How ONR Can Speed AI Research Without Losing Rigor

A decade is an eternity in AI. Yet in defense R&D, “more than a decade to field new capabilities” is still a familiar timeline—one even government watchdogs keep calling out. That gap between commercial innovation cycles (months) and defense delivery cycles (years) is the quiet risk sitting behind most “AI readiness” briefings.

The Navy’s Office of Naval Research (ONR) sits right in the middle of that problem. ONR funds early-stage science and transitions promising tech toward real operational use—everything from autonomy to ocean sensing. So when the Navy installs a new leader with a reputation for bureaucracy-busting—Rachel Riley, a DOGE alum and former McKinsey partner—people notice.

I’m optimistic about this kind of appointment, but not starry-eyed. ONR doesn’t just need “faster.” It needs faster where speed matters, and more disciplined where science and national security demand patience. The services that get this balance right will be the ones that actually field trustworthy AI, resilient autonomy, and usable decision-support—rather than producing endless prototypes and slide decks.

Why ONR’s leadership shift matters for military AI

Answer first: ONR’s new leadership matters because AI and autonomy don’t fail in labs—they fail in transition to fleets, programs of record, and operational doctrine.

The RSS story highlights observers who believe Riley could bring “urgent reform” to ONR, and it’s easy to see why. She’s associated with a broader government efficiency push, and her consulting background is steeped in diagnosing bottlenecks: committee sprawl, risk aversion, and slow decision cycles.

For the Navy, those bottlenecks have concrete consequences:

• Autonomy prototypes never get ruggedized for maritime operations.
• AI models don’t get the data pipelines and security accreditation needed to deploy.
• Promising tech gets trapped in multi-year “experiments” that never become capabilities.

The uncomfortable truth: in 2025, the Navy isn’t competing only with peer adversaries. It’s also competing with the pace of technology itself. If you can’t field AI-enabled sensing, targeting support, cyber defense, and autonomous systems on a relevant timeline, you’re effectively choosing to fight with yesterday’s toolset.

The “private sector is faster” argument is real—but incomplete

The source article quotes officials arguing that parts of ONR can resemble a slower, more expensive version of what private tech firms already do. That criticism lands because commercial AI stacks have matured quickly: data engineering, model training, evaluation tooling, MLOps, and simulation.

But the private sector’s speed advantage comes with an asterisk: commercial success is driven by revenue and scale, while the military cares about robustness under deception, contested networks, edge deployment, and mission assurance. That’s where ONR should be strongest.

ONR shouldn’t try to out-Google Google. It should do what the market won’t: fund hard problems like resilient undersea communications, adversarially robust autonomy, and trustworthy AI in degraded environments.

The real bottleneck: transition, not ideas

Answer first: The Navy doesn’t have an “innovation” problem as much as it has a transition problem—getting research to ships, submarines, aviation, and cyber units with repeatable pathways.

ONR’s portfolio includes everything from basic science to applied projects. That breadth is valuable. It’s also where effort gets diluted.

A practical way to think about it is to divide work into three lanes:

1. Science that must exist (even if it’s slow)

   • Oceanography, climate and maritime environment modeling
   • Cryptography and advanced security research
   • Undersea sensing physics and materials

2. Tech that should be bought, adapted, and secured

   • Commercial perception and planning components
   • Data labeling, synthetic data, simulation tooling
   • Many autonomy building blocks that are dual-use

3. Military-unique engineering that must be delivered fast

   • Edge AI deployment on constrained platforms
   • Integration with tactical networks and C2
   • Test and evaluation in representative maritime conditions

Most organizations get this wrong by treating all three lanes the same. They run them through the same funding gates, the same review boards, the same documentation expectations, and the same “we’ll transition later” promises.

The railgun lesson: money doesn’t guarantee fielding

The source mentions the Navy’s canceled $500 million electromagnetic railgun effort as an example of R&D that didn’t translate into a fielded capability. You can debate the technical merits, but the acquisition lesson is crisp: if the program can’t prove a viable pathway to operational deployment, it becomes an expensive science project.

For AI, the equivalent failure mode is common:

• impressive lab demos
• unclear data rights and data readiness
• no integration owner
• undefined authority-to-operate path
• no sustainment plan for models (retraining, monitoring, drift)

If Riley’s team focuses on anything, it should be this: transition is a product discipline, not an end-of-project paperwork task.

What “efficiency” should mean in Navy AI R&D

Answer first: Efficiency in defense AI isn’t about indiscriminate cuts; it’s about time-to-field, measurable outcomes, and killing work that doesn’t graduate.

DOGE’s broader reputation in government—disruption, hasty cuts, controversy—creates understandable skepticism. But ONR reform doesn’t need blunt-force cost cutting. It needs a better operating model.

Here are four changes that would materially improve Navy AI outcomes without sacrificing rigor.

1) Make timelines a first-class requirement

Every applied AI and autonomy effort should publish a timeline that answers:

• What can be demonstrated in 90 days?
• What can be tested at-sea or in realistic environments in 6–12 months?
• What is the transition target (program office, fleet type command, warfare center)?

If the timeline can’t be stated plainly, the project is probably not ready for applied funding.

A strong stance: ONR should refuse “three-year research plans” that can’t show a six-month operational experiment. Not because science is easy, but because operational learning is the fastest way to expose what’s missing.

2) Standardize evaluation for trustworthy AI

AI in defense fails when leaders can’t trust outputs under pressure. ONR can raise the bar by requiring shared evaluation patterns:

• adversarial robustness checks (spoofing, deception, domain shift)
• calibration and uncertainty reporting (knowing when the model doesn’t know)
• data provenance and audit trails
• safety constraints for autonomous behaviors

This shouldn’t be optional “responsible AI” paperwork. It should be a technical exit criterion for moving from prototype to pilot.

A useful internal rule: if a model can’t explain its confidence and failure modes, it’s not ready to influence operations.

3) Treat data as a deliverable, not a dependency

Most Navy AI projects slow down because the data is messy, inaccessible, classified in unhelpful ways, or owned by systems that were never designed for ML.

ONR can change incentives by funding:

• data pipelines and labeling strategies
• synthetic data generation for rare events
• secure data-sharing approaches across commands
• maritime “data readiness levels” alongside tech readiness levels

If a project proposes a model but doesn’t propose the data work, it’s not an AI project—it’s a demo.

4) Use dual-use commercial tech—then harden it for war

The best version of “buy what’s commercially available” isn’t procurement theater. It’s disciplined engineering:

• adopt commercial components for perception, planning, and MLOps where they fit
• run them through military-grade security and resilience testing
• build the missing layers: contested comms behaviors, mission constraints, human-on-the-loop controls

This is how venture-backed defense tech tends to move quickly: reuse proven building blocks, then specialize.

What ONR must protect: the research the market won’t fund

Answer first: ONR must keep funding strategically vital science—especially maritime research and advanced security—that won’t attract commercial R&D even in a booming defense tech market.

The RSS article makes a point that should shape every reform plan: the Defense Department is increasingly the “last refuge” for certain research areas as civilian science funding faces pressure.

That matters for AI in maritime and national security contexts because:

• Ocean conditions drive sensor performance. If you don’t understand the environment, your “AI sensing” is fragile.
• Undersea warfare is data-poor. You need physics-informed models, simulation, and careful measurement.
• Cryptography and high-end cyber research rarely maps cleanly to commercial incentives.

So yes—cut the zombie projects. But don’t starve the science that becomes decisive five to ten years out.

A clean way to manage this is to create explicit portfolio guardrails:

• applied AI programs: measured on time-to-experiment and transition
• basic science: measured on publication quality, unique datasets, and long-horizon payoff
• strategic “no-commercial-market” work: protected funding with clear national security justification

People also ask: What will reform look like in practice?

Will ONR become “more like a startup”?

Not in the ways people mean. ONR can’t behave like a startup because it’s a steward of public funds and national security research. But it can adopt startup-like cadence: faster iterations, clearer ownership, and an insistence on learning quickly through real tests.

Does faster AI fielding increase risk?

Yes—if you skip evaluation, safety engineering, and sustainment planning. But the opposite is also true: slow fielding is a risk when adversaries adapt faster and commercial tech outpaces defense cycles. The solution is not slowing down; it’s making rigor repeatable.

How does this connect to the Navy’s autonomy push?

AI-enabled autonomy depends on three things ONR can directly influence: data, evaluation, and transition pathways. Get those right and you’ll see more operationally relevant autonomous maritime systems, not just prototypes.

What leaders and contractors should do next (practical steps)

Answer first: If you work with Navy R&D, you’ll win in 2026 by showing measurable progress in months, not promises across budget cycles.

Here’s what works, whether you’re in government, a prime, or a venture-backed firm:

1. Show a 90-day operational experiment plan. Even if it’s a surrogate environment, make it realistic.
2. Bring an evaluation pack. Document model performance, uncertainty, robustness tests, and failure cases.
3. Own the data plan. Data access, labeling, security, and sustainment are part of your proposal.
4. Name the transition customer early. If no one will own it after ONR, it’s not transitioning.
5. Budget for sustainment. Models drift; sensors change; environments shift. Plan for monitoring and retraining.

If ONR under new leadership rewards these behaviors consistently, the ecosystem will respond quickly.

Where this goes in 2026: AI advantage is a management problem

Military AI advantage isn’t primarily about having a bigger model. It’s about fielding reliable systems faster than adversaries can adapt, then sustaining those systems responsibly. That’s why ONR leadership and operating model choices matter.

Rachel Riley’s appointment—paired with the broader defense push toward acquisition reform and commercial adoption signals—suggests the Navy is ready to be more aggressive about timelines and accountability. I think that’s overdue.

If you’re building AI for defense and national security, this moment is also an opportunity: teams that can combine speed, evaluation rigor, and real transition planning will be the ones still standing when pilot programs turn into fleet-wide requirements.

Where do you think the Navy should draw the line between “buy commercial AI now” and “fund the science only government will do”?