Determine staff planning to support an optimum routine maintenance scheduling based on cost, time and average wear and tear.

Using live cameras or photos to help remote troubleshooting. Tougher issues will typically be flagged for human intervention.

Optimise job scheduling based on factors such as weather, estimated travel times, technical capabilities and parts availability. There is a strong link with mobile staff routing use cases.

Optimise staff transportation routing based on factors such as weather, traffic, changing job loads and shifting priorities. Given the cost of mobile workforces productvity improvements can significantly alter economics.

Self-checkout systems, also known as cashierless, cashier-free or automated checkouts, have increasing functionality built in to them - some of it increasingly powered by AI.

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.

Analyse and determine technician competency based on inout factors such as time on site, materials consumer and first-time fix rates or output factors such as machine vision monitoring of repairs or Internet of Things sensor data on ongoing performance of the repaired parts.

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.

In environments of high complexity and potential risk being able to label components automatically can help with mutliple operational tasks.

Construction work is very reliant on the quality of both staff and site management, roles which may be harder to deliver in hard to access or inhospitable locations. Machine vision can be used to ensure that quality standards are being met and errors minimised and to ensure a rapid feedback loop to avoid potential cost (and engineering safety) issues.

Updating and replacing legacy analogue meters with digital meters can be an expensive and complicated process. If instead IoT cameras are positioned to capture images of the read-outs and translate those to digital read-outs then the information can be captured automatically. This enables greater speed, frequency and consistency of data capture, along with reduced risk and cost from human checking - potentially enabling multiple other use cases.

Automate financial planning and other costly back-office functions so as to reduce repetitive work and likely cut back on processing errors. This would be a restructuring of the process to manage with machine learning as opposed to the automation of individual human tasks which is covered under Robot Process Automation (RPA).

Optimising maintenance scheduling (technicians with the right skill sets, replacement parts, maintenance equipment, etc.) in order to minimise the costs for replacement and/or upgrading of failing or under-performing parts or products.

Monitoring and identifying the illicit use of natural resources for activities such as illegal farming, logging, deforestation, etc.

Improve administrative productivity with Robotic Process Automation. Aviva for example claim a 15x improvements on their administrative productivity.

Improve preventative maintenance and Maintenance, Repair and Overhaul (MRO) performance with greater predictive accuracy to the component and part-level. Predictive maintenance predicts when certain products or devices are in need of maintenance what sort of maintenance, the likely maintenance and replacement materials, and technician skill sets.

Optimise treatment of processed materials to enable reuse, saving cost, and to reduce environmental impact, also potentially reducing legal and negative PR risk.

Flexible robots that can mimic and work alongside humans are sometimes known as Cobots. This is key as humans and robots can still do different jobs better and if they can work alongside each other without risking injury to humans then this increases flexibility for deployment in manufacturing.

Digitisation of processes in weeks without replacing legacy systems which can take years. Bots can operate many of the repetitive tasks currently done by humans interacting with their computers. Robotic agents copy the variety of tasks performed on a GUI (graphical user interface - e.g. a PC screen) without needing deeper software integration.

Automate collection of banking data from non-standard documentation (e.g. in shipping paperwork) - this will typically be turned in to standardised banking documentation

Maintenance, repair, and operations equipment inventory optimisation balances kit inventory with predicted maintenance needs in order to reduce inventory costs and minimise obsolete and excessive inventory.

Recognise documents (eg handwriting) to extract information to streamline functions like account creation, loan and insurance origination and documentation. This is especially useful with high volume consumer products.

Optimise the quality of the delivery of the service and product (e.g. improve video streaming quality)

People experience side effects on drugs or procedures across the globe and it can be hard to gather the information needed to improve and refine both products and services. AI can be used to collate patient data from departments such as A&E and ultimately use this to predict the impact and help set procedures to minimise these side effects. For example, AI can be used to enhance procedures so as to minimise exposure to potentially dangerous substances during MRI scans.

Robotics can automate many route activities that typically require human actions. This is inevitably a very broad use case and the requirements will vary - from reducing potential physical risk to humans, to maximise process uptime or simply to reduce variable costs. These will drive different configurations of project.

Predictive maintenance predicts when network nodes are in need of maintenance, what sort of maintenance, the likely maintenance and replacement materials, and technician skill sets.

Optimise retail network locations based on multiple signals of demand (e.g., social data, footfall, transactions). This would - for example - help a retailer to plan their expansion in to a new market. Alternatuvely this might enable cost savings across a retail banking operation where it would likely cover both branches and ATMs - at the risk of medium to long term revenue loss and potential negative customer and press reaction.

Network security identifies, analyses and recommends the most appropriate response to unauthorised network access, device or node compromise or incoming usage volume across the network (e.g. denial of service attacks).

Optimise supply chain including logistics, procurement timing and inventory distribution across warehouses and stores

Theft and revenue protection identifies, understands, and recommends the most appropriate actions based on situations across the network. This supports deployment of new technology such as RFIDs.