Annual Review & Outlook 2025
Logistics
Executive Commentary
real estate cost analytics and forecasting technologies will provide the planning savvy and visibility currently missing in the process .
Given the forecasted 18 % annual growth for warehouse automation , we expect a significant impact on the industrial real estate sector in two key areas .
First , building design will have to adjust . Retailers and e-commerce companies will push for taller buildings , more electric infrastructure and heavier structural and mechanical systems to house robotics , sortation systems , and ASRS modules . The traditional “ four walls and a roof ” design will become obsolete . Speculative development will have to take all of this into account going forward .
Second , property developers will have to capitalize non-traditional improvements such as material-handling systems and robotics in order to attract sophisticated clients . This is uncharted territory for them and will require their financial partners and lenders to enter into a new domain of financial risk with more agility and understanding .
Looking to 2025 , consumer demand is expected to remain steady , leading to continued growth and expansion . Investment in automation will stretch the industry and predictive cost modeling will be imperative to achieve operational success .
Chain . io
Brian Glick
Founder and CEO www . chain . io
In 2025 , the hype around artificial intelligence will begin to crystallize into practical applications . We ’ ll see a wave of pilot projects evolve into full-scale implementations , but also watch as nonessential AI uses get filtered out due to costs and environmental impacts .
Data quality will take center stage in logistics , as smarter systems demand consistent , high-quality data for effective , real-time communication . This shift echoes the EDI transition of three decades ago , when typewriters vanished
“ AI can be applied not only for data analysis but also for data connectivity , which is a prerequisite .”
Gisli Herjolfsson
“ The next frontier in futureproofing pharma supply chains , particularly for delivering a new wave of treatments , will require innovations that enable end-toend visibility , reliably tracking shipments from production to patient down to the unit level .”
Jaakko Elovaara
“ Beware of solutions that claim to solve broken processes with AI alone rather than addressing core issues .”
Brian Glick from workflows . Email will become the “ new typewriter ” holding back seamless operations . Beware of solutions that claim to solve broken processes with AI alone rather than addressing core issues .
A notable shift next year : shippers , not carriers or forwarders , will likely drive the most impactful AI innovations . To unlock AI ’ s full potential , data must flow across functions such as marketing , finance and planning . Carriers and forwarders will provide essential data , but transformative insights will come from shippers and software providers who see the complete logistics landscape .
coneksion
Jaakko Elovaara
Co-founder and CEO www . coneksion . com
Controlant
Gisli Herjolfsson
Co-founder and CEO www . controlant . com While pharma companies are at the forefront of innovation in developing new treatments that stand to improve people ’ s health outcomes , their supply chains often lag . Many still rely on outdated
Nowadays , artificial intelligence algorithms can quickly and accurately analyze vast amounts of data , identifying patterns and making connections that would take humans significantly longer to accomplish . Especially in container shipping , this capability can be invaluable for predictive analytics , helping companies anticipate demand fluctuations and optimize routing . Additionally , AI facilitates process automation and minimizes manual intervention , ultimately boosting operational efficiency and enhancing supply chain performance . Enabling the constant flow and orchestration of these vast amounts of data , based on which AI algorithms can then deliver these advantages , requires efficient data connectivity solutions .
In fact , AI can be applied not only for data analysis but also for data connectivity , which is a prerequisite . There ’ s a lot of raw data residing in various formats that can be made valuable ; stakeholders often face challenges with data format mapping , primarily due to a lack of common data standards and outdated technology . Traditionally , data mapping processes have been labor-intensive and error-prone , requiring extensive manual input and oversight . Even now that common messaging and data standards have emerged ( e . g ., DCSA and SMDG ), message conversion is considered a pain . This not only slows down operations but also increases the likelihood of mistakes , which can be costly in terms of time and resources .
Data connectivity solutions enhanced by AI , when combined with human domain expertise , enable companies to automate execution and visibility processes , streamlining operations and allowing them to focus on strategic decision-making , rather than getting bogged down in tedious mapping or integration tasks .
AI ’ s capabilities are invaluable in logistics and container shipping , especially in data exchange , where timely , accurate information is vital . Its automation and optimization capabilities make it a game changer in container shipping , enhancing efficiency and supporting long-term business growth .
www . joc . com January 6 , 2025 | Journal of Commerce 91