Programme Overview
A two-day programme for transport authorities, logistics operators, port and airport professionals and public-transport leaders. It covers how AI is reshaping transport planning, routing, asset management, passenger experience and logistics execution. The hands-on work uses anonymised transport and logistics data and realistic operational scenarios. The content is grounded in the Saudi transport context, including the Kingdom’s ambition as a logistics hub, the Transport General Authority (TGA), sector regulators and the major infrastructure under construction.
Learning Objectives
By the end of this programme, participants will be able to:
- Describe how AI is reshaping transport and logistics across planning, execution, assets and customer experience.
- Use AI tools for routing, scheduling, demand forecasting, asset management and operational reporting.
- Apply AI to fleet and asset optimisation, passenger flow and logistics-network design.
- Evaluate AI-based transport and logistics vendors and platforms for technical fit and sovereignty.
- Recognise transport-specific AI risks, including safety, resilience and automation complacency.
- Apply the Transport General Authority (TGA), the General Authority of Civil Aviation (GACA), port authorities and SDAIA to AI use in transport and logistics.
Programme Content & Modules
Day 1: The transport landscape and AI in practice
How AI is reshaping transport, logistics, ports, aviation and public transport. Predictive maintenance, demand sensing, dynamic routing and passenger-experience AI. Where AI has matured in the sector and where it is still emerging. The Saudi logistics-hub context and the transport-sector transformation.
Practical work with AI tools for routing, scheduling, demand forecasting, asset monitoring and operational reporting. Prompt engineering for transport accuracy. A tour of AI-enhanced transport-management, fleet-management and logistics platforms.
Day 2: AI-supported transport and responsible practice
Using AI for network design, scenario planning, disruption response and asset optimisation. Combining AI output with operational judgement. A group exercise on a realistic transport or logistics scenario supported by AI.
Safety-critical AI and the limits of automation in transport. Governance of transport and passenger data. PDPL, TGA, GACA and port-authority considerations. SDAIA’s AI Ethics Principles in a transport context. Data-sovereignty and infrastructure considerations for critical logistics. Applied capstone project: each participant designs an AI-adoption plan for one transport or logistics operation.
Suggested Duration
Two training days.
Target Audience & Prerequisites
Target audience: Transport planners, logistics managers, fleet operators, port and airport managers, rail and metro operations leaders, public-transport planners, freight and last-mile leaders and senior staff in transport authorities, airlines, ports, rail operators and logistics companies.
Prerequisites: Practical experience in transport or logistics. Basic computer literacy. No prior background in AI or programming is required. A laptop is required for the hands-on sessions.
