Published by Splash247

The industry body that operates one of global shipping’s most significant blockchain networks has published a pointed analysis of where artificial intelligence stands in its hype cycle – and the verdict is that shipping should be far more cautious than the current frenzy of AI investment suggests.

The Global Shipping Business Network (GSBN), whose not-for-profit consortium handles data for more than half of the world’s containerised cargo, argues in its latest Insights report that AI today more closely resembles the internet in 1997 than a mature, deployable technology. The report, Blockchain vs. AI: Navigating the Hype Cycle, draws heavily on GSBN’s own hard-won experience building blockchain infrastructure to warn that AI’s enterprise moment has yet to arrive – and that shipping companies racing to deploy it risk repeating the same expensive mistakes the industry made with blockchain a decade ago.

“Blockchain’s experience in global trade offers useful signals for how this phase of AI adoption may unfold,” the report states, adding that the transition from hype-driven experimentation to utility-first discovery is neither fast nor painless.

Hype is finite but long-term value is cumulative

The parallel GSBN draws is structurally sharp. Both blockchain and AI attracted capital and talent at a pace that far exceeded their near-term ability to deliver. Both moved rapidly from innovation trigger to peak of inflated expectations on Gartner’s Hype Cycle. And in both cases, the industry’s instinct was to chase the most visible use case rather than the most structurally important one. For blockchain in shipping, that meant years of expensive, ultimately failed attempts to solve visibility and transparency – problems that existing platforms already handled adequately. TradeLens, We.trade, Marco Polo and Contour all collapsed under the weight of governance failures, misaligned incentives, and the fundamental absence of a genuine product-market fit.

The lesson that eventually emerged was not that blockchain was wrong, but that it was being aimed at the wrong problems. Value only materialised, the report argues, when the technology was redirected at structural challenges in cross-party coordination – and when external conditions, specifically the legal recognition of digital trade documents, gave it a foundation to stand on. Electronic bills of lading are now the clearest expression of that maturation: blockchain functioning as invisible but essential infrastructure rather than topline disruption.

GSBN’s assessment of AI’s current position is equally direct. Only 21% of enterprises are significantly using agentic AI, according to Lenovo’s CIO Survey 2026, while 55% remain at the exploration or early pilot stage and 24% have no plans to adopt it at all. Despite a capital investment environment that has seen hyperscaler capex triple since 2023 – with forecasts pointing to more than $2.7 trillion in cumulative AI-related spending through to 2029 – durable, scaled use cases in global trade remain largely absent.

The report is particularly sharp on the risk of adopting AI as a reactive overlay rather than as a capability built around genuine operational advantage. The contrast it draws between shoe brand Allbirds’ pivot into AI to offset deteriorating business fundamentals and Kodak’s late-stage embrace of blockchain as a survival narrative is a deliberately uncomfortable one for an industry currently spending heavily on technology it does not fully understand. Adopting fashionable technologies, the report observes, does not by itself create defensible value.

Where the analysis becomes genuinely forward-looking is in its argument about convergence. GSBN’s central thesis is that AI and blockchain are not competing technologies but complementary ones whose structural limitations mirror each other. AI is a capability layer – extracting meaning, predicting outcomes, automating decisions. Blockchain is a coordination layer – establishing shared trust, immutability, and auditability across parties that do not fully trust one another. Global trade, with its twenty-plus parties per shipment, its paper-heavy documentation flows, and its legally consequential decisions, is precisely the environment where both are needed simultaneously.

The electronic bill of lading sits at the centre of this convergence argument. As eBL adoption scales, each shipment generates more structured and authenticated trade data. Better data enables more reliable AI. More reliable AI strengthens the economic case for deeper automation. The virtuous cycle, GSBN argues, is not driven by hype but by tangible operational value – fewer disputes, faster settlement, stronger compliance, reduced friction.

Two near-term applications illustrate the point. In customs, where what was once a compliance obligation has become a direct source of financial risk, AI can classify goods, detect anomalies, and forecast duty exposure in real time – but only if the underlying data is trusted. Blockchain preserves that provenance. In trade finance, as AI agents begin to manage financing and trigger payments, eBLs allow settlement to be conditioned on shipment milestones, with blockchain guaranteeing that the shared record of what has occurred is beyond dispute.

The report’s implicit warning for shipowners and operators is clear enough. The companies that will capture value from AI are not those that deploy it fastest but those that build it on trusted data, clearly defined execution rights, and operational workflows that govern real economic outcomes. Without that foundation, agentic AI systems will not scale value – they will scale uncertainty.

“If there’s one lesson here,” the report concludes, “it is that hype is finite but long-term value is cumulative.”

Blockchain took the better part of a decade to learn that. AI, GSBN suggests, does not have the luxury of the same timeline.