AI Use Cases 

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.

Predict failure and recommend proactive maintenance for farming and production equipment saving costs and reducing downtime.

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.

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).

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

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).

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.

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

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

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.

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.

Reduce unplanned downtime to identify, monitor and pre-emptively predict the failure of the drivers of unplanned network downtime. Model complex networks and analyse historic data to understand probable causes of major problems and interruptions.

Examine network traffic to triage network traffic bottlenecks and provide real-time incentives and/or intervention to reduce or re-route traffic during overload situations. Load balancing identifies and rebalances network traffic based on current and forecasted traffic needs and current network capacity.

Use technologies, such as machine vision, to better detect quality control issues during key processes - vegetable sortign for quality for example. This will potentially help generate a better understanding of which internal processes, workflows and factor contribute most and least to meeting quality objectives.

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.

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.

Network performance optimisation predicts and optimises field network performance across multiple usage scenarios (network traffic, weather, seasonality, holidays, special events) - potentially in real-time.

Forecasting network demand (average demand, surge demand, minimal viable demand) based on predicted network usage behaviours, patterns, trends and likely upcoming events (e.g. cold weather). Helping determine future capacity needs (e.g. retail locations, new plant, new networks) ensures better planning outcomes including potential (what if) working situations.

Network layout optimisation optimises network layout in order to minimise traffic bottlenecks and deliver higher volume network bandwidth and throughput.

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

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

Understanding the link between digital spending and real world reactions by consumers - in this case seeing an online ad and then going in to a store - is one of the holy grails of advertising. Machine learning makes the inherent data scale and connections behind this manageable. However, getting the right partnerships and data capture approach as well as consumer acceptance and data privacy right will be the key to long term commercialisation.

Optimise online search results - possibly the most frequently experienced form of AI for consumers. Frequently the optimisation is focused on maximising commercial marketing returns.

Personalise search results for individual's preferences. A key question is who sets these - ideally it is the individual's needs that do so rather than the need to optimise the commercial serving of advertising.

Identifying new customer growth segments and opportunities - for example those that are underserved by the traditional operating approach. One example might be customers for whom there is limited credit history.

Streamline insurance application processes - examples would include pre-populating forms or automatically linking with relevant information sources (e.g. public registers etc).

Evaluate and advise the most appropriate product and supplier to meet consumer need (e.g. mortgages). This should improve consumer outcomes.