AI Use Cases
Improve image (and video) quality
Use deep neural networks to remove clutter from images - this can vary from blurring to watermarks. This will often be used when poor quality image data has been captured and mass data cleaning will help improve training data for other AI applications.
Automate matching of 3rd party data sets such as IDs
Leveraging 3rd party data to better understand existing customers and target potential customers is critical. However, the basics of ensuring correct data matching among different sets is essential to deliver this and can be very labour intensive.
Detect anomalies in software stacks prior to deployment
Detect anomalies in software stacks (e.g. security or downtime risks). Fro example, banks tend to have complicated legacy systems based both on multiple generations of development and software codes but also driven by historic merger activity in the industry.
Analyse large text datasets to uncover trends from documentary evidence
Across large text datasets - for example, historical archives - there will be trends or insights that can be analysed but which it would take a human observer too much time to process. Although still at a relatively rudimentary stage this will become an increasingly powerful tool for creating new insights and levels of knowledge.
Deliver appropriate information to user at relevant times
Knowledge management systems can be configured to predict and provide relevant information at an apporpriate moment based on different indicators such as calendar or other contextual information. This has the potential to signifciantly improve user efficiency and effectiveness - and occasioanlly disorientation (although typically such a feature can be disabled in standard software configurations).
Update and improve corporate Intranet content
Update and improve corporate Intranet content through auto-edited and standardised content tags - for example ensuring that engineers have up to date sample code and best practice guidelines collated on a standardised basis. User searching is used to improve the data tagging.
Navigate, extract relevant information and automate interaction with legacy software systems
Legacy systems can be a significant challenge in the process of increasing efficiency and effectiveness of organisations. There may well be a multitude of such systems with limited inter-operability. Deploying machine learning (ML) and robotic process automation (RPA) can ensure that these systems can become part of a newly effective process - reducing the new for system replacement and streamlining processes. Platforms are being developed to do precisely this.
Enable self-learning option generation in CAD software
Support 3D CAD (Computer Aided Design) software - e.g. Autodesk Dreamcatcher - to build options in to the design process. Environmental, traffic data used to help analyse potential outcomes. Architects / design staff can make educated decisions with visualised data-rich options.
Mininise need for sensors through generating likely input data from other sources
Sensors can be expensive, hard to maintain or simply unavailable for what can be important data. Using data from other sources can enable the predictive modelling of other data sets. Note that this can create a series of new risks, especially if historic patterns break down or feedback effects occur.