WHITE PAPER
Navigating the future. Harnessing AI in transportation and logistics
WHITE PAPER
Navigating your future. Harnessing AI in transportation and logistics
As you read this, AI is delivering measurable operational improvements across the logistics sector.
McKinsey findings reveal:
65%
(up to) reduction in lost sales and stockouts with AI integration
25-40%
drop in administrative overhead through automation
30-50%
decrease in unplanned downtime and 20-40% extended equipment life through predictive maintenance
65%
(up to) reduction in lost sales and stockouts with AI integration
25-40%
drop in administrative overhead through automation
30-50%
decrease in unplanned downtime and 20-40% extended equipment life through predictive maintenance
Meanwhile, logistics players face a perfect storm of operational challenges
The driver shortage has reached crisis levels, with 24,000 empty truck seats costing the US freight sector $95.5 million weekly, while more than half of European logistics companies are unable to expand their operations due to a lack of skilled drivers. Supply chain disruptions also jumped 38% in 2024, with weather incidents up 119% and civil unrest surging 285%, according to Resilinc’s EventWatch data for H1 2024. Customer expectations, shaped by e-commerce giants, now demand real-time visibility and same-day delivery across all sectors.
What’s next and strategic imperatives for the years ahead
Today, the broader reality demonstrates that route optimisation algorithms are already cutting fuel costs by 15% (World Economic Forum), automated warehouses are operating at 99.9% accuracy, and demand forecasting is reducing inventory levels by 30% without stockouts (McKinsey).
The coming years will see transformative AI implementation, with agentic AI systems becoming integral to the workforce. Gartner forecasts autonomous decision-making in 50% of supply chain solutions by 2030. Transformative AI implementation is imminent, including AI agents negotiating freight rates in microseconds, quantum computing optimizing complex routing, AI-human co-managed, near-zero-FTE departments, and carbon-aware algorithms integrated into all routing decisions.
To stay ahead, logistics must transition from forecasting to autonomous processes by merging prescriptive analytics with agentic execution. This demands cultural transformation towards hybrid human-AI teams, investment in learning, and development of AI-augmented leadership. Early movers gain sustainable competitive advantages in agility and operational efficiency.
Action priorities:
- Develop AI governance frameworks balancing automation with human agency
- Invest in workforce development and continuous learning cultures
- Build ecosystem partnerships for collaborative innovation
- Integrate ethical AI principles and sustainability metrics
Soon
Digital twins paired with cognitive supply chains will enable real-time network simulation and autonomous resource reallocation.
Later
Digital twins paired with cognitive supply chains will enable real-time network simulation and autonomous resource reallocation.
Later still
Looking even further ahead, multimodal autonomous logistics will emerge – massive AI-coordinated fleets of self-driving trucks, drones, ships and trains coordinating harmony.
Digital twins paired with cognitive supply chains will enable real-time network simulation and autonomous resource reallocation.
Digital twins paired with cognitive supply chains will enable real-time network simulation and autonomous resource reallocation.
Looking even further ahead, multimodal autonomous logistics will emerge – massive AI-coordinated fleets of self-driving trucks, drones, ships and trains coordinating harmony.
A roadmap for turning these technological capabilities into competitive advantages for your business, this Transporeon whitepaper guides you through what AI signals for the future, how logistics is transforming today, and how to leverage early-mover advantages.
© 2025 Trimble Inc.
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© 2025 Trimble Inc.