Expected future developments
Agentic AI becomes operational staff
Looking to the next few years, logistics and supply chain professionals can expect to see a slew of transformative developments that will reshape their business’ operational efficiency.
But among these innovations, AI agents hold perhaps the most potential to deliver real changes to the way we do business. Gartner has forecast that such agents will power autonomous decision-making in 50% of cross-functional supply chain management solutions by 2030. This represents a fundamental shift toward agentic AI systems that function as virtual workforces, capable of assisting, supplementing and enhancing both human capabilities and conventional software applications.
Shippers
By 2030, logistics directors will work alongside AI agents acting as experienced planners — contributing insights in strategy meetings and challenging assumptions about seasonal demand.
Logistics service providers
Operations managers can expect to double their operational capacity, without increasing their staff — as AI takes care of routine coordination, freeing people to focus on relationships and strategy.
At Trimble and Transporeon, we are creating a platform where humans and AI agents collaborate to maximise supply chain efficiency and business growth. By embedding generative AI at the core, we aim to transform logistics into intelligent systems that autonomously manage complex workflows, empowering our customers with AI agents that achieve optimal outcomes.
Jonah Mcintire
Transportation Chief Platform and Technology Officer at Trimble
At Trimble and Transporeon, we are creating a platform where humans and AI agents collaborate to maximise supply chain efficiency and business growth. By embedding generative AI at the core, we aim to transform logistics into intelligent systems that autonomously manage complex workflows, empowering our customers with AI agents that achieve optimal outcomes.
Jonah Mcintire
Transportation Chief Platform and Technology Officer at Trimble
Concrete steps to take now:
Start by mapping the top 10 repetitive decisions your team makes daily and capture the logic behind them. This creates the training foundation for future AI agents to learn your business rules.
Implement APIs and integrations that capture shipping updates, inventory levels and market conditions as they happen. AI agents can’t make intelligent decisions without continuous, quality data feeds.
Deploy basic AI assistants for specific tasks like demand forecasting, teaching your managers how to prompt, guide and oversee AI outputs. This builds the management skills needed for directing future autonomous agents.
Read more...
Cognitive supply chains & digital twins
By the end of this decade, cognitive supply chains paired with digital twin technology will fundamentally reshape how logistics networks operate. Companies are already moving beyond reactive firefighting, adopting autonomous systems that predict disruptions, reallocate resources and adjust operations faster than any human team could.
The digital twins that are emerging now go far beyond mere tracking systems. They create parallel universes where every supplier, warehouse and delivery route exists in virtual form, responding to the same conditions as their physical counterparts. These comprehensive simulations encompass entire logistics ecosystems in real time.
Shippers
Right now, most shippers spend monthly reviews looking backward — figuring out why delays happened instead of preventing them. With digital twin technology, those meetings become proactive planning sessions where teams can simulate disruptions and fine-tune responses before issues ever surface.
Logistics service providers
For carriers, AI and digital twins make it possible to evaluate new routes or equipment investments virtually — running thousands of simulations to compare financial outcomes across different market conditions, without committing resources to uncertain strategies.
Concrete steps to take now:
Start small. Create a virtual model of your most critical shipping lane or highest-volume distribution centre first, before attempting to digitise your entire network.
If you’re among the 40% still using basic reporting, invest in predictive capabilities for demand forecasting and delivery ETAs first. You can’t leap to prescriptive AI without mastering prediction as the foundation.
Install real-time tracking at your most congested ports, warehouses, or transit points. Digital twins are only as good as their data feeds.
Read more...
Real-time network virtualisation
In this future, technical architectures will continuously ingest IoT sensor data, transportation telemetry and market intelligence...
Prescriptive vs. predictive
Traditional predictive analytics, while valuable in their own right, are only the foundation for what’s now emerging...
Multimodal autonomous logistics
Autonomous vehicles at massive scale might not be on the horizon in the next few years, but when this development does arrive, it will represent a profound shift in how goods are moved globally.
The comprehensive integration of autonomous trucks, drones, ships and trains within unified AI-coordinated networks will enable an intelligent ecosystem that responds dynamically to changing conditions.
Concrete steps to take now:
Partner with autonomous vehicle providers to pilot self-driving trucks on fixed routes between your warehouses or deploy autonomous forklifts in designated warehouse zones.
Integrate tracking systems across your trucking, rail and ocean partners into a single control tower that can compare routes and costs in real time. This creates the foundation for AI to eventually orchestrate autonomous assets across all transport modes.
Start with a single high-volume, repetitive task like pick-and-pack or sortation rather than full automation. Choose systems that can communicate with other robots and scale up as you expand.
Read more...
AI-driven negotiation & marketplaces
Autonomous vehicles at massive scale might not be on the horizon in the next few years, but when this development does arrive, it will represent a profound shift in how goods are moved globally. The comprehensive integration of autonomous trucks, drones, ships and trains within unified AI-coordinated networks will enable an intelligent ecosystem that responds dynamically to changing conditions.
Freight managers often watch profitable opportunities disappear during lengthy manual negotiations, while logistics directors lose cost advantages when market rates shift faster than procurement cycles.
Transporeon is a pioneer in AI-enabled freight negotiations. Solutions like Autonomous Procurement and Autonomous Quotation leverage AI and behavioural science to automate spot freight negotiations, load matching and assignment, while AI agents vet carriers joining the freight marketplace and network, allowing human talent to focus on strategic tasks.
Mauro Pederzolli
CEO, SIMA
Transporeon is a pioneer in AI-enabled freight negotiations. Solutions like Autonomous Procurement and Autonomous Quotation leverage AI and behavioural science to automate spot freight negotiations, load matching and assignment, while AI agents vet carriers joining the freight marketplace and network, allowing human talent to focus on strategic tasks.
Mauro Pederzolli
CEO, SIMA
Concrete steps to take now:
Convert your scattered email quotes, PDF contracts, and spreadsheet records into a unified digital format. This historical data becomes the training set for AI to understand your specific market patterns and negotiation outcomes.
Move beyond email RFPs by establishing direct system integrations that enable real-time rate queries and automated booking confirmations.
Test platforms that compare your quotes against market rates in real time, even if you still negotiate manually. Learning to trust AI’s market intelligence for rate validation builds confidence before allowing it to handle actual negotiations autonomously.
Read more...
Quantum optimisation
Major logistics operators are investigating quantum computing applications as traditional computational methods hit their limits handling complex operational challenges. Companies managing extensive distribution networks face exponentially growing complexity that classical algorithms struggle to address efficiently.
For logistics directors, distributing components across multiple European sites creates millions of route possibilities that exceed what conventional optimisation can handle. For LSPs, cross-border operations add layers of complexity — fluctuating fuel costs, customs bottlenecks, and capacity limits.
Fortunately, quantum systems can evaluate multiple pathway combinations simultaneously through superposition principles. Early research indicates this parallel processing capability could identify significantly more efficient routes, directly reducing fuel consumption and delivery windows. Quantum algorithms can process vast datasets simultaneously to predict and accommodate demand shifts, preventing costly production bottlenecks and distribution disruptions.
Concrete steps to take now:
Map out the complex routing, network design, or demand forecasting challenges where your current systems time out or require oversimplification.
Access quantum simulators to run small-scale experiments on vehicle routing or warehouse placement problems.
Design hybrid systems where classical computers prepare data and interpret results, while quantum processors handle core optimisation. Start with a manageable problem, like single-city routing, then scale up.
© 2025 Trimble Inc.
CONNECT WITH US
CONNECT WITH US
© 2025 Trimble Inc.