Generative AI is already saving hours in forwarding offices. The harder question is whether it is reducing the fully loaded cost of processing a shipment.
| THE CENTRAL TEST AI productivity is credible only when the fully loaded cost per successfully completed shipment falls while service quality is maintained. |
The information revolution around the cargo
For the last few years, Generative AI has been surrounded by extraordinary hype. Depending on whom you listen to, it will transform the global economy, eliminate millions of jobs—or become another costly technology that fails to live up to expectations. [4]
Shipping and logistics has heard this before. Blockchain was going to revolutionise the bill of lading. Digital freight forwarders would sweep away traditional forwarding. Yet containers still get delayed, customers still ask where their cargo is, documentation still needs correcting, and experienced operators still spend much of their day chasing information. [4,5,13]
GenAI is unlikely to begin by moving cargo. It is beginning by transforming the enormous amount of information surrounding it: emails, tenders, contracts, invoices, bills of lading, SOPs, spreadsheets and presentations. Traditional logistics AI predicts ETAs, forecasts demand and optimises routes. GenAI reads, interprets, summarises and generates language, including the messy, unstructured information on which forwarding still runs. [4]
What adoption looks like on the forwarding floor
Evidence from Japan suggests that the technology has already moved beyond occasional experimentation. One Danish logistics company said its staff use AI daily for routine and legal translation, summarising tenders and contracts, completing and analysing Excel files, filling gaps in Word documents, customer research, business planning and presentation preparation. Usage is not formally measured, but employees appear willing and happy to use it. [1]
One example makes the time saving tangible. A major Dubai warehouse tender was managed from Japan. Summarising the tender package and completing the response would previously have taken several days. Using Claude, the team completed the work in a matter of hours. This is not a futuristic demonstration; it is a live commercial workflow in which AI compressed days of administrative work into hours. [1]
Feedback from a large Swiss / German logistics company describes a similar direction. The company is exploring GenAI for data collection, reconciliation and validation, and it already supports some repetitive administrative tasks. The stated objective is not simply to remove staff, but to improve productivity and quality so teams can spend more time with customers and on exceptions requiring human expertise. [2]
Time saved is not the same as economic productivity
These examples prove that AI can make particular tasks faster. They do not by themselves prove that it has reduced the total cost of running a forwarding business. That distinction should sit at the centre of the AI debate. [4,8,9,13]
If a forwarder handles the same volume with fewer employees, shipments per employee rises. But that metric does not capture enterprise software, cloud infrastructure, integration, cybersecurity, governance, consultants, data cleaning, human checking, restructuring, legacy systems, errors or rework. Nor does it show whether service quality was maintained or whether the remaining staff simply absorbed a heavier workload. [4,8,9,13]
The more demanding measure is the fully loaded cost per successfully completed shipment. A credible calculation should include labour savings, avoided rework, additional capacity and incremental margin, then subtract AI ownership costs, restructuring costs and quality failures. Corporate claims should also separate savings already achieved through headcount reduction, operational improvements demonstrated in data, and future AI benefits that remain prospective. [4]
Where the business-level savings are already visible
One logistics company said its track-and-trace system offers a useful example of real scale. Operators worldwide still input shipment data, and prediction quality depends on that input. However, customers can check predicted cargo movement and arrival themselves. The company estimates that this self-service capability saves many hundreds or thousands of customer-service hours. Here, AI is not merely helping one employee draft faster; it is reducing a large volume of repeat enquiries across the network. [1]
The benefit is therefore a mix. The system lowers the cost of providing routine visibility, while general-purpose tools help staff manage workloads and improve output. Yet there is a clear boundary: the company does not use an AI customer-service chatbot, and shipment exceptions are still handled by people in different offices. It has not yet solved autonomous exception management. [1]
That is a revealing picture of the market in 2026. AI is strong at summarising, drafting, completing, reconciling and predicting. It is weaker where information is incomplete, responsibility is dispersed and a decision carries operational, commercial or regulatory consequences. [4]
The data-quality paradox
GenAI has an advantage over earlier waves of logistics technology because it can work in an unstandardised environment. But unstructured does not mean unreliable. A former senior executive at a global logistics company warns that outdated, duplicated or contradictory SOPs must be reviewed before they are placed behind an AI assistant. Otherwise, the old principle remains: garbage in, garbage out. [1,3,4,8]
The same problem appears in track and trace. AI can predict only from the events operators record. Poor input does not disappear because a sophisticated model sits above it. Companies therefore need clear data ownership, decision rights and accountability—areas that are often ambiguous in logistics, particularly where global organisations, local offices, shared-service centres and subcontractors meet. [4]
There is also a human competence risk. If employees spend years reviewing and approving AI output rather than doing the underlying work, will they retain enough operational understanding to recognise a subtle error? The industry may create roles such as AI workflow designer and exception-management specialist, while simultaneously weakening the entry-level pathway through which tomorrow’s experts learn. [4]
BPO, jobs and the uncomfortable sequence of change
Standardised shared-service and BPO activities are likely to face the earliest disruption. They are repetitive, governed by established SOPs and KPIs, and were outsourced precisely because they could be standardised. This has implications not only for logistics companies but also for BPO-heavy economies such as the Philippines and India. [3,5,12]
A former senior executive at a global logistics company offers a more uncomfortable hypothesis: management may reduce workforce before productivity has demonstrably risen in order to force organisational change. The broader incentive problem is real. Why should employees enthusiastically automate their own work if the perceived reward is fewer colleagues, greater workloads or redundancy? Leadership must give staff a credible stake in the transformation. [3,11,14]
The sensible question is therefore not simply, ‘How many jobs can AI remove?’ It is, ‘How much low-value work can we remove, and how will we use the capacity released?’ The answer might be lower headcount, but it might also be faster tender responses, better service, more customers per employee, fewer errors or more time spent on complex exceptions. [4,8,9,11,13]
Japan may be the perfect stress test
Japan combines a severe labour shortage with layers of subcontracting, customer-specific SOPs, uneven digitalisation between global corporations and SMEs, continued use of fax and a business culture in which personal relationships remain important. That makes it both an attractive market for automation and a difficult environment in which to scale it. [3,6,7,14]
Physical automation matters too. Driver and warehouse shortages create a strong case for robotics, and a capability learned by one robot can in principle be replicated across an entire fleet. But in the near term, the fastest gains are still likely to come from the office: translation, document checks, tender work, knowledge retrieval, tracking enquiries and routine customer communication. [3,6,7,12,14]
A practical test for the next AI announcement
When a logistics company announces an AI programme, four questions matter.
- Which end-to-end workflow changed?
- How much employee time, rework or customer effort disappeared?
- Was service quality maintained or improved?
- And did the fully loaded cost per successful shipment fall after all technology and transformation costs were included?
[4,8,9,11,13]
The evidence so far is neither hype nor revolution. A tender completed in hours rather than days is real. Thousands of customer-service hours avoided through predictive self-service is real. Data reconciliation and repetitive administration supported by GenAI is real. But fully autonomous exception management remains rare, and most companies still do not publish enough information to prove a lower total unit cost. [1,2]
The winning combination is experienced logistics people, good data and AI. The technology processes information at extraordinary speed; people understand customers, risk, relationships and what really happens when a shipment misses a connection at 2 a.m. The companies that win will not be those that merely ‘have AI’. Eventually, everybody will. They will be the companies that can show—workflow by workflow—that AI removed cost or friction without weakening service, accountability or human expertise. [1,2,4,8,9,11,13]
That transformation will not arrive as one dramatic moment. It is arriving through millions of boring, repetitive tasks quietly disappearing from the working day. For an industry still powered by emails, spreadsheets, and human follow-up, that may be revolutionary enough. [1,2,4,8,9,11,13]
REFERENCES 1 Senior executive, Danish logistics company, private correspondence, 27–28 Aug 2026 (identity withheld). • 2 Kuehne+Nagel, Head of Ocean Logistics Japan, private correspondence, 28 Aug 2026 (identity withheld). • 3 Former senior executive, global logistics company, private correspondence, 27 Aug 2026 (identity withheld). • 4 NIST, AI Risk Management Framework: Generative AI Profile, AI 600-1 (2024), doi:10.6028/NIST.AI.600-1. • 5 ILO, Generative AI and Jobs, Working Paper 96 (2023), doi:10.54394/FHEM8239. • 6 Digital Agency Japan/MLIT, Current Situation and Challenges of Logistics, 24 Jan 2025, digital.go.jp. • 7 METI Japan, Comprehensive Physical Distribution Policy, 31 Mar 2026, meti.go.jp. • 8 OECD, Implementation of the OECD AI Principles, C/MIN(2024)17. • 9 Brynjolfsson, Li & Raymond, Generative AI at Work, NBER WP31161 (2023; rev. 2025). • 10 Lee et al., The Impact of Generative AI on Critical Thinking, CHI/Microsoft Research (2025). • 11 OECD, Ensuring Trustworthy AI in the Workplace, Employment Outlook (2023). • 12 Anthropic, Economic Index research series (2025–26), anthropic.com/economic-index. • 13 Filippucci et al., AI, Productivity, Distribution and Growth, OECD AI Paper 15 (2024), doi:10.1787/8d900037-en. • 14 OECD, Artificial Intelligence and the Labour Market in Japan (2025), doi:10.1787/b825563e-en.
