Article
Published
14 October 2026
The AI landscape: traditional, generative, and agentic AI
Traditional artificial intelligence (AI) refers to machine learning and statistical models that operate on structured data to automate, predict, and optimise decision-making processes. These systems are designed to identify patterns and improve outcomes in areas such as forecasting, risk management, and operational efficiency.
Generative AI (GenAI) extends these capabilities by using large language models (LLMs) and other generative architectures that are built to handle general inputs and outputs. GenAI can analyse and create content from unstructured data such as text, images, or audio, enabling more interactive and context-aware use of information. An AI agent (often used interchangeably with ‘assistant’) is an LLM configured to perform a specific task through prompt engineering and data access.
Agentic AI systems equip LLMs with a level of autonomy and access to a predefined toolbox, allowing models to perform complex tasks such as writing code, drafting slides, querying databases, and accessing systems.
We’re embedding AI across our operations – from predicting disruptions to reducing emissions… We need to make AI less scary.
Charles van der Steene, President North America, Maersk
The state of AI in shipping and logistics
Global supply chains are being reshaped by persistent disruptions, geopolitical instability, labour shortages, port congestion, inflation, and climate-related events. Meanwhile, customer expectations for faster, more transparent, and sustainable deliveries continue to rise. Traditional operations, often dependent on siloed systems and manual processes, are struggling to keep up.
In this high-pressure environment, AI is emerging not just as a support function, but as a strategic lever for resilience, speed, and competitive differentiation. From predictive maintenance and intelligent routing to just-in-time arrivals and real-time cargo visibility, AI offers tangible answers to some of the sector’s most urgent challenges.
Shipping may not be structurally transformed overnight, but the way it operates will. As highlighted in recent C-level conversations we have conducted at Implement Consulting Group, companies that invest in applied AI today will gain a lasting edge. Those that delay risk falling behind.
This document explores why AI is critical now and presents some of the highest impact use cases for AI across shipping and logistics.
Research indicate that shipping companies are experimenting, but broad adoption is lagging …
Current state of AI adoption in shipping…
AI is widely used for tasks such as route planning or invoice processing. However, there is significant potential for further adoption and exploration, particularly with emerging technologies based on generative AI.
Success stories showcase the concrete impact these tools can have on established businesses. Nevertheless, adoption is lagging.
… so, what is hindering adoption?
Despite strong leadership intent and clear AI potential, adoption is often slowed by cultural and organisational barriers, not technical ones. While tech readiness is essential, the real challenge lies in shifting mindsets and managing change strategically.
AI creates impact across the entire S&L value chain
AI is already transforming logistics operations and supply chain planning, creating competitive advantages for early adopters.
Highlighted AI use cases in S&L
Several advanced AI use cases are implemented at full industrial scale and can deliver significant returns on investment from day one.
Reference cases
Selected AI reference cases in S&L
The two projects demonstrate how AI is being integrated into shipping and logistics operations, delivering significant results and creating substantial impact for early adopters…
Reducing fuel consumption, lowering emissions, and boosting operational efficiency through AI-powered routing for a leading shipping company
Regulated enterprise // Compliance GenAI
A leading global shipping company sought to leverage AI to optimise voyage routing for greater fuel efficiency, lower emissions, and enhanced safety. We supported the company in defining sustainability goals, identifying high-impact use cases, and selecting AI partners. The work included piloting optimisation tools, assessing ROI potential, and shaping an operating model and transformation roadmap to scale AI across fleet operations.
Impact
- Voyage efficiency improved by 7%
- More than 60 mUSD in annual fleet savings
- Project return on investment of 26x
Increasing uptime through AI-enabled maintenance and automation
Customer operations // Agentic AI
A global shipping operator sought to reduce downtime and improve asset longevity by enhancing its maintenance processes. The company faced challenges with manual corrosion tracking, inefficient paint maintenance, and inconsistent reporting across its fleet. To address this, they implemented an AI-based solution that automates corrosion monitoring, estimates paint needs, and delivers intuitive dashboards for proactive decision-making.
Impact
- Cut downtime by 20% through automated maintenance planning
- Boosted efficiency by reducing manual work by 30–40%
The first step to AI success...
...is setting a strategic direction and defining the boundaries for exploration.
In our experience, you need to explore these six strategic questions to unlock the full potential of AI.
Future outlook
AI’s role in shipping and logistics is growing fast. Autonomous trucks, ships, and drones are moving from pilots to full-scale deployment, and analysts expect these technologies to cut delivery costs by up to 30%. At the same time, infrastructure is getting smarter. From connected ports to AI-enabled warehouses and sensor-equipped roads, the entire value chain is becoming more responsive and adaptive.
Generative AI (GenAI) brings new opportunities. It can summarise customs documents, find insights in complex data, and power virtual assistants for tracking shipments, handling delays, or answering multilingual queries. In the back office, GenAI tools can automate reports and help with planning tasks, boosting speed and accuracy.
AI-driven digital twins are also gaining ground, offering full visibility across supply chains. These models can simulate disruptions such as congestion or delays and help managers reroute goods or adjust inventory before issues escalate. AI-based forecasting uses market trends and demand signals to optimise pricing, capacity, and delivery planning.
To keep up, shipping and logistics companies need to build strong data and AI strategies and adopt more flexible operating models. More than just tools, AI technologies should be seen as enablers of smarter networks, new business models, and faster, more data-driven decisions across the value chain.
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