Whether the MSS is indicative of an unfolding RMA remains a subject of debate. At a minimum, it represents a significant leap in how modern militaries sense, decide, and act in combat. From a scholarly perspective, RMAs are not defined by single technological breakthroughs but by clusters of innovations that fundamentally transform the conduct of warfare.

They typically involve shifts in doctrine, tactics, organization, culture, and technology. Unlike broader military revolutions, which reshape societies and political systems, RMAs are confined to the military sphere—and they often unfold quietly, only recognized in hindsight.

Several RMAs were identified in the past, providing a framework to anticipate future ones. In The Dynamics of Military Revolutions: 1300–2050, MacGregor Knox and Williamson Murray outline five significant military revolutions in the West since 1618. Each one, they argue, set off a chain of revolutionary changes in military affairs.

These include the emergence of the modern state with its standing armies, the political and social upheavals brought on by the French Revolution, the industrialization of warfare in the 19th century, the era of total war in the 20th century, and the transformative impact of nuclear weapons. If a new RMA is underway, we may not fully recognize it until it has already matured.

The concept of RMA has drawn justified criticism for being abstract, amorphous, and debated to the point of analytical paralysis. After the Gulf War, the DoD’s fixation on identifying the “next RMA” often overshadowed the operational impact of emerging capabilities. Scholars frequently focus on definitional purity rather than assessing real battlefield transformation.

Whether the MSS fits a textbook definition, adopted by the DoW or derived from historical theory, is less important than its functional impact. If an RMA is indeed emerging or approaching, there should be tangible real-world consequences. Otherwise, theory becomes disconnected from practice. In this light, the MSS may serve as a bridge between the long-unfolding information RMA and a new, AI-driven transformation.

The MSS could be indicative of another significant shift in command and control (C2). While the US Army’s command post computing environment (CPCE) already integrates legacy systems into a modular, cloud-capable architecture for multi-domain operations, the MSS pushes these capabilities toward revolutionary real-time situational awareness.

While initially developed to automate drone feed analysis, the MSS has evolved into an AI-powered battlefield intelligence engine. It fuses intelligence, surveillance, and reconnaissance (ISR) data, enables real-time targeting, and supports distributed decision-making. As with the telegraph in the 19th century, the MSS may redefine the military’s relationship with information and time.

Historically, C2 was slow and fragmented. Commanders relied on flags, runners, and direct observation, limited by geography and transmission delay. The Industrial Revolution began to change this. Introduced in 1793, Claude Chappe invented the optical telegraph which allowed faster coordination across long distances. It was Samuel Morse’s electrical telegraph, patented in 1837, that truly revolutionized communication.

AI is reshaping combat just as electricity once did. Electricity transformed communication by creating the foundation for critical innovation, like the internet. The harnessing of electricity for industrial use itself was not an RMA, but it was the essential prerequisite for one. Without it, the revolution in communication that began with the telegraph would not have been possible. AI may not constitute a full RMA on its own, but it is the enabling foundation for one.

During the Crimean War and the American Civil War, the telegraph enabled real-time command for the first time. In the US, President Lincoln relied on the War Department telegraph office to direct Union forces and enforce strategic decisions. Strategic-level C2 became possible, and expectations for real-time situational awareness took hold. The rise of the steam-powered printing press and the expansion of railways accelerated this transformation, making war reporting nearly instantaneous—a precursor to modern information warfare.

Similarly, Project Maven, initiated in 2017, began as a machine learning initiative to automate drone video analysis. Since then, the MSS has grown to integrate cloud computing, ISR fusion, and targeting. The MSS delivers intelligence to the tactical edge at machine speed on enterprise cloud infrastructure. It processes unfathomable amounts of data in milliseconds— augmenting analysts and automating portions of the workflow.

Just like the electric telegraph centralized control and supported linear commander decisions, the MSS introduces machine learning, machine inference, and adaptive analytics to take command and control. The MSS provides a picture of the theater that is not merely quantitative, but qualitative.

A true RMA requires more than new technology. It demands operational adaptation, organizational restructuring, and doctrinal evolution. The MSS checks many of these boxes. Technologically, the MSS merges AI, edge computing, and cloud infrastructure in a holistic fashion. Operationally, it uses human-machine teaming to accelerate kill chains. Organizationally, it catalyzed the creation of institutions such as the Joint AI Center (JAIC) and the Chief Digital and Artificial Intelligence Office. Doctrinally, it promotes shifts toward algorithmic and mosaic warfare, which are adaptive, data-driven models of conflict.

The MSS could signal a broader shift in military operations, much like the telegraph reshaped communication in the 19th century. By combining intelligence, surveillance, and reconnaissance (ISR) with artificial intelligence at operational speed, the MSS is changing how armed forces interpret the battlespace, make decisions, and coordinate action—all while improving the shared situational picture. Yet without a corresponding cultural shift, even the best tools can fail to yield a true RMA. Whether the Department of War can fully adapt its doctrine and institutions to leverage the MSS remains to be seen.

Lieutenant Colonel Matthew J. Fecteau is an information operations officer working with artificial intelligence. The views expressed in this report are those of the author and do not necessarily reflect the official policy or position of the Department of the Army, the Department of War, or the US Government. 

Signals of a New Revolution: Maven Smart System and the AI-RMA Horizon was originally published on Global Security Review.

Agentic AI quickly became a buzzword in the technology industry, drawing the attention of innovators, corporations, and policymakers alike. By combining machine learning, natural language processing, and real-time analytics, agentic AI systems can interpret nuanced data and execute multi-step tasks previously thought to require human intervention. This capability unlocks immense potential across industries, from healthcare and finance to manufacturing and customer service.

However, the rise of this transformative technology is accompanied by profound challenges. The potential for significant job displacement is actual and imminent. Roles that involve repetitive or analytical tasks could be rendered obsolete, leaving swathes of the workforce vulnerable. This shift is sparking resistance from employees and unions concerned about job security and the ethical implications of AI in the workplace.

In the workplace, agentic AI is already making waves. Businesses leverage these systems to automate complex workflows, enhance productivity, and reduce operational costs. For instance, AI agents are being deployed to streamline supply chains, personalize customer interactions, and even assist in groundbreaking research, such as drug discovery and financial forecasting. As organizations increasingly adopt this technology, the workplace transforms, promising greater efficiency and innovation.

However, the rise of agentic AI is not without challenges. Concerns about job displacement, ethical considerations, and security risks have fueled resistance from employees, unions, and policymakers. Trust in AI decision-making and transparency in its operations remain critical issues that must be addressed for widespread acceptance. Moreover, questions about accountability and regulatory oversight are at the forefront of discussions as the line between human and machine decision-making becomes increasingly blurred.

As this trend continues to gain momentum, agentic AI promises to become a cornerstone of the future economy. Its ability to think, learn, and act autonomously offers unprecedented opportunities to reshape industries and redefine the relationship between humans and technology. With careful implementation and robust ethical guidelines, agentic AI could usher in a new era of collaboration between intelligent systems and human ingenuity, setting the stage for innovations yet to be imagined. 

Basics of Agentic AI

Agentic AI represents a significant advancement in artificial intelligence, characterized by its autonomy in decision-making and action execution without direct human intervention. Unlike traditional AI systems that rely on predefined rules, agentic AI adapts to dynamic environments, processes vast data, and formulates strategies to achieve specific objectives. This autonomy enables agentic AI to handle complex, multi-step problems across various industries.

Integrating agentic AI into the workplace transforms business operations by automating intricate tasks and enhancing efficiency. Companies like ServiceNow are embedding agentic AI into their enterprise software to act as control towers for AI-driven business transformations, facilitating seamless system cooperation. Additionally, organizations such as Johnson & Johnson and Moody’s are deploying AI agents for tasks ranging from drug discovery to financial analysis, demonstrating the versatility and impact of Agentic AI in various sectors.

Potential Benefits

Autonomy and adaptability are critical as agentic AI systems operate independently, making decisions and adapting to changing inputs to achieve specific goals without human oversight. Enhanced productivity is vital because agentic AI automates complex tasks, allowing employees to focus on strategic initiatives, thereby increasing overall productivity.

Agentic AI aids industry applications when it is utilized across various industries, including healthcare, manufacturing, and retail, to optimize processes and improve efficiency.

Emerging Concerns

 While promising enhanced productivity and operational efficiency, the integration of agentic AI in the workplace faces significant resistance from various groups due to various concerns. These objections stem from ethical, economic, and cultural implications. Worker advocacy organizations and unions express concern about the potential for large-scale job displacement.

Agentic AI, with its capacity to perform complex, multi-step tasks autonomously, threatens roles that rely on repetitive or analytical tasks, such as data analysis, customer service, and logistics coordination. These groups argue that businesses may prioritize short-term cost savings over employee welfare, exacerbating unemployment and wage stagnation.

Employees in affected industries, such as manufacturing, finance, and retail, fear job insecurity and skill redundancy. Their resistance stems from the perception that companies are deploying AI solutions without clear plans for retraining or upskilling displaced workers. Furthermore, employees often express discomfort working alongside autonomous systems due to a lack of trust and transparency in AI decision-making.

Civil rights groups and ethics committees raise alarm bells over the potential misuse of Agentic AI in surveillance, decision-making, and predictive analytics. Concerns center on the lack of accountability for autonomous decisions, biases in AI algorithms, and the possibility of violating individual privacy rights. These groups argue that deploying AI without clear ethical guidelines undermines trust and could harm marginalized communities disproportionately.

Many managers view agentic AI as threatening their authority and decision-making roles. With AI systems taking on responsibilities like resource allocation and strategic planning, resistance arises from concerns about diminished relevance and influence in organizational hierarchies.

C-Suite Leadership and Industry Best Practices

 Executives, particularly C-suite leadership, often resist large-scale AI adoption due to high implementation costs, cybersecurity risks, and the challenges of integrating AI into legacy systems. They also face pressure to ensure ethical compliance, which slows decision-making processes and creates resistance to adopting agentic AI systems.

As agentic AI reshapes the corporate landscape, C-suite leaders face a dual imperative: embracing this transformative technology, driving innovation, and remaining attractive to top talent while supporting existing employees. Forward-thinking executives recognize that successfully navigating this shift requires technical adoption and a strategic focus on building a resilient and inclusive workplace culture.

C-suite leaders emphasize reskilling and upskilling programs to keep talent coming. By investing in continuous learning initiatives, leaders can empower employees to thrive alongside agentic AI, positioning their companies as industry leaders prioritizing professional growth. Additionally, fostering partnerships with academic institutions and specialized training providers enables businesses to cultivate a pipeline of skilled professionals eager to work with cutting-edge technologies.

Equally important is addressing employee concerns about job security and ethical AI deployment. Transparent communication about how agentic AI is integrated and its benefits to the organization and workforce helps build trust.

C-suite leaders should also protect their organizations by establishing best practices for ethical AI use, including governance frameworks, data privacy safeguards, and inclusive decision-making processes. These measures mitigate risks and position companies as responsible innovators, enhancing their reputation among employees, customers, and investors.

 Ethics and Governance

 In the broader policy perspective, regulatory authorities resist rapid adoption due to insufficient legislation governing AI ethics, accountability, and security. Policymakers advocate for stricter oversight and clearer governance frameworks to mitigate autonomy and data security risks.

While this innovation has much to be praised, consumer resistance stems from a lack of understanding and trust in agentic AI systems. Concerns about transparency in AI-driven decisions—such as pricing models, recommendations, or dispute resolutions—lead to skepticism about businesses that fully rely on such systems.

 Tech Companies Eye Financial Windfall from Agentic AI

 Tech giants are racing to position themselves at the forefront of the agentic AI revolution, recognizing its immense potential to drive financial growth. Companies like Google, Microsoft, and Amazon invest heavily in research and development to create advanced AI agents capable of autonomous decision-making. These firms view agentic AI as a transformative technology that can enhance their existing platforms, open new revenue streams, and cement their dominance in the AI landscape. For instance, enterprise solutions powered by agentic AI are being marketed as tools to revolutionize industries by automating complex tasks, optimizing workflows, and delivering unprecedented efficiency.

Start-ups and established firms compete to attract venture capital funding, focusing on niche applications such as healthcare diagnostics, financial analytics, and personalized customer experiences. These targeted deployments promise significant cost savings for businesses, making them attractive investments. Additionally, tech companies are integrating agentic AI into cloud computing services, offering businesses scalable, AI-powered solutions that can be tailored to diverse needs.

In short, Agentic AI is coming. It will shape industry, government, and the military, and preparing for that future is critical.

Mr. Greg Sharpe is a Fellow and the director of Communications and Marketing for the National Institute for Deterrence Studies and the Managing Design Editor for the Global Security Review. He has 25+ years in marketing and communications with a focus on digital communications, organizational and institutional change, and analysis.  Greg has over 35 years of military, federal civilian, and defense contractor experience in the fields of database development, digital marketing & analytics, and organizational outreach and engagement, technology use case exploration and assessment.

Navigating the New Frontier: Agentic AI’s Promise and Challenges was originally published on Global Security Review.