Why are ports investing in AI if only 4% see measurable earnings gains?

Operators are motivated by perceived operational benefits: 74% report efficiency improvements and 44% report faster decision-making. However, these operational gains lack systematic financial tracking. The survey reveals that confidence at the C-suite level (79% of leaders don't cite unclear ROI as a barrier) exceeds the evidence-based business case available to implementation teams. Investment inertia and competitive pressure to adopt emerging technology are likely additional factors.

What are the top barriers preventing wider AI adoption in ports?

According to the survey, the three primary barriers are: (1) unclear return on investment (41% of respondents), (2) data quality issues (32%), and (3) difficulties integrating AI with existing systems (30%). Notably, there is a significant gap between senior leadership and operational staff perspectives on ROI clarity, with 44% of non-C-suite respondents citing unclear returns compared to only 21% of executives.

Which AI use cases are ports currently prioritizing?

The survey assessed 43 AI use cases. The two most common implementations are: (1) extracting data from shipping documents (62% exploring or implementing) and (2) predicting equipment failures in cranes and straddle carriers (48% exploring or implementing). The study projects growing interest in integrating AI into daily operational workflows, expected to rise from 60% to 74% over the forecast period.

What do port operators need to do to realize AI value?

BCG and TIC4.0 recommend three key actions: (1) establish measurable evidence of value using performance baselines accepted by operational managers before scaling deployments, (2) invest in shared data foundations across businesses rather than disconnected point solutions at individual terminals, and (3) develop customer-facing services alongside customers, since 61% of respondents identified freight forwarders as a source of demand for AI-enabled services.

Is there demand from customers for AI-enabled port services?

Yes, significant commercial interest exists. The survey found that freight forwarders (61% of respondents identified them as a demand source), third-party logistics providers (58%), and container shipping lines (52%) are actively seeking AI-enhanced terminal services. This customer-side pull represents a potential path to value realization that ports can leverage by developing services aligned with existing commercial demand.

Technology & Innovation

Moderate

AI at ports is scaling but earnings gains remain elusive

Nov 3, 2026

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The signal

A Boston Consulting Group survey of 103 industry experts across five continents reveals a critical disconnect in port and terminal AI adoption: while 44% of respondents have moved beyond trials to scaling stage, only 4% have documented measurable earnings gains from their AI investments. The research highlights that operators are successfully deploying AI for shipping document extraction, equipment failure prediction, and operational decision support, with 74% reporting improved efficiency, yet these operational improvements are not translating into tracked financial performance.

The biggest obstacle to wider adoption is unclear return on investment, cited by 41% of respondents, particularly among operational staff (44%) compared to C-suite executives (21%), suggesting a trust and transparency gap between leadership and implementation teams. Port operators are continuing to invest heavily, with 59% planning to hire AI specialists and 55% allocating R&D funding, but without clear measurement frameworks or evidence of value creation before scaling.

This gap between deployment velocity and financial accountability creates a critical challenge for the sector.

Companies affected by this story:

Gap Inc.

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Frequently Asked Questions

What This Means for Your Supply Chain

Simulation Suggestion

this month

What if ports implement measurement frameworks and capture 15% documented ROI within 12 months?

Scenario: Port terminals systematically establish performance baselines and track AI contribution to operational costs and margins. By implementing BCG's recommended measurement practices, assume 15% of current AI-deploying ports document measurable ROI within 12 months. Simulate impact on: (1) continued AI investment commitment across the sector, (2) speed of AI adoption scaling, (3) competitive positioning of early-measurement adopters, (4) confidence levels of operational staff in technology initiatives.

Run this scenario

Simulation Suggestion

this month

What if customer demand accelerates AI adoption at ports serving freight forwarders?

Scenario: Freight forwarders and 3PLs actively demand AI-enabled terminal services (cited by 61% and 58% of survey respondents respectively). Assume that terminal operators prioritizing customer-co-developed AI services achieve measurable efficiency gains 18 months faster than those pursuing internal optimization only. Simulate impact on: (1) lead times and service levels to freight forwarding customers, (2) port terminal competitive positioning, (3) speed of ROI realization, (4) investment capital reallocation toward customer-facing AI.

Run this scenario

Simulation Suggestion

strategic

What if data quality issues prevent 30% of ports from scaling AI beyond pilot stage?

Scenario: The 32% of survey respondents citing data quality as a barrier reflects a systemic problem. Assume that without investment in shared data foundations, 30% of ports remain unable to move AI deployments from pilot to operational scale. Simulate impact on: (1) global port terminal productivity growth, (2) competitive advantage of ports with strong data infrastructure, (3) freight forwarding service levels and costs, (4) timeline for industry-wide AI adoption benefits.

Run this scenario

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