AI Use Cases 

Determine root causes for quality issues originating prior to the manufacturing process. This might include supply sources or logistic process issues. Close human analyst oversight recommended.

Optimising matches between transplant patients and donors benefits all patients across the transplant waiting list as it results in more saved lives. For example, through paired kidney donations, AI is able to identify potential donors and recipients who are biologically suited for one another and can take into account certain criteria to optimise the service such as prioritising the hardest to match patients.

Consider automation of delivery and other transportation cases through drones and other automated vehicles

Optimise purchasing mix across suppliers and locations should enable better pricing negotiations and reduced wastage on purchased product.

Supplier decommits/recommits analytics understand optimal production capacities of suppliers and contract manufacturers in order to properly rebalance manufacturing needs caused by supply chain disruptions (strikes, storms, wars, raw material shortages).

Supplier network analytics triage product and supplier problems more quickly by understanding the dynamics of the underlying supplier and contract manufacturer relationships and inter-dependencies.

Identify and predict which suppliers meet performance characteristics such as cost effectiveness, timeliness, order completeness, quality, regulatory compliance and social responsibility. Determine most cost effective and optimal suppliers.

Identify opportunities to use robotics to automate warehouses. Use of robots to support retail deliveries is one of the fastest growing use cases for the machines.

Anticipatory logistics are based on predictive algorithms running on big data. The practice allows logistics professionals to improve efficiency and quality by predicting demand before a consumer places an order. A cost-efficient and effective returns system is a key element in the value chain. Returns to scale are usually key to making this work economically.

Use scanned images - often from portable cameraphone - to identify physical properties. a key function of this is the potential for mobile apps, greatly increasing the potential specific use situations.

Support review and design of supplier contracts potentially including analysing key elements to focus on in contract design. This may be semi-automated with limited human involvement.

Use historic data to model supply chains to identify and predict the way potentially complex and opaque demand patterns ripple through the system under different scenarios (e.g. weather changes). This can be used to predict potential pricing.

Predict likely demand for products and model rapidly changing scenarios (e.g. weather) to limit out of stock situations.

Comparable data across multiple suppliers (or internal relationships) can allow for potentially significant negotiation power and cost reduction opportunities. Automated data aggregation and comparison from multiple sources significantly reduces the challenges to getting an aggregate and normalised view across organisations and suppliers.

Predict commodity requirements - typically to optimise procurement strategy for large industrial organisations.

A variety of AI techniques can be deployed to help capture insights on opposing sporting team approaches and strategies (for example recognise patterns in movement). AI can support modelling through alternatuive game strategies that coaches or managers wish to test.

Support data visualisation and use predictive analytics to help individuals and teams spot issues or opportunities for self-improvement - or weaknesses in opponents - to optimise athletic performance. Sensor data may be a significant element in the inputs.

Optimise distribution network cost effectiveness (balancing capital and operating expenditure). Factors include route mapping, load balancing, capacity and demand analysis. Complicating issues may include weather and other exogenous factors.

Use conversational interfaces to analyse business data. The ability to ask conversational questions (e.g. "what is driving this correlation?") offers potential convenience, speed and reduced Business Intelligence costs. Will increasingly become a tool layered in to other applications / use cases.

Reducing data training set requirements is a significant move towards lowering the cost and challenge of data deployment for modelling purposes, however it does have risks.

Building and maintaining high quality data for advanced analytics

Combine source data from different sources into meaningful and valuable information

Take data from raw formats with data quality problems and develop in to a clean, ready to analyse and deploy format. This may be as simple as mismatching Excel columns or the aggregation of completely different data sources and types. Linking object IDs is key - hence human reinforcement and tagging potentially part of the data management process.

Unless using unsupervised learning systems, high quality labeled data is critical. Labelling data minimises the risks inherent in using it. This can potentially be part-automated.

Empower teams with data and tools to run advanced analyses on the business and adjacencies.

Generate cloned voice - at this stage largely for artistic, trouble-making and media purposes. This has potentially troubling implications for so-called "fake news" applications amongst many potential uses.

Avoid garbage in, garbage out by ensuring quality of data with appropriate data cleaning process

Personalised data feeds, potentially structured on a team / functional / hierarchical level will help speed up processes and decision-making. Differing organisational cultures around information-sharing will be a key issue here.

Determine whether there are new business model opportunities to be examined- for example algorithmic deployment. These can be modelled and tested for potential scenarios.

Identify high-value B2B leads by analysing internal and external data. Internal data will include CRM and sales channel data. External data might include press releases, 3rd party information sources and social media.