The Department of Defense is racing to deploy artificial intelligence from central command to the tactical edge to ensure decision-dominance in future conflicts. However, military leaders face a fundamental obstacle that threatens to undermine their progress: Deploying autonomous AI agents without a deeper foundation of trust and operational control poses significant risks of fragmentation, flawed outcomes, and mission failure, say AI experts and former military intelligence officials in a new report.

The stakes for managing AI effectively in the military are increasing as global opponents speed up their use of commercial AI, and leaders face the emerging threat of what one AI expert in the report called “algorithmic warfare.” Given the growing amount of commercial and customized AI acquired by the U.S. military that operates inside so-called “black boxes,” experts warn that the lack of trust in AI output will hinder the Pentagon’s AI progress, especially if commanders lack confidence in the ability to verify or trust the data.

The report suggests that without a shift toward transparent, configurable, and explainable AI, the DoD risks mission failure and ceding the advantage to its rivals, even if it continues to invest billions in modernization.

The new report, titled “The AI control advantage: Trusted autonomy, on your terms,” produced by Scoop News Group on behalf of Seekr, argues that to achieve true decision dominance, defense leaders must move beyond acquiring fragmented, siloed AI tools. It lays out the case for taking a broader platform-based approach that provides a command-and-control layer for AI itself, ensuring that autonomous agents operate with explainable logic and in alignment with commander’s intent, from the enterprise cloud to the tactical edge.

The report, based on insights from former senior military and intelligence officials, highlights three major factors shaping the military’s approach to AI:

Confronting insight gaps and trust deficits

The DoD’s aging systems and dashboards generally fail to provide the insights needed to make quick decisions on the ground. This “insight gap” is exacerbated by a dangerous “trust deficit” in AI output, says Lisa Costa, former U.S. Space Force Chief Technology and Innovation Officer and now a Senior Advisor to Seekr in the report. Many AI applications function as black boxes, obscuring how they arrive at a recommendation. This lack of transparency makes it nearly impossible for commanders to verify the logic or trust the source of AI-generated recommendations. That poses potentially fatal risks in high-stakes operational environments when humans only have seconds to make critical decisions.

This forces an untenable choice between speed and safety, says Costa. “Our adversaries are moving forward with commercial AI. Waiting isn’t an option. However, trust is not an option, even if commercial AI is used. How can a commander execute a mission based on an AI recommendation if they cannot verify its reasoning or trust its source?”

True autonomy requires orchestration from the enterprise to the edge.

Additionally, the report says, effective military AI cannot be confined to a central cloud. It must be deployable as autonomous agents to the warfighter, operating in disconnected and denied environments. This requires an infrastructure that can create and manage these agents, pushing them from powerful, centralized resources out to a small form-factor device on the front lines, explains Derek Britton, SVP of Government at Seekr and a former U.S. Air Force intelligence officer.

“It’s all about creating the agentic processes at the various levels, using enterprise cloud capabilities… to develop human-centric AI agents, but then having the ability to push them out from the enterprise cloud to the tactical cloud node, then all the way out to the edge on a PC or a small form-factor device,” he says.

Fragmented solutions cannot keep pace with ‘Algorithmic Warfare.’

The future of conflict will continue to evolve as adversaries directly target U.S. capabilities dynamically and at machine speeds, and vice versa, creating a mounting contest between algorithms. A defense strategy built on disparate point solutions, each with its own vulnerabilities and no common framework for updates, is dangerously fragile, warns John Chao, Seekr’s Director of Federal Products and a former U.S. Marine Corps Special Operations Command Intelligence Operator.

He argues that defense leaders need to look beyond isolated AI tools and consider adopting a unified platform approach capable of developing, deploying and orchestrating trustworthy AI agents that can be updated rapidly across the enterprise and out to the tactical edge to maintain a competitive advantage.

Key takeaways for defense leaders

The report maintains that to gain the AI advantage, the imperative is to act now. “Mission owners can start by solving discreet but critical and urgent problems using pre-built, out-of-the-box commercial AI solutions that are transparent and configurable for their needs, without compromising safety and trust,” says Britton.

The report highlights four “non-negotiable principles” for embracing this platform approach. Among them is an AI platform that stresses data and algorithmic transparency, radical explainability, correctability, continuous improvement, and training agility. It also emphasizes the need for speed and points to the success Seekr has achieved with its AI-Ready Data Engine, which automates data preparation 2.5 times faster and 90% less expensive than traditional data preparation methods.

Download the full report.

Listen to a “deep dive” podcast discussion highlighting the findings and recommendations of the report, created by Scoop News Group using NotebookLM.

This article and the full report were produced by Scoop News Group for DefenseScoop and sponsored by Seekr.

Federal agencies are facing an onslaught of artificial intelligence requirements, a new government watchdog report detailed, with callouts coming from executive orders, federal laws, advisory guidance and other sources.

As of July, there were nearly 100 different objectives related to the emerging technology that might be considered government-wide standards, according to the Government Accountability Office. 

“AI technologies can drive economic growth and support scientific advancements that improve the conditions of our world,” the GAO said in its correspondence to Congress. “It also holds substantial promise for improving the operations of government agencies. However, AI technologies also pose risks that can negatively impact individuals, groups, organizations, communities, society, and the environment.”

The goal of the report, the watchdog said, was to understand the various AI requirements facing the government and which bodies hold responsibility related to the technology. 

The review included current requirements for federal agencies, like creating inventories of AI use cases and updating AI use policies. It also examined broader efforts, like the National AI Initiative, which focuses on goals like increasing research and development of the technology and investing in computing resources. 

“Federal agencies’ efforts to implement AI have been guided by a variety of legislative and executive actions, as well as federal guidance,” GAO continued. “Congress has enacted legislation, and the President has issued EOs, to assist agencies in implementing AI in the federal government.”

The office reviewed new artificial intelligence initiatives created by the current and former administrations, stretching from the first Trump administration’s executive order on artificial intelligence and the signing of the AI Training Act to more recent guidance from the Office of Management and Budget.

Overall, GAO found that 10 different bodies had a stake in reviewing the U.S. government’s AI efforts, and that federal laws, executive orders, and guidance had produced 94 different expectations related to the technology, including reviews related to risk mitigation, investment strategies, and usage policies. 

The GAO sent a draft of the report to OMB, the Office of Science and Technology Policy, the Commerce Department, the General Services Administration and the National Science Foundation. OSTP, Commerce and NSF responded with technical comments, while GSA declined to provide comments and OMB did not respond to GAO’s request for comments on its findings.

The IBM Institute for Business Value study shows that generative AI is live in customer care for 69% of telecoms. Meanwhile, agentic AI—capable of autonomous decision-making—is being used by 44% of CSPs. These technologies are enabling real-time insights, personalized experiences and operational efficiency across the board.

Momentum is building in areas such as network automation, edge intelligence and service assurance, but leading CSPs are already pushing further. 

For example, Bharti Airtel, a leading CSP in India, has deployed an AI-powered anti-SPAM network that flags over 8 billion spam calls and 1 billion spam SMS messages. It identifies nearly 1 million spammers daily. The company also launched an AI-driven RAN energy management solution, expected to save USD 12 million annually while reducing its carbon footprint.

Meanwhile, China Mobile has introduced over 24 AI products. One of them, Lingxi—an intelligent customer assistant—handles 90% of first-line inquiries and has boosted customer satisfaction by 10% in pilot regions. The company also uses AI-powered predictive analytics to reduce network repair times by 30% and AI-based energy management to dynamically optimize power usage across its RAN infrastructure.

As AI becomes embedded in critical infrastructure, telecom providers are turning to performance dashboards to bring transparency and accountability to AI-driven initiatives. These tools help shift AI from a black box to a visible engine for business value—tracking model drift, triggering retraining and alerting teams when KPIs fall below thresholds. Governance dashboards also support regulatory compliance by offering transparency logs for audit purposes.

To ensure sustained impact, continuous monitoring and agile feedback loops are essential. But measuring the right things matters equally. Focusing solely on cost can obscure gains in customer experience or business growth.

That insight is why leading telecom adopters track a balanced set of KPIs—most often cost savings, customer satisfaction, AI-driven revenue growth and operating margin. Over the past year, CSPs have reported real, measurable improvements across these high-priority performance areas.

By anchoring AI initiatives in business outcomes and operational KPIs, CSPs can ensure that innovation translates into growth, efficiency and long-term competitive advantage.