AI Use Cases 

Predict outcomes from tests. This may mean that fewer experiments are required, thus reducing experimental R&D costs and time to market. With appopriate data and a sensitivity to risk trade-offs it may also mean that a go / no go decision can be made before the cost of a testing regime is initiated.

Enhance offering by embedding AI tools and big data (e.g. improve search)

Predict market potential for new product proposition - e.g. based on product innovation. This may involve scanning social media feeds to understand consumer activity and react to emerging trends, for example in fashion. The analysis may be used for production planning, pricing or marketing purposes.

Optimise resource allocation in R&D, leveraging multiple data sources (e.g., communications, documentation) to track progress

Identifying negative responses (monitor social networks for early problems with drugs)

Data visualisation typically supports better analytics and decision making - collating and standardising data from potentially multiple sources. AI will enable faster and scalable data visualisation with the potential to respond to real time issues. This will be a set of tools increasingly embedded in other applications and use cases.

Interpret human gestures to add new modes of interactivity to products. Early consumer products have included gaming applications - but nothing has yet reached the levels shown in the Tom Cruise film 'Minority Report'.

Identify critical factors to improve R&D efficiency - e.g. to reduce the number of required experiments for research and testing process (an examples might include component testing). Optimising configuration of processes and operations will work better where the parameters under development remain stable.

Leveraging neuroscience and biometric sensors to understand how proposed content impacts on audience’s responses remains at an early stage of development. Marketing executives enjoy the quasi-scientific output and CFO's typically wince at the cost but AI potentially enables this to be scaled.

Determine whether there are new product opportunities by rethinking data and tools.

Uncover product usage insights to support product development process to improve products. May also be used to support customer service outlets and model use cases for new propositions.

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

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.

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

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.

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.

Optimise field force labour allocation - engineers and support staff. This is especially important at moments of network crisis (e.g. in the event of natural disaster) - although this may also be when humans are most likely to override any algorithmic decisions.

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

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.

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.

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.

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.

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.

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

Optimise resource allocation to support branch and ATM network. Factors that may be open to alternative deployment and focus include staffing, cash delivery trucks, opening hours or mobile assets

Use satellite image feeds to assess likely commodity prices - this could include likely crop yields for agricultural commodities or shipment levels for mining products.

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

Forecast asset pricing based on market patterns. This can be for investment banking trading and back office teams.

Discover anomalies across fleet of vehicle sensor data to identify potential failure risks. This may enable companies to pre-empt expensive and embarassing recalls, often driven by negative PR.