The sample consisted of a healthy mix of companies across sectors using ocean, air, and overland freight, yielding a well-rounded view of shipping realities across both modes and regions. In January 2026, BCG and Alpega conducted an in-depth survey of a broad spectrum of more than 180 experts at both LSPs and shippers across Europe, North America, Asia, and the Middle East. The authors would like to thank Carlo Alberto Castelli, Gaurav Kumar, and the Alpega team for their support in creating the survey and analyzing the results. Our survey of logistics industry leaders reveals the progress both logistics providers and shippers have made in implementing AI. This article does a strong job of outlining the 25 AI-enabled logistics and supply chain startups transforming global trade.
- AI replaces fragmented tasks with a single view of what’s where, what’s needed, and what’s next.
- This fragmentation makes it difficult to deploy a single AI system across all operations — each jurisdiction may require different constraint parameters, compliance checks, and audit trail formats.
- One of AI’s most exciting prospects is its ability to forecast future events by inferring intricate patterns from data.
- AI can facilitate transparency over the entire network, restructuring each moving part informed by a single source of truth.
- It’s no surprise that leading logistics and e-commerce companies have quickly embraced AI to enhance efficiency, reduce costs, and stay competitive.
- It highlights how AI can preempt threats, automate compliance, and maintain business and customer continuity during unexpected events.
They are building proprietary differentiating algorithms that add value by shaping operational quality and customer experience, and buying standard AI capabilities from vendors for purposes such as back-office automation. Despite shippers’ growing expectations that their LSP partners offer AI capabilities, AI adoption among LSPs still lags significantly. A detailed analysis of the survey results makes clear where LSPs and their customers are making gains in their AI efforts and where they are meeting resistance. This year’s participants were considerably more convinced of its value and are showing first signs of adoption, although progress remains behind what is likely required for impact at scale. It covers real-world case studies, practical strategies, and key applications of AI Agents, GenAI, and automation to help businesses reduce costs, improve efficiency, and build resilient logistics networks. AI is no longer an emerging trend in supply chains, it is a key driver of efficiency, automation, and resilience.
Einride specializes in providing electric and autonomous transportation solutions, focusing on reducing emissions in the freight industry. By automatically identifying sudden acceleration, sharp turns, and severe braking, the system enables fleets to proactively address risky driving behaviors, thereby enhancing safety and reducing potential accidents. Leveraging AI, Konexial’s My20 LogiCam AI integrates road and driver-facing cameras with live telemetry data to detect, correct, and prevent incidents before they escalate.
Real-World Examples of AI Logistics Software in Action
- The survey highlights a clear distinction between what shippers expect from AI and where LSPs capture the most value.
- Supply chains are transforming with artificial intelligence (AI), improving efficiency, visibility, and automation across freight management, warehousing, and transportation.
- AI is set to revolutionize logistics even further, with advancements that will drive smarter, more efficient, and sustainable supply chain operations.
- Results include materially fewer locker-overflow events, faster customer collection times, and a lower misrouting rate after full process redesign.
- The company has experienced significant growth, with its SaaS revenue increasing by 193% in 2020 and tripling in 2021.
- Pickrr specializes in providing AI-powered logistics solutions for e-commerce and direct-to-consumer (D2C) brands, focusing on optimizing delivery operations and enhancing customer satisfaction.
Therefore, the business will be able to reduce shipping costs and speed up the shipping process. Route optimization utilizes shortest-path algorithms in the field of graph analytics to determine the most efficient route for logistics trucks. These AI systems detect patterns across thousands of data points to forecast market movements and execute timely price adjustments. Modern pricing software, powered by machine learning algorithms and AI technology, enables companies to analyze data, including historical sales data, customer data, and competitor benchmarks, in real-time. In the fast-paced https://investnews24.net/tels-global-the-best-international-logistics-company.html logistics landscape, where cost structures and customer behavior evolve rapidly, static pricing models can lead to lost revenue opportunities or inefficient resource allocation.
AI-powered demand forecasting is transforming how logistics companies anticipate and respond to demand fluctuations. This growth reflects a strong global commitment to leveraging AI for cost savings, efficiency gains, and competitive advantage in logistics operations. Codewave is a UX first design thinking & digital transformation services company, designing & engineering innovative mobile apps, cloud, & edge solutions. AI can be trained to flag documentation errors, detect compliance risks, and adapt workflows for different customs regulations. AI can optimise routes to reduce fuel usage, balance load capacity to minimise trips, and forecast demand to avoid overproduction. For focused use cases with clean, integrated data, companies often see measurable results, such as reduced delivery times or fuel costs, within 3–6 months.
Sophia acts as an intelligent supply chain companion, delivering real-time https://power-at-work.com/lifts-streamlining-logistics-in-high-rise-construction-projects/ analysis and action based on unique supply chain data. Their platform offers real-time visibility, predictive analytics, and automation tools to streamline logistics processes. The company’s OmniFlow software acts as an ecommerce command center, providing brand operators with the visibility and insights required to drive success. Flowspace empowers brand operators with the tools needed to manage their supply chains effectively, ensuring seamless delivery experiences across various channels.
Their platform combines AI, advanced analytics, and graph technology to analyze billions of supply chain data points, offering long-range strategic risk scores at the material, supplier, and facility levels. Everstream Analytics specializes in providing advanced supply chain risk management solutions, focusing on delivering end-to-end visibility and predictive insights https://ulstergrandprix.net/meet-the-sponsors-ifs-logistics/ to enhance supply chain resilience and agility. These features help streamline operations by reducing human error, optimizing supply channels, and balancing production supply and demand.
Freight forwarders say 37% of their apparel and fashion customers expect AI-powered solutions, the highest of all industry verticals, followed by industrial and pharmaceutical segments, at 26% each. More than 40% of shippers say they now take LSPs’ AI capabilities into account when selecting their logistics partners. The survey highlights a clear distinction between what shippers expect from AI and where LSPs capture the most value.
- AI can track warehouse systems, factory equipment, and freight infrastructure.
- TrackChain offers features such as carrier procurement, intelligent load assignment, real-time shipment tracking, automated freight audit & payments, and predictive transportation analytics.
- Everstream Analytics specializes in providing advanced supply chain risk management solutions, focusing on delivering end-to-end visibility and predictive insights to enhance supply chain resilience and agility.
- Their electric trucks are designed without a driver’s cab and can be remotely controlled, offering a sustainable alternative to traditional diesel vehicles.
- The 35% adoption rate means early movers capture disproportionate competitive advantage.
- Algorithms can also factor in vehicle capacity and driver shifts to optimise utilisation.
The Real Barriers Are ROI Clarity and Internal Capabilities
Parade specializes in providing a platform for freight brokers and carriers to streamline their operations, focusing on automating tasks and improving efficiency in the logistics industry. Nautilus Labs utilizes AI to analyze vessel performance data, offering predictive insights that help shipping companies optimize routes, reduce emissions, and improve fuel efficiency. Osa Commerce utilizes AI and blockchain technologies to enhance supply chain operations by providing real-time visibility and intelligent decision-making capabilities.
In Europe, organizational resistance to change ranks as a key barrier, cited by almost a quarter of respondents. The question is now whether organizations can execute effectively. Today, the technology is far easier to access and use, and adoption costs have dropped. Instead, roughly 40% of survey respondents—both LSPs and shippers—cited unclear return on investment and internal capability gaps as the top barriers. These operational use cases are often less visible but can deliver substantial impact at scale. At the same time, AI is enabling faster and more convenient customer interactions—such as responding to inquiries, managing complaints, and providing proactive shipment updates through chatbots and automated notifications.
It requires managing fulfillment networks, flow paths, supplier orchestration, and distribution nodes simultaneously. For enterprises evaluating where to begin, the most common entry points are demand forecasting, route optimization, and warehouse automation — all of which are covered in the examples below. This article will delve into 17 examples of AI in logistics and supply chain management. If logistics and supply chains are to support these business process transformations, AI adoption becomes essential. The critical transition from Stage 1 to Stage 2 requires simultaneous investment in data infrastructure, edge computing, and workforce enablement — this is where 65% of logistics AI initiatives stall. We help logistics and supply chain operators move from ad-hoc AI experiments to production systems that affect the P&L.
