The logistics industry could soon look very different, driven by artificial intelligence that accelerates operations and reshapes decision-making.
“The first workflows to go will be the repetitive ones,” Archival Garcia, CEO of Fluent Cargo, explained. “Tasks like manually checking container locations across different carrier websites, manually checking pricing to put a quote together, copying data between systems, building reports by pulling information from various sources. These are time-consuming tasks that AI handles instantly.”
“Mid-sized forwarders will eliminate entire workflow steps, because rather than needing to check five different systems to get one piece of information, they’re able to ask one question and get a comprehensive answer. This matters because institutional knowledge about how to navigate systems becomes less valuable than knowing what questions to ask.”
Faster decisions, better data
As AI replaces traditional dashboards and reports with conversational, layered analysis, speed does not have to come at the expense of decision quality. Traditional dashboards force you to accept the data they present. Conversational AI lets you challenge assumptions and test scenarios. It’s also what will allow shippers and freight service providers to pull data from different sources in the way they want, which will answer complex questions faster.
Garcia cited the Red Sea crisis as a practical example: “During the Red Sea crisis, operators used AI to quickly evaluate alternative routes, but the most valuable part was being able to ask ‘what if’ questions: What if this port also becomes congested? What’s the cost difference? How does this affect my delivery commitments? That’s where the better decisions happen. Freight operators don’t need to wait days for a report on capacity alternatives, they can explore options in minutes, which means they can be more thorough.”
Underlying all this capability is the need for integrated and clean data. The industry can’t have meaningful AI if the data is fragmented – it is only as good as the data you feed it, and if ocean freight data sits in one system, air in another, and road transport somewhere else entirely, you won’t have a stable, clean foundation to work from.
“The biggest gap I see is in data standardisation. Companies have spent years accumulating data in different formats, using different naming conventions, and different update frequencies. At Fluent Cargo, we spent five years cleaning and standardising data before we started implementing meaningful AI capabilities,” Garcia outlined. “Many systems still update once or twice daily. That worked for dashboards, but AI exposes how slow that is. When you can ask questions instantly, you expect current answers. A dirty secret in the industry is that real time data means people manually going into websites and updating reports and dashboards. It’s very costly and the risk of errors is very high.”
Redefining roles and performance
For logistics professionals, the rise of AI means a shift in skill priorities. When AI handles ‘where is it?’ the valuable question becomes ‘how do we improve our network?’ That requires understanding the business, not just operating the systems. Logistics professionals will need to know how to ask better questions. They’ll need to interrogate the right areas, challenge assumptions, and identify the gaps in what the AI is saying.
Performance metrics, Garcia predicted, will evolve as well. “You can’t measure people on how many tracking updates they provided or how quickly they generated reports. Focus will shift towards decision quality, customer satisfaction, cost improvements, and risk mitigation. AI will provide the standardisation of the language and questions within the ecosystem. Limitations on systems will be challenged as customers will expect immediate and contextual answers.”
Curiosity emerges as a key differentiator for AI-forward teams. AI-forward teams dig deeper, ask follow-ups, and explore alternatives. Teams struggling with adoption treat AI like a slightly faster search engine rather than a tool for analysis. Traditional systems gave us defined fields and reports. AI opens up new possibilities, which means you need people willing to explore undefined territory. Some people find that energising, others find it uncomfortable. There will be a huge disruption, particularly in the offshore service provider models. The positive though is that resources will manage more valuable tasks and shift focus back to the customer.
Preparing for the next frontier
Garcia offered a real-world glimpse of conversational AI at work: “A forwarder gets word that a major customer needs to accelerate a shipment currently scheduled to arrive in three weeks. Rather than spending hours researching options, they ask the AI system to evaluate alternatives. The initial query triggers analysis across multiple factors: current vessel location, available services that meet timeframe SLAs on that trade lane, market index costs, and whether faster routing affects emissions reporting. The AI presents three options with specific trade-offs. Organisation-specific data such as capacity and contract pricing will already provide a better set of considerations.”
But the task becomes conversational rather than just a search result. The forwarder asks follow-up questions: ‘What if we split the shipment between air and ocean? How does that change the cost profile?’ Then: ‘Are there any disruptions forecasted on these routes?’ The system pulls in live disruption intelligence, notes potential port congestion on one option, and adjusts recommendations. In a short space of time, the forwarder has explored several different scenarios, understood the cost and risk profile of each, and can present informed options to their customer. That same analysis traditionally takes most of a day and involved multiple people checking different systems.
Transparency is another non-negotiable, Garcia insisted. “When AI recommends a routing change, logistics leaders need to know: Is this based on real-time schedule data or last week’s information? Does it account for my contract rates or just spot market pricing? Is the emissions calculation using actual vessel specifications or industry averages? If AI suggests avoiding a particular trade lane, the system needs to explain why – port congestion, schedule reliability issues, cost factors. Leaders won’t follow recommendations they can’t explain to their own customers or management. As time progresses and AI integration becomes more mainstream, the demand for transparency about data sources, calculation methods and confidence levels will grow.”
AI shifts disruption management from reactive to anticipatory. Instead of learning about a problem when a shipment is delayed, operators can identify potential disruptions before they impact cargo and model alternatives immediately. Operators can run continuous scenario analysis. Previously, this kind of analysis only happened during major planning cycles. Now it’s ongoing, which means operators maintain contingency plans rather than outdated disaster recovery documents. This also impacts customer communication. When you can assess disruption impact instantly, you can proactively inform customers before they ask.
By 2026, Garcia predicted AI will be table stakes for freight operations. Customers will demand real-time tracking, instant quotes, and proactive disruption alerts. If you can’t provide these, you won’t be competitive. But sophisticated use of AI – the ability to run complex multimodal analysis, optimise across cost and emissions, provide strategic insights rather than just operational updates – that will still be an advantage as there are organisations still formulating their AI strategies. For mid-sized operators, AI levels the playing field. A well-implemented AI system can give a smaller forwarder capabilities that previously required more staff and expensive contracts.
“The biggest barrier is data quality. Cleaning up data – inconsistent formats, multiple sources that don’t align, gaps in coverage, update frequencies that vary by system – takes time and resources that often isn’t budgeted for,” Garcia said. “The technical barrier isn’t usually the AI itself – that technology is increasingly accessible. It’s integration. Companies have accumulated decades of legacy systems, custom integrations, and workarounds. Getting AI to work across this landscape is complex. The cultural barrier is significant. There is so much noise around AI that it can be difficult to understand what is true and valuable versus what is wishful thinking. It is important for companies to form the right partnerships now before getting left behind.”
Looking beyond 2026, predictive capabilities are seen as the next frontier. AI won’t just tell you where capacity exists today, it will predict where shortages will emerge weeks in advance based on booking patterns, seasonal trends, and market indicators. Companies should be building the data infrastructure to support this now.
“The bigger shift is AI that learns from outcomes, not just data. Current systems analyse historical information. Next-generation systems will track which recommendations produced good outcomes and refine their models accordingly. As AI is adopted across the freight ecosystem, expect disruptions and intense competition across the board in the next few years,” Garcia concluded.
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Author: Edward Hardy
