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Future Tech

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)

Podcast

Sea Control 553 – Tracks on the Ocean with Dr. Sara Caputo

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By Jared Samuelson

Dr. Sara Caputo joins the program to discuss her new book, Tracks on the Ocean: A History of Trailblazing, Maps and Maritime Travel. Sara is Director of Studies in History, History and Politics, and History and Modern Languages, Magdalene College. 

Download Sea Control 553 – Tracks on the Ocean with Dr. Sara Caputo

Links

1. Tracks on the Ocean: A History of Trailblazing, Maps and Maritime Travel, by Sara Caputo, Profile Books, September 2024.  

2. Sea Control 353 – The Medical Culture of the British Seaman with Dr. Sara Caputo, by Jared Samuelson, CIMSEC, June 12, 2022.

3. Sea Control 527 – The Wide Wide Sea with Hampton Sides, by Jared Samuelson, CIMSEC, June 13, 2024.

Jared Samuelson is Co-Host and Executive Producer of the Sea Control podcast. Contact him at Seacontrol@cimsec.org.

Podcast

Sea Control 552 – Diego Garcia with Nitya Labh

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By Jonathan Selling

The recent announcement that the United Kingdom will be returning Diego Garcia and the rest of the Chagos Islands to Mauritius brought to an end this long-running dispute. Nitya Labh joins the program to discuss the dispute over the Chagos Islands and the benefit to the UK and US for its return to Mauritius. Nitya Labh is a former James C. Gaither fellow for the South Asia Program and Tata Chair for strategic affairs at the Carnegie Endowment for International Peace. She is an incoming Schwarzman Scholar at Tsinghua University in Beijing.

Download Sea Control 552 – Diego Garcia with Nitya Labh

Links

1. “Why Diego Garcia Matters,” by Nitya Labh, Foreign Policy, May 30, 2024.

Jonathan Selling is co-host and executive producer of the Sea Control podcast. Contact the podcast team at Seacontrol@cimsec.org.

Capability Analysis

Building Resilient Killchains for the Stand-In Force

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By Aaron Barlow, Patrick Reilly, and Sean Harper

Introduction

As the Marine Corps prepares to contest the regional superiority of the People’s Republic of China (PRC) in the Indo-Pacific alongside the Navy and the joint force, the service must strengthen its organic killchains and ensure that each new capability acquisition aligns to the concepts that the service must execute. While joint integration will rightfully remain critical to successful campaigns, the Marine Corps – as the isolatable forward edge of the joint force in the Indo-Pacific – must ensure that its presence adds credible theater combat capability even when joint sensing, communication, and fires cannot support the stand-in force. The Marine Corps should therefore focus on acquiring platforms that present a different risk profile than the joint force; prioritize organic ownership of all components of certain killchains from sensor to shooter; value resilient, risk-worthy platforms over the highly capable but expensive; and focus on diversity and depth in the types of munitions it brings to the fight.

 Strategic Context

Over the past five years, the Marine Corps has confidently and rapidly altered its force structure to meet changing national strategic priorities. As articulated in the 2018 National Defense Strategy (NDS) and echoed in its 2022 sequel, the United States must meet the 2020s as a “decisive decade” and defend U.S. national security interests by effectively deterring its adversaries, using the People’s Republic of China (PRC) as a benchmark to measure the pace of advancement. In an August 2024 report, the Commission on the 2022 NDS charted the Marine Corps’ modernization progress to date, stating “The service deserves high marks for displaying the agility that DoD often yearns for but rarely achieves.” The report further lauded the Marine Corps’ Force Design efforts as a “coherent way for the Marine Corps to operate in the Indo-Pacific against the pacing threat while retaining the ability to serve as the nation’s emergency response for crises as they materialize.”

However, the 39th Commandant’s Planning Guidance recently reinforced that modernization remains a “righteous” but incomplete journey. Using the service’s Concept for Stand-in Forces as a yardstick, recent acquisitions overestimate and over-rely on the availability of joint and national capabilities in the highly contested environment in which they must perform. Equally, other acquisition and force design decisions seem fundamentally misaligned to stand-in force imperatives like footprint, signature, and risk minimization.

The value proposition of stand-in forces best manifests in the context of a hypothetical PRC campaign to achieve reunification with Taiwan by force, in which the PLA will leverage its significant regional firepower advantage to assert all-domain superiority well East of the first island chain. Confronted by an adversary capable of devastating maritime precision strikes, the joint force will likely withdraw the preponderance of its high-end capabilities beyond the range of PRC threats. Further, Chinese capabilities will be focused on disrupting the long-range communications networks necessary for these high-end capabilities to close killchains from safer distances.

Nonetheless, the joint force will still require the ability to contest Chinese all-domain control in the first island chain. Enter the stand-in force, positioned on forward locations throughout the battlespace. Fighting as an extension of the fleet and joint force, the stand-in force will leverage disaggregation to create reconnaissance and targeting dilemmas for adversaries reliant on precision strike regimes. Stand-in forces will employ asymmetric capabilities and tactics to hold adversaries at risk in multiple domains, ultimately preventing the accumulation of regional superiority.

The Marine Corps’ perspective on how to execute A Concept for Stand-in Forces has evolved since the project began in 2020. The services Force Design annual updates allow us to trace this evolution. Foundational Force Design guidance initially prioritized the development of “smaller but better-connected formations that organically possess a complete killchain appropriate to echelon.” However, the 2022 Force Design Annual Update walked back this vision “from an initial focus on generating organic lethal capabilities…to a more balanced focus that includes persisting forward in a contested area to win the [reconnaissance/counter-reconnaissance] battle and complete joint kill webs.” The 2022 annual update also raises unresolved questions about what this balance might look like, reiterating that “certain capabilities must be organic to our Stand-in Forces, such as organic sensors and long-range precision fires to close kill webs when external capabilities are not present or available.”

Based on this guidance, the stand-in force’s risk of isolation from the joint force clearly persists. How intensely should the Marine Corps hedge against this risk, and how should the service define the balance it seeks? Recent service acquisitions suggest that the Marine Corps has overinvested in capabilities that are inappropriate for a stand-in force, at the expense of building robust organic killchains that provide a guaranteed capability baseline in the event of isolation.

The Value Proposition of Organic Killchains

The disaggregated nature of stand-in force formations and the tyranny of distance imposed by the littoral operating environment combine with the nature of the PRC threat to illustrate the value proposition of organic killchains. For example, consider the dependence of the combined joint all-domain command and control (CJADC2) concept on the resilience and availability of joint information networks. Under CJADC2, the joint force and partners seek to project all-domain effects by seamlessly closing killchains comprised of national and joint sensors, processors, and shooters. CJADC2 represents a legitimate integration challenge, and to date the services have been inching towards minimum viable capabilities.

The 39th Commandant’s Planning Guidance articulates how the Marine Corps sees its contributions to CJADC2: “Marines will act as the ‘JTAC of the Joint Force’ – sensing, making sense, and communicating to the rest of the Joint Force with an ‘any sensor, any shooter’ mindset.” Until recently the Marine Corps has followed in the wake of other services’ initiatives through participation in the Navy’s Project Overmatch and the Army’s Project Convergence, both of which have sought to develop and exercise the interconnectedness and interoperability required for the joint services to share information and close killchains. The Marine Corps has successfully exercised acquiring and maintaining custody of targets with organic sensors while passing this information to joint command-and-control applications, recently at Exercise Valiant Shield, which included an Indo-Pacific Command-level exercise of its Joint Fires Network. These initiatives and exercises represent obvious technical progress, but as demonstrations of concepts, they risk overestimating the reliability and availability of joint information networks in a way that unbalances the Stand-in Force in favor of brittle kill webs.

This imbalance becomes especially evident in the context of how the PLA plans to prosecute future conflicts. The PLA believes that modern warfare is not “a contest of annihilation between opposing military forces, but rather a clash between opposing operational systems.” The PLA’s derived concept – Systems Destruction Warfare – prioritizes attacking “the flow of information within the adversary’s operational system.” Under this paradigm, if the joint force envisions CJADC2 as a fundamental center of gravity that enables hard-hitting joint killchains, the PLA must view the same system as the joint force’s critical vulnerability and deploy proportional operational resources to target and disrupt it. What is the value proposition of the stand-in force if joint information networks must be available to unlock its contribution to potent joint capabilities?

A U.S. Marine Corps AN/TPS-80 Ground/Air Task Oriented Radar is deployed during exercise Resolute Dragon 24 in Okinawa, Japan, July 31, 2024. The radar was deployed to support training with enhanced sensing and targeting data between the 12th Marine Littoral Regiment and the JSDF during RD 24. (U.S. Marine Corps photo by Lance Cpl. Matthew Morales)

To deliver on its value proposition, the stand-in force must retain the capability to hold the adversary at risk with credible killchains in contested environments when the rest of the joint force cannot. When CJADC2 is uncontested and operating at its peak it will make extensive use of C2 platforms in the air and space domains. However, the questionable survivability and persistence of these platforms is in part the impetus of the stand-in force concept. Thus, reliance on these high-end joint networks introduces a contradiction in the stand-in force’s conceptual framework.

A potential overestimation of the resilience of emerging commercial, proliferated low-earth orbit constellations also underpins the Marine Corps’ conceptual reasoning. Systems such as SpaceX’s Starlink may indeed enable a more robust space-based command and control architecture compared to legacy systems. However, these constellations have increasingly been touted as a communications panacea, especially after Starlink’s success in Ukraine. Meanwhile, adversaries are rigorously searching for effective counters, hunting for exploitation opportunities, or developing options to remove the space layer altogether. Though a credible 21st-century force cannot ignore emerging space layer technologies, the Marine Corps should not overestimate the resilience of commercial P-LEO solutions at the expense of organic spectrum-diverse information networks.

Earlier this year, the Marine Corps initiated Project Dynamis as a service bid to gain initiative in shaping contributions to CJADC2. The Marine Corps should leverage this opportunity to refocus command and control modernization to better align the service’s balance of information capabilities with the stand-in-force concept. The service should specifically refine robust, diverse information capabilities that enable the stand-in force to contest adversary all-domain control in ways that multiply combat power through the availability of joint networks, but crucially do not require them. Further, the end-to-end organic ownership of certain critical killchains by the stand-in force has the dual benefit of providing a credible means of contesting all-domain control when the joint force cannot be present and providing an alternative information path for the joint force inside contested areas.

An Organic and Asymmetric Munitions Mix

If spectrum-diverse information networks provide the connective linkages for an end-to-end organic killchain, a deep and varied arsenal of service-owned munitions must provide the kinetic edge. Though the Marine Corps has long constructed capabilities around a variety of indirect fire munitions, the 38th Commandant’s Planning Guidance prioritized the service’s first ever acquisition of a ground based medium-range anti-ship missile. The service’s portfolio has since grown to include Naval Strike Missiles, long-range anti-ship missiles, and Tomahawk cruise missiles, each in different phases of acquisition and with varying concepts of employment. While these munitions will provide the stand-in force with the capability to hold high-value targets at risk, they also represent relatively high-cost, low-density investments. Deriving estimates from total program acquisition costs published in the Department of Defense Fiscal Year 2024 Budget Request, the Naval Strike Missile (90 units), Tomahawk (34 units), and long range anti-ship missile (91 units) carry units costs of $2.32M, $3.09M, and $7.02M respectively.1

The per-shot expense of these munitions raises questions about whether the Marine Corps will have the magazine depth to necessary to sustain a protracted sea denial campaign. Additionally, the many lower-tier maritime targets that the stand-in force could easily hold at risk may not rise to the threshold of significance necessary for engagement with low density munitions; if the stand-in force cannot engage these targets it forgoes opportunities for credible sea denial contributions. The acquisition of exquisite medium-range munitions should not be abandoned, but greater diversity and depth in the Marine Corps portfolio of munitions could enable the service to operate more effectively as a stand-in force. 

For example, a large arsenal of relatively low-cost loitering munitions will provide the stand-in force with an asymmetric advantage against littoral targets, since a single operator can control multiple munitions that cooperatively overwhelm adversary air defenses. Practical munitions trade-offs could also reduce the volume of information exchange necessary to execute killchains. For example, capabilities imbued with a layer of autonomy, such as kamikaze drones and suicide surface and sub-surface vehicles may reduce the required frequency and fidelity of sensor and operator inputs compared to traditional munitions, unburdening limited network resources. The Marine Corps should therefore intentionally balance its high-cost fires systems with deep magazines of effective yet relatively inexpensive loitering and one-way attack munitions.

Matching Capabilities to Concepts

As the Marine Corps considers the appropriate balance of organic and joint investments, the service should also consider how well its future platforms align to the concepts the service must execute. The 38th Commandant’s Planning Guidance clearly defined the types of platforms appropriate to future amphibious and stand-in forces: “We must continue to seek the affordable and plentiful at the expense of the exquisite and few when conceiving of the future amphibious portion of the fleet.” Equally, stand-in forces must “confront aggressor naval forces with an array of low signature, affordable, and risk-worthy platforms and payloads.” The latest 39th Commandant’s Planning Guidance suggests that the service has not wholly altered this philosophy, reiterating that the service must “not design our own exquisite low volume platforms.” However, considerations of affordability and riskworthiness do not receive explicit mention.

The Marine Corps should not compromise on cost and risk here. As the service constructs killchains, it should avoid the pattern of investing in expensive, exquisite, and excessively overengineered platforms that directly mirror or present the same risk profile as existing joint capabilities. The service should instead focus acquisitions on platforms that diversify the risks faced by the joint force. Marine Corps platform attributes should closely resemble the original value proposition for Force Design and A Concept for Stand-in Forces: highly expeditionary, risk-worthy, operationally and logistically supportable in protracted conflict, and respectful of the fiscal realities faced by the service.

As an illustrative example, consider the Marine Corps’ recent acquisition of the MQ-9A Reaper platform, part of a service unmanned aerial system strategy that actually preceded Force Design. Now integrated into air combat element formations, the MQ-9A provides the service with a credible organic long-endurance airborne surveillance and command-and-control capability in competition. However, recent battlefield evidence suggests that the Reaper may not be survivable when targeted in conflict without additional supporting capabilities. Iranian proxy groups, most notably Yemen’s Houthi rebels, appear to have downed at least four MQ-9s since October 7, 2023 (and possibly far more, with acknowledged numbers increasing frequently). If affected today, these losses would halve the Marine Corps’ current fleet of MQ-9A platforms, or quarter the projected fleet in 2025. Unmanned aerial system operations in Ukraine also offer insights into the utility and survivability of large, loitering unmanned platforms in peer conflict. Though used to great effect at the outset of the war, recent reports have suggested that Ukraine has significantly curtailed the sorties flown by their Turkish Group 5-equivalent Bayraktar TB2 drones, due in part to the deployment of a more sophisticated Russian integrated air defense network along the front. Further, a platform with a 3000-foot runway requirement and a unique maintainer MOS arguably does not conform to Force Design and stand-in force principles like footprint and signature minimization. Finally, though not a novel and exquisite platform, the service’s MQ-9s do not seem fiscally risk-worthy at the current rate of acquisition, especially considering recent shoot-down rates. In FY2024, the Marine Corps paid an effective unit cost of $37.5M each for five MQ-9A platforms, which would provide a Houthi-equivalent adversary with several months of target practice. The PLA is likely another story, and the MQ-9A will almost certainly be a priority target based on the platforms’ potential value as killchain enabler.

General Atomics, perhaps sensing that the service lacks compelling alternatives, appears ready to upsell the Marine Corps on the more capable but likely far more expensive MQ-9B in the near future. At present, while the MQ-9A may serve as an invaluable enabler in competition, the platform appears too rare, too capable, and too imminently targetable to persist and survive as the stand-in force transitions to conflict.

U.S. Marine Corps Captain Joshua Brooks, an unmanned aircraft system representative, and Master Sergeant Willie Cheeseboro Jr., an enlisted aircrew coordinator with Marine Unmanned Aerial Vehicle Squadron 1, prepare to launch and operate the first Marine Corps owned MQ-9A Reaper on Marine Corps Air Station Yuma, Ariz. Aug. 30, 2021. (U.S. Marine Corps photo)

Consider instead the application of a different solution paradigm to the same problem: the acquisition of high numbers of comparatively low-cost medium-size semi-autonomous unmanned aerial systems (UAS) like Shield AI’s V-BAT or the Platform Aerospace Vanilla UAS to support surveillance, command and control, and targeting missions. Distributed throughout contested areas, launched from austere locations under vertical/short takeoff and landing regimes, and operated in swarms with a different payload on each airframe, these platforms could support or heavily augment large, low-density systems like MQ-9A in conflict. In one-to-one comparisons, medium UAS clearly cannot match the capability of larger systems like MQ-9A. However, when operated at scale and especially when integrated with other long-range littoral sensors, medium UAS platforms can provide an acceptable solution to the stand-in force’s surveillance and command and control requirements while presenting an asymmetric cost and targeting dilemma to adversaries.

While we have focused on the MQ-9, the Marine Corps portfolio is replete with platforms that carry similar contradictions when examined through the Force Design and stand-in force lens. Instead of replicating the acquisitions of the past, Marine Corps should specifically develop capabilities around diverse, risk-worthy, high-density, and relatively low-cost platforms and consider reducing investments in highly capable but overly precious and concentrated capabilities that mirror those in the joint force. 

The Future of Force Design

The 39th Commandant’s Planning Guidance reiterates that “Force Design remains our strategic priority and we cannot slow down.” Force Design provides the Marine Corps a unique opportunity to differentiate itself from past operating concepts and acquisition decisions while building an asymmetric value proposition in the joint fight against peer adversaries. The Marine Corps cannot afford to own every node of every kill web, but selective end-to-end ownership of specific killchains will enable relevant and credible service contributions to the joint force in competition and at the onset of a protracted conflict. Moreover, a Marine Corps with enhanced magazine depths and a plethora of affordable, risk-worthy platforms operating forward in first island chain will challenge adversary all-domain control and set conditions for US domination in the later stages of any maritime campaign. Likewise, any improvements that the Marine Corps makes in the alignment of its expeditionary capabilities to threat-informed concepts will concurrently prepare the service to effectively fulfill its role as a crisis response force, primed for contingencies in support of national mission objectives in accordance with the shifting realities of modern war.

Major Aaron Barlow, Captain Patrick Reilly, and Major Sean Harper are currently serving as operations research analysts assigned to the Deputy Commandant for Combat Development and Integration in Quantico, Virginia.

These views are presented in a personal capacity and do not necessarily reflect the official views of any U.S. government entity. 

Notes

1. Data reported for USN. USMC specific data not available for FY2024.

Featured Image: U.S. Marine Corps Lance Cpl. Terrell Chandler, left, and U.S. Marine Corps Lance Cpl. Melvin Monet, both low-altitude-air defense gunners with 3d Littoral Anti-Air Battalion, 3d Marine Littoral Regiment, 3d Marine Division, set security with an FIM-92 Stinger during Marine Littoral Regiment Training Exercise (MLR-TE) at Marine Corps Air Station Yuma, Arizona, Jan. 28, 2023. (U.S. Marine Corps photo by Sgt. Israel Chincio)