Ship Engineers
53-5031.00Median wage $109,530/yr8,400 employed (US)Rank #602 of 923 scored · top 65% by substitution
Supervise and coordinate activities of crew engaged in operating and maintaining engines, boilers, deck machinery, and electrical, sanitary, and refrigeration equipment aboard ship.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution23
Exposure23
Augmentation50
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
17 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
6%
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%25
panel mean rating 2.0/5 → substitution pressure 25/100
Technical feasibility todayw 20%21
panel mean rating 1.8/5 → substitution pressure 21/100
Cost vs. human wagew 15%22
panel mean rating 1.9/5 → substitution pressure 22/100
Adoption barriersw 20%inverted — strong barriers lower the score27
panel mean rating 3.9/5 (barrier strength) → substitution pressure 27/100
Sector adoption velocityw 10%14
panel mean rating 1.6/5 → substitution pressure 14/100
Task breakdown (17 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.
Order and receive engine room stores, such as oil or spare parts, maintain inventories, and record usage of supplies.
72CI 65–79 · exposure 70 · augmentation 63 · importance 4.0/5 · click for rater detail
Order and receive engine room stores, such as oil or spare parts, maintain inventories, and record usage of supplies.
72| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Maritime and industrial sectors show strong adoption of automated inventory and procurement systems; major shipping operators and naval yards routinely deploy these systems. Information-sector digitization patterns dominate, with rapid rollout across fleets and facilities. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Maritime/shipping is a traditionally slow-digitizing sector with high physical and regulatory friction, so software adoption for back-office tasks like this lags behind information-sector norms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI assists engineers by automating routine ordering and tracking, freeing them to focus on technical analysis and exception handling. The system provides useful visibility and recommendations, though the engineer's judgment on part quality and inventory levels remains valuable. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Inventory management software, barcode/RFID tracking, and automated reorder alerts substantially reduce manual burden and error for engineers handling stores and usage logs. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Most of this task is highly automatable: AI can place orders based on inventory thresholds, process receipts against purchase orders, update inventory records, and log usage automatically through ERP integration. The main human bottleneck is physical inspection and exception handling of received items, which is relatively minor. |
| Task automatability | claude-sonnet-5 | 4/5 | Ordering, inventory tracking, and usage recording are largely structured data-entry and procurement workflows that existing inventory management and ERP software with AI-assisted reordering can handle with significant time savings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers prevent automation of routine supply ordering and inventory tracking on ships. Some organizational friction around process change and the need for human sign-off on exceptional orders exist, but nothing requires a licensed engineer to perform routine ordering or record-keeping. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a certified engineer perform inventory logging itself, though the engineer's broader certification role and shipboard authority create some organizational friction against fully removing them from oversight. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven inventory and order management systems are orders of magnitude cheaper per transaction than human labor for routine ordering, receiving documentation, and record maintenance. API-based integration and maintenance costs are minimal relative to the labor they displace. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated inventory and procurement systems are cheap to run per transaction compared to engineer time spent on paperwork, though initial system setup and integration with ship systems add cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature ERP and inventory management systems (SAP, Oracle, Intune) perform these functions reliably in production at scale across maritime and industrial sectors. Automated reordering, receipt matching, and usage logging are standard, deployed capabilities with documented track records. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Maritime inventory/procurement software exists and is used on many vessels, but full integration with parts catalogs, supplier ordering, and physical receiving still requires human confirmation, so reliability varies across fleets. |
Maintain complete records of engineering department activities, including machine operations.
55CI 43–67 · exposure 58 · augmentation 75 · importance 4.5/5 · click for rater detail
Maintain complete records of engineering department activities, including machine operations.
55| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Maritime and shipping sectors show moderate adoption of digital record systems and automated logging, with some vessels using integrated engine-room monitoring, but many operations remain traditional or transitional. Full AI-driven record automation is not yet standard across the industry despite clear technical feasibility. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Shipping is a traditionally slow-digitizing, capital-intensive physical industry where automation of recordkeeping is progressing but lags far behind sectors like finance or professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems can substantially augment engineers by automatically capturing, cross-referencing, and flagging anomalies in operational records, while the engineer retains oversight and interpretation responsibilities. This transforms the engineer's ability to maintain accurate, comprehensive records and identify trends without eliminating human judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enabled sensor dashboards, automated data capture, and digital logbook software can significantly reduce manual transcription burden and improve accuracy while the engineer remains responsible for oversight and sign-off. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Most of this task involves structured data entry and log consolidation, which current AI systems and automation tools can handle efficiently. Parsing machine telemetry, timestamping events, and organizing records into standardized formats are largely automatable with >50% time savings. However, some judgment about which operational events warrant detailed annotation may still require human review. |
| Task automatability | claude-sonnet-5 | 3/5 | Recording and logging tasks can be partially automated with sensor data logging and templated report generation, but synthesizing narrative records and judgment-based entries still requires human input aboard ship. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Maritime regulations and flag-state authorities often require that certain records be signed off or verified by licensed officers, creating legal compliance friction. Although automation can generate and store records, regulatory and liability concerns around data authenticity and chain-of-custody impose meaningful oversight and validation requirements. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Maritime regulations (e.g., SOLAS, flag state requirements) mandate certain official logs be maintained and signed off by licensed engineering officers, creating moderate regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated record-keeping systems have low per-task inference costs and high leverage across many operations. Once integrated, the cost per maintained record is orders of magnitude cheaper than paying an engineer to manually log and organize the same data. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated sensor logging is cheap once installed, but integration with legacy ship systems, connectivity constraints at sea, and compliance recordkeeping requirements keep overall cost comparable to human effort in many fleets. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products for automated data logging, compliance record management, and machine monitoring already exist in maritime and industrial sectors. Systems can reliably extract, organize, and archive engineering records from multiple sources with minimal error rates in production environments, though some sectors still use manual processes. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some maritime digital logbook and sensor-integrated monitoring systems exist, but most vessels still rely on manual or semi-manual logging by engineers rather than fully automated recordkeeping products. |
Monitor and test operations of engines or other equipment so that malfunctions and their causes can be identified.
49CI 25–72 · exposure 50 · augmentation 88 · importance 4.7/5 · click for rater detail
Monitor and test operations of engines or other equipment so that malfunctions and their causes can be identified.
49| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Shipping is moderately digitized with growing adoption of IoT and predictive maintenance platforms, but full automation faces crew-management inertia and regulatory conservatism; pilots and hybrid approaches are common, but production-wide autonomous monitoring is still emerging rather than mainstream. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Maritime/shipping is a traditionally slow-digitizing physical-operations sector; while predictive maintenance sensors are being adopted, deep AI-driven diagnostic automation in engine rooms remains in early pilot stages. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI monitoring systems significantly augment ship engineers by providing real-time alerts, root-cause hypotheses, and diagnostic dashboards that enable faster problem identification and maintenance planning while the engineer remains responsible for decision-making and response. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-based condition monitoring, anomaly detection, and predictive maintenance tools significantly help engineers anticipate and diagnose malfunctions faster, meaningfully boosting productivity while the engineer remains in control of testing and repair decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Engine and equipment monitoring can be largely automated through sensor networks, real-time diagnostics, and AI-driven anomaly detection; current systems can identify most malfunctions and root causes without human intervention, though complex diagnostics may still require human oversight, easily achieving 50%+ time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | Continuous monitoring can be sensor-automated, but diagnosing malfunctions and testing equipment aboard a vessel requires physical presence, hands-on inspection, and judgment that current AI cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Shipping regulations (IMO, flag state) mandate human crew presence and responsibility, but increasingly permit autonomous or semi-autonomous monitoring systems; liability still rests with the vessel operator, not strictly requiring a licensed engineer to perform the monitoring task itself. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime regulations (STCW, flag state requirements) mandate licensed engineers to be present and responsible for engine room operations and safety, creating strong regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-based monitoring systems (sensors, cloud analytics, modest compute) are significantly cheaper than continuous human operator salaries; even accounting for integration and maintenance, the cost per monitored engine is far below the loaded cost of a ship engineer dedicated to monitoring. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Sensor and analytics systems have upfront and maintenance costs comparable to or exceeding savings from partial monitoring automation, while the human engineer's hands-on testing role remains largely intact and necessary, keeping cost ratio close to parity. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products exist in marine and industrial sectors (e.g., predictive maintenance platforms, IoT monitoring systems) that perform continuous equipment monitoring and malfunction detection in production; some gaps remain in rare failure modes, but reliability is high for standard marine engine diagnostics. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Condition monitoring and predictive maintenance systems exist and are deployed in marine engineering, but they augment rather than replace the engineer's direct testing and physical diagnostic work, and reliability for full autonomous diagnosis is limited. |
Record orders for changes in ship speed or direction, and note gauge readings or test data, such as revolutions per minute or voltage output, in engineering logs or bellbooks.
43CI 25–60 · exposure 45 · augmentation 63 · importance 4.0/5 · click for rater detail
Record orders for changes in ship speed or direction, and note gauge readings or test data, such as revolutions per minute or voltage output, in engineering logs or bellbooks.
43| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Maritime industries move slowly on automation due to regulatory conservatism, safety criticality, and the distributed nature of older ship fleets. While some vessel monitoring systems exist, widespread AI displacement of log-keeping duties in production is limited to new, high-tech vessels. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Maritime shipping is a traditionally slow-adopting, capital-intensive, physically-oriented sector where digitization of engine room logs is progressing but not yet widespread or fast. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by auto-populating routine gauge readings, flagging anomalies, or summarizing logs, helping engineers focus on interpretation and decisions. However, the core task—maintaining an accurate, certified record—remains human-centered, so augmentation is moderate rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enabled sensor dashboards and automated logging tools can significantly reduce manual transcription burden and improve accuracy while engineers remain responsible for oversight and decision-making. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While data recording itself is straightforward, this task requires real-time interpretation of gauge readings in a dynamic marine environment and judgment about which readings are significant. Current AI can transcribe or log structured numeric data, but contextual understanding of what anomalies matter and integration with ship systems remains incomplete. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording orders and gauge readings is largely structured data logging that could be captured and transcribed automatically via sensor integration and speech-to-text/OCR systems, though verification against physical instruments still requires some human oversight. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime regulations (IMO, flag state authorities) mandate that engineering logs be signed off by licensed officers and serve as legal records of ship operations. A qualified engineer must certify data, creating a hard licensing and liability barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Maritime regulations often require official engineering logs to be maintained and signed off by certified engineers, creating some regulatory and liability friction even if data capture itself is automated. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI sensor data logging and transcription are cheap, but the cost of reliable integration into a certified ship's engineering systems, validation, and required human oversight approaches or exceeds the cost of a qualified marine engineer recording the same data. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated sensor logging and digital record-keeping systems are cheap to run continuously compared to dedicating engineer time to manual transcription, though initial sensor/integration investment is needed. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production system reliably captures and logs the full context of marine engine orders and gauge readings autonomously. Marine automation exists for some parameters, but comprehensive, integrated logging that meets maritime safety documentation standards is not deployed at scale without human verification. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Automated data logging systems and digital engine monitoring exist on many modern vessels, but many ships still rely on manual bellbook entries and legacy analog gauges, limiting universal deployment. |
Maintain electrical power, heating, ventilation, refrigeration, water, or sewerage systems.
28CI 5–51 · exposure 33 · augmentation 63 · importance 4.2/5 · click for rater detail
Maintain electrical power, heating, ventilation, refrigeration, water, or sewerage systems.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Shipping is a traditionally conservative, low-digitization sector with aging fleets; while some digital monitoring tools have entered the market, production deployment of AI-driven maintenance automation remains limited compared to land-based industries, and crew presence requirements slow adoption velocity. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime engineering is a physical, low-digitization sector with slow adoption of AI for hands-on equipment maintenance despite some predictive monitoring pilots. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist engineers through real-time system monitoring dashboards, predictive alerts, diagnostic recommendations, and automated logging, enabling faster decision-making and fewer missed issues while the engineer retains full control and final sign-off on repairs. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-driven predictive maintenance and diagnostic sensor analytics can help engineers anticipate failures and prioritize repairs, improving efficiency without replacing hands-on work. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI could automate many diagnostic, monitoring, and scheduling components of system maintenance (predictive analytics, sensor data analysis, work-order generation) with significant time savings, but hands-on physical repairs and system resets still require human technicians, limiting full end-to-end automation to roughly 70-80% of the workflow. |
| Task automatability | claude-sonnet-5 | 1/5 | This is hands-on physical maintenance and repair of shipboard mechanical/electrical systems, requiring manual inspection, diagnosis, and repair work that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime regulations (SOLAS, IMO STCW) require licensed engineers to be physically present and responsible for critical systems; liability for system failures at sea creates strong legal and insurance barriers to full automation; and shore-based backup requirements further restrict autonomous operation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime regulations (STCW, flag state rules) require certified, licensed ship engineers to be responsible for these systems, and safety-critical shipboard systems carry high liability for failures at sea. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI monitoring and diagnostics can reduce labor hours for routine checks and scheduling, but integration into maritime systems, regulatory compliance infrastructure, and required human oversight keep total costs roughly comparable to skilled maritime engineer wages rather than an order of magnitude cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical maintenance itself, so cost comparison favors the human engineer who must be present regardless. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed AI systems exist for equipment diagnostics, anomaly detection, and predictive maintenance (e.g., Siemens, GE, Honeywell platforms), but most maritime operators rely on human-in-the-loop oversight, and full autonomous system management remains uncommon at scale in shipping due to safety and liability concerns. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously maintains shipboard electrical, HVAC, refrigeration, or sewerage systems; at best there are sensor-based monitoring dashboards, not maintenance execution. |
Monitor engine, machinery, or equipment indicators when vessels are underway, and report abnormalities to appropriate shipboard staff.
25CI 20–30 · exposure 30 · augmentation 63 · importance 4.8/5 · click for rater detail
Monitor engine, machinery, or equipment indicators when vessels are underway, and report abnormalities to appropriate shipboard staff.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Shipping is a traditionally conservative, asset-heavy sector with slow digital transformation. While some large operators pilot remote monitoring, the majority of vessels still rely on onboard engineering staff with minimal autonomous systems. Adoption remains in pilot phase rather than production-wide displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Shipping is a traditionally slow-adopting, capital-intensive, physically-oriented industry; while automation is increasing on newer vessels, fleet-wide adoption is gradual and constrained by regulation and retrofit costs. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Automated dashboards, alert systems, and predictive maintenance analytics meaningfully assist engineers in identifying trends and prioritizing attention. These tools improve productivity on monitoring tasks but require human interpretation and decision-making, making augmentation useful but incomplete. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Automated sensors, predictive maintenance analytics, and alarm systems significantly enhance an engineer's ability to detect and diagnose abnormalities faster and with less manual monitoring effort. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can monitor sensor data and detect anomalies in machinery readings, the task requires real-time judgment in a safety-critical maritime environment where false positives/negatives carry high operational cost. Current systems can flag deviations but cannot fully replace the contextual decision-making and communication loop that a human engineer performs. |
| Task automatability | claude-sonnet-5 | 2/5 | Sensor-based condition monitoring and alarm systems can automate parts of this, but continuous integrated judgment across mechanical, electrical, and propulsion systems on a moving vessel still requires human oversight and physical presence for verification and response. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | International maritime regulations (SOLAS, class society rules) mandate that certified officers maintain direct supervision and responsibility for machinery; automation cannot legally replace the engineer's sign-off on safety-critical conditions. Liability asymmetry and regulatory requirement for human accountability create substantial adoption friction. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Maritime law (STCW, SOLAS, flag-state regulations) mandates certified marine engineers to be present and responsible for engine room operations and safety, making full automation legally prohibited on crewed vessels. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integrated sensor systems, edge computing, and cloud infrastructure for vessel monitoring are capital-intensive and require specialized marine integration. When amortized across a single ship's operations, the total cost per monitored task remains comparable to or higher than a marine engineer's loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Automated monitoring hardware and software require significant upfront investment, integration, and maintenance, and do not eliminate the need for a certified engineer aboard, so cost savings versus a human engineer's wage are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Sensor monitoring and anomaly detection systems exist in shipping (e.g., IoT platforms, condition-based monitoring), but deployment remains patchy and typically advisory rather than autonomous. No mature product reliably performs end-to-end monitoring and reporting without significant human oversight in production maritime operations. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Modern ships already deploy automated engine monitoring and alarm systems (integrated bridge/engine control systems) that flag abnormalities, but these are decision-support tools rather than full replacements for a licensed engineer's judgment and response. |
Start engines to propel ships, and regulate engines and power transmissions to control speeds of ships, according to directions from captains or bridge computers.
23CI 20–25 · exposure 25 · augmentation 63 · importance 4.7/5 · click for rater detail
Start engines to propel ships, and regulate engines and power transmissions to control speeds of ships, according to directions from captains or bridge computers.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Shipping remains a conservative, heavily regulated sector with slow adoption of autonomous systems; current digitization includes monitoring and data logging but not widespread replacement of propulsion control. Pilots exist (autonomous vessels) but production-scale displacement of engine engineers is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | The maritime shipping industry is historically slow to digitize and adopt autonomous systems, with widespread autonomous vessel deployment still largely in pilot and trial phases. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted monitoring dashboards and predictive maintenance tools can help engineers optimize engine performance and anticipate failures, providing useful real-time assistance on parts of the task. However, augmentation is limited by the relatively straightforward nature of modern engine controls and the primacy of safety checks. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Modern engine monitoring systems, predictive maintenance software, and automated control interfaces significantly assist engineers in monitoring and adjusting engine performance more efficiently. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While engine monitoring and speed regulation could be partially automated via existing marine control systems, physically starting engines and managing the full propulsion sequence still requires human intervention and oversight in current maritime practice. Modern ships have some automation, but the task of end-to-end engine start and power transmission control remains heavily human-dependent with no 50% time-saving automation standard deployed. |
| Task automatability | claude-sonnet-5 | 2/5 | While automated engine control systems exist, this task requires physical presence, real-time monitoring of mechanical systems, and response to emergent conditions that current AI cannot fully replace end-to-end without human oversight. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime regulations (IMO, flag-state rules) and insurance requirements mandate licensed engineering officers oversee propulsion systems; liability and safety-critical system requirements create strong legal and organizational barriers to autonomous operation. A licensed human engineer is typically required by law to be responsible for engine operations. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Maritime law (SOLAS, STCW) and flag-state regulations require certified marine engineers to operate and be responsible for propulsion systems, creating a hard legal barrier to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-based marine automation systems require substantial capital investment, integration, and ongoing oversight, while ship engineers' wages are relatively modest given the safety-critical nature of the role. The all-in cost of reliable automated propulsion control does not yet undercut the human operator significantly. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Automation systems require significant capital investment, retrofitting, and redundant safety systems, making them costly relative to employing a ship engineer, especially for older vessel fleets. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some marine automation systems exist for engine monitoring and speed regulation, but fully autonomous engine start and propulsion control without human operators are not deployed in commercial shipping at scale. Existing products handle narrow aspects (RPM regulation) but not the complete task reliably or independently. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated engine control and integrated bridge systems are deployed on some modern vessels, but fully autonomous engine room operation without a licensed engineer is not standard practice in commercial shipping today. |
Monitor the availability, use, or condition of lifesaving equipment or pollution preventatives to ensure that international regulations are followed.
19CI 13–25 · exposure 20 · augmentation 50 · importance 4.8/5 · click for rater detail
Monitor the availability, use, or condition of lifesaving equipment or pollution preventatives to ensure that international regulations are followed.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Maritime shipping is a regulated, conservative industry with slow digitization; while some large operators pilot IoT monitoring, the majority of vessels still rely on manual inspection logs and human judgment, with adoption lagging faster-moving sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime engineering is a low-digitization, physically-embedded, heavily regulated sector with slow AI adoption for compliance-critical safety tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted monitoring dashboards, automated alerts for expiration dates, and predictive maintenance flags can improve engineer productivity in tracking equipment across large inventories, though the final compliance judgment and physical inspection remain human responsibilities. |
| Augmentation potential | claude-sonnet-5 | 3/5 | IoT sensors, predictive maintenance software, and digital checklists can help track equipment condition and flag issues, aiding the engineer's monitoring and record-keeping tasks. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist in tracking inventory and flagging expired equipment via sensors or databases, the task requires physical inspection of lifesaving gear condition, judgment calls on compliance status, and regulatory interpretation that demand human verification and sign-off today. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical inspection of equipment condition, presence checks, and hands-on verification aboard a vessel, which current AI cannot perform end-to-end without embodied sensing and physical access.'}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | International maritime regulations (SOLAS, MARPOL) mandate that qualified personnel verify equipment condition and compliance; liability for equipment failure rests on the vessel and its certified crew, creating legal barriers that prevent full automation without licensed human sign-off. |
| Adoption barriers | claude-sonnet-5 | 5/5 | International maritime regulations (SOLAS, MARPOL) require certified ship engineers/officers to personally verify and certify safety and pollution equipment, making this a hard licensing and liability barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Sensor systems and monitoring software require significant upfront capital and integration costs; the ongoing oversight and human verification needed to satisfy liability and regulatory requirements means total cost often exceeds the wage of a ship engineer performing spot checks. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Physical inspection still requires a human engineer on-site; any AI/sensor system adds cost as an augmentation rather than a full substitute, so total cost is not clearly lower than the human alone. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Monitoring systems exist in some ships (IoT sensors, inventory tracking), but reliable end-to-end automation of lifesaving equipment condition assessment and pollution preventative compliance verification with full regulatory accountability remains limited to narrow, well-defined data streams rather than comprehensive production deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously performs physical lifesaving/pollution equipment inspections and compliance sign-off; sensor-based monitoring exists only in narrow, supplementary forms. |
Fabricate engine replacement parts, such as valves, stay rods, or bolts, using metalworking machinery.
15CI 5–25 · exposure 13 · augmentation 38 · importance 3.7/5 · click for rater detail
Fabricate engine replacement parts, such as valves, stay rods, or bolts, using metalworking machinery.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Shipping and marine engineering are traditionally conservative, capital-constrained sectors with long asset lifecycles. Adoption of autonomous fabrication in shipyards is nascent; most fabrication still relies on skilled tradespeople and conventional CNC programming rather than AI-driven autonomous systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime engineering and shipboard machining are low-digitization, physically isolated environments with minimal AI/robotics adoption for fabrication tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted design tools and CNC optimization can help engineers and machinists improve efficiency in planning and programming, but the core fabrication and quality control remain human-driven. Moderate augmentation through CAD/CAM and simulation tools is realistic, but the human remains central to the process. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-assisted CAD/CAM design tools or diagnostic software could help plan replacement parts, but the core fabrication process itself receives little productivity benefit from current AI. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While CNC and automated metalworking systems can produce many standardized parts, fabricating ship engine replacement parts often requires custom design, material selection, and precision fitting to existing engines—tasks that demand human judgment and setup. Current AI cannot reliably handle the full end-to-end task including design, material handling, and quality verification without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical machining and fabrication task requiring manual manipulation of lathes, mills, and metalworking tools aboard a ship; no current AI system can perform hands-on fabrication end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime engineering has strict regulatory requirements (classification societies, safety standards) that typically mandate documented fabrication by qualified personnel and third-party inspection. Liability for engine failure and mandatory human sign-off on critical parts create hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Ship engineering involves licensing requirements, safety-critical engine components, and maritime regulatory oversight, creating strong barriers to full automation of part fabrication and installation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Setup, programming, materials oversight, and quality control for custom marine parts remain labor-intensive and costly. Fully autonomous fabrication would require expensive AI-integrated systems that remain unproven for this specialized domain, making total cost-per-part likely comparable to or higher than skilled human fabricators. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI has no direct role in physical fabrication; any automation would require expensive CNC/robotic hardware investment far exceeding the marginal cost of a ship engineer's labor for occasional part fabrication. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Automated metalworking machinery exists and is deployed, but autonomous fabrication of bespoke marine engine parts at the required tolerance and quality standards is not a demonstrated product capability. Existing systems require significant human programming, setup, and intervention for non-standard or retrofit work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously fabricates custom mechanical parts like valves or bolts on a ship; CNC automation exists but requires human setup, operation, and quality control in this context. |
Perform general marine vessel maintenance or repair work, such as repairing leaks, finishing interiors, refueling, or maintaining decks.
12CI 5–19 · exposure 8 · augmentation 25 · importance 4.5/5 · click for rater detail
Perform general marine vessel maintenance or repair work, such as repairing leaks, finishing interiors, refueling, or maintaining decks.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime industries are capital-intensive, conservative, and slow to adopt automation; crew-based maintenance is entrenched and required by international maritime law. Adoption of robotics for general repair is minimal despite decades of potential. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine trades and physical vessel maintenance are a low-digitization, low-AI-adoption sector with minimal deployment of AI/robotics for these tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Digital tools (maintenance planning software, remote diagnostics) provide modest assistance, but AI offers limited real-time help during hands-on repair work. The physical and sensory demands of marine maintenance leave little room for meaningful AI augmentation of the human worker. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostics, maintenance scheduling, or parts identification via manuals/apps, but offers minimal help with the core physical repair and maintenance work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some narrow aspects (e.g., scheduling maintenance, documentation) could be partially automated, the core task—physically repairing leaks, finishing interiors, refueling, maintaining decks—requires embodied manipulation and real-time problem-solving in unpredictable marine environments that current AI cannot reliably perform. Current robots lack the dexterity, environmental adaptation, and diagnostic capability for end-to-end repair work. |
| Task automatability | claude-sonnet-5 | 1/5 | This is hands-on physical maintenance and repair work requiring manual dexterity, mobility, and physical presence aboard a vessel; no current AI system can perform these physical tasks. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Ship maintenance involves regulatory compliance (marine safety codes, classification societies, liability for vessel integrity), crew certification requirements, and the need for human judgment in high-stakes repairs. Liability and error cost asymmetry are severe; no automation can legally substitute for a licensed marine engineer's sign-off on critical repairs. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no formal licensing mandates a human specifically for routine maintenance, safety regulations, vessel certification requirements, and the physical/judgment-intensive nature of shipboard repair create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Marine engineering labor is highly specialized; automation hardware (underwater robots, manipulators) with integration and oversight would be substantially more expensive than human crews per vessel. The capital cost of deployment in harsh marine environments is prohibitive. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical labor, so any comparison favors the human worker entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic systems reliably perform general marine vessel maintenance and repair at production scale. While remote inspection and diagnostics exist in research, actual repair execution remains entirely human-dependent in operational vessels. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform physical marine repair, refueling, or deck maintenance; robotics for this specific unstructured physical work remain research-stage at best. |
Maintain or repair engines, electric motors, pumps, winches, or other mechanical or electrical equipment, or assist other crew members with maintenance or repair duties.
12CI 5–19 · exposure 13 · augmentation 50 · importance 4.5/5 · click for rater detail
Maintain or repair engines, electric motors, pumps, winches, or other mechanical or electrical equipment, or assist other crew members with maintenance or repair duties.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime engineering remains a laggard sector for autonomous AI adoption. While remote monitoring and predictive analytics are emerging, the physical, skilled nature of repairs and the highly regulated maritime environment mean adoption of autonomous maintenance systems is negligible in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime engineering and shipboard maintenance is a physical, low-digitization sector with minimal AI/robotics adoption for hands-on repair tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI augmentation is moderate: diagnostic tools, remote monitoring dashboards, and predictive maintenance systems can assist engineers in identifying problems and planning interventions, but the core task of hands-on repair and testing still rests entirely with the human engineer. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist via diagnostic support, predictive maintenance alerts, digital manuals, and troubleshooting guidance, improving efficiency even though the physical repair itself remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with diagnostics and some planning of repairs, the physical manipulation, testing, and troubleshooting of complex marine equipment requires hands-on intervention that current automation cannot perform at scale. Current AI systems lack the embodied capability to physically access, repair, and test engines and electrical systems in confined ship spaces. |
| Task automatability | claude-sonnet-5 | 1/5 | This is hands-on physical maintenance and repair of shipboard mechanical/electrical equipment requiring manual dexterity, tool use, and physical presence in confined spaces—current AI cannot perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong regulatory, safety, and liability barriers protect this task: maritime regulations require licensed engineers for vessel machinery operation and maintenance; classification societies impose standards; and error consequences (engine failure at sea) create asymmetric liability. Legal authority and human accountability requirements are embedded in maritime law. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime engineering work typically requires certified marine engineers per safety and classification society regulations, and equipment failures at sea carry high liability, creating strong regulatory and safety barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of an automated system capable of safely entering engine rooms, diagnosing failures, and performing repairs would far exceed the loaded wage of a ship engineer, and current AI systems cannot perform this work at any cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical labor, so any AI cost comparison is moot—human labor remains the only functional option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform full maintenance and repair of ship engines and marine equipment autonomously. Predictive maintenance tools and diagnostic systems exist but fall far short of performing actual repairs; human engineers remain required in production maritime operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical engine/pump/winch repair aboard vessels; robotics for this remains research-stage or nonexistent in production. |
Clean engine parts and keep engine rooms clean.
12CI 5–19 · exposure 8 · augmentation 25 · importance 4.2/5 · click for rater detail
Clean engine parts and keep engine rooms clean.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime and shipboard maintenance sectors show slow digital transformation and minimal adoption of autonomous cleaning systems. These are traditionally labor-intensive, human-dependent operations with limited incentive or infrastructure for robotization. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime engine room maintenance is a low-digitization, physical-labor-heavy sector with minimal AI/robotic adoption for cleaning tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could assist with scheduling, monitoring surfaces via sensors, or planning cleaning routes, but the core manual labor of scrubbing, removing deposits, and physical cleaning offers limited augmentation value beyond human supervision and basic decision support. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with scheduling maintenance/cleaning cycles or diagnosing where cleaning is needed via sensors, but offers little direct assistance with the physical act of cleaning. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical cleaning of engine parts and engine rooms requires dexterous robotic systems that must navigate complex, confined spaces with varied contaminants and part geometries. While robotic cleaning exists in controlled environments, real-world engine room conditions—corrosion, variable layouts, hazardous materials—present significant barriers to autonomous execution at 50% time savings without substantial customization. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual cleaning task in a confined mechanical space; no current AI system (software or robotics) can perform this end-to-end.6 Robotic cleaning solutions for irregular industrial engine rooms are not deployed. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Engine room cleaning involves hazardous material handling, confined space entry certification, and occupational safety regulations that in many jurisdictions require human workers with specific training and liability oversight. Regulatory and human-contact requirements create meaningful protective barriers against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically governs cleaning, but confined-space safety rules, maritime crewing requirements, and the physical/hazardous nature of engine rooms create real organizational and safety barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Capable robotic systems for engine room cleaning would require significant capital investment, specialized deployment, and ongoing maintenance, making the all-in cost substantially higher than human labor for this task in most maritime contexts. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so any hypothetical automation would require expensive custom robotics far exceeding the cost of a crew member performing routine cleaning. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs end-to-end engine room or engine part cleaning autonomously in maritime or industrial production settings. Existing industrial robots handle narrow, repetitive cleaning tasks in controlled factories, not the diverse, unstructured conditions of actual engine rooms. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercial product performs autonomous cleaning of ship engine rooms or engine parts; this remains firmly in the human labor domain. |
Act as a liaison between a ship's captain and shore personnel to ensure that schedules and budgets are maintained and that the ship is operated safely and efficiently.
9CI 5–14 · exposure 5 · augmentation 50 · importance 4.0/5 · click for rater detail
Act as a liaison between a ship's captain and shore personnel to ensure that schedules and budgets are maintained and that the ship is operated safely and efficiently.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime operations are highly regulated, conservative, and slow to adopt autonomous systems. Digitization in shipping is advancing, but the core liaison function remains tightly bound to human expertise and regulatory compliance with minimal AI displacement in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipping is a traditionally low-digitization, physically-oriented sector with slow AI adoption for operational command and coordination roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools could meaningfully assist with schedule optimization, budget forecasting, and data consolidation from ship sensors, enabling faster decision-making by the engineer. However, the critical judgment and accountability elements limit augmentation to specific analytical subtasks. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can assist with scheduling, budget tracking, weather/route data, and communication drafting, but the core liaison judgment and relationship management remain human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires real-time human judgment, stakeholder negotiation, and decision-making authority that AI cannot execute end-to-end. It involves complex interpersonal communication, conflict resolution, and accountability that current AI systems cannot reliably handle in a maritime safety context. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time human judgment, trust, and communication across parties in dynamic, safety-critical maritime operations; no AI system performs this liaison role end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime regulations (SOLAS, ISM Code) establish that a qualified ship engineer must hold official certification and bear responsibility for operational safety. Legal liability for ship safety and efficiency is tied to licensed personnel, creating hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime safety regulations, chain-of-command requirements, and liability for vessel operations create strong barriers requiring qualified licensed personnel in this coordinating role. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI could reduce some administrative overhead in scheduling and reporting, but the loaded cost of a ship engineer remains lower than the total cost (infrastructure, oversight, liability) of deploying and maintaining an AI system for this safety-critical role. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this coordination function, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with scheduling optimization and budget tracking, no deployed system can reliably perform the full liaison role autonomously. Current products cannot navigate the nuanced negotiations and real-time problem-solving between captain and shore teams in high-stakes maritime operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product acts as an autonomous liaison between ship command and shore personnel; this remains a human relational and coordination role. |
Supervise marine engine technicians engaged in the maintenance or repair of mechanical or electrical marine vessels, and inspect their work to ensure that it is performed properly.
9CI 0–19 · exposure 13 · augmentation 38 · importance 3.8/5 · click for rater detail
Supervise marine engine technicians engaged in the maintenance or repair of mechanical or electrical marine vessels, and inspect their work to ensure that it is performed properly.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime industries are historically slow to adopt emerging technologies due to safety regulations, long vessel lifecycles, and conservative operational culture; adoption of AI supervisory systems is minimal and concentrated in pilot projects. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime engineering and physical vessel maintenance is a low-digitization, physically dispersed sector with minimal AI agent deployment in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist supervisors by automating maintenance log analysis, flagging anomalies in repair work, and scheduling inspections, thereby raising supervisor productivity—but the human supervisor remains essential for final judgment and certification. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with maintenance scheduling, diagnostics data logging, or predictive maintenance alerts, but offers limited direct help with the physical supervision and inspection itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with documentation review and scheduling of maintenance tasks, the core supervision and inspection work requires real-time physical presence, judgment about technician competence, and hands-on verification of repairs—tasks where current AI systems cannot meaningfully substitute for an experienced human supervisor. |
| Task automatability | claude-sonnet-5 | 1/5 | Supervising and inspecting hands-on physical repair work aboard a vessel requires direct human presence, judgment, and accountability that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime regulations (IMO, flag-state requirements) and vessel classification societies typically mandate that a qualified human engineer supervisor must certify maintenance work; liability and safety-critical responsibilities create strong legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Ship engineers must hold maritime licenses/certifications and legal responsibility for vessel safety, and regulatory bodies require licensed personnel to supervise and sign off on such work. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI inspection systems (cameras, sensors, integration, human oversight) combined with the regulatory and safety liabilities would exceed the loaded wage of a marine engineer supervisor who provides irreplaceable judgment and sign-off authority. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this supervisory/inspection role, so AI cost is not comparable—human labor is the only option today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI products reliably perform supervisory oversight and quality inspection of marine mechanical/electrical work at scale; this requires contextual judgment, safety verification, and accountability that current systems cannot deliver in production environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product supervises or inspects marine engine repair work; this remains firmly in the domain of certified human ship engineers. |
Operate or maintain off-loading liquid pumps or valves.
7CI 0–14 · exposure 8 · augmentation 38 · importance 4.4/5 · click for rater detail
Operate or maintain off-loading liquid pumps or valves.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime engineering is a traditionally slow-adopting sector with stringent regulatory oversight, long equipment lifecycles, and high safety requirements. Automation of core pump/valve operation and maintenance is rare or absent in production fleets. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime engineering is a physical, safety-critical, low-digitization sector where AI adoption for hands-on mechanical tasks remains at the pilot or sensor-monitoring stage, not deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Diagnostic sensors, predictive maintenance software, and real-time monitoring systems can assist engineers by flagging anomalies and reducing inspection time. However, these are advisory tools that support human decision-making rather than transforming the core manual operation and maintenance work itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based sensor analytics and predictive maintenance systems can flag valve/pump anomalies or schedule maintenance, offering some assistive value, but the physical operation and repair remain manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Off-loading liquid pumps and valves require physical manipulation in hazardous marine environments, real-time problem-solving, and rapid response to safety anomalies. While monitoring and diagnostics can be partially automated, the core manipulation and maintenance work cannot be automated end-to-end by current systems, and no meaningful time savings at equal quality are achievable today. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical operation and maintenance of shipboard pumps and valves, involving manual manipulation, inspection, and repair that current AI systems cannot perform end-to-end without robotic embodiment far beyond deployed capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime law and international shipping regulations (SOLAS, IMO codes) mandate that qualified human officers oversee critical ship systems, particularly hazardous liquid handling. Legal liability, safety certification, and the requirement for immediate human judgment in emergency situations create hard regulatory and operational barriers. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime safety regulations (e.g., STCW, flag state rules) require certified engineers to oversee and perform critical engine room and cargo-handling operations, and error costs (spills, explosions) are high, creating strong regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of marine-grade robotic systems capable of operating and maintaining pumps/valves, plus integration and ongoing oversight by qualified engineers, far exceeds the loaded wage of a single ship engineer performing the task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical task, so any comparison favors the human engineer who can physically operate and repair the equipment. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems perform hands-on pump and valve operation or maintenance in marine contexts. Robotic arms exist but integrating them into existing ship infrastructure with the required reliability and safety certification is not demonstrated in production at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously operates or maintains shipboard liquid off-loading systems; existing marine automation provides monitoring/alerts but not autonomous physical maintenance or valve operation. |
Install engine controls, propeller shafts, or propellers.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Install engine controls, propeller shafts, or propellers.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The maritime sector shows low AI adoption for core engineering tasks, with installations remaining labor-intensive and dependent on highly specialized human expertise. Digital transformation in shipping is slow and typically limited to navigation and logistics, not mechanical installation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Shipbuilding and marine engineering is a physical, low-digitization sector with minimal AI/robotic adoption for heavy mechanical installation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide minimal assistance through design visualization or maintenance planning tools, but offers little productivity enhancement for the hands-on installation work itself, which requires spatial reasoning, troubleshooting, and real-time adjustment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, documentation, torque specifications, or diagnostics related to the installation, but offers little direct help with the physical assembly work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical installation of large mechanical components on ships, requiring precise alignment, welding, fitting, and on-site problem-solving. Current AI systems cannot perform end-to-end physical installation work in maritime environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on mechanical installation task requiring physical manipulation of heavy machinery in confined vessel spaces; no AI system can perform this physical labor. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime safety regulations, classification society requirements, and liability frameworks mandate that installation of critical propulsion components must be performed or directly supervised by licensed marine engineers. Regulatory barriers are substantial and legally binding. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Marine engineering installation work typically requires certified engineers/technicians and is subject to safety and classification society inspection requirements, creating strong regulatory and liability barriers to any automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous robotic systems capable of performing precision ship engine installation would be significantly more expensive than the loaded wage of a ship engineer, requiring custom hardware, integration, and extensive oversight. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for the physical installation work, so the human labor cost is the only viable option, making AI infeasible rather than merely more expensive. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs autonomous installation of engine controls, propeller shafts, or propellers. This remains entirely within the domain of human technicians and engineers; no production systems exist for this work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product installs engine controls, shafts, or propellers; this remains purely a manual skilled-trade task performed by humans with tools and cranes. |
Perform or participate in emergency drills, as required.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.6/5 · click for rater detail
Perform or participate in emergency drills, as required.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime sectors are heavily regulated with strict compliance requirements; emergency drills remain entirely human-executed and show no meaningful AI adoption. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime shipboard operations are a physical, highly regulated, low-digitization sector with minimal AI agent deployment for physical emergency response tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with drill scheduling, documentation, or post-drill analysis, but offers minimal productivity enhancement to the core task of physically executing emergency procedures. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with drill scheduling, scenario planning, or record-keeping, but offers minimal assistance to the actual physical performance of drills. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Emergency drills require physical presence, real-time decision-making, and human coordination on a vessel. AI cannot meaningfully participate in or lead these drills without human supervision and physical execution. |
| Task automatability | claude-sonnet-5 | 1/5 | Emergency drills require physical presence, hands-on operation of shipboard equipment, and real-time human coordination that AI cannot perform or substitute for today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | International maritime law (SOLAS) mandates that ship crews conduct regular emergency drills with certified personnel; human participation and sign-off are legally required. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Maritime regulations (SOLAS, flag state requirements) mandate licensed crew participation in emergency drills, making this a hard legal and safety-critical requirement for human involvement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Emergency drills are a regulatory and safety requirement performed by existing crew at no additional cost; deploying AI systems would add expense rather than reduce it. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that can substitute for the human physical participation required, so cost comparison is not applicable/favorable to AI. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems can autonomously perform or participate in ship emergency drills; this task fundamentally requires human personnel physically present and trained to execute emergency procedures. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that can physically participate in or perform shipboard emergency drills; this is a physical, embodied task. |
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.