Autopilots are transiting to AI-powered devices. This will potentially shift the boundaries of what will be possible - and in time possibly customer expectations and willingness to accept risk (e.g. airplanes without pilots).

Identify performance and risk for pilots through flying patterns and other data. Similar analysis can be done on road drivers although the data is inevitably more likely to be complicated by ground-level interactions that would be more traceable in the air (e.g. other vehicular movement).

Personalise loyalty programs and promotional offerings to individual customers - typically this will involve balancing availability of time-sensitive supplier capacity (e.g. airline seats) with customer demand and preference profile

Personalise product recommendations to target individual consumers reflecting multiple pricing, product and service preference indicators. The time sensitive nature of the product offering increases the need to find an appropriate market-clearing price.

Support management of real time data and decision-making in traffic control - for example at airports. Automatic data flows from key assets and data modelling enhanced from multiple sources (e.g. weather models) help with flow optimisation, operator support and risk prediction.

Ensure rapid response - and fraud risk analysis - on claims by customers whose trains have been delayed. By nature this will be peaky (given that train delays frequently cascade) - and customers will expect brisk turnaround (the timing of this may also be open to regulatory monitoring). AI enables this challenge to be met and can also check for those attempting to use the situation to defraud the business.

Read addresses/bar codes in mail/parcel sorting machines to improve efficiency and reduce data human error

Optimise staffing levels and asset placement in real time, for example for customer service organisations facing weather or similar network disruption

Predict failure and recommend proactive maintenance for planes, trucks, and other moving equipment

Identify performance and risk for drivers through driving patterns and other data. This can be used for a variety of drivers including those of taxis, HGV or delivery vehicle operators.

Optimise vehicle routing in real time (e.g., airlines, logistics, last mile routing for complex event processing)

Predict local sales and demand trends for transportation to help optimise pricing, capacity planning and network management.

Optimise pricing and scheduling based on real time demand updates for time sensitive products - e.g. airline seats, less than full truckload shipping, mobility services

Real time information such as images or live footage can provide information on crowd flow in areas where congestion is undesirable (e.g. airports or train stations). Machine vision is being used to process the data and prompt the necessary management actions to optimise passenger flow.

Deploy AI to automate the network signalling - needs to be deployed through a centralised and heavily automated network.

Most railway networks are large, complex, frequently old, subject to all manner of environmental hazard and prone to degradation. Using sensors and cameras to monitor tracks and predict required maintenance can have a considerable cost and safety impact.

Fully automated drone piloting and resupply (e.g. energy power ups). Typically this would be deployed in areas of low human habitation to minimise risk - for example in agricultural or mining applications. As reliability improves these will be deployed in more urban environments.