Monitor application performance for cloud compute resource allocation - scaling up or down and switching on or off capacity in response to web and mobile application usage numbers.

Minimisation of compute resources to achieve low latency and reduced energy usage. This will be a critical of growing Edge computing applications.

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.

Ensure high quality record keeping by transcribing key medical conversations - lowering risk of potential errors due to (famously) poor medic handwriting. Provides detailed, comprehensive information capture.

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.

While intended to assist the hearing impaired, this may have additioanl widespread applications. The technology must work in real-time and may be used on portable devices, which adds the challenge of limited available compute power.

Assist developers by using AI to help them accelerate code development by automatically spotting errors

Monitor application KPIs and performance for resource management to identify issues before they become widespread or too costly

Leverage speech library-SDK-APIs to quickly and cost-effectively to quickly and cost-effectively build new applications and system deployments.

Leverage image recognition library-SDK-APIs to quickly and cost-effectively build capacity and support new system deployments.

Leverage speech recognition library-SDK-APIs to quickly and cost-effectively build new applications and system deployments

Leverage deep learning library-SDK-APIs to quickly and cost-effectively build capacity and support new system deployments.

AI can be used to automate aspects of project management such as reporting, progress tracking, version control, production roll out, resource estimation, risk prediction etc

AI writes code autonomously usually within set parameters to enable rapid scaling of new applications and tools

Leverage Natural Language Processing library-SDK-APIs to quickly and cost-effectively build new applications and system deployments.

Automate testing of web applications and apps as part of continuous development - opportunities include ensuring less downtime and continued business improvement as well as potentially A/B testing at scale

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.

"Codifying business expertise so that is more widely available and understandable. For example investment decision-making approach or economic modelling skills

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.

In a world where information is at anyone's fingertips plagiarism is a constant challenge, especially in academic circles. NLP can be used to match content and identify suspect material.

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).

Searching for data from 3rd party sources on websites to capture, and potentially quantify, knowledge

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.

E-records in health systems are a growing set of critically important documents. Machien learning enables them to be created more efficiently and then updated and (where necessary) consolidated more effectively and cheaply.

Provide personalised learning programmes - with regular testing / feedback loops used to assess and deliver against topics that individual students find more challenging and a pace, and potentially style, of interaction tailored to the individual.

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.

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.

Detect potentially dangerous electrical current surges. This potentially enables systems and devices to automatically react in an appropriate fashion to minimise risk to users and devices.

Network packets must be correctly identified and routed in order to maintain network functionality including controlling traffic flow, operationalising firewalls, and measuring networks.

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.