Sailors and Marine Oilers
53-5011.00Median wage $51,520/yr31,670 employed (US)Rank #823 of 923 scored · top 89% by substitution
Stand watch to look for obstructions in path of vessel, measure water depth, turn wheel on bridge, or use emergency equipment as directed by captain, mate, or pilot. Break out, rig, overhaul, and store cargo-handling gear, stationary rigging, and running gear. Perform a variety of maintenance tasks to preserve the painted surface of the ship and to maintain line and ship equipment. Must hold government-issued certification and tankerman certification when working aboard liquid-carrying vessels. Includes able seamen and ordinary seamen.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution14
Exposure10
Augmentation32
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
28 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%10
panel mean rating 1.4/5 → substitution pressure 10/100
Technical feasibility todayw 20%9
panel mean rating 1.4/5 → substitution pressure 9/100
Cost vs. human wagew 15%11
panel mean rating 1.5/5 → substitution pressure 11/100
Adoption barriersw 20%inverted — strong barriers lower the score31
panel mean rating 3.8/5 (barrier strength) → substitution pressure 31/100
Sector adoption velocityw 10%6
panel mean rating 1.2/5 → substitution pressure 6/100
Task breakdown (28 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Read pressure and temperature gauges or displays and record data in engineering logs.
66CI 47–85 · exposure 66 · augmentation 75 · importance 4.3/5 · click for rater detail
Read pressure and temperature gauges or displays and record data in engineering logs.
66| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Maritime digitization is accelerating (smart ship initiatives, IoT adoption in large fleets), but adoption remains uneven: modern container ships and tankers are faster adopters, while smaller vessels lag. Pilots are common among major shipping lines, but widespread production deployment is still emerging. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Maritime shipping is a capital-intensive, slow-turnover industry with long vessel lifespans, so automation of engine monitoring is adopted gradually rather than rapidly compared to information-sector tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-driven sensor monitoring and automated log preparation significantly assist human engineers by surfacing anomalies, trends, and alerts in real time, freeing them to focus on diagnosis and troubleshooting rather than manual transcription. The human operator remains responsible for validation and action. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Sensor dashboards, automated alerts, and digital logging tools significantly reduce the burden of manual reading and recording, letting engineers/oilers focus on interpretation and response rather than rote data capture. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Reading analog or digital gauges and recording data is fully automatable via computer vision (for analog gauges) or direct sensor integration (for digital displays), with straightforward structured data entry into logs. Current systems can achieve >50% time savings at equal or better accuracy by eliminating manual reading and transcription errors. |
| Task automatability | claude-sonnet-5 | 3/5 | Reading and logging sensor data is technically simple to automate via IoT sensors and automated data logging systems, but the physical gauge-reading and integration on legacy vessels still requires setup and retrofit.dis Many ships still use analog or non-networked gauges requiring a human to physically check them. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Maritime safety regulations (SOLAS, class society rules) typically require human verification and sign-off of engineering logs rather than mandating manual reading. This creates oversight requirements rather than hard legal bars to automation; autonomous systems can feed data to human reviewers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates human gauge-reading specifically, though maritime safety regulations and classification society rules often require certain checks and logs to be maintained under crew responsibility, creating some regulatory friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated sensor reading and logging costs pennies per reading (sensor + cloud logging), while a sailor's hourly wage-loaded cost for the same task is $25–50+. The ratio is easily 10:1 or better in favor of automation. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Where automated sensor networks already exist, digital logging is essentially free at scale; but retrofitting older vessels with sensors and telemetry involves non-trivial capital cost compared to a sailor's marginal labor cost for this sub-task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Computer vision systems deployed in industrial settings can reliably read digital displays and many analog gauges; integration with IoT sensor networks is mature in modern maritime vessels. Minor gaps remain in edge cases (obscured gauges, legacy analog instruments), but production systems handle the core task reliably. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Modern vessels with integrated bridge/engine monitoring systems can automatically log sensor data, but many ships (especially older ones) still rely on manual gauge checks and paper/manual logs, so this is not universally deployed. |
Chip and clean rust spots on decks, superstructures, or sides of ships, using wire brushes and hand or air chipping machines.
48CI 15–81 · exposure 45 · augmentation 25 · importance 3.6/5 · click for rater detail
Chip and clean rust spots on decks, superstructures, or sides of ships, using wire brushes and hand or air chipping machines.
48| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Shipping and shipyard sectors are moderately digitized with growing automation investment, but adoption of robotic hull-cleaning remains patchy—common in large yards, slower in small vessels and aboard operating ships. Pilots and early deployments are documented, but fleet-wide substitution is not yet widespread. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime deck maintenance is a low-digitization, physically demanding sector with minimal AI/robotics adoption for this kind of manual upkeep work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Automated systems can assist workers by handling large expanses quickly, leaving humans to inspect quality, handle complex geometries, and oversee safety; this raises crew productivity compared to all-manual work, though the human role becomes supervisory rather than primary. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance to a sailor physically chipping and cleaning rust with hand or air tools. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Rust removal on ships is a repetitive, well-defined mechanical task. Automated systems (robotic arm mounted on vessels or drones with brushes/grinding tools) can inspect, map corrosion, and perform chipping/cleaning at high speed with equal or superior quality to manual work, easily achieving >50% time savings with current robotics and computer vision. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual labor task requiring dexterity, mobility over uneven ship surfaces, and tool handling that current AI systems (software or robotics) cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal regulatory barriers to automating this mechanical task; maritime safety rules govern work conditions but do not mandate human labor for rust removal. Some organizational inertia and vessel-retrofit logistics exist, but no licensing or legal requirement protects human work here. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically protects this task, but physical environment hazards, ship classification/safety rules, and lack of mature robotic alternatives create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Robotic cleaning systems have high upfront capital cost but low per-hour operating cost; when amortized over high-volume use (large commercial fleets, shipyards), the cost per task is substantially lower than paying a sailor or marine worker hourly wages plus benefits and safety oversight. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Deploying robotic hardware capable of navigating ship decks and superstructures would be far more expensive than paying a sailor to do manual chipping with hand tools. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Automated blasting and robotic deck cleaning systems are deployed in shipyards and on vessels today (e.g., automated grit blasting, robotic arm systems). While human oversight and repositioning for complex geometries remain common, proven products reliably handle large flat and curved surfaces in production environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic products reliably chip rust and clean ship surfaces in production; any relevant robotics remain research-stage or highly specialized/limited pilots. |
Record data in ships' logs, such as weather conditions or distances traveled.
45CI 25–65 · exposure 45 · augmentation 63 · importance 3.5/5 · click for rater detail
Record data in ships' logs, such as weather conditions or distances traveled.
45| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Maritime is a traditionally conservative, heavily regulated sector with slow digitization outside specialized fleet management companies. Most vessels continue paper or basic digital logs maintained manually by crew rather than automated systems, indicating laggard adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Maritime shipping is a traditionally slow-to-digitize industry with uneven adoption of automated systems, especially on older vessels and smaller operators. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by auto-formatting data, suggesting log entries based on sensor inputs, or flagging anomalies for crew review, but the human sailor must observe conditions and remain responsible for log accuracy. Assistance is useful but modest given the task's simplicity. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted or automated logging systems significantly reduce manual burden and error, letting crew focus on verification and other duties while data entry is streamlined. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Recording log entries requires observing real-time environmental conditions and manually transcribing them into structured logs. While AI could format and store data, it cannot independently observe actual weather or ship movement without human input or integrated sensors, and the task inherently depends on human observation and verification. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording standardized data like weather conditions and distances is a structured, repetitive data-entry task that AI/automated sensor systems can largely handle by pulling from instruments and auto-populating logs. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime regulations (e.g., SOLAS, IMO conventions) legally require a licensed officer or qualified crew member to maintain and certify ships' logs. Records are official documents with legal standing in accidents or disputes, creating strong liability and regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Ship logs have some regulatory/legal significance (official records for maritime authorities), requiring accuracy and occasionally human verification, but automated data logging is already accepted in many maritime contexts. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automating log recording would require significant infrastructure investment in sensors, integration, and oversight systems. The cost of deploying and maintaining such systems on vessels exceeds the relatively low wage of the data-entry portion of the task itself. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated sensor-to-log data capture is cheap to run continuously compared to paying crew time for manual logging, though initial system integration adds some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI system reliably performs end-to-end ship logging today. Sensor data collection exists, but maritime logs require human certification, contextual judgment about conditions, and integration with established maritime protocols that current AI tools do not handle in production environments. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Electronic logbooks and integrated bridge systems with automated data capture exist and are used on some vessels, but many ships still rely on manual entry and legacy logging practices, limiting universal deployment. |
Stand watch in ships' bows or bridge wings to look for obstructions in a ship's path or to locate navigational aids, such as buoys or lighthouses.
25CI 25–25 · exposure 25 · augmentation 63 · importance 4.2/5 · click for rater detail
Stand watch in ships' bows or bridge wings to look for obstructions in a ship's path or to locate navigational aids, such as buoys or lighthouses.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While large shipping companies are exploring autonomous vessels, the regulatory environment remains restrictive and most commercial fleets still employ traditional watch-standing. Adoption of full automation is slow due to international maritime law, insurance requirements, and the high cost of failure in a safety-critical domain. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Maritime shipping is a slower-adopting, capital-intensive physical sector; autonomous vessel technology remains in trials and niche deployments rather than widespread production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered radar enhancement, automated buoy/lighthouse detection displays, and real-time obstacle alerts can meaningfully assist human watchstanders by reducing cognitive load and improving situational awareness, though the human remains the primary decision-maker and safety guardian. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Radar, AIS, thermal cameras, and automated alert systems substantially augment a human lookout's ability to detect hazards and navigational aids, especially in poor visibility. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can detect objects and navigational aids in images, the task requires real-time 360° environmental monitoring, dynamic obstacle avoidance decision-making, and integration with ship navigation systems that current AI cannot reliably perform end-to-end without substantial human oversight. Current systems achieve only partial automation of visual scanning. |
| Task automatability | claude-sonnet-5 | 2/5 | Radar, AIS, and camera-based sensor fusion can detect many obstructions and navigational aids, but the physical watch-standing role combining human judgment, weather adaptation, and legal duty is not yet fully replaceable end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime law (SOLAS, IMO regulations) mandates that human lookouts and qualified officers maintain watch responsibility; automation is heavily regulated and liability for collision/grounding remains with the vessel operator. Human presence in navigation is a legal requirement, creating a hard barrier to full substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | SOLAS and maritime regulations mandate lookout watch-keeping by qualified personnel for safety and legal liability, creating strong regulatory barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | A watch-standing sailor's loaded cost (wages, training, benefits) is relatively modest compared to the infrastructure cost of AI vision systems, integration with ship automation, regulatory oversight, and the liability exposure for navigation failures. Current AI deployment cost exceeds the annual salary of a single sailor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Outfitting a vessel with redundant sensor arrays, radar, and monitoring software plus required human oversight is costly relative to a sailor's wage, though costs are falling with autonomous shipping investment. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some maritime vessels use automated radar and AIS systems for obstacle detection, and computer vision prototypes exist for buoy/lighthouse identification, but no deployed product reliably performs full watch-standing duties across varied weather, lighting, and sea conditions without human supervision. Existing systems require extensive human interpretation and validation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Autonomous ship sensor suites and collision-avoidance systems exist in pilot and limited commercial deployments, but no mature product reliably replaces human lookout duty across the full range of merchant vessels today. |
Measure depth of water in shallow or unfamiliar waters, using leadlines, and telephone or shout depth information to vessel bridges.
24CI 14–34 · exposure 17 · augmentation 38 · importance 2.9/5 · click for rater detail
Measure depth of water in shallow or unfamiliar waters, using leadlines, and telephone or shout depth information to vessel bridges.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime industries remain highly conservative, labor-intensive, and physically distributed. Adoption of AI agents for deck operations is negligible; vessels continue to rely on human crews for shallow-water navigation tasks. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Maritime shipping is a physically-oriented, moderately digitized sector where electronic instruments are common but full automation of navigational tasks and crew reduction moves slowly due to safety regulation and legacy practices. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Modern electronic depth finders and GPS systems provide some augmentation by offering complementary data to humans taking soundings, but AI adds limited direct assistance to the physical task of leadline operation or depth communication itself. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Electronic depth-sounding and communication systems already augment sailors performing this task by providing real-time backup data, though the leadline method itself remains a manual skill with limited AI enhancement. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical measurement in hazardous shallow waters and direct communication to vessel bridge personnel. Current AI systems cannot autonomously operate leadlines, assess water conditions in unfamiliar waters, or safely position themselves on a vessel in dynamic maritime conditions. |
| Task automatability | claude-sonnet-5 | 2/5 | Depth measurement in shallow/unfamiliar waters can be automated via sonar/depth sounders, but the specific manual leadline task with physical presence and verbal communication requires a human on deck; full end-to-end automation of this exact physical task is not yet standard. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime regulations (IMO, national maritime authorities) and vessel classification societies mandate human crew for safety-critical operations including depth measurement and communication. Liability and safety requirements create strong legal and regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Safety-critical navigation decisions often require human verification and there are maritime regulations/certifications around navigational safety equipment and crewing requirements, creating moderate barriers to full removal of human involvement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The capital cost of specialized maritime automation equipment, integration, and maintenance would likely exceed or be comparable to the loaded wage of a sailor performing occasional soundings, particularly given the task's infrequency in modern navigation. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Electronic sonar/depth sounders are cheap and already installed on most vessels, making the automated alternative cost-competitive, though the manual leadline task itself isn't 'AI' per se and still requires a person for backup or specific conditions. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While depth sensors and autonomous measurement systems exist in maritime contexts, no deployed AI product reliably performs the full task of measuring with leadlines and communicating findings in real-time to bridge personnel across varied shallow-water conditions. Human sailors remain essential for this safety-critical operation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Electronic depth sounders are mature products, but they replace the technology rather than the human task as described (manual leadline use plus verbal reporting), which remains a backup/traditional method still performed by sailors in many contexts. |
Steer ships under the direction of commanders or navigating officers or direct helmsmen to steer, following designated courses.
21CI 18–25 · exposure 25 · augmentation 63 · importance 4.0/5 · click for rater detail
Steer ships under the direction of commanders or navigating officers or direct helmsmen to steer, following designated courses.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime shipping is a physically distributed, heavily regulated, conservative sector with long asset lifecycles. Adoption of autonomous steering remains minimal; most deployed vessels rely on human helmsmen, with automation limited to assistance features on newer ships. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Shipping is a traditionally slow-adopting, physically-oriented sector; autonomous steering pilots exist but fleet-wide adoption of AI-directed helming is still nascent and mostly experimental. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Modern ship navigation systems provide helmsmen with autopilot features, route visualization, and collision-avoidance alerts that improve situational awareness and reduce fatigue, offering meaningful but incremental productivity gains while the helmsman remains operationally in charge. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Autopilot, route optimization, and navigation-assist systems meaningfully reduce workload and improve course-keeping precision for helmsmen and officers, while humans remain in the loop for command decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While autonomous navigation systems exist and can follow predetermined courses in controlled conditions, steering ships under dynamic commands from commanders/officers in real-time with proper response to situational changes requires continuous human oversight and decision-making that current AI cannot reliably replicate end-to-end at the quality and safety standards required for maritime operations. |
| Task automatability | claude-sonnet-5 | 2/5 | Autonomous ship steering exists in experimental and limited commercial contexts, but general steering under officer direction on crewed vessels remains largely manual and supervised by humans, not something off-the-shelf AI replaces end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime law (SOLAS, flag-state regulations) and international conventions legally mandate qualified human crew on bridge; liability, insurance, and safety certification create hard barriers to full automation. Human helmsman authorization is effectively a regulatory requirement. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime regulations (SOLAS, flag state rules) and safety/liability concerns generally require certified crew to be in control or supervising steering, creating strong regulatory and licensing barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Autonomous steering systems remain expensive to install, integrate, and maintain relative to a sailor's wage; the total cost of ownership, including redundancy, liability coverage, and oversight infrastructure, exceeds or barely matches the cost of human crew. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Autopilot hardware/software costs are moderate and integration with crewed vessel operations still requires human oversight, so cost savings versus a helmsman are only partial, not order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Autonomous vessel systems are in early trials and limited deployment; no mature, production-scale AI systems fully replace helmsmen or steer independently following real-time commander direction across diverse conditions. Current maritime automation handles routing and course-keeping in narrow scenarios, not general command-following. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Autopilot and dynamic positioning systems are deployed and reliable for maintaining a course, but full 'steer under direction/direct helmsmen' tasking involving real-time judgment and communication is not yet handled by deployed autonomous products at scale. |
Lubricate machinery, equipment, or engine parts, such as gears, shafts, or bearings.
19CI 10–28 · exposure 13 · augmentation 25 · importance 4.0/5 · click for rater detail
Lubricate machinery, equipment, or engine parts, such as gears, shafts, or bearings.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime and heavy equipment sectors lag in digital automation; most commercial and military vessels use manual lubrication schedules. Adoption of automated systems is slow, confined to new large-tonnage vessels, and rare in smaller craft or shore-based equipment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime engine room maintenance is a low-digitization, physically demanding sector with minimal AI/robotics adoption for hands-on mechanical upkeep tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-assisted monitoring (sensors, predictive maintenance alerts) can help schedule lubrication, but the task itself—physical application of lubricant—offers limited augmentation. Scheduling and condition tracking are more augmentable than the manual execution. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based predictive maintenance systems can flag when lubrication is needed or monitor equipment health, offering some indirect assistance, but does not meaningfully change how the physical lubrication task itself is performed. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Lubricating machinery requires physical manipulation in confined, moving environments with precise application needs. While some lubricant-dispensing systems exist, end-to-end automation with equal quality and >50% time saving is not demonstrated at scale; human judgment on quantity, frequency, and access remains essential. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation in a shipboard engine room environment—applying grease, checking fittings, accessing tight mechanical spaces—which current AI and robotics cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Regulatory frameworks (maritime safety codes, classification societies) require documented maintenance and inspection; however, there is no hard legal mandate that a licensed human must personally perform lubrication, only that it be properly logged and executed. Organizational preference for human oversight and liability concerns create moderate friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing specifically bars automation, but maritime safety regulations, confined shipboard environments, and reliability requirements for engine operation create meaningful practical barriers to introducing robotic substitutes. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Purpose-built automated lubrication systems have high capital and integration costs; the ongoing operational expense of maintaining such systems typically exceeds the loaded wage of a marine oiler, especially for smaller or aging vessels. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical task, so any hypothetical automation would require expensive specialized robotics far more costly than a human oiler performing routine lubrication. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Automated lubrication systems exist in some industrial settings, but they are task-specific, narrowly scoped installations—not general deployable products. Most marine vessels and field equipment still rely on manual lubrication by trained personnel; production reliability at scale is not established. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously lubricates marine machinery today; this remains a manual maintenance task performed by crew, with no commercial robotic solution in production. |
Examine machinery to verify specified pressures or lubricant flows.
18CI 5–30 · exposure 13 · augmentation 50 · importance 4.2/5 · click for rater detail
Examine machinery to verify specified pressures or lubricant flows.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Maritime industries show modest adoption of AI monitoring systems, with many vessels still relying on traditional crew-based inspections. While digitalization is increasing, capital constraints on shipping and the dispersion of vessel operations slow deep adoption of automated examination systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipping and marine engineering are low-digitization, physically demanding sectors with minimal AI/robotic deployment for hands-on machinery inspection tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist sailors by providing real-time sensor alerts, historical trend analysis, and predictive maintenance recommendations that reduce the cognitive load of manual monitoring. However, the augmentation is limited to decision support; the physical examination and certification remain human responsibilities. |
| Augmentation potential | claude-sonnet-5 | 3/5 | IoT sensors and predictive maintenance software can augment human inspection by flagging anomalies in pressure or lubricant data, helping sailors prioritize physical checks, though the core physical verification remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI-powered systems could analyze sensor data for pressure and flow readings, the task requires physical examination of machinery and visual verification that is not feasible for current AI systems. The sensorimotor component—accessing machinery, observing physical indicators, and responding to detected anomalies—cannot be automated end-to-end without significant manual setup and human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical inspection, visual/tactile checks, and mobility around shipboard machinery spaces that current AI systems cannot perform without embodied robotics, which are not deployed for this purpose today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime regulations and classification society rules (ABS, DNV, Lloyds) typically require a licensed marine engineer or qualified rating to certify machinery condition and sign off on maintenance logs. This creates a legal and regulatory barrier to full automation, as human certification is often mandated. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed in the sense of a doctor, maritime safety regulations, classification society requirements, and shipboard safety protocols require crew members to perform and log these inspections, creating regulatory and organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While sensor monitoring systems are cost-effective at scale, the requirement for physical on-site examination means human personnel must still be present. The all-in cost of an AI monitoring solution plus required human oversight remains comparable to, or exceeds, the cost of a single trained marine engineer performing the inspection. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only substitute for the physical inspection component, so any comparison would require expensive robotic hardware plus sensors, making it far more costly than a human sailor currently performing the task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed AI systems can monitor digital sensor streams and flag anomalies, but the inspection task as stated requires on-site physical examination and verification. No current product reliably performs the full task of independently examining machinery in a marine engine room environment without human verification of the visual and tactile findings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical machinery inspection aboard vessels; sensor-based monitoring exists but does not replace the physical examination task described. |
Sweep, mop, and wash down decks to remove oil, dirt, and debris, using brooms, mops, brushes, and hoses.
15CI 15–15 · exposure 0 · augmentation 13 · importance 3.8/5 · click for rater detail
Sweep, mop, and wash down decks to remove oil, dirt, and debris, using brooms, mops, brushes, and hoses.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Shipping and maritime industries are digitizing slowly compared to information-intensive sectors, and deck maintenance remains largely manual. Robotics adoption in this space is nascent and confined to narrow applications. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime deck maintenance is a low-digitization, physically demanding sector with negligible AI/robotic adoption for routine cleaning tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Powered cleaning equipment and improved hose systems offer some productivity gains, but AI offers minimal direct assistance to a sailor performing physical deck cleaning. Voice-directed scheduling or sensor-based debris detection could provide marginal augmentation, but not transformative. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance to a sailor sweeping, mopping, or hosing down a deck, as this is a purely physical, manual task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Sweeping, mopping, and washing decks requires physical manipulation in unstructured maritime environments with dynamic obstacles, moving vessels, and safety hazards. Current AI systems lack the embodied dexterity, spatial reasoning in wet/slippery conditions, and real-time hazard avoidance needed to perform this end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual labor task requiring mobility, dexterity, and adaptation to a moving vessel environment; no current AI system (as opposed to robotics) performs this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Maritime labor regulations and union agreements may protect these roles, and vessel operators prefer human workers for safety and reliability in hazardous marine conditions. However, no formal legal requirement mandates human performance of this specific cleaning task. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically, but the physical marine environment, safety concerns, and lack of robotic infrastructure onboard create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous cleaning robots with marine-grade durability, safety compliance, and the ability to operate on pitching decks cost significantly more than a sailor's loaded wage, and integration/maintenance costs are substantial. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic solution deployed for this task, so any hypothetical system would be far more costly than simply having a sailor perform manual cleaning. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform deck cleaning autonomously. Robotics research exists for cleaning tasks, but production systems capable of working safely on moving ships in marine conditions are not yet available at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously clean ship decks at sea; deck-cleaning robots exist only in limited research or land-based contexts, not for marine vessels. |
Clean and polish wood trim, brass, or other metal parts.
15CI 15–15 · exposure 0 · augmentation 13 · importance 2.6/5 · click for rater detail
Clean and polish wood trim, brass, or other metal parts.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime industries are traditionally slow to adopt automation for routine maintenance tasks, with limited digitization of vessel operations and strong reliance on human labor for onboard work in physically constrained environments. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipboard maintenance is a low-digitization, physical-labor sector with minimal AI/robotic adoption for such tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could offer modest assistance through robotic polishing arms in constrained spaces or computer vision systems to detect corrosion, but the task fundamentally depends on human judgment about surface finish quality and safe equipment handling in a marine context. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for physical polishing and cleaning of trim and metal parts aboard a ship. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Cleaning and polishing wood trim and metal parts requires physical manipulation in unstructured environments (ships/vessels) with variable surfaces, finishes, and corrosion patterns. Current AI systems lack the embodied dexterity, real-time environmental adaptation, and quality assessment capabilities to perform this end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual physical cleaning/polishing task requiring dexterity and physical presence on a vessel; no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing requirements for this manual task, organizational and logistical friction is moderate—ships operate in confined spaces with limited infrastructure for deploying and maintaining specialized equipment, and human workers are already embedded in maritime operations. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human do this specific cleaning task, though it's embedded in broader seafarer duties with some organizational routine, not a hard barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The hardware, integration, and oversight costs for a robotic system capable of cleaning and polishing diverse marine surfaces would far exceed the loaded wage of a sailor or marine oiler performing this routine maintenance. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute; the human labor cost is the only viable option, so AI is not cheaper (or even applicable) here. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems or robotic products reliably perform general-purpose cleaning and polishing of mixed materials in marine settings at production scale. This remains a manual task performed by human workers. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product performs shipboard cleaning and polishing of trim and metal fittings; this remains purely manual labor. |
Splice and repair ropes, wire cables, or cordage, using marlinespikes, wire cutters, twine, and hand tools.
13CI 10–15 · exposure 0 · augmentation 13 · importance 3.7/5 · click for rater detail
Splice and repair ropes, wire cables, or cordage, using marlinespikes, wire cutters, twine, and hand tools.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime operations remain relatively low-digitization and labor-intensive sectors. Shipboard maintenance tasks like rope repair are performed by crew with limited technological disruption; adoption of automation is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime trades and shipboard physical maintenance tasks show minimal AI/robotic adoption; this sector remains low-digitization for manual craft work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-based tools might provide marginal assistance through rope-type identification or damage assessment via computer vision, but the hands-on splicing and repair work itself leaves little room for meaningful augmentation of human productivity. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance to the physical act of splicing rope or cable; there is no meaningful software or AI tool that enhances this manual task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Splicing and repairing ropes and cables requires dexterous manipulation of flexible materials, precise knot-tying, and real-time tactile feedback in three-dimensional space—capabilities far beyond current AI systems. No deployed automation exists for this core maritime task. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, dexterity-intensive manual craft task requiring hand-eye coordination and tactile skill; no current AI system or robot can perform splicing/repair of ropes and cables end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Maritime safety regulations and vessel classification societies (ABS, Lloyd's, etc.) have established standards for rope and cable maintenance that may implicitly or explicitly require human inspection and certification of critical repairs, creating moderate regulatory friction for full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for rope splicing, but the task requires physical presence on a vessel and specialized manual skill that limits remote or software-based substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The specialized robotics hardware required to splice ropes—with sufficient dexterity, durability in marine environments, and ability to handle variable cordage types—would far exceed the loaded cost of a skilled sailor performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this physical task, so any hypothetical automation would require expensive custom robotics far costlier than a sailor's labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No commercial products reliably perform rope splicing and cable repair end-to-end. While industrial robotics research has explored flexible material handling, nothing production-grade automates this task in the maritime domain. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that autonomously splices rope or cable aboard vessels; this remains purely a human manual skill with no robotics automation in production. |
Paint or varnish decks, superstructures, lifeboats, or sides of ships.
10CI 5–15 · exposure 0 · augmentation 13 · importance 3.4/5 · click for rater detail
Paint or varnish decks, superstructures, lifeboats, or sides of ships.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Shipping and maritime maintenance remain among the lowest-digitization sectors; adoption of automation in shipboard maintenance is minimal, with vessels still operating on decades-old schedules and predominantly manual labor due to the high fixed costs of ship modifications and regulatory conservatism. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipboard maintenance is a low-digitization, physical-labor-heavy sector with minimal AI/robotics adoption for surface painting tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited augmentation potential: surface inspection tools and paint-mixing automation could assist planning, but the core task of application requires human judgment on technique, coverage, and environmental conditions that AI cannot materially enhance while the human remains in control. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance to the physical act of painting or varnishing ship surfaces. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Painting and varnishing ship surfaces requires navigating complex curved geometries, working at heights and angles, assessing surface preparation quality, and maintaining precise application standards in marine conditions—tasks requiring dexterous manipulation and real-time environmental adaptation that current AI and robotics cannot reliably perform end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring painting/varnishing large ship surfaces; no current AI system can perform the physical application of paint end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime work is heavily regulated by international maritime law, flag-state regulations, and classification society rules; painting and coating standards (corrosion protection, environmental compliance) carry legal liability, and human oversight and certification are typically required for quality assurance and regulatory compliance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement restricts who can paint a ship, but the maritime physical environment (motion, weather, confined access, safety gear) creates practical barriers to any automated equipment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized marine painting equipment, surface preparation machinery, robotic systems capable of maritime conditions, and the integration overhead would substantially exceed the loaded wage of a skilled marine painter, particularly given the low-volume, custom nature of ship maintenance. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical labor, so any hypothetical automation (specialized robotics) would be far more capital-intensive than paying a sailor's wage for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products today reliably perform full painting/varnishing of ship decks and structures autonomously; isolated robotic painting systems exist in controlled manufacturing environments but not for the irregular, multi-story maritime context with its environmental variables and safety constraints. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs shipboard painting autonomously; robotic painting systems for hull maintenance exist only in narrow dry-dock research contexts, not for general deck/superstructure painting by crew. |
Operate, maintain, or repair ship equipment, such as winches, cranes, derricks, or weapons system.
9CI 0–18 · exposure 8 · augmentation 38 · importance 3.4/5 · click for rater detail
Operate, maintain, or repair ship equipment, such as winches, cranes, derricks, or weapons system.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime industries are among the slowest sectors to adopt automation due to safety regulations, unionization, physical environment constraints, and regulatory oversight. Although autonomous vessels are in early trials, operational ship equipment maintenance remains dominated by human crews with minimal AI displacement in practice. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime/shipping is a physically intensive, low-digitization sector with minimal AI/robotics adoption for equipment operation and repair tasks; automation here lags far behind office and information work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist with predictive maintenance monitoring, diagnostics via sensor analysis, and procedure documentation, improving a sailor's ability to maintain equipment more efficiently. However, the core tasks of physical operation and repair still require substantial human judgment and manual work, limiting transformative augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with predictive maintenance scheduling, diagnostic data analysis, or repair manuals/troubleshooting guidance, but offers minimal help with the core physical operation and hands-on repair work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Operating and maintaining ship equipment requires physical manipulation, real-time situational awareness, and troubleshooting in complex maritime environments. While AI could assist with diagnostics and planning, current systems cannot reliably perform hands-on repair or emergency equipment operation without human oversight, and achieving 50% time savings end-to-end is not feasible today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is hands-on physical mechanical work requiring manual dexterity, physical strength, and on-site presence to operate and repair large shipboard machinery; no current AI system performs physical repair or operation of winches, cranes, or weapons systems. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and regulatory barriers exist: maritime regulations (SOLAS, national maritime law) typically require licensed sailors to operate and maintain critical ship systems; liability for equipment failure remains with the ship operator; and weapons systems have strict authorization requirements. Human presence and certification are legally mandated. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime safety regulations, certification requirements for crew operating cranes and weapons systems, and liability concerns for shipboard equipment failure create strong barriers to automation, though not an absolute legal requirement for human sign-off in all cases. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI systems for ship equipment operation (sensors, robotics, integration, maintenance) combined with required human oversight still exceeds the loaded wage of a sailor for most maritime applications. Full autonomy would be needed for cost advantage, which is not yet practical. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing physical operation and repair, so any comparison favors the human worker; the cost of robotic hardware capable of this work would far exceed a sailor's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems reliably operate ship equipment like winches or weapons systems autonomously in production maritime environments. While remote-operation and some diagnostics tools exist, they require human operators to perform the actual task rather than AI performing it independently. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously operates or repairs marine deck equipment; robotics for maritime maintenance remain research-stage or highly limited to specific inspection tasks, not full operation/repair. |
Load or unload materials, vehicles, or passengers from vessels.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Load or unload materials, vehicles, or passengers from vessels.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The maritime sector shows slow adoption of full automation in cargo operations. While some container terminals use automated equipment, the broader sector of vessel loading/unloading remains labor-dependent with minimal AI-driven displacement. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipping and marine transport are low-digitization, physically-oriented sectors with minimal AI/robotics adoption for manual cargo and passenger handling. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with route planning or inventory tracking for cargo operations, but the core physical task of loading and unloading offers limited augmentation potential. Current AI provides minimal on-site productivity gains for the actual material handling work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with logistics planning, load optimization, or scheduling around this task, but offers little direct assistance to the physical act of loading/unloading itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Loading and unloading cargo from vessels requires real-time physical manipulation in dynamic maritime environments with safety-critical constraints. Current AI and robotics cannot reliably perform this end-to-end task in the variety of vessel configurations, weather conditions, and material types encountered in practice. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical materials-handling task requiring manual labor, mobility, and dexterity aboard vessels, none of which current AI systems can perform without robotic embodiment far beyond deployed capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime operations are heavily regulated by international maritime law (IMO, SOLAS conventions) and national port authorities, with legal liability for cargo safety resting on licensed maritime personnel. Human presence is typically required for compliance and liability purposes. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no specific license is required for loading/unloading itself, maritime safety regulations, crew certification requirements, and physical liability around passenger and cargo handling create moderate institutional friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous maritime cargo systems require significant capital investment, specialized infrastructure, and ongoing maintenance. The loaded cost per task far exceeds that of human sailors and marine oilers, whose wages represent the baseline for economic substitution. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any hypothetical automation (specialized cargo robotics) would require far greater capital investment than paying a sailor's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic systems reliably perform full cargo loading/unloading operations in production maritime settings. While some automated cargo handling exists in controlled port environments, it does not extend to vessel-specific loading scenarios with the flexibility required. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product loads/unloads cargo, vehicles, or passengers from ships; such work remains research-stage robotics at best, with no production systems in this domain. |
Give directions to crew members engaged in cleaning wheelhouses or quarterdecks.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.1/5 · click for rater detail
Give directions to crew members engaged in cleaning wheelhouses or quarterdecks.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime shipping remains highly conservative and fragmented, with slow digitization of operational practices. Crew direction is tied to hierarchical, human-dependent command structures; adoption of AI agents for real-time crew direction is negligible in the industry. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipboard operations are a low-digitization, physically embedded sector with minimal AI agent deployment for direct crew supervision tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with task planning or scheduling (e.g., suggesting optimal cleaning sequences), but the core task—dynamic, context-sensitive crew direction during active work—requires human judgment, authority, and immediate responsiveness that AI cannot meaningfully augment today. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could support scheduling or checklist generation for cleaning tasks, but it offers little direct assistance to the real-time act of giving verbal directions to crew in a physical environment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Directing crew members requires real-time spatial awareness, dynamic adjustment to ongoing work, and interpersonal communication tailored to individual workers. Current AI cannot reliably perceive, interpret, and adapt directional guidance in the unstructured, mobile maritime environment. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence on a vessel, real-time observation of crew activity, and situational spoken direction tied to physical conditions—no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime operations are regulated by IMO, national maritime authorities, and vessel-specific safety protocols. Crew safety, deck operations, and command hierarchy create legal and organizational barriers to machine direction-giving; a human supervisor must maintain accountability for crew safety during cleaning operations. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed, maritime hierarchy, safety protocols, and the need for an accountable human supervisor aboard a vessel create real organizational and practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A sailor or supervisor giving directions costs a maritime wage (fully loaded, often $50k–$90k annually). AI would require ship-based sensing, integration, and continuous oversight that would exceed the cost of a human already present on deck performing other duties. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this supervisory task, so the comparison favors the human at essentially any wage since AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed system today autonomously directs maritime crew during active cleaning operations. While AI can generate generic instructions, maritime-specific real-time direction-giving in production environments does not exist as a reliable product. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product supervises or directs shipboard cleaning crews; this remains purely a human supervisory task performed in person. |
Maintain government-issued certifications, as required.
6CI 0–11 · exposure 5 · augmentation 50 · importance 4.2/5 · click for rater detail
Maintain government-issued certifications, as required.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Maritime certification compliance is a slow-moving, regulatory-driven domain; adoption of AI assistants for reminders or tracking exists but is not widespread in production across shipping sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime credentialing processes are paper/exam-based and slow to digitize, with essentially no AI displacement occurring in this specific compliance task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by automating deadline tracking, form preparation, and compliance documentation review, meaningfully reducing administrative burden while the sailor remains responsible for submission and credential maintenance. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help track renewal deadlines, organize documentation, and provide study aids for certification exams, offering moderate administrative assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires human agency to apply for, renew, and maintain official certifications through government bureaucracy; no AI system can autonomously obtain or sustain legal credentials in a human's name. |
| Task automatability | claude-sonnet-5 | 1/5 | Maintaining certifications requires the individual to undergo training, testing, and physical exams administered by regulatory bodies (e.g., Coast Guard) which cannot be performed by AI on the person's behalf.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Government maritime certifications (e.g., USCG merchant mariner credentials) are legally required credentials that only the licensed individual can hold and renew; regulatory frameworks mandate human accountability. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Government licensing requirements mandate that the individual personally hold and renew certifications; this is a hard legal barrier to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for compliance tracking have modest costs, but the core task—maintaining certifications through government channels—cannot be replaced by software; human effort dominates the cost structure. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for the human undergoing required exams/training, so AI cannot reduce cost of this task itself. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can track certification deadlines and generate reminder emails, the actual submission, exam-taking, and credential maintenance must be performed by the human sailor, making end-to-end automation infeasible despite partial workflow support. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product can obtain or renew a human's legally required maritime certification; this is inherently a personal compliance action. |
Attach hoses and operate pumps to transfer substances to and from liquid cargo tanks.
5CI 5–5 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail
Attach hoses and operate pumps to transfer substances to and from liquid cargo tanks.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Shipping remains a low-digitization sector with high capital lock-in and legacy vessel designs. Adoption of cargo automation is minimal; most vessels still rely entirely on manual crew operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipping and physical dockside operations are a low-digitization, slow-adopting sector for AI/robotics in hands-on tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with monitoring tank levels, pressure sensors, and flow rates via real-time dashboards, but the core task of physically attaching hoses and operating pumps remains predominantly manual with limited augmentation from current systems. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensors and monitoring software can assist with flow rates, leak detection, or scheduling, but the physical hose attachment and pump operation itself receives minimal AI augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attaching hoses and operating pumps requires physical manipulation in unpredictable maritime environments and real-time detection of hazards, leaks, and pressure conditions. Current AI systems cannot perform these embodied tasks end-to-end with the required safety margins. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy hoses, valves, and pumps aboard a vessel in a dynamic marine environment; no current AI or robotic system can perform this end-to-end task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strict maritime safety regulations, international conventions (MARPOL, SOLAS), and classification society rules require certified human oversight and sign-off on cargo operations. Liability for spills and environmental damage creates strong regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime cargo operations are subject to strict safety and environmental regulations, often requiring certified personnel to handle hazardous liquid cargo transfers, creating strong regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Physical marine robotics capable of safe, independent cargo transfer remains expensive and requires substantial integration. A sailor's loaded wage is competitive with the capex and opex of seaworthy autonomous pumping systems. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this task, so any hypothetical automation would require costly specialized robotics far exceeding current human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system reliably performs independent hose attachment and pump operation in maritime settings. Robotic systems for cargo transfer remain research prototypes; production automation is confined to fixed-facility chemical plants, not ships. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that autonomously connects hoses and operates cargo transfer pumps on ships; this remains a manual, physically embodied task. |
Handle lines to moor vessels to wharfs, to tie up vessels to other vessels, or to rig towing lines.
5CI 5–5 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail
Handle lines to moor vessels to wharfs, to tie up vessels to other vessels, or to rig towing lines.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime is a traditional, heavily regulated sector with slow digitization of core operations; line handling remains fundamentally manual across commercial and military fleets with minimal automation adoption to date. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipping and deckhand work is a low-digitization, physical-labor sector with minimal AI/robotic adoption for hands-on mooring tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance for the physical act of handling lines, though it could provide real-time mooring guidance or weather/tide data to support human decision-making, but does not materially transform sailor productivity on the core task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with route planning or communication logistics around mooring, but offers little direct augmentation to the physical act of handling lines. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Line handling requires physical dexterity, spatial reasoning in dynamic maritime conditions, and real-time adaptation to vessel movement and water conditions that current AI systems cannot perform. No autonomous system can reliably handle, tie, or rig physical lines on moving vessels today. |
| Task automatability | claude-sonnet-5 | 1/5 | Handling mooring lines requires physical dexterity, strength, and real-time judgment in dynamic marine conditions that no current AI system can perform end-to-end; this is a manual, embodied task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory frameworks (SOLAS, maritime labor conventions) and liability requirements for vessel safety operations create strong barriers; mooring operations are legally and safety-critical functions where human accountability is mandated and automation carries extreme liability risk. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety regulations, maritime crewing requirements, and the physical/liability risks of mooring operations create strong barriers to automation, though not a strict licensing requirement for this specific subtask. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of developing, deploying, and maintaining robotic systems capable of handling heavy maritime lines would vastly exceed the loaded wage of a sailor performing this work, particularly given the safety-critical nature and operational variability. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical labor, so any 'AI' approach would require expensive robotic/mechanical systems far exceeding the cost of a human sailor performing this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs this task autonomously in production. While robotic systems exist in controlled environments, maritime line-handling in real operational conditions (weather, vessel motion, variable dock configurations) remains entirely manual. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously handle mooring lines on vessels; automated mooring systems exist only in limited port infrastructure trials, not as AI performing the sailor's physical task. |
Break out, rig, and stow cargo-handling gear, stationary rigging, or running gear.
5CI 0–10 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail
Break out, rig, and stow cargo-handling gear, stationary rigging, or running gear.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime shipping remains a heavily human-dependent, conservative sector with slow digital transformation and minimal adoption of autonomous cargo-handling systems. Most vessels continue to rely on traditional human crew for these tasks. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipboard labor is a low-digitization, physical-labor sector with minimal AI or robotic adoption for manual rigging tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance on this task today. Digital checklists and safety monitoring systems provide minor augmentation, but AI cannot substantially enhance a sailor's ability to physically break out, rig, and stow gear in a meaningful way. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer essentially no assistance for the physical act of rigging, stowing, or handling cargo gear on a vessel. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical manipulation of heavy cargo-handling equipment and rigging in a maritime environment, requiring dexterity, spatial reasoning, and real-time adaptation to conditions that current AI systems cannot perform end-to-end. No autonomous systems today can reliably break out, rig, and stow nautical gear at sea with safety margins equivalent to human workers. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task requiring manual manipulation of heavy cargo gear, rigging, and lines aboard a vessel, which no current AI system can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime law, international maritime conventions, and vessel classification society rules impose strict liability and human-sign-off requirements for cargo handling and rigging operations. Licensed mariners must certify safety-critical rigging work, creating hard legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed in a strict legal sense, maritime safety regulations, physical dexterity requirements, and crew training standards create practical barriers to automating this hands-on task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of specialized maritime robotics capable of this task, combined with integration, maintenance, and safety oversight, far exceeds the loaded wage of a skilled sailor or marine oiler in any realistic maritime deployment scenario. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical labor, so any hypothetical robotic solution would be far more expensive than a human sailor's wage given current robotics costs and lack of maturity. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products perform this task autonomously in production maritime settings. While robotics research exists, practical maritime cargo-handling automation remains at early prototype stages, not in reliable operational use on commercial vessels. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical rigging or gear stowage on ships; this remains entirely manual work done by deckhands. |
Stand by wheels when ships are on automatic pilot, and verify accuracy of courses, using magnetic compasses.
5CI 0–10 · exposure 5 · augmentation 25 · importance 4.0/5 · click for rater detail
Stand by wheels when ships are on automatic pilot, and verify accuracy of courses, using magnetic compasses.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime shipping is a heavily regulated, conservative sector with strong international conventions mandating human watchkeeping. Autonomous vessels remain experimental; established fleets continue employing traditional crew structures with minimal AI adoption for bridge watch functions. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipping is a slow-adopting, highly regulated, physically-embedded sector; autonomous ship technology remains largely in pilot/testing phases, not production deployment for crew replacement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-assisted navigation aids (electronic chart displays, automated course monitoring alerts) do provide some assistance to watchkeepers, but the core task of standing by the wheel and verifying compass accuracy remains fundamentally human-dependent with limited augmentation scope. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Electronic navigation aids and automated course-monitoring systems can alert crew to deviations, offering minor assistance, but the core physical standby and verification role sees little AI augmentation today. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical presence on a ship's bridge, continuous monitoring of magnetic compass readings, and manual intervention capability if automatic pilot deviates. Current AI cannot physically stand watch or operate ship's wheels, and the maritime safety requirement for a human watchkeeper means the task cannot be end-to-end automated today. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence aboard a vessel to monitor steering and verify compass accuracy as a safety redundancy; no current AI system can physically stand watch or serve as a fail-safe human backup on a ship.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | International maritime law (STCW Convention) explicitly requires a qualified human officer to maintain a continuous bridge watch and be capable of taking manual action. This is a hard legal/regulatory barrier that prevents automation regardless of technical capability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime safety regulations (SOLAS, STCW) require crewed watchkeeping and human oversight of navigation for safety-critical redundancy, creating strong regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A sailor's loaded wage (including benefits, training, certification) is relatively modest, and the infrastructure required to install autonomous vessel control capable of legal bridge watch operation would be extremely expensive, making AI substitution economically unfavorable for this specific task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI product performing this task, so no meaningful cost comparison exists; a human sailor's wage is the only current cost basis. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI systems can analyze compass data and detect course deviations in simulation, no deployed maritime system replaces the human bridge watch entirely. Some vessels use electronic monitoring and alerts, but international maritime law (STCW) mandates a human watchkeeper on the bridge, making full automation legally infeasible in practice. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this specific watchkeeping/backup steering verification role; autonomous ship navigation systems exist in trials but not as a substitute for this specific manual standby function. |
Provide engineers with assistance in repairing or adjusting machinery.
5CI 0–10 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail
Provide engineers with assistance in repairing or adjusting machinery.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime and marine operations are heavily regulated, geographically dispersed, and depend on certified personnel. Adoption of AI for physical repair assistance has been negligible, with sectors relying on established human crews and apprenticeship models. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime and shipboard engineering work is a low-digitization, physically demanding sector with minimal AI or robotics adoption for hands-on machinery repair tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While diagnostic AI tools could potentially assist engineers in troubleshooting, the core task of providing hands-on repair assistance remains fundamentally physical and human-dependent. Limited augmentation exists beyond digital diagnostics, which are only one component of the broader task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could offer some assistance via diagnostic tools, manuals, or troubleshooting guidance delivered through tablets or AR, but this does not substantially transform the physical assistance task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires hands-on physical interaction with machinery, real-time troubleshooting judgment, and collaborative problem-solving with human engineers in complex marine environments. Current AI systems cannot manipulate physical equipment or provide genuine in-situ technical assistance. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, hands-on task requiring manual dexterity, tool handling, and presence in engine rooms or machinery spaces; no current AI system can physically assist in repairing or adjusting shipboard machinery. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime regulations, safety certifications, and liability requirements legally mandate that qualified human engineers oversee and personally perform machinery repairs on vessels. Automation is restricted by regulatory bodies and industry standards requiring licensed personnel. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed for this specific sub-task, maritime safety regulations, shipboard certification requirements, and the physical/safety-critical nature of engine room work create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems capable of physical machinery repair and hands-on engineering assistance do not exist at commercial scale, making cost comparison infeasible; the task remains far more economically viable to perform with human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system capable of performing this physical assistance task, so any comparison would require robotic hardware that does not exist for this purpose at any competitive cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs physical machinery repair assistance or real-time adaptive support in marine engineering contexts. This requires embodied presence and real-time mechanical manipulation that falls outside current automation capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform physical mechanical assistance tasks aboard vessels; this remains firmly in the domain of human labor with no robotic or AI substitute in production. |
Stand gangway watches to prevent unauthorized persons from boarding ships while in port.
4CI 0–9 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Stand gangway watches to prevent unauthorized persons from boarding ships while in port.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime security remains highly conservative and regulation-bound; adoption of automation for gangway watch is minimal to non-existent. The sector is slow-moving on digitization for physical security roles, and regulatory frameworks still mandate human watchkeepers. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipboard security is a low-digitization, physically embedded task with minimal AI/robotics deployment in this specific function industry-wide. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While CCTV and access-control sensors can assist a watchkeeper by logging entries and alerting to motion, they provide only marginal assistance to the core judgment task of authenticating credentials and preventing unauthorized boarding. The human must remain the primary decision-maker. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-enabled cameras, motion sensors, or access-logging systems can support a watchstander by flagging anomalies, but the core watch task still depends on human presence and judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Standing gangway watch requires physical presence at a ship's entry point to visually assess and make judgment calls about who may board. Current AI systems cannot physically prevent unauthorized access or make real-time human-judgment decisions about port security in an embodied way; this task is fundamentally dependent on human presence and authority. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence, real-world perception, and the ability to physically intervene or challenge boarders, which current AI systems cannot perform end-to-end without embodied robotics far beyond deployed capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Port security and vessel boarding are heavily regulated by maritime law, port authorities, and flag-state regulations that typically require a licensed human watchkeeper on duty. Liability for unauthorized boarding remains with the ship and captain, creating a hard legal and operational barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Port security and maritime safety often have regulatory and liability requirements for having a responsible crew member on watch, plus the need for physical intervention capability limits pure AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of surveillance or sensor systems adequate to monitor a gangway, plus integration and oversight, would exceed the wage of a single sailor standing watch. Additionally, liability and insurance costs would likely increase if automated, making the cost ratio unfavorable. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While cameras/sensors are cheap, a full substitute requiring physical deterrence and judgment would need robotics or constant human monitoring of feeds, which doesn't clearly beat a low-wage watch role in cost today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can independently perform this task. While cameras and sensors can assist, they cannot legally or practically substitute for a human security watch responsible for port access control. This remains a human-only operational requirement in maritime security. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical gangway watch duties; camera-based surveillance exists but does not replace the human presence and authority required for this task. |
Tie barges together into tow units for tugboats to handle, inspecting barges periodically during voyages and disconnecting them when destinations are reached.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.6/5 · click for rater detail
Tie barges together into tow units for tugboats to handle, inspecting barges periodically during voyages and disconnecting them when destinations are reached.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime industries, particularly tugboat and barge operations, have low digital transformation and automation adoption rates; the sector remains labor-intensive and geographically dispersed with strong regulatory constraints on automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipping and towing operations are a low-digitization, physically intensive sector with minimal AI/robotics adoption for hands-on deck work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist through real-time monitoring or inspection cameras to aid human workers, but the core physical coupling and decoupling tasks offer limited opportunity for human-AI productivity enhancement. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensors, weather routing, and monitoring tools can inform crew decisions about tow inspections, but the core physical tying/inspecting/disconnecting task itself sees little AI-driven productivity boost. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Tying barges together, inspecting their condition, and disconnecting them are physical tasks requiring in-person work on water in dynamic maritime environments. Current AI systems cannot perform these hands-on maritime operations end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy mooring lines, knots, and hardware on moving vessels in variable weather, well beyond current robotic or AI capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime law, Coast Guard regulations, and international maritime conventions require licensed personnel to oversee vessel operations and cargo handling. Human presence and accountability are legally mandated, creating hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime safety regulations, licensing (merchant mariner credentials), and liability for cargo/vessel safety in open water create strong barriers, though not a formal 'sign-off' requirement per se. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The equipment, safety systems, and personnel required to automate barge handling operations would far exceed the cost of a skilled sailor or marine oiler performing the task manually. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical task, so any hypothetical automation system would be far more expensive than a sailor's wage today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed autonomous systems reliably perform barge coupling/decoupling and periodic inspection at scale in production maritime operations. This remains a human-performed task in working tugboat operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs barge lashing, periodic inspection, or disconnection autonomously; this remains entirely manual maritime labor. |
Maintain a ship's engines under the direction of the ship's engineering officers.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Maintain a ship's engines under the direction of the ship's engineering officers.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime shipping remains a traditionally low-digitization sector with strong union presence, regulatory conservatism, and safety-critical operations that inhibit rapid AI adoption. Automation of crew positions faces significant regulatory and operational friction. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipping is a physically intensive, historically low-digitization sector where hands-on mechanical roles see minimal AI-driven displacement to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Diagnostic monitoring systems and predictive maintenance software can assist sailors in identifying issues earlier, but the hands-on repair work remains human-centered and the assistance is limited to data interpretation rather than transformation of core task productivity. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based predictive maintenance and sensor analytics can help engineering officers flag issues, offering modest assistance, but the oiler's hands-on maintenance work itself is largely unaided by AI tools. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Ship engine maintenance requires hands-on physical inspection, repair, and troubleshooting in a complex mechanical environment. Current AI systems cannot perform the tactile, site-specific diagnostic and maintenance work that this task demands. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence aboard a vessel to perform hands-on mechanical maintenance, lubrication, and monitoring of engine equipment; no current AI system can physically execute this labor.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime labor is heavily regulated; crews must be licensed and certified to operate vessels and maintain engines under international maritime law (STCW). The human officer supervision and the legal requirement for qualified personnel to be present create hard legal barriers. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime engine rooms require certified crew under STCW regulations and safety-critical human oversight, creating strong regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The physical presence and expertise of trained sailors is essential and currently far cheaper than developing autonomous systems capable of working in maritime engine rooms with the required safety and reliability standards. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical labor involved, so cost comparison favors the human worker entirely; robotics for this task remain unavailable commercially. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can reliably perform maritime engine maintenance today. While remote monitoring and diagnostics exist, they support humans rather than replace the core maintenance work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical shipboard engine maintenance; existing marine AI is limited to monitoring/diagnostics dashboards, not the physical task itself. |
Relay specified signals to other ships, using visual signaling devices, such as blinker lights or semaphores.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Relay specified signals to other ships, using visual signaling devices, such as blinker lights or semaphores.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Shipping and maritime sectors rely on established human-centered protocols and regulations; automation of signaling has seen minimal adoption. Modern vessels use electronic communication (radio, AIS) rather than visual signals, making this task itself legacy-oriented. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipping and physical vessel operations are a low-digitization, slow-adopting sector for this type of manual physical task, with essentially no visible AI displacement in this specific function. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist by identifying other vessels or suggesting standardized signal sequences, but the core task—physically relaying signals via specialized devices—offers limited augmentation opportunity and remains fundamentally human-operated. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could conceivably assist with training simulations or signal decoding reference tools, but it offers minimal real-time assistance to a sailor physically operating signaling equipment on deck. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time visual communication with other vessels at sea using specialized signaling equipment. Current AI systems cannot reliably operate manual signaling devices or interpret context-dependent maritime communication protocols in live nautical conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires a physical presence on a moving vessel operating manual devices (blinker lights, semaphore flags) and interpreting maritime signal codes in real-time environmental conditions; no off-the-shelf AI system performs this physical task end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime law and international maritime regulations (IMO, COLREG) require human operators to maintain signaling capability and situational awareness. A licensed sailor must personally perform or verify these safety-critical communication tasks. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime signaling often falls under safety-critical communication protocols and STCW crew certification requirements, and physical presence aboard a vessel with hands-on equipment operation creates strong practical and regulatory barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The infrastructure cost of automating visual signaling devices (robotics, sensors, integration) plus the liability and verification overhead would exceed the loaded wage of a trained sailor performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system currently substituting for this task, so no cost comparison favors AI; a human sailor performing this specialized duty remains the only viable option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs maritime visual signaling autonomously today. This task requires physical operation of blinker lights and semaphores, environmental awareness of sea conditions, and adherence to strict maritime protocols—beyond current AI capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that autonomously operates physical semaphore or blinker light signaling on ships; this is a niche maritime communication skill with no commercial automation. |
Overhaul lifeboats or lifeboat gear and lower or raise lifeboats with winches or falls.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail
Overhaul lifeboats or lifeboat gear and lower or raise lifeboats with winches or falls.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime operations are among the most conservative industries regarding automation of safety-critical tasks. Vessel operations remain heavily regulated and crew-dependent; there is minimal evidence of AI or robotics adoption for lifeboat maintenance or winch operations even in advanced shipping fleets. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipboard maintenance and safety equipment operation is a physically-intensive, low-digitization sector with minimal AI/robotics adoption in this specific function. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI could potentially assist with diagnostic support or maintenance scheduling for lifeboat systems, the core task of physical overhaul and equipment operation offers limited scope for meaningful augmentation given the hands-on, safety-critical nature of the work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with maintenance scheduling, inspection checklists, or diagnostic monitoring of winch systems, but offers little direct help with the physical overhaul and raising/lowering work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical manipulation of heavy maritime equipment in a marine environment, requiring hands-on work with mechanical systems and safety-critical operations. Current AI systems cannot perform end-to-end physical tasks like winch operation, lifeboat maintenance, or equipment handling on vessels. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical maintenance and mechanical operation task requiring manual dexterity, strength, and physical presence aboard a vessel; no current AI system can perform it end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime law, flag-state regulations, and international maritime safety codes (IMO SOLAS) legally mandate that licensed crew members maintain and operate lifeboats and associated safety equipment. Human certification and sign-off are non-negotiable safety and legal requirements. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime safety regulations (e.g., SOLAS) mandate crew training and certification for lifeboat and lifesaving equipment operation, creating strong regulatory and safety-liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automated systems capable of maritime equipment maintenance and winch operation would require specialized robotics, integration with ship systems, and ongoing maintenance costs that far exceed the wage of a marine oiler, making the economic case unfavorable. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any hypothetical robotic solution would be far more costly than a sailor's wage for equivalent output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously overhaul lifeboats or operate vessel winches in real maritime conditions. The task requires physical presence on a moving ship, mechanical problem-solving during repairs, and real-time adjustment to equipment wear—capabilities that exist only in research or prototype form if at all. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical lifeboat overhaul or winch operation; this remains firmly in the domain of human maritime labor and marine robotics research at best. |
Lower and man lifeboats when emergencies occur.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Lower and man lifeboats when emergencies occur.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This is a safety-critical, heavily regulated maritime function. Adoption of automation is negligible because regulatory and legal barriers prevent any shift away from human crew responsibility for lifeboat operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime emergency response is a highly physical, safety-regulated domain with minimal AI adoption for hands-on rescue operations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide minor assistance such as real-time lifeboat status monitoring or passenger manifest verification, but the core task of physical deployment and crew management remains fundamentally human-dependent with limited room for augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with training simulations, emergency drill scheduling, or decision-support systems for evacuation planning, but offers little real-time assistance during the physical act of lowering and manning lifeboats. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical coordination, situational judgment under extreme stress, and human presence in lifeboats during emergencies. Current AI systems cannot physically operate winches, secure passengers, or manage the dynamic conditions of lifeboat deployment and evacuation. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical emergency response task requiring bodily presence, strength, and real-time judgment in chaotic conditions; no AI system can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime law and international regulations (SOLAS) mandate that trained, certified crew members must physically lower and staff lifeboats during emergencies. Legal liability, crew certification requirements, and the non-delegable duty of care create absolute barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Maritime safety regulations (SOLAS, STCW) mandate trained, certified crew members to operate lifeboats and conduct emergency drills, making this a hard legal and safety-critical barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task fundamentally requires human physical presence and judgment that cannot be replicated by AI at any cost. A human sailor is legally and practically necessary; there is no cost comparison possible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical rescue action, so any cost comparison favors the human crew by default since AI cannot perform the task at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products can autonomously lower and man lifeboats in emergency scenarios. This requires integrated physical manipulation, real-time decision-making, and direct human care of people in life-threatening situations—all beyond current autonomous system capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product lowers or mans lifeboats; this remains purely a human physical and safety task with no automation product in production. |
Participate in shore patrols.
0CI 0–0 · exposure 0 · augmentation 25 · importance 2.5/5 · click for rater detail
Participate in shore patrols.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Military and maritime organizations, while technology-forward in some domains, have not adopted autonomous or remotely-piloted systems for routine shore patrols at scale. Human presence and judgment remain legally and organizationally required. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime and military-adjacent physical security roles show minimal AI adoption; this is a low-digitization, physically embodied task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited augmentation is possible through surveillance drones or sensors that assist a human patrol team with situational awareness, but these represent minor enhancements rather than transformative productivity gains for the core patrol task itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with communication logs, surveillance camera monitoring, or route planning, but offers limited direct assistance to the core patrol activity itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Shore patrols require physical presence in geographic locations, situational awareness, interpersonal judgment with civilians, and safety assessments that current AI cannot execute in the embodied world. This task is fundamentally dependent on human sensory perception, movement, and decision-making in real-world maritime environments. |
| Task automatability | claude-sonnet-5 | 1/5 | Shore patrol requires physical presence, situational judgment, security enforcement, and potential physical intervention in unpredictable environments—none of which current AI can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Shore patrols are performed by military or government personnel under strict legal authority and chain of command. Only authorized personnel can lawfully conduct security patrols, and liability for security failures is legally assigned to human operators and their organizations. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Shore patrol involves security, legal authority, and safety responsibilities typically requiring authorized personnel, creating strong institutional and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current AI systems cannot perform this task, making cost comparison inapplicable. The operational cost of any autonomous system capable of physical patrolling would vastly exceed the labor cost of a deployed sailor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical patrol task, so AI cost is not comparable—human labor remains the only functional option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs autonomous shore patrols. This would require autonomous robotics, real-time environmental navigation, and authority to exercise judgment in law enforcement contexts—none of which exist as production systems today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs autonomous shore patrol duties involving physical security and human interaction; this remains firmly in the human domain. |
Related occupations — Transportation & Material Moving
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.