top of page


AI Use Cases

Accelerate data analytics with conversational interface


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.

Deliver scaled, real time, interactive analytics platform to accelerate Big Data use


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

Deliver personalised, real time analytics feed according to individual and team requirements


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.

Reduce data training set requirements

Data Science

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.

Scale and support data management and monitoring

Data Science

Building and maintaining high quality data for advanced analytics

Accelerate data integration from multiple sources

Data Science

Combine source data from different sources into meaningful and valuable information

Automate preparation of data for inclusion in analytics platform

Data Science

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.

Predict appropriate data labelling to support data analytics work

Data Science

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.

Generate cloned voices

Data Science

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.

Automate data cleansing and validation

Data Science

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

Predict commodity requirements


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

Optimise distribution network cost effectiveness


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.

Support strategic planning for sports teams


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 performance improvement in athletes


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.

Test and optimise new business models

Strategic Planning

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

bottom of page