AI scales at ports, but just 4% report earnings gains

By James Calloway

Only 4% of 103 port and terminal industry experts reported measurable earnings gains from artificial intelligence, even as 44% said their organizations had reached the scaling stage or beyond.

The disconnect is equally stark: only 11% systematically tracked AI’s contribution to efficiency or margins. Boston Consulting Group and Terminal Industry Committee 4.0 say ports are moving beyond trials, but most have yet to connect deployments with financial results.

What did the survey cover?

BCG and TIC4.0 conducted the survey in August 2026, drawing on terminal operators, port authorities, container shipping lines and port vendors across five continents.

TIC4.0 is an international, non-profit association established in 2019 and based in Brussels. BCG published the findings in October, following the August survey.

The study assessed 43 AI use cases across container ports and terminals:

• 62% of respondents were exploring or implementing AI to extract data from shipping documents.
• 48% were exploring or using AI to predict equipment failures, including failures involving cranes and straddle carriers.

These deployments target two costly operational problems: processing the information needed to move containers and maintaining the equipment required to handle them.

For shippers, carriers and forwarders, the commercial opportunity lies in converting those improvements into dependable service, lower terminal operating costs and better margins rather than simply expanding the number of pilots.

Are efficiency gains reaching the bottom line?

Respondents reported their clearest gains inside daily operations:

• 74% identified improved operational efficiency.
• 44% reported faster decision-making.
• 41% reported cost reductions.
• 18% reported an improvement in customer experience.

The low customer-experience figure shows that adoption has concentrated on internal performance rather than customer-facing services. Faster workflows and reduced equipment downtime may help operators, but they do not automatically establish how much financial value AI creates.

The gap is straightforward. Most respondents reported operational benefits, yet only 4% documented earnings gains and only 11% systematically measured AI’s contribution to efficiency or margins. An operator can streamline work without proving that the savings reached the business.

That matters commercially across the container chain. Carriers and shippers can benefit from productive terminals and fewer operating delays, while forwarders can package more reliable services, but the survey shows that evidence supporting those claims remains limited.

What is blocking wider adoption?

Respondents identified three leading barriers:

• Unclear return on investment, cited by 41%.
• Data quality, cited by 32%.
• Difficulty integrating AI with existing systems, cited by 30%.

A separate divide appeared between senior executives and employees closer to implementation. Among respondents below C-suite level, 44% identified unclear returns as a barrier, compared with 21% of senior leaders.

That gap suggests leadership confidence has not always produced a business case that operational teams recognize. The people responsible for deployment may lack the data and performance evidence needed to justify expansion.

Are operators still investing?

The survey indicates that financial uncertainty has not halted planned spending:

• 59% planned to hire or train AI specialists.
• 55% were allocating funding to research and development.
• 47% planned to add AI modules to existing systems.

Those plans point to continued investment in skills, software and infrastructure. The commercial test will be whether that spending produces measurable operating and financial gains rather than isolated projects without shared data or baselines.

How can terminals prove value?

BCG and TIC4.0 recommended that operators establish evidence of value before expanding deployments, using performance baselines accepted by the operational managers closest to the work.

They also called for investment in shared data foundations across businesses rather than disconnected projects at individual terminals. Their approach links each new deployment to a defined operating result and gives terminal teams a common basis for measuring performance.

Customer demand offers another route to value:

• Freight forwarders were cited by 61% of respondents as a source of demand for AI-enabled terminal services.
• Third-party logistics providers accounted for 58%.
• Container shipping lines represented 52%.

Freight forwarders were therefore the strongest demand signal in the survey. BCG urged operators to develop services alongside customers, connecting technical capabilities to needs that already carry commercial interest.

What comes next?

BCG expects interest in integrating AI into daily operational workflows to rise from 60% to 74%. The share expecting to benefit from forecasting operational conditions and their causes is projected to increase from 49% to 67%.

With 59% of respondents planning specialist hiring or training and 55% funding research and development, AI deployment at container terminals remains set to expand. Documented margins, rather than further trials, will determine whether that investment becomes a durable earnings story.