By John Babick
The Pentagon’s artificial intelligence strategy documents promise decision advantage at scale. Defense contractors pitch predictive logistics and machine-speed targeting. The narrative is seductive… and largely irrelevant to a destroyer executive officer (XO) at sea.
At the deckplate level, the questions are fundamentally different. To the degree that an XO has time to think, the question is not “How transformative is AI?” but, “Will it work when the satellite link drops? Will my sailors trust it enough to use it, but not so much that they stop thinking for themselves? Will its absence during a high-end fight create a liability I haven’t trained for?”
These are practical, even deadly realities that determine whether a technology earns its place aboard a warship or becomes dead weight in a server rack. If artificial intelligence is to earn a place aboard surface combatants, it must be evaluated against operational reality, not against a PowerPoint brief delivered in a climate-controlled conference room. By 2027, the destroyer XO should expect onboard AI that is bounded, transparent, and resilient. They should be equally prepared for the moments when it is not.
The Environment AI Must Survive
A destroyer is a paradox. It is an austere and unforgiving operating environment that continuously produces more data than practically any other platform. Engineering systems generate continuous data streams across propulsion, electrical, and auxiliary plants, which go mostly ignored today. Combat systems fuse inputs from multiple sensors simultaneously, each with its own refresh rate, classification level, and failure modes. Administrative demands, from supply chain management to personnel qualification tracking, compete with operational demands for the crew’s every available hour. Within this data-rich environment, watch standers work under sustained fatigue, often standing six-on, six-off rotations for weeks on end.
The intuitive response is off-platform connectivity, but connectivity cannot be assumed. Even during routine steaming, communications bandwidth is finite and intermittent at best. Emissions control requirements, satellite geometry, adversary interference, and occasional equipment failure can all degrade or eliminate communications without warning. In a contested maritime environment, the very scenario the United States Navy should and is preparing to fight in, network access may be deliberately denied by the adversary for extended periods, making AI capability that depends on persistent cloud reach-back operationally irrelevant. Even without deliberate electronic interference, transmitting can be a beacon for enemy targeting. Cloud-based AI is a peacetime tool wearing a warfighting costume.
The baseline requirement is therefore unambiguous. Onboard AI must run locally, on ship-approved hardware, without external dependencies. If a system fails when the network drops, it has no place at sea. This is not some abstract aspirational standard. It is the minimum threshold for credibility in combat environments.
What Onboard AI Should Actually Do
A destroyer XO is responsible for, among other things, the standardized training of every division across the ship. By 2027, a destroyer XO should expect AI to assist the trained warfighter in all aspects of naval warfare, not as a replacement for judgment, but as a force multiplier. The most promising applications reduce friction for existing processes, freeing the crew’s cognitive bandwidth for the decisions that actually require human expertise. Applications with immediate value fall into several broad categories.
Maintenance triage. Engineering departments manage a continuous flow of discrepancies, preventive maintenance actions, and corrective work orders across complex, interdependent systems. A gas turbine engine does not fail in isolation. Rather, its degradation cascades through reduction gears, lube oil systems, and electrical generation in patterns that are difficult to track manually across thousands of maintenance entries. A mature onboard AI tool should analyze historical maintenance logs, identify recurring fault patterns before they escalate, and recommend actions based on the ship’s own operational history, not a generic baseline developed from fleet averages that may not reflect a ship’s specific material condition.
This does not displace the engineering team’s expertise. It structures information that already exists but is nearly impossible to synthesize under time pressure. The ship already has the data. AI makes the data useful. The difference between a well-maintained warship and a materially degraded one is often not a lack of information but a lack of time to process it. Critically, the tool must be transparent about its reasoning. When it flags a discrepancy as high-priority, watch standers need to understand why. They need to understand the historical pattern that drove the recommendation and how confident the model is. This is what makes an maintenance AI agent question is not a diagnostic tool. It is a black box wearing a maintenance hat.
Log and report synthesis. Warships generate an enormous quantity of written records. This includes deck logs, engineering logs, operational reports, casualty reports, and administrative correspondence, all of which accumulates daily. An effective AI assistant should compress and synthesize that data, summarize logs into concise operational briefs, flag negative trends, and cross-reference issues across departments that would otherwise remain siloed. When the combat systems officer’s maintenance trends correlate with the chief engineer’s power generation anomalies, the XO needs to see that connection without manually reading two hundred pages of logs. The value is not novelty. It is time recovery. Hours currently spent reviewing administrative records become minutes, and those recovered hours translate directly into training time, rest, or tactical planning. The caveat, as with maintenance AI, is transparency and the consequent trust: a synthesis tool that obscures the source data behind its conclusions cannot be trusted, because the XO has no way to assess whether the summary has missed something operationally significant.
Procedural recall under stress. When a flooding boundary is expanding, smoke is filling a space, or the repair locker team needs to know the location of the nearest fire main valve, cognitive load is at its highest and error tolerance is at its lowest. An AI system should rapidly surface relevant instructions and present structured guidance tied to approved technical documents. It should not generate novel doctrine on the fly. Instead, it should organize what already exists and make it accessible under pressure. The caveat here is critical: the system must draw exclusively from verified, configuration-controlled source documents. A tool that synthesizes procedures from multiple sources, or worse, generates plausible-sounding guidance from training data rather than authoritative technical manuals, introduces risk precisely when the stakes are highest. The value of procedural AI is speed and clarity, not creativity.
Distributed training support. Training time is scarce and constantly competed against by maintenance demands, watch rotations, and administrative requirements. AI can assist by generating scenario-based prompts, tracking qualification progress across divisions, and reinforcing procedural understanding between formal evolutions. A junior officer preparing for a professional knowledge board, or a damage controlman reviewing casualty procedures between drills, benefits from on-demand support that does not require pulling a senior sailor off watch. The limitation worth acknowledging is that AI-assisted training is a supplement, not a substitute, for the mentorship and direct observation that develop genuine competence. Scenario prompts can reinforce knowledge. They cannot replicate the judgment that comes from working alongside experienced petty officers over time.
The Dependency Trap
The more subtle risk of onboard AI is that sailors stop performing without it.
If the Combat Information Center (CIC) watch relies on AI-generated track correlation, if the engineering watch relies on AI-prioritized maintenance queues, if the quarterdeck watch relies on AI-drafted reports, then their absence during combat operations will create friction at exactly the wrong moment. This is not a hypothetical. It is the predictable consequence of any capability that integrates faster than the training culture adapts. Every augmentation is an amputation. History offers ample precedent. GPS navigation degraded celestial navigation skills across the fleet within a generation, and the Navy is still working to recover that competency. AI presents the same risk at greater scale and speed.
A destroyer XO must treat onboard AI as an augmentation layer, not as infrastructure. The distinction is critical. An augmentation layer improves performance when present but does not create a capability gap when absent. Infrastructure, by contrast, is load-bearing. Its removal causes structural failure. Before fielding any AI tool, the XO should ask three questions: “What manual processes exist if the system fails?”, “Are watchstanders trained to operate independently of AI assistance?”, and “Does institutional knowledge live in the crew, or has it migrated into the tool?”
Redundancy in mechanical systems is standard practice at sea. The same logic must apply to cognitive systems. If AI becomes a silent dependency, its failure will produce operational surprise, and operational surprise is the one thing a warship cannot afford in a contested environment.
When AI Gets It Wrong
Probabilistic models generate confident outputs. They also generate confident wrong outputs. Even mission-specific, locally deployed AI systems can misinterpret data, surface misleading correlations, or fail silently in ways that are not immediately apparent. A maintenance prediction model trained on peacetime steaming patterns may produce dangerously inaccurate recommendations during high-tempo combat operations where equipment is driven well beyond normal parameters. The architecture of modern machine learning does not provide natural safeguards against this, though deliberate architectures can mitigate some risks. The real safeguard, however, is the human in the loop.
An XO should expect any onboard AI system to offer transparent reasoning pathways, traceability to source data, and clear articulation of uncertainty. A system that presents conclusions without exposing its basis is a liability, not an asset. The correct operational posture is disciplined skepticism, neither blind acceptance of AI output nor reflexive dismissal of AI insights. Watch standers should be trained not only to use AI tools, but to interrogate them. What data drove this recommendation? What is the confidence interval? What was excluded from the analysis?
That training is not optional. It is the difference between AI as a capability and AI as a vulnerability. The fleet has always understood that a tool is only as good as the sailor operating it. AI does not change that principle. It raises the stakes.
Command Authority Does Not Transfer
Artificial intelligence should compress cognitive load. It cannot compress accountability. Onboard AI must remain subordinate to command authority at every layer. It can assist with pattern recognition and information synthesis. It cannot bear responsibility for the decisions that follow. Final authority must always rest with the accountable officers.
The Navy’s culture of command responsibility did not emerge arbitrarily. It reflects hard-earned understanding, often paid for in lives, of what happens when accountability diffuses. AI does not change that calculus. If anything, the introduction of capable AI tools makes the deliberate preservation of command culture more important, not less. When a machine recommends a course of action and a commanding officer accepts it, the responsibility for the outcome belongs entirely to the officer. Technology should reinforce that culture, not erode it by degrees through the gradual normalization of deference to algorithmic output.
The Standard
Artificial intelligence aboard a warship should not look like a revolution. It should look like a well-trained petty officer who never sleeps, never tires, and never lets a maintenance log fall through the cracks.
By 2027, a destroyer XO should demand AI that operates fully offline without degradation, exposes transparent and traceable reasoning, integrates without destabilizing shipboard networks, reduces administrative and cognitive burden measurably, and fails gracefully, loudly enough that watch standers notice before the gap becomes a hazard.
If a system meets those criteria, it earns a place aboard. If it promises transformation while depending on perfect conditions, it is not yet ready for sea. The Navy does not field systems that only function in permissive environments. Neither should it field AI that does.
The decisive variable in AI adoption is not model sophistication. It is operational fit, and operational fit is proven at sea, not in a vendor demonstration. The fleet’s standard has always been simple: perform when conditions are imperfect, and give commanders the tools to succeed. That standard does not change because the technology is new.
John Babick is a retired naval officer and defense technology professional with experience in edge AI deployment for maritime operations. He currently works for EdgeRunner. The views expressed are his own and do not represent the views of the Department of Defense or the United States Navy.
Featured image: Jim Blesse, standing, from the Office of Naval Research, explains project BlueShark to Lt. Col. John Moore from the Marine Corps Warfighting Laboratory. Project BlueShark is an ONR effort to create a high-tech, futuristic environment to demonstrate what operational work environments might look like and what emerging innovative technologies might provide in the next decade. (U.S. Navy photo by John F. Williams)