AI Use Cases
Pilot and resupply drone independently
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.
Evaluate technician competency
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.
Optimise traffic/passenger flow through visual data including video and images
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.
Improving construction process quality by detecting error
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.
Digitise analogue meter reading through computer vision
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 back-office functions
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).
Predict maintenance requirements
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.
Accelerate and support key processes with improved human-robot collaboration
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.
Digitise and automate processes using Robotic Process Automation (RPA)
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.
Recognise unstructured text, e.g. handwriting, in documents to extract information to streamline processes like account creation, loan and insurance origination and documentation
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.
Reduce side effects by collating patient data and optimising processes
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.
Deploy robots to replace human staff
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.
Optimise retail network based on demand modelling
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.
Optimise network security response to unauthorised access to network nodes or connections
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).