Optimise manufacturing process in real time—determine where to dedicate resources to reduce bottlenecks and cycle time. This requires building clear data-led models, often with IoT input and can lead to significant savings - although getting it wrong can be disastrous.

Optimise lane choices and path routing based on multiple data sources to reduce length, cost or environmental impact of a trip.

Provide valuations of second hand products. Typically this will be based on market prices being applied to individual products offered for sale. Absent specific sample details prices these may be a base for negotiation rather than a fixed price (e.g. in second hand vehicles).

Driving assistant - example tasks might be auto-parking or autopilot features on highways. Both enable the driver to sit back but require continued monitoring of the situation.

Vehicle cybersecurity is a hugely growing area of focus both because of large-scale risks but also the threat of targeted attacks. Potential spin-off criminality include blackmail, terrorist activity and activity by hostile state actors.

Recognise complex voice commands to access a greater variety of services and features. Issues might include multi-demands in a single sentence as well as ambient noise - voice recognition then translates to NLP to system commands to ensure job is done.

Self-driving vehicles are a poster child for AI. In fact there are multiple levels of risk, autonomy and functionality that this encompasses. There will likely be many linked network vehciles or protected environment deployments before we see fully autonomous vehicles deployed in a non-test environment on city streets.

Personalise in-vehicle suggestions based on location data and passenger requests. This might include recommending local attractions, garages for proactive maintenance and finding parking places for drivers.

Being unable to find a parking place can be a nightmre for drivers - and a missed revenue opportunity for parking space providers (and an issue for the communities that they operate in). Using AI to synthesise multiple data sources and predict space availability is a strong use case for optimising what was not historcially considered a market for practical purposes.

Discover anomalies across fleet of vehicle sensor data to identify potential failure risks. This may enable companies to pre-empt expensive and embarassing recalls, often driven by negative PR.

Predict failure and recommend proactive maintenance on vehicle components. This will be increasingly important for services such as automated transport-on-demand business models.

Identify and navigate roads and obstructions in real-time for autonomous driving. This requires a mapping of both relatively static elements - such as the road layout - but also dynamic threat assessment.

Vision systems that enable risk and reality recognition are key to a host of autonomous and semi-autonomous vehicle use cases. Typically these rely on a battery of AI ranging from image recognition to predictive responses.

Modify driver experience based on emotion tracking through facial recognition system. This will for example lead to different music being played, lighting set up and driving advice offered.

Predict outcomes using fewer experiments to reduce experimental R&D costs. Examples would include simpler component testing and using models to minimise the requirement for expensive and time-consuming track testing.

Managing event security has the benefit of operating inside a closed environment where huge volumes of data can be generated by multiple video, sensor and other data feeds. Integrating this and ensuring fast and reliable data visualisation and risk monitoring is a sophisticated AI use case.

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.

Artificial Intelligence has become salient and understandable to the public at times when machines have beaten humans at high profile games - Deep Blue winning at chess and AlphaGo at Go. This has now extended to a series of video games. This has significantly helped the development of AI overall.

Use 3D sensors to support the decision making process for judges in sports that require scoring or judgement calls. An example of this would be gymnastics where the tool can be trained on routine peformances. These tools may also be used to support individual's althletic training regimes.

Automated captioning of video (or audio) from sporting events enables the creation of sophisticated video libraries that can be searched without the need for a huge amount of (costly) human trawling to find relevant media moments. The creation of meta-tagging hugely simplifies the indexing process. The potential to then build sophisticated, targeted media feeds - for example individual player highlights - is then possible at acceptable cost.

Predict chance of given outcomes form sporting events based on historic data - this will include individual's performance data, team statistics and potentially broader data sets such as weather or geo-location of events. Given the array of elements that impact on sporting success the track record of success is mixed.

Track and analyse individual players to predict likelihood of success - this has clear ramifications for scouting and talent development. Data gathered may also be used in consumer-facing applications - ranging from sports gaming to betting applications. Data gathering faces potential issues especially where it is not publicly available - e.g. training data rather than public footage - with trade-offs emerging around privacy and ownership issues.

It is a matter of debate what the nature of creativity is. If it is to gather multiple inputs and then to combine these to deliver a new take - in this case as a piece of art, whether painted, digitally created or even scuplted with basic robotic functionality - then this is creativity. If creativity requires emotion or understanding then this is not it.

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.

Predicting risk of injury from personalised data monitoring and big data analysis of historic activity can help athletes and their teams minimise activity liable to cause problems and to initiate mitigation strategies. Potential legal issues around privacy, data ownership and liability need management but this could have a positive impact on teams and individuals.

Playing poker has an additional level of information challenge to other games that AI has conquered - namely the element of bluff.

Musicians have historically used innovative ways to create their music, such as digital and electronic music - but the limit has always been their muse. Generative AI can be used to create songs, melodies, and instrumentation by being trained on previously recorded music. The results can be tuned to match artists' unique characteristic genre and style - for example creating music in the style of a long-dead master.

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.

Sports betting is a hugely growing area, where increased data volumes on individual, team performance and market statistics means machine learning can build betting odds for bookmakers - and also for bettors to attempt to game the odds offered. Some areas - reading human emotions mid-game for example - remain harder to access reliably. This creates the opportunity to offer more targeted products (for example: chances of a given player scoring in the next x minutes).

An application of this type would categorise the food pictured in an image and send the user to an approprate recipe to enable them to create the food pictured. With the growth of social media this creates multiple opportunities.