switch for their wired communications connecting the CP to each launch site.

Talking over radio was possible. Talking over SATCOM was possible. But, this close to the PLA Navy, even a radio squelch invited a missile or a drone while wired communication stayed out of earshot and only suffered from a busted wire here and there. So, they used old school wire to talk and only monitored SATCOM to receive critical tasking.     

About a quarter mile offshore was an array of submarine batteries installed on the floor of the island’s shelf that pulled energy off the telecommunications line, converted it, and fed it into this box. That was enough power to keep them going indefinitely.  

The box was humming and its contents were intact. Garcia connected his tablet into a port and watched the screen. He reviewed diagnostics for the battery systems, the subsea cable line, and the cable line’s sensors. Then, he got on the net, authenticated his crypto, and typed a quick message:

“ROWAN3, FRESNO9. SITREP. PLA-N MISSILE ATTACK. 2 KIA. 1 LAUNCHER DESTROYED. SITUATION WELL IN HAND.”

Garcia’s island was a small but important outpost. The Company was based on the “big island,” which was a misnomer because the “big island” was only five miles long. There were detachments manning other small outposts on outlying islands, but Garcia’s was the northernmost, meaning they had the greatest range but were also the most exposed. The Marines and missiles sprinkled around the Philippine Sea were meant to deny the PLA Navy freedom of operation in these constrained waters and augment the combat capacity of the waning US Navy surface fleet.

Garcia saw an alert flash on his screen for an inbound message.

“FRESNO9, ROWAN3. ACK. BE ADVISED. INCREASED PLA-N SURFACE/SUBSURFACE ACTIVITY ANTICIPATED IN AO. HOLD CURRENT POS DESTROY ANY EN OVER II THRESHOLD. RELIEF AS SCHEDULED NOT BEFORE.”

“Shit.”

As dusk was beginning to set in, Garcia hurried back into the CP. He saw Santo Biyernes, a big island local who served as an auxiliary member of their unit, unpacking a number of large waterproof bags lined up against the wall, and exclaimed with relief.

“What did we get!?”

Santo turned around and smiled a welcome as Brenner walked out from the tactical operations room.

“Mostly food. But also two new tube-launched drones, a couple of replacement satellite arrays, and de-sal kits. I saw the boat caught out in a reef, so I got a little wet dragging it in.” Brenner said, swelling with pride as if he were a hunter who had killed his meal instead of dragging in one of the thousands of surface maritime drones that were slowly but surely supplying the static island campaign.

“Awesome. Are comms up? I think it’s just a wire shunt.”

“Yeah. I found the shunt and patched it. We’re up. I saw a message came in, but couldn’t read it.”

“I have it here,” Garcia said, raising his tablet. “Red is coming our way in a big way and we need to be ready.”

“Where’s the Lieutenant?” Brenner asked, wide eyed.

“He got hit last night, but we’re going to get ours tonight.”

With communications re-established with Masterson and Hubert on the leeward side of the island and the rest of the Company on the big island, Garcia leapt into action. He needed to find the enemy.

Each of the missile sites had a number of rotary and tube-launched fixed wing drones equipped with sensor arrays to identify enemy ships and guide missiles into them. Garcia got the long-range drones into the air and traveling west to the vicinity of known sea corridors. He didn’t have to worry about controlling them because their AI understood the mission.

Garcia had been an artilleryman for the better part of two decades. As he booted his reconnaissance and targeting systems up, he thought about how much his tools had evolved. He was first trained on rudimentary and temperamental AFATDS fire control software on the Oklahoma plains, then on the KillSwitch mobile app in the California hills. Now, seated on a makeshift bench hunched over two screens, Garcia activated the distributed acoustic sensor suite along his island’s subsea cables. In addition to a single connection between his island and the big island, the cable was festooned along the coastline. This festoon created multiple redundant cable landing access points and also allowed Garcia to monitor the depths of the sea around him. On his other screen, he received video feeds from the aerial drones. He now had eyes and ears in the sky and the sea.

With the missiles loaded and activated, he called his Marines back to the CP. Masterson and Hubert shuffled in with a renewed sense of urgency and purpose. Masterson took a seat next to Garcia while Hubert quickly pulled the .50 caliber machine gun from the recesses of the cave and set it to cover the bay. Corporal Masterson and Lance Corporal Desmond monitored launcher diagnostics on their own tablets while keeping an eye on Garcia. Now it was a waiting game.

“We’re looking for anything over threshold two, so more than 7,000 tons. That means we’re looking for Type 61 or 57 destroyers, or even an old Type 55 Renhai if we have to settle,” Garcia muttered as he watched images from the airborne drones pop up on his feed.

The small fleet of drones, both from Garcia and the rest of the Company on the big island, communicated with each other and coordinated their search path. They had cues about where the enemy fleet was likely steaming from and where they were likely steaming to, so their AI could anticipate the likely path. Sure enough, well into the night, the first targets began to materialize on Garcia’s screen.

Garcia saw the highlighted outline of a Type 61 destroyer appear and felt a wave of adrenaline flush into his bloodstream. His fingers tingled and shook as the drone cycled through different sensor spectrums to identify the vessel.

“Standing by to fire, Staff Sergeant,” one of the Corporals whispered, dripping with anticipation.

“Alright. Relax. We have to wait until we identify more, and the AI matches us,” Staff Sergeant Garcia soothed. Firing at the first identified target would spoil the surprise. They would have to wait for the AI to calculate the ideal flight path of each of the Company’s launch sites, match their launcher to the right ship, and deconflict through the Navy’s antiquated JADC2 targeting network. Garcia hoped the AI would do its job right.

Another tense 20 minutes passed. The number of targets acquired was quickly growing. After finding the first ship, the AI could easily anticipate the enemy’s order of battle. It seemed obvious that the PLA Navy fleet was heading directly for the US Fleet at Camilo Osias Naval Base under cloud cover and darkness. With few U.S. Navy surface ships in the South China Sea, they must’ve felt uncontested.

Then, the target list abruptly started to shrink. Garcia stared at his screen, growing impatient and increasingly concerned with each passing minute as images blinked off the screen, targets fell off the list, and yet he had not received an order to launch.

“What the hell, Staff Sergeant?” one of the Corporals muttered.

Garcia was at a loss. His team was ready. They had done everything right. The list had been full of ripe targets – lumbering surface vessels with meager defenses just begging for a naval strike missile. A target allocation to his team would have justified his last eighteen months of semi-starvation. It would have justified the daily battle drills that he had forced his team to sweat through in full PPE over and over again. It would have justified eighteen months away from his wife and two daughters, who he was scared wouldn’t recognize him when he came home. It would mean that the Lieutenant’s missing jaw and Reston’s shattered limbs would have had a purpose – a purpose other than fulfilling some General’s wet dream of what the new Marine Corps should be. Tears welled up in Garcia’s eyes as he clenched his fists and tried to stop himself from screaming.

The target list dwindled down to vessels below their threshold – tenders, minesweepers, ammunition boats. There must be something wrong with his systems. He tested the connections, running his shaking fingers over each wire and port. Nothing.

Garcia looked at the screen of his cable sensing system. The diagnostic dashboard showed no problems. Then he looked at the time in the corner of the screen. It read 9:47pm. The screen had been frozen for hours. Garcia furiously grabbed the tablet, closed out of its programs and restarted. The boot procedure stretched on for what felt like eternity. As the cable sensing system came online, the acoustic disturbances in the water surrounding the subsea cables north of his island gave him a clear picture.

“It’s CV-35!” CV-35 Shaoshan was the PLA Navy’s cutting-edge aircraft carrier. She was escorted by a pair of destroyers and an amphibious ship and seemed to be making a quiet run around the southern tip of Taiwan to break out of the first island chain into the Philippine Sea.

“AI must have known CV-35 was missing!” Garcia cried out.

The AI finished its calculations, reorienting the remaining missiles from Staff Sergeant Garcia’s launchers to target CV-35, and flashed a message to Garcia.

“Fire.” 

Geoffrey Irving works for the Department of Commerce’s Bureau of Industry and Security identifying and addressing vulnerabilities in information and communications technology supply chains. Geoff previously served on active duty with the U.S. Marine Corps and currently serves in the U.S. Marine Corps Reserve. Geoff is a graduate of Tsinghua University College of Law and writes about the national security implications of economic and technological competition.

Featured Image: Art made with Midjourney AI.

Future Tech

Alexa, Write my OPORD: Promise and Pitfalls of Machine Learning for Commanders in Combat

1 Comment

By Jeff Wong

Introduction

Jump into the time machine and fast forward a few years into the future. The United States is at war, things are not going well, and the brass wants to try something new. John McCarthy,1 a Marine lieutenant colonel whose knowledge of technology is limited to the Microsoft Office applications on his molasses-slow government laptop, mulls over his tasks, as specified by his two-star boss, the commander of Joint Task Force 58:

1. Convene a joint planning group to develop a plan for the upcoming counteroffensive. (Check.)

2. Leverage representatives from every staff section and subject-matter experts participating virtually from headquarters in Hawaii or CONUS. (Roger.)

3. Use an experimental machine-learning application to support the planning and execution of the operation. (We’re screwed.)

Nearly 7,000 miles from a home he might never see again, McCarthy considered two aphorisms. The first was from Marcus Aurelius, the second-century Roman emperor and stoic thinker: “Never let the future disturb you. You will meet it, if you have to, with the same weapons of reason which today arm you against the present.” 2 The second was from Mike Tyson, the fearsome boxer, convicted felon, and unlikely philosopher: “Everybody has a plan until they get punched in the mouth.”3

Artificial intelligence (AI), including large-language models (LLMs) such as ChatGPT, is driving a societal revolution that will impact all aspects of life, including how nations wage war and secure their economic prosperity. “The ability to innovate faster and better – the foundation on which military, economic, and cultural power now rest – will determine the outcome of great-power competition between the United States and China,” notes Eric Schmidt, the former chief executive officer of Google and chair of the Special Competitive Studies Project.4 The branch of AI that uses neural networks to mimic human cognition — machine learning (ML) — offers military planners a powerful tool for planning and executing missions with greater accuracy, flexibility, and speed. Senior political and military leaders in China share this view and have made global technological supremacy a national imperative.5

Through analyzing vast amounts of data and applying complex algorithms, ML can enhance situational awareness, anticipate threats, optimize resources, and adapt to generate more successful outcomes. However, as ML becomes more widespread and drives technological competition against China, American military thinkers must develop frameworks to address the role of human judgment and accountability in decision-making and the potential risks and unintended consequences of relying on automated systems in war.

To illustrate the promise and pitfalls of using ML to support decision-making in combat, imagine the pages of a wall calendar flying to some point when America must fight a war against a peer adversary. Taking center stage in this fictional journey are two figures: The first is McCarthy, an officer of average intelligence who only graduated from the Eisenhower School for National Security and Resource Strategy thanks to a miraculous string of B+ papers at the end of the academic year. The second is “MaL,” a multimodal, LLM that is arguably the most capable – yet most immature – member of McCarthy’s planning team. This four-act drama explores how McCarthy and his staff scope the problems they want MaL to help them solve, how they leverage ML to support operational planning, and how they use MaL to support decision-making during execution. The final act concludes by offering recommendations for doctrine, training and education, and leadership and policies to better harness this capability in the wars to come.

Act One: “What Problems Do We Want This Thing to Solve?”

The task force was previously known as “JTF-X,” an experimental formation that blended conventional legacy platforms with AI and autonomous capabilities. As the tides of war turned against the United States and its allies, the Secretary of Defense (SecDef) pressured senior commanders to expand the use of AI-enabled experimental capabilities. Rather than distribute its capabilities across the rest of the joint force, the SecDef ordered JTF-X into duty as a single unit to serve as the theater reserve for a geographic combatant commander. “Necessity breeds innovation… sometimes kicking and screaming,” she said.

 Aboard the JTF command ship in a cramped room full of maps, satellite imagery, and charts thick with unit designators, McCarthy stared at a blinking cursor on a big-screen projection of MaL. Other members of his OPT looked over his shoulder as he impulsively typed out, “Alexa, write my OpOrd.” Undulating dots suggested MaL was formulating a response before MaL responded, “I’m not sure what you’re asking for.”

The JPG chief, an Air Force senior master sergeant, voiced what the humans in the room were thinking: “Sir, what problems do we want this thing to solve?”

The incredible capacity of ML tools to absorb, organize, and generate insights from large volumes of data suggests that they hold great promise to support operational planning. Still, leaders, planners, and ML tool developers must determine how best to harness the capability to solve defined military problems. For instance, the Ukrainian military uses AI to collect and assess intelligence, surveillance, and reconnaissance (ISR) data from numerous sources in the Russia-Ukraine conflict.6 But as Ukrainian planners are probably discovering today, they must do more than throw current ML tools at problem sets. Current LLMs fall short of desired capabilities to help human planners infer and make sense within the operating environment. Military professionals must tailor the problem sets to match the capabilities and limitations of the ML solutions.

Although tools supporting decision advantage comprised a small fraction of the 685 AI-related projects and accounts within the DoD as of 2021, existing efforts align with critical functions such as the collection and fusion of data from multiple domains (akin to the DoD’s vision for Joint All-Domain Command and Control (JADC2)); multidomain decision support for a combatant command headquarters; automated analysis of signals across the electromagnetic spectrum; and location of bots to support defensive cyber operations.7 There are numerous tools with various tasks and functions, but the crux of the problem will be focusing the right tool or set of tools on the appropriate problem sets. Users need to frame planning tasks and precisely define the desired outputs for a more immature LLM capability, such as the fictional MaL.

McCarthy mapped out a task organization for the JPG to align deliverables with available expertise. The team chief scribbled dates and times for upcoming deliverables on a whiteboard, including the confirmation brief for the commander in 24 hours. An Army corporal sat before a desktop computer to serve as the group’s primary interface with MaL. To help the group develop useful queries and troubleshoot, MaL’s developers in Hawaii participated via a secure video teleconference.

MaL was already able to access volumes of existing data – operations and contingency plans, planning artifacts from previous operations, ISR data from sensors ranging from national assets to stand-in forces in theater, fragmentary orders, and mountains of open-source information.

Act Two: MaL Gets Busy to the ‘Left of the Bang’

Some observers believe that ML capabilities can flatten the “orient” and “decide” steps of the late military theorist John Boyd’s observe-orient-decide-act decision (OODA) loop, expanding a commander’s opportunity to understand context, gain an appreciation of the battlespace, frame courses of action, and explore branches and sequels to inform decisions.8 Nevertheless, the greater capacity that ML tools provide does not eliminate the need for leaders to remain intimately involved in the planning process and work with the platform to define decision points as they weigh options, opportunities, risks, and possible outcomes.

Planners should examine frameworks such as the OODA Loop, IPB, and the Joint Operational Planning Process (JOPP) to guide where they could apply ML to maximum effect. To support IPB, ML can automate aspects of collection and processing, such as identifying objects, selecting potential targets for collection, and guiding sensors. ML capabilities for image and audio classification and natural language processing are already in commercial use. They support essential functions for autonomous vehicles, language translation, and transit scheduling. These commercial examples mirror military use cases, as nascent platforms fuse disparate data from multiple sources in all five warfighting domains.9

MaL’s digital library included the most relevant intelligence reports; adversary tactics, techniques, and procedures summaries; videos of possible target locations taken by uncrewed systems; raw signals intelligence; and assessments of the enemy orders of battle and operational tendencies from the early months of the conflict. The corpus of data also included online news stories and social media postings scraped by an all-source intelligence aggregator.

 McCarthy said, “As a first step, let’s have MaL paint the picture for us based on the theater-level operational context, then create an intelligence preparation of the battlespace presentation with no more than 25 PowerPoint slides.” After the clerk entered the query, the graphic of a human hand drumming its fingers on a table appeared.

Two minutes later, MaL saved a PowerPoint file on the computer’s desktop and announced in a metallic voice, “Done, sir, done!” McCarthy and his J-2 reviewed the IPB brief, which precisely assessed the enemy, terrain, weather, and civil considerations. MaL detailed the enemy’s most likely and dangerous courses of action, discussed adversary capabilities and limitations across all domains, and provided a target-value analysis aligning with the most recent intelligence. The J-2 reviewed the product and said, “Not bad.” She added, “Should I worry about losing my job?”

“Maybe I should worry about losing mine,” McCarthy said. “Let’s go through the planning process with MaL and have it generate three COAs based on the commander’s planning guidance and intent statement.”

American military planning frameworks – JOPP and its nearly identical counterparts across the services – are systematic processes that detail the operational environment, the higher commander’s intent, specified and implied tasks, friendly and enemy COAs, and estimates of supportability across all warfighting functions. They benefit the joint force by establishing uniform expectations about the information needed to support a commander’s estimate of the situation. However, current planning frameworks may hinder decision-making because a commander and his staff may become too focused on the process instead of devoting their energies and mental bandwidth to quickly orienting themselves to a situation and making decisions. Milan Vego, a professor of operational art at the U.S. Naval War College, writes of “a great temptation to steadily expand scope and the amount of information in the (commander’s) estimate. All this could be avoided if commanders and staffs are focused on the mental process and making a quick and good decision.”10

An ML-enabled decision-support capability could help planners stay above process minutiae by suggesting options for matching available weapon systems to targets, generating COAs informed by real-time data, and assessing the likelihood of success for various options in a contemporary battlespace, which features space and cyberspace as contested warfighting domains.11

MaL developed three unacceptable COAs which variously exceeded the unit’s authorities as outlined in the JTF’s initiating directive or extended kinetic operations into the adversary’s mainland, risking escalation.

McCarthy rubbed his face and said, “Time for a reboot. Let’s better define constraints and restraints, contain operations in our joint operational area, and have it develop assessments for risks to mission and force.” He added, “And this time, we’ll try not to provoke a nuclear apocalypse.”

The planning team spent several more hours refining their thoughts, submitting prompts, and reviewing the results. Eventually, MaL generated three COAs that were feasible, acceptable, complete, and supportable. MaL tailored battlespace architecture, fire support coordination measures, and a detailed sustainment plan for each COA and mapped critical decision points throughout the operation. MaL also assisted the JTF air officer develop three distinct aviation support plans for each COA. 

The planning team worked with MaL to develop staff estimates for each COA. The logistics and communications representatives were surprised at how quickly MaL produced coherent staff estimates following a few hours of queries. The fires, intelligence, and maneuver representatives similarly worked with MaL to develop initial fire support plans to synchronize with the group’s recommended COA.

McCarthy marveled at MaL’s ability to make sense of large amounts of data, but he was also surprised at the ML platform’s tendency to misinterpret words. For instance, it continually conflated the words “delay,” “disrupt,” and “destroy,” which were distinct tactical tasks with different effects on enemy formations. The planning team reviewed MaL’s COA overviews and edited the platform’s work. The staff estimates were detailed, and insightful, but still required corrections.

During the confirmation brief, the JTF commander debated several details about MaL’s outputs and risk assessments of the planning team’s recommended COA. McCarthy said, “Respectfully, Admiral, this is your call. MaL is one tool in your toolbox. We can tweak the COA to suit your desires. We can also calibrate the level of automation in the kill chain based on your intent.”

After a moment, the admiral said, “I’ll follow the planning team’s recommendation. Notice that I didn’t say MaL’s recommendation because MaL is just one part of your team.”

Act Three: MaL Lends a Hand to the Right of the ‘Bang’

Contemporary military thinkers believe that ML-enabled tools could improve decision-making during the execution of an operation, leveraging better situational awareness to suggest more effective solutions for problems that emerge in a dynamic battlespace. However, critics argue that developing even a nascent ML-enabled capability is impossible because of the inherent limits of ML-enabled platforms to generate human-like reasoning and infer wisdom from incomplete masses of unstructured data emanating from a 21st-century battlefield. Some are also concerned about the joint force’s ability to send and receive data from a secure cloud subject to possible malicious cyber activities or adversarial ML. Prussian military thinker Carl von Clausewitz reminds practitioners of the operational art that “War is the realm of uncertainty; three-quarters of the factors on which action in war is based are wrapped in a fog of greater or lesser uncertainty.”12 Technological solutions such as ML-enabled capabilities can temporarily lift parts of this fog for defined periods. Still, users must understand the best use of these capabilities and be wary of inferring certainty from their outputs.

Emerging capabilities suggest that ML can augment and assist human decision-making in several ways. First, ML systems can enhance situational awareness by establishing and maintaining a real-time common operational picture derived from multiple sensors in multiple domains. Greater battlespace awareness provides context to support better decision-making by commanders and more accurate assessments of events on the battlefield. Second, ML can improve the effectiveness and efficiency of kill-chain analytics by quickly matching available sensors and shooters with high-value or high-payoff targets.13 This efficiency is essential in the contemporary battlespace, where ubiquitous sensors can quickly detect a target based on a unit’s emissions in the electromagnetic spectrum or signatures from previously innocuous activities like an Instagram post or a unit’s financial transaction with a host-nation civilian contractor.

Indeed, some AI observers in the U.S. defense analytic community argue that warfighters must quickly adopt ML to maintain a competitive edge against the People’s Liberation Army, which some observers believe will favor the possible gains in warfighting effectiveness and efficiency over concerns about ethical issues such as implementing human-off-the-loop AI strategies.14 ML-enabled feedback constructs will enhance the control aspects of command and control to employ a large, adaptable, and complex multidomain force.15

It was now D+21. JTF-58 achieved its primary objectives, but the campaign did not unfold as intended. During the shaping phase of the operation, several high-value targets, including mobile anti-ship cruise missile batteries, escaped kinetic targeting efforts, living to fight another day and putting U.S. naval shipping at risk for the latter phases of the campaign. MaL failed to update the developers’ advertised “dynamic operating picture” despite attempts by forward-deployed sensors and reconnaissance teams to report their movement. Incredibly, the DoD still did not have access to data from some weapon systems due to manufacturer stipulations.16

MaL’s developers insisted that the forward-deployed sensors should have had enough computing power to push edge data to the JTF’s secure cloud. The CommO speculated that environmental conditions or adversary jamming could have affected connectivity. McCarthy shook his head and said, “We need to do better.”

MaL performed predictably well in some areas to the right of the bang. The task force commander approved using MaL to run networked force protection systems, including a Patriot battery that successfully intercepted an inbound missile and electronic-warfare (EW) systems that neutralized small unmanned aerial systems targeting a fuel farm. MaL’s use in these scenarios did not stretch anyone’s comfort level since these employment methods were barely different than the automation of systems like the U.S. Navy’s Phalanx close-in weapon system (CIWS), which has detected, evaluated, tracked, engaged, and conducted kill assessments of airborne threats for more than four decades.17

MaL’s communications and logistics staff estimates were precise and valuable for the staff. The CommO adjusted the tactical communications architecture based on MaL’s predictions about enemy EW methods and the effects of weather and terrain on forward maneuver elements. Similarly, the LogO worked with the OpsO to establish additional forward-arming and refueling points (FARPs) based on MaL’s fuel and munitions consumption projections.

In the middle of the operation, the task force commander issued a fragmentary order to take advantage of an unfolding opportunity. MaL leveraged open-source data from host-nation news websites and social media postings by enemy soldiers to inform battle damage assessment of kinetic strikes. Some of that information was fake and skewed the assessment until the intelligence officer corrected it by comparing it with satellite imagery and human intelligence reporting.

As with any emerging capability, commanders and their staffs must consider the risks of integrating ML into the planning, execution, and assessment of operations. One of the risks is inherent in forecasting, as the ML platform artificially closes the feedback loop to a decision-maker sooner than one would expect during real-world operations. Retired U.S. Navy Captain Adam Aycock and Naval War College professor William Glenney IV assert that this lag might make ML outputs moot when a commander makes a decision. “The operational commander might not receive feedback, and the effects of one move might not be recognized until several subsequent moves have been made,” Aycock and Glenney write. “Furthermore, a competent enemy would likely attempt to mask or manipulate this feedback. Under such circumstances … it is difficult to ‘learn’ from a first move in order to craft the second.”18

Another risk is that the data used by ML platforms are, in some way, inaccurate, incomplete, or unstructured. Whether real or training, flawed data will lead to inaccurate outputs and likely foul an ML-enabled tool’s assessment of the environment and COA development. “Unintentional failure modes can result from training data that do not represent the full range of conditions or inputs that a system will face in deployment,” write Wyatt Hoffman and Heeu Millie Kim, researchers with the Center for Security and Emerging Technology at Georgetown University. “The environment can change in ways that cause the data used by the model during deployment to differ substantially from the data used to train the model.”19

The corollary to inaccurate data is adversarial ML, in which an enemy manipulates data to trick an ML system, degrade or disrupt optimal performance, and erode users’ trust in the capability. Adversarial ML tricks can trick an ML model into misidentifying potential targets or mischaracterizing terrain and weather. In one notable demonstration of adversarial ML, researchers at the Chinese technology giant Tencent placed stickers on a road to trick the lane recognition system of a Tesla semi-autonomous car, causing it to swerve into the wrong lane.20 Just the possibility of a so-called “hidden failure mode” could exacerbate fears about the reliability of any ML-enabled system. “Operators and military commanders need to trust that ML systems will operate reliably under the realistic conditions of a conflict,” Hoffman and Kim write. “Ideally, this will militate against a rush to deploy untested systems. However, developers, testers, policymakers, and commanders within and between countries may have very different risk tolerances and understandings of trust in AI.”21

Act Four: Hotwash

McCarthy took advantage of an operational pause to conduct a hotwash. Over lukewarm coffee and Monsters, the conversation gravitated toward how they could use MaL better. The group scribbled a few recommendations concerning integrating ML into doctrine, training and education, and leadership and policies until the ship’s 1-MC sounds general quarters. 

Doctrine: To realize the utility of ML, military leaders should consider two changes to existing doctrine. First, doctrine developers and the operational community should consider the concept of “human command, machine control,” in which ML would use an auction-bid process akin to ride-hailing applications to advertise and fulfill operational requirements across the warfighting functions. Under this construct, a commander publishes or broadcasts tasks, including constraints, priorities, metrics, and objectives. “A distributed ML-enabled control system would award-winning bids to a force package that could execute the tasking and direct the relevant forces to initiate the operation,” write naval theorists Harrison Schramm and Bryan Clark. “Forces or platforms that accept the commander’s bid conducts (or attempts to conduct) the mission and reports the results “when communications availability allows.”22 This concept supports mission-type orders/mission command and allows C2 architectures to flex to instances and areas subject to low-bandwidth constraints.23

Second, doctrine developers should adjust joint, service, and domain-centric planning processes to account for additional planning aids, such as LLMs, modeling and simulation, and digital twins, which can more deeply explore COAs, branches, and sequels and accelerate understanding of threats and the operating environment. Explicitly changing planning doctrine to account for these emerging capabilities will encourage their use and emphasize their criticality to effective planning.

Training and Education: Tactical units must train and continually develop ML technical experts capable of conducting on-the-spot troubleshooting and maintenance of the tool. Meanwhile, the services should develop curricula to train budding junior leaders — corporals, sergeants, lieutenants, ensigns, and captains — that introduce them to machine-learning tools applicable to their warfighting domains, provide best principles for generating productive outputs, and articulate risks – and risk mitigations – due to skewed data and poor problem framing.

Best practices should also be documented and shared across the DoD. Use of ML capabilities should become part of a JPG’s battle drill, featuring a designated individual whose primary duty is to serve as the human interface with a decision-support tool such as MaL. Rather than work from scratch at the start of every planning effort, JPGs should have a list of queries readily available for a range of scenarios that can inform a commander’s estimate of the situation and subsequent planning guidance, and formulation of intent based on an initial understanding of the operating environment. Prompts that solicit ML-enabled recommendations on task organization, force composition and disposition, risks to force or mission, targeting, and other essential decisions should be ready for immediate use to speed the JPG’s planning tempo and, ultimately, a unit’s speed of action as well. The information management officer (IMO), which in some headquarters staffs is relegated to updating SharePoint portals, should be the staff’s subject matter expert for managing ML capabilities. IMOs would be the military equivalent of prompt engineers to coax and guide AI/ML models to generate relevant, coherent, and consistent outputs to support the unit’s mission.24

Leadership and Policies: There are implications for senior leaders for warfighting and policy. Within a warfighting context, senior defense leaders must identify, debate, and develop frameworks for how commanders might use ML to support decision-making in wartime scenarios. It seems intuitive to use a multimodal LLM tool such as the fictitious MaL to support IPB, targeting, and kill chain actions; in the same way, campaign models are used to inform combatant commander planning for crises and contingencies.

However, leaders and their staffs must also understand the limitations of such tools to support a commander’s decision-making. “Do not ask for an AI-enabled solution without first deciding what decision it will influence and what the ‘left and right limits’ of the decision will be,” Schramm and Clark warn.25 Likewise, AI might not be the appropriate tool to solve all tactical and operational problems. “Large data-centric web merchants such as Amazon are very good at drawing inferences on what people may be interested in purchasing on a given evening because they have a functionally infinite sample space of previous actions upon which to build the model,” they write. “This is radically different from military problems where the amount of data on previous interactions is extremely small and the adversary might have tactics and systems that have not been observed previously. Making inference where there is little to no data is the domain of natural intelligence.”26

Meanwhile, future acquisition arrangements with defense contractors must provide the DoD with data rights – particularly data generated by purchased weapon systems and sensors – to optimize the potential of ML architecture in a warfighting environment.27 Such a change would require the DoD to work with firms in the defense industrial base to adjudicate disagreements over the right to use, licensing, and ownership of data – each of which might bear different costs to a purchaser.

Epilogue

Technologists and policy wonks constantly remind the defense community that the Department must “fail fast” to mature emerging technologies and integrate them into the joint force as quickly as possible. The same principle should guide the development of AI/ML-enabled warfighting solutions. Commanders and their staffs must understand that this is a capable tool that, if used wisely, can significantly enhance the joint force’s ability to absorb data from disparate sources, make sense of that information, and close kill chains based on an ML tool’s assessment.

If used unwisely, without a solid understanding of what decisions ML will support, the joint force may be playing a rigged game against a peer adversary. ML-enabled capabilities can absorb large amounts of data, process and organize it, and generate insights for humans who work at a relative snail’s pace. However, these nascent tools cannot reason and interpret words or events as a competent military professional can. As strategic competition between the United States and China intensifies over Taiwan, the South China Sea, the Russian-Ukraine war, and other geopolitical issues, American political and military leaders must develop a better understanding of when and how to use ML to support joint force planning, execution, and assessment in combat, lest U.S. service members pay an ungodly sum of the butcher’s bill.

Lieutenant Colonel Jeff Wong, a U.S. Marine Corps reserve infantry officer, studied artificial intelligence at the Eisenhower School for National Security and Resource Strategy, National Defense University in the 2022-2023 academic year. In his civilian work, he plans wargames and exercises for various clients across the Department of Defense.

The views expressed in this paper are those of the author and do not necessarily reflect the official policy or position of the National Defense University, the Department of Defense, or the U.S. Government.

References

1. The fictional hero of this story, John McCarthy, is named after the Massachusetts Institute of Technology researcher who first coined the term “artificial intelligence.” Gil Press, “A Very Short History of Artificial Intelligence,” Forbes, December 30, 2016, https://www.forbes.com/sites/gilpress/2016/12/30/a-very-short-history-of-artificial-intelligence-ai/?sh=51ea3d156fba.

2. Marcus Aurelius, Meditations, audiobook.

3. Mike Berardino, “Mike Tyson Explains One of His Most Famous Quotes,” South Florida Sun-Sentinel, November 9, 2012, https://www.sun-sentinel.com/sports/fl-xpm-2012-11-09-sfl-mike-tyson-explains-one-of-his-most-famous-quotes-20121109-story.html.

4. Eric Schmidt, “Innovation Power: Why Technology Will Define the Future of Geopolitics,” Foreign Affairs, March/April 2023.

5. “Military-Civil Fusion and the People’s Republic of China,” U.S. Department of State, May 2020.

6. Eric Schmidt, “Innovation Power: Why Technology Will Define the Future of Geopolitics,” Foreign Affairs, March/April 2023.

7. Wyatt Hoffman and Heeu Millie Kim, “Reducing the Risks of Artificial Intelligence for Military Decision Advantage,” Center for Security and Emerging Technology Policy Paper (Washington, D.C.: Georgetown University, March 2023), 12.

8. James Johnson, “Automating the OODA Loop in the Age of AI,” Center for Strategic and International Studies, July 25, 2022, https://nuclearnetwork.csis.org/automating-the-ooda-loop-in-the-age-of-ai/.

9. Hoffman and Kim, “Reducing the Risks of Artificial Intelligence for Military Decision Advantage,” 7.

10. Milan Vego, “The Bureaucratization of the U.S. Military Decision-making Process,” Joint Force Quarterly 88, January 9, 2018, https://ndupress.ndu.edu/Publications/Article/1411771/the-bureaucratization-of-the-us-military-decisionmaking-process/.

11. Hoffman and Kim, 7.

12. Carl von Clausewitz, On War, ed. and trans. Michael Howard and Peter Paret (Princeton: Princeton University Press, 1976), 101.

 13. Hoffman and Kim, 7.

 14. Elsa Kania, “AI Weapons” in China’s Military Innovation, Brookings Institution, April 2020.

 15. Harrison Schramm and Bryan Clark, “Artificial Intelligence and Future Force Design,” in AI at War (Annapolis, Md.: Naval Institute Press, 2021), 240-241.

16. Josh Lospinoso, Testimony on the State of Artificial Intelligence and Machine Learning Applications to Improve Department of Defense Operations before the Subcommittee on Cybersecurity, U.S. Senate Armed Services Committee, April 19, 2023, https://www.armed-services.senate.gov/hearings/to-receive-testimony-on-the-state-of-artificial-intelligence-and-machine-learning-applications-to-improve-department-of-defense-operations. “Today, the Department of Defense does not have anywhere near sufficient access to weapon system data. We do not – and in some cases, due to contractual obligations, the Department cannot — extract this data that feeds and enables the AI capabilities we will need to maintain our competitive edge.”

 17. MK15 – Phalanx Close-In Weapon System (CIWS), U.S. Navy, September 20, 2021, https://www.navy.mil/resources/fact-files/display-factfiles/article/2167831/mk-15-phalanx-close-in-weapon-system-ciws/.

18. Adam Aycock and William Glenney IV, “Trying to Put Mahan in a Box,” in AI at War, 269-270.

19. Hoffman and Kim, CSET Policy Brief, 8.

20. Ibid, 8-9.

21. Ibid, 11.

22. Schramm and Clark, “Artificial Intelligence and Future Force Design,” 239-241.

23. AI at War, 241.

24. Craig S. Smith, “Mom, Dad, I Want To Be A Prompt Engineer,” Forbes, April 5, 2023, https://www.forbes.com/sites/craigsmith/2023/04/05/mom-dad-i-want-to-be-a-prompt-engineer/amp/.

25. AI at War, 247-248.

26. AI at War, 248.

27. Heidi M. Peters, “Intellectual Property and Technical Data in DoD Acquisitions,” Congressional Research Service In-Focus, IF12083, April 22, 2022, https://crsreports.congress.gov/product/pdf/IF/IF12083.

Featured Image: PHILIPPINE SEA (Sept. 22, 2020) Cpl. Clayton A. Phillips, a network administrator with Marine Air Control Group 18 Detachment, 31st Marine Expeditionary Unit (MEU), and a native of Beech Bluff, Tennessee, tests the connectivity of the Networking On-the-Move Airborne (NOTM-A) communications system during flight operations from the amphibious assault ship, USS America (LHA 6). (U.S. Marine Corps photo by Lance Cpl. Brienna Tuck)

Culture

Upgrading the Mindset: Modernizing Sea Service Culture for Trust in Artificial Intelligence

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By Scott A. Humr

Winning on the future battlefield will undoubtedly require an organizational culture that promotes human trust in artificial intelligent systems. Research within and outside of the US military has already shown that organizational culture has an impact on technology acceptance, let alone, trust. However, Dawn Meyerriecks, Deputy Director for CIA technology development, remarked in a November 2020 report by the Congressional Research Service that senior leaders may be unwilling, “to accept AI-generated analysis.” The Deputy Director goes on to state that, “the defense establishment’s risk-averse culture may pose greater challenges to future competitiveness than the pace of adversary technology development.” More emphatically, Dr. Adam Grant, a Wharton professor and well-known author, called the Department of Defense’s culture, “a threat to national security.” In light of those remarks, the Commandant of the Marine Corps, General David H. Berger, stated at a gathering of the National Defense Industrial Association that, “The same way a squad leader trusts his or her Marine, they have to trust his or her machine.” The points of view in the aforementioned quotes raise an important question: Do Service cultures influence how its military personnel trust AI systems?

While much has been written about the need for explainable AI (XAI) and need for increasing trust between the operator and AI tools, the research literature is sparse on how military organizational culture influences the trust personnel place in AI imbued technologies. If culture holds sway over how service personnel may employ AI within a military context, culture then becomes an antecedent for developing trust and subsequent use of AI technologies. As the Marine Corps’s latest publication on competing states, “culture will have an impact on many aspects of competition, including decision making and how information is perceived.” If true, military personnel will view information provided by AI agents through the lens of their Service cultures as well.

Our naval culture must appropriately adapt to the changing realities of the new Cognitive Age. The Sea Services must therefore evolve their Service cultures to promote the types of behaviors and attitudes that fully leverage the benefits of these advanced applications. To compete effectively with AI technologies over the next decade, the Sea Services must first understand their organizational cultures, implement necessary cultural changes, and promote double-loop learning to support beneficial cultural adaptations.

Technology and Culture Nexus

Understanding the latest AI applications and naval culture requires an environment where experienced personnel and technologies are brought together through experimentation to better understand trust in AI systems. Fortunately, the Sea Service’s preeminent education and research institution, the Naval Postgraduate School (NPS), provides the perfect link between experienced educators and students who come together to advance future naval concepts. The large population of experienced mid-grade naval service officers at NPS provides an ideal place to help understand Sea Service culture while exploring the benefits and limitations of AI systems.

Not surprisingly, NPS student research has investigated trust in AI, technology acceptance, and culture. Previous NPS research has explored trust through interactive Machine Learning (iML) in virtual environments for understanding Navy cultural and individual barriers to technology adoption. These and other studies have brought important insights on the intersection of people and technologies.

One important aspect of this intersection is culture and how it is measured. For instance, the Competing Values Framework (CVF) has helped researchers understand organizational culture. Paired with additional survey instruments such as E-Trust or the Technology Acceptance Models (TAM), researchers can better understand if particular cultures trust technologies more than other types. CVF is measured across six different organizational dimensions that are summarized by structure and focus. The structure axis ranges from control to flexibility, while focus axis ranges from people to organization, see figure 1.

Figure 1 – The Competing Values Framework – culture, leadership, value from Cameron, Kim S., Robert E. Quinn, Jeff DeGraff, and Anjan V. Thakor. Competing Values Leadership, Edward Elgar Publishing, 2014.

Most organizational cultures contain some measure of each of the four characteristics of the CVF. The adhocracy quadrant of the CVF, for instance, is characterized by innovation, flexibility, and increased speed of solutions. To this point, an NPS student researcher found that Marine Corps organizational culture was characterized as mostly hierarchical. The same researcher found that this particular group of Marine officers also preferred the Marine Corps move from a hierarchical culture towards an adhocracy culture. While the population in the study was by no means representative of the entire Marine Corps, it does generate useful insights for forming initial hypotheses and the need for additional research which explores whether hierarchical cultures impede trust in AI technologies. While closing this gap is important for assessing how a culture may need to adapt, actually changing deeply rooted cultures requires significant introspection and the willingness to change.

The ABCs: Adaptations for a Beneficial Culture

“Culture eats strategy for breakfast,” quipped the revered management guru, Peter Drucker—and for good reason. Strategies that seek to adopt new technologies which may replace or augment human capabilities, must also address culture. Cultural adaptations that require significant changes to behaviors and other deeply entrenched processes will not come easy. Modifications to culture require significant leadership and participation at all levels. Fortunately, organizational theorists have provided ways for understanding culture. One well-known organizational theorist, Edgar Schein, provides a framework for assessing organizational culture. Specifically, culture can be viewed at three different levels which consist of artifacts, espoused values, and underlying assumptions.

The Schein Model provides another important level of analysis for investigating the military organizational culture. In the Schein model, artifacts within militaries would include elements such as dress, formations, doctrine, and other visible attributes. Espoused values are the vision statements, slogans, and codified core values of an organization. Underlying assumptions are the unconscious and unspoken beliefs and thoughts that undergird the culture. Implementing cultural change without addressing underlying assumptions is the equivalent to rearranging the deck chairs on the Titanic. Therefore, what underlying cultural assumptions could prevent the Sea Services from effectively trusting AI applications?

One of the oldest and most ubiquitous underlying assumptions of how militaries function is the hierarchy. While hierarchy does have beneficial functions for militaries, it may overly inhibit how personnel embrace new technologies and decisions recommended by AI systems. Information, intelligence, and orders within the militaries largely flow along well-defined lines of communication and nodes through the hierarchy. In one meta-analytic review on culture and innovation, researchers found that hierarchical cultures, as defined by CVF, tightly control information distribution. Organizational researchers Christopher McDermott and Gregory Stock stated, “An organization whose culture is characterized by flexibility and spontaneity will most likely be able to deal with uncertainty better than one characterized by control and stability.” While hierarchical structures can help reduce ambiguity and promote stability, they can also be detrimental to innovation. NPS student researchers in 2018, not surprisingly, found that the hierarchical culture in one Navy command had a restraining effect on innovation and technology adoption.

CVF defined adhocracy cultures on the other hand are characterized by innovation and higher tolerances for risk taking. For instance, AI applications could also upend well-defined Military Decision Making Processes (MDMP). MDMP is a classical manifestation of codified processes that supports underlying cultural assumptions on how major decisions are planned and executed. The Sea Services should therefore reevaluate and update its underlying assumptions on decision making processes to better incorporate insights from AI.

In fact, exploring and promoting other forms of organizational design could help empower its personnel to innovate and leverage AI systems more effectively. The late, famous systems thinking researcher, Donella Meadows, aptly stated, “The original purpose of a hierarchy is always to help its originating subsystems do their jobs better.” Therefore, recognizing the benefits, and more importantly the limits of hierarchy, will help leaders properly shape Sea Service culture to appropriately develop trustworthy AI systems. Ensuring change goes beyond a temporary fix, however, requires continually updating the organization’s underlying assumptions. This takes double-loop learning.

Double-loop Learning

Double-loop learning is by no means a new concept. First conceptualized by Chris Argyris and Donald Schön in 1974, double-loop learning is the process of updating one’s underlying assumptions. While many organizations can often survive through regular use of single-loop learning, they will not thrive. Unquestioned organizational wisdom can perpetuate poor solutions. Such cookie-cutter solutions often fail to adequately address new problems and are discovered to no longer work. Rather than question the supporting underlying assumptions, organizations will instead double-down on tried-and-true methods only to fail again, thus neglecting deeper introspection.

Such failures should instead provide pause to allow uninhibited, candid feedback to surface from the deck-plate all the way up the chain of command. This feedback, however, is often rare and typically muted, thus becoming ineffectual to the people who need to hear it the most. Such problems are further exacerbated by endemic personnel rotation policies combined with feedback delays that rarely hold the original decision makers accountable for their actions (or inactions).

Implementation and trust of AI systems will take double-loop learning to change the underlying cultural assumptions which inhibit progress. Yet, this can be accomplished in several ways which go against the normative behaviors of entrenched cultures. Generals, Admirals, and Senior Executive Service (SES) leaders should create their own focus groups of diverse junior officers, enlisted personnel, and civilians to solicit unfiltered feedback on programs, technologies, and most importantly, organizational culture inhibitors which hold back AI adoption and trust. Membership and units could be anonymized in order to shield junior personnel from reprisals while promoting the unfiltered candor senior leadership needs to hear in order to change the underlying cultural assumptions. Moreover, direct feedback from the operators using AI technologies would also avoid the layers of bureaucracy which can slow the speed of criticisms back to leadership.

Why is this time different?

Arguably, the naval services have past records of adapting to shifts in technology and pursuing innovations needed to help win future wars. Innovators of their day such as Admiral William Sims developing advanced naval gunnery techniques and the Marine Corps developing and improving amphibious landing capabilities in the long shadow of the Gallipoli campaign reinforce current Service cultural histories. However, many technologies of the last century were evolutionary improvements to what was already accepted technologies and tactics. AI is fundamentally different and is akin to how electricity changed many aspects of society and could fundamentally disrupt how we approach war.

In the early 20th century, the change from steam to electricity did not immediately change manufacturing processes, nor significantly improve productivity. Inefficient processes and machines driven by steam or systems of belts were never reconfigured once they were individually equipped with electric motors. Thus, many benefits of electricity were not realized for some time. Similarly, Sea Service culture will need to make a step change to fully take advantage of AI technologies. If not, the Services will likely experience a “productivity paradox” where large investments in AI do not fully deliver the efficiencies promised. 

Today’s militaries are sociotechnical systems and underlying assumptions are its cultural operating system. Attempting to plug AI application into a culture that is not adapted to use it, nor trusts it, is the equivalent of trying to install an Android application on a Windows operating system. In other words, it will not work, or at best, not work as intended. We must, therefore, investigate how naval service cultures may need to appropriately adapt if we want to fully embrace the many advantages these technologies may provide.

Conclusion

In a 2017 report from Chatham House titled, “Artificial Intelligence and the Future of Warfare,” Professor Missy Cummings stated, “There are many reasons for the lack of success in bringing these technologies to maturity, including cost and unforeseen technical issues, but equally problematic are organizational and cultural barriers.” Echoing this point, the former Director of the Joint Artificial Intelligence Center (JAIC), Marine Lieutenant General Michael Groen, stated “culture” is the obstacle, not the technology, for developing the Joint All-Domain, Command and Control (JADC2) system, which is supported by AI. Yet, AI/ML technologies have the potential to provide a cognitive-edge that can potentially increase the speed, quality, and effectiveness of decision-making. Trusting the outputs of AI will undoubtedly require significant changes to certain aspects of our collective naval cultures. The Sea Services must take stock of their organizational cultures and apply the necessary cultural adaptations, while fostering double-loop learning in order to promote trust in AI systems.

Today, the Naval Services have a rare opportunity to reap the benefits of a double-loop learning. Through the COVID-19 pandemic, the Sea Services have shown that they can adapt responsively and effectively to dynamic circumstances while fulfilling their assigned missions. The Services have developed more efficient means to leverage technology to allow greater flexibility across the force through remote work and education. If, however, the Services return to the status quo after the pandemic, they will have failed to update many of its outdated underlying assumptions by changing the Service culture.

If we cannot change the culture in light of the last three years, it portends poor prospects for promoting trust in AI for the future. Therefore, we cannot squander these moments. Let it not be said of this generation of Sailors and Marines that we misused this valuable opportunity to make a step-change in our culture for a better approach to future warfighting.

Scott Humr is an active-duty Lieutenant Colonel in the United States Marine Corps. He is currently a PhD candidate at the Naval Postgraduate School as part of the Commandant’s PhD-Technical Program. His research interests include trust in AI, sociotechnical systems, and decision-making in human-machine teams. 

Featured Image: An F-35C Lightning aircraft, assigned to Strike Fighter Squadron (VFA) 125, prepares to launch from the flight deck of the aircraft carrier USS George H. W. Bush (CVN 77) during flight operations. (U.S. Navy photo by Mass Communication Specialist 3rd Class Brandon Roberson)