Global Logistics Focus
Special Report
Streamlining services
The industry hype around AI is deafening, so what will the AI investments mean for cargo-owning customers of the forwarders?
DSV CIO Jesper Riis said an important initial step has been to bring data together in one place and describe it in a consistent way across divisions.
“ When bookings are brought together in a common booking domain and described according to the same enterprise data model, we get a much clearer view across customers, divisions and geographies,” Riis told the Journal of Commerce in an interview at the company’ s Copenhagen-area headquarters.“ We can then enrich and validate parts of the booking data before it reaches the transport management systems, rather than giving the tasks to freight forwarders to correct the same type of information manually in local processes. This way, we can solve problems once for large global processes, instead of through smaller fragmented, local solutions.”
An example of how AI could improve DSV processes was with address validation, Riis said. If a customer sends an address in one format of data quality but before that information is used operationally, it can be compared against trusted data sources, validated and corrected where needed.
“ That may sound simple, but when you apply it across very large global booking volumes, it removes a significant amount of manual work and improves the quality of the data that reaches our operational systems,” Riis said.
DSV is using technologies such as Optical Character Recognition( OCR) and AI on large global processes including vendor invoices and customs documentation. The technology reads information from documents, extracts and structures the relevant data, and maps it into DSV’ s enterprise data model.
“ Customs is more complex due to regulatory requirements and country variations, but AI can significantly reduce the manual effort and improve consistency,” Riis said.“ One of the important advantages is that corrections are fed back into the AI model, so the solution improves over time... as those exceptions are reviewed and corrected, the model learns from that feedback.
“ That is where AI becomes powerful in our context,” he added.“ Not as a standalone solution, but as part of a controlled process where data quality, human expertise and continuous learning help us move toward more complete and accurate bookings.”
Benefits of direct control
K + N’ s proprietary cloud-based AI platform gives the company control over quality, cost, resilience, and speed of deployment, which was rare in a market where most forwarders depend on external vendors for their core transport management system( TMS).
Markus Johannsen, SVP of global sea logistics and head of technology and business intelligence at K + N, explained the benefits of that direct control.
“ K + N’ s ownership of its own system provides not only a strong groundwork but also enables enhancements or fixes and hyper-detail to shipment information rather than waiting for patches from a third-party TMS vendor that are available for all customers and hence do not provide any competitive edge,” Johannsen told the Journal of Commerce in an interview.
He listed some of the AI-driven processes that are being deployed across the K + N business areas.
AI is helping with rate management by structuring rates from ocean carriers received via EDI or API and adding regional pricing and domestic tariffs for landside charges, including detention, demurrage and per diem. The approach was delivering accuracy in the high 90 % range, generating faster quotes and lessening disputes, Johannsen noted.
AI allows shippers to use their own documents and allows K + N to confirm a booking in less than two hours compared with an industry average of 1 to 1.5 days.
AI also helps with blank sailings by allowing K + N to allocate customers’ volumes to available capacity.
“ This is done automatically following clear business rules and with a human sign-off and is far speedier than sending emails out to execute allocation changes,” Johannsen said.
“ This way, we can solve problems once for large global processes.”
K + N is also using agentic AI to speed up confirmation of delivery. If there is no response, a human agent will follow up, but the agentic AI makes the first attempt automatic, saving human time.
This is all part of a broader AI strategy to create what K + N calls“ sustainable operating leverage” and improve productivity, customer service and margins.
“ Our efforts to centralize and standardize repetitive workflows are already having a positive effect across the group, and they lay the groundwork for AI-driven automation with material traction expected in 2027,” Nemati said.
“ In sales, AI has begun to generate customer briefings, ultimate meeting documentation and assist the contract review of incoming RFQs,” he added.“ Presently, we expect a productivity increase of around 10 %.”
In terms of where these gains will first appear, the initial 2027 traction will relate to K + N’ s sea logistics, air logistics and functional areas such as sales, finance, IT and HR, with use cases specific to road and contract logistics also under development.
email: greg. knowler @ spglobal. com email: mark. szakonyi @ spglobal. com
28 Journal of Commerce | October 5, 2026 www. joc. com