For decades, freight brokers and forwarders have relied on (often proprietary) software platforms to connect shippers with carriers and manage the flow of cargo. That model is now being tested. AI could make it easier for start-ups to mimic enterprise software-as-a-service offerings—or maybe even sidestep the need for them.
Some logistics start-ups have already reported using AI-fueled software to create shipping efficiency gains. It’s therefore no surprise that incumbent, asset-light logistics forwarders and brokers are coming under pressure, as AI threatens to erode the moats around their software platforms.
There are two potential risks. The first is that AI might let new entrants quickly and cheaply replicate powerful logistics software interfaces. The second is that AI agents could just stitch together all the necessary functionality on their own using open data, thereby completely bypassing a customer’s need for any specialized logistics platform.
Should incumbents be concerned that new, nimble competitors could “vibe code” robust logistics software interfaces, reducing the considerable time and money previously required to develop or obtain access to such tools? Will incumbents’ carefully designed, often proprietary tech platforms no longer provide sustainable competitive advantages? Will shippers just start routing all their freight using large-language-model-based chat portals?
It’s certainly true that AI is changing logistics, and incumbents will need to adapt. But those that can combine their formidable scale, relationships, experience, and data with a forward-looking approach to tech—embracing innovation faster and implementing it better than competitors—could be well positioned not only to solidify their leadership advantages but also to capture the ROI upside that AI could help deliver to the sector.
Logistics incumbents’ strengths are hard to mimic
Incumbent logistics providers have advantages that they have developed and bolstered over time and that software can’t easily replicate.
- Scale. Asset-light logistics providers (such as brokers and forwarders) manage substantial volume, which helps them secure volume discounts from carriers. Their wider networks give incumbents more options for moving their customers’ cargo.
- Personal connections. The human element—including relationships developed over years and decades—helps increase trust between shippers and broker/forwarders, as well as between broker/forwarders and carriers. Customers have more confidence that promises will be kept, and they know who to call should anything go wrong. Start-ups (especially those focused more on tech solutions than on personal relationships) have not had time to earn the same amount of goodwill.
- Experience. More time on task—at an enterprise level—means that incumbents have seen it all (or at least most of it) before. They deeply understand the needs of both shippers and carriers.
- Data. Incumbent brokers and forwarders maintain large, diverse sets of proprietary data, gathered from millions of shipments and customer interactions that span years of activity. This data include not just successful transactions but also records of which shipping rates customers have declined and in which contexts. Large data flows can help train AI models on real-world freight behavior. Newer entrants won’t have had time to amass robust, detailed data sets of their own.
(Asset-heavy logistics providers tend to be more insulated from AI disruption, given the capital required to replicate a physical network. In addition, they can benefit from AI in areas such as maintenance, asset utilization, and network optimization.)
Leading logistics incumbents are already embracing AI
The potential for AI to disrupt the logistics sector is real. AI has the capacity to radically transform how transportation and logistics companies operate, propelling significant increases in productivity.
But some institutional analysts who cover the space have downplayed the near-term risks of disruption for asset-light logistics incumbents. Why? Logistics incumbents are aware of this evolution in the sector, and they aren’t resting on their laurels. They’re combining the advantages detailed above with the cutting-edge benefits of AI.
One transportation company has implemented an AI-enabled supply chain platform that has boosted productivity by more than 40 percent since 2022. The platform can automate tasks and has been applied to areas including pricing, capacity sourcing, freight tracking, and document handling. Another transportation company has deployed 50 AI agents that have allowed it to automate 60 percent of check calls (which update the status of cargo), 73 percent of order acceptances, 80 percent of paper invoice payments, and two million quotes—saving tens of thousands of hours of labor.
Adapt and innovate—or the train leaves without you
Understanding and implementing AI is now imperative for transportation and logistics companies. Those that can’t integrate AI into their operating models could struggle to keep pace with those that can.
But AI can’t simply be layered on top of existing processes and ways of working. It needs to be woven into the core elements of a company’s strategy and execution. McKinsey research has revealed approaches that can help companies rewire—reimagining how their business works, accelerating their innovation cycles, and translating AI investments into bottom-line returns.
The first step is to establish a clear strategy. The organization must align on what the business should look like in the digital age. Any strategy should include a detailed road map that makes clear which capabilities will be needed to generate impact. Successful organizations tend to focus on domains that are big enough to generate meaningful value without disrupting large parts of the business. The commercial domain is typically one such area—for instance, AI can be used as a lead generation engine or to improve account management. AI can also reshape operations, for instance, by automating the creation of bills of lading or by functioning as a training agent for workers.
Once the strategy is clear, focus can shift toward developing capabilities, which tend to fall into four buckets:
- Talent. Digital excellence can’t be outsourced. It’s critical to acquire or develop internal talent that can build and evolve proprietary digital solutions (and equally critical to create an environment in which this talent can thrive).
- Operating model. It’s difficult to alter operating models because they sit at the core of an organization and how its people work together. But adapting the operating model is what allows the organization to accommodate faster and more flexible innovation.
- Technology. Building a distributed technology environment can enable easier access to data, applications, and software development tools. The goal is for technology to be readily usable in ways that allow for rapid iteration and high-quality solutions.
- Data. Organizations can develop a data architecture that allows data to be easily consumed and reused by different teams. A strong data approach is critical because the value that can be extracted will be dependent on the data’s quality, relevance, and availability.
All of this fundamentally relies on an organization’s willingness and ability to be purposeful with change management. Leaders should get customers and employees to adopt the new solutions and track usage at sufficiently granular levels, with clear KPIs, to ensure that real value is being captured. Changing processes that have worked for many years can be difficult. Successful rewiring requires leadership to be invested and involved—actively influencing teams to adopt promising AI initiatives.
AI is unlikely to fully breach the moats that incumbent logistics providers have established. But it could reshape some of the advantages that have often separated leaders from laggards. The most effective companies will find ways to fuse AI with the assets that are hardest to replicate: large networks, trusted relationships, deep operational experience, and proprietary data at scale. In this environment, incumbents that treat AI as an accelerant—rather than a bolt-on tool—can turn a potential disruption into a source of durable competitive edge. Companies that move decisively to embed AI into their core workflows, talent models, technology stacks, data libraries, and customer interactions could expand margins, improve service, and capture share.
