AI Use Cases 

Advanced analytics on all customer contact data across multiple channels (Including real world monitoring) to uncover insights to improve customer satisfaction and build a holistic picture of their status.

Predict call volume for the purposes of staff scheduling and optimisation of resources.

Authenticate customers without passwords leveraging biometry to improve customer satisfaction, reduce wait times and speed up service access, leading to lower drop rates. Key impact should be to lower fraud risk.

Using voice and text analysis to uncover overall customer sentiment - negative or positive - sometimes in real-time as displayed when they contact the company; for example through the contact centre.

Automate key workflow steps in the mergers and acquisition (M&A) process (e.g. document capture) . Banks such as Goldman Sachs can reduce reliance on relatively expensive staff doing low value-add tasks.

Automation of documentation steps for Initial Public Offerings (IPOs) of new shares to financial markets helps free up relatively expensive staff time. Automated processes help ensure efficiency, effectiveness and compliance matching (like arranging and tracking legal and compliance reviews, filling in forms and generating reports).

Machine learning used to analyse and co-ordinate team schedules across multiple software platforms (eg Google calendars, Trello etc). Service will typically schedule meetings across teams, often on the basis of analysed emails - current internal use focus will expand.

Monitoring and indentifying potential fraud and theft from utility networks

Optimise energy scheduling and management of power plants based on energy pricing, weather and other real-time data

Optimise location and building specifications in construction of power generation equipment based on previous sites and other relevant data including visual imagery (e.g. from satellites or drones). Demand projection scenarios need to be fed in to the predictive model.

Smart home devices manage consumer energy expenditure by, for example, turning lighting, heating on and off depending on human presence, pre-planned variable margins (e.g. average home temperature) and weather / time of day.

Replicate (and replace) human-made decisions in control room environments to reduce cost and human error

Optimise pricing and promotional targeting to energy customers. Factors will include issues such as weather projections, spot supply pricing and forward cost estimations.

Predict energy demand trends based on data sources ranging from sensors to social media. In a competitive market the more varied and unique the data sources accessed the more refined (if not always more accurate) the model can become.

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

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).

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

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

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

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.

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

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.

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

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.

Prioritise capex investment recommendations across network through assessment of network needs and potential capacity challenges

Predict regional demand trends for voice, data, other telecoms traffic. This will enable marketing, pricing, capacity and network support decisions to be made - as well as preparing telecoms firms for potential customer service activity should issues emerge.

Optimise resource allocation in network vs. current and future loads. Resources would typically include compute capacity.

Predict failure and recommend proactive maintenance for fixed (substations, poles) and moving equipment