Tug and workboat owners are accelerating adoption of AI-driven software to improve chartering, scheduling, logistics and day-to-day fleet efficiency. The push reflects a broader shift toward data-driven operations where algorithms can process operational information faster than manual workflows, helping owners reduce idle time, identify bottlenecks, and lower emissions through better planning and higher asset utilisation.

 

Jifmar Builds a Unified Digital Ecosystem After Fleet Expansion

 

Jifmar Group is integrating its fleet of more than 80 vessels into a single digital ecosystem through a partnership with French technology provider Seavium, following the company’s recent fleet growth through acquisition. The collaboration was initiated through CMA CGM’s Zeebox innovation accelerator and is intended to standardise technical information, improve operational transparency, and support data-driven decision-making across departments. The move also follows Jifmar’s acquisition of workboats from Seacontractors in late 2025, which strengthened its Middle East footprint, where it now has a significant share of its fleet deployed.

 

Emar Targets Logistics Workflow Efficiency and Centralised Knowledge

 

Emar Offshore Services is investing in digitalisation this year through work with Dutch start-up Wave AI Solutions to improve purchasing and logistics workflows tied to supplying vessels operating in West Africa. In parallel, Emar plans to build a central internal knowledge hub to consolidate legislation, crewing data, technical documentation and other information streams into a single reference point, aiming to reduce friction caused by fragmented data and inconsistent information access. The company is also preparing for fleet growth, expecting delivery of a new Damen-built azimuth stern drive tug in July 2026 that will enter service for term charters and towage later in the year.

 

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Kotug Advances Scheduling Software Ahead of a Major Release

 

Kotug International is upgrading its OptiPort fleet scheduling software as it prepares version 2.0, building on functionality released in late 2025. Recent enhancements include the ability to share tug timelines and operational replays with third parties via secure links, automated prompts for missing data and weather-related delays, and tighter synchronisation with enterprise resource planning systems. Kotug is also expanding analytics and automated detection capabilities to classify tug operations, spot operational constraints and identify efficiency opportunities, a sign that scheduling tools are evolving into broader operational intelligence platforms.

 

Ultranav Scales AI Safety and Risk Detection Across a Large Fleet

 

Ultranav is expanding AI deployment across more than 420 managed vessels, including a substantial harbour tug and offshore support portfolio, after a pilot within one of its business units. The company plans to roll out ShipIn Systems’ FleetVision across the fleet, integrating AI modules with onboard camera systems to improve visibility of risks during operations and strengthen navigation and safety decision-making. The approach reflects growing interest in AI as a practical safety layer that can complement established management systems by detecting hazards and operational issues in real time.

 

What This Means for Tug and Workboat Operations

 

The common thread across these deployments is that AI is being applied to the operational core of tug businesses rather than treated as an add-on. Scheduling optimisation, logistics planning, and safety monitoring all translate directly into higher utilisation, lower delays, and reduced fuel burn, which can cut emissions without waiting for new propulsion systems. As these tools mature, the competitive gap is likely to widen between operators that can standardise data, integrate systems across departments, and act on analytics quickly, and those still relying on fragmented workflows and manual coordination.