Predict new high value crop strain performance based on past crop trends, weather and soil data. Effectively speeds up traditional farming innovation but can face some PR challenges.

Predict market potential for new product proposition - e.g. based on product innovation. This may involve scanning social media feeds to understand consumer activity and react to emerging trends, for example in fashion. The analysis may be used for production planning, pricing or marketing purposes.

Capture, collate and visualise multiple sources of data to build projected site layouts for architectural purposes. Data sources will include geo-relevant public data sources, private data sets and potentially sensor data.

Collate data from multiple sources (public, private and potentially real time sensor includng aerial or even satellite imagery) to build a clear visualisation of potential building site. Can be used to assess potential risks, visualise opportunities and project planning issues.

Analyse social, traffic data to measure potential impact of new building developments. Modeled outcomes might indicate hazards (e.g. fire evacuation risks) or design features for consideration (e.g. siting of facilities etc)

Leveraging neuroscience and biometric sensors to understand how proposed content impacts on audience’s responses remains at an early stage of development. Marketing executives enjoy the quasi-scientific output and CFO's typically wince at the cost but AI potentially enables this to be scaled.

Improving audio quality - for example by eliminating background noise or static - has numerous follow-on applications: helping deliver better outcomes from translation or hearing impairement support devices, delivering cleaner data for NLP products and providing better consumer experiences.

Researchers are taking advantage of computational power to analyse vast amounts of data on drugs and their interactions. Enhancing the drug research process by advancing data mining, integration and analysis as well as testing can result in quicker drug and cure discovery for known diseases.

Personalise product offerings to target individual consumers based on multiple data sources (e..g mobile, social media, location, etc.) and historic / comparable behaviour patterns.

Enhance offering by embedding AI tools and big data (e.g. improve search)

Scan social media to discover references to product and competitors for product management purposes. This may include customer sentiment analysis on key product or service attributes.

Accelerate product discovery based on modelling of components - potentially millions of input options can be modelled to ascertain the most promising.

Interpret human gestures to add new modes of interactivity to products. Early consumer products have included gaming applications - but nothing has yet reached the levels shown in the Tom Cruise film 'Minority Report'.

Successful social media sites tend to have more content available for users than all but the most addicted (and socially restricted) could possibly read. This means that they have to use multiple indicators to power the algorithms that rank the content displayed. In this they are in a constant battle to stay one step ahead of the content marketeers trying to game the system.

Determine whether there are new product opportunities by rethinking data and tools.

Uncover product usage insights to support product development process to improve products. May also be used to support customer service outlets and model use cases for new propositions.

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Identify drug compounds with current regulatory approval which could be used in new ways to treat other conditions. Machine learning assists by searching through existing research literature for known and inferred relationships

Once a drug target has been identified, the drug compound itself must be evaluated for safe use in living organisms before live testing can begin. This includes researching how the drug will be metabolised by the body and identifying potential toxic interactions and side effects.

Identify critical factors to improve R&D efficiency - e.g. to reduce the number of required experiments for research and testing process (an examples might include component testing). Optimising configuration of processes and operations will work better where the parameters under development remain stable.

Analyse the data set (cost / benefit analysis) of potential development projects to assist with prioritisation of research effort and resource allocation in the product development process.

Analysis of clinical outcomes to adapt clinical trial design

Identifying biomarkers for boxed warnings on marketed products. Drug labelling may contain information on genomic biomarkers and can describe issues such as drug exposure and clinical response variability, risk for adverse events, genotype-specific dosing, mechanisms of drug action, polymorphic drug target and disposition genes, and trial design features.

Optimise the testing and manufacturing processes to enable efficient turnaround and throughput for drugs to be trialled during the research and development phase.

Combining medications can produce negative side effects - and potentially mitigate the positive impact. Issues for this include limited overlap case studies, decentralised information and unclear cause and effect. Using AI on appropriate data sets can uncover previously unnoticed correlations. Note that 11% of the US population claim to have used at least 5 medications in a given 30-day period.

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.

Even with advances in computing power there will come a moment when too much data has been gathered to be economically stored - for example during high spec science experiments. At that stage, in real time, machine learning can be used to help decide which data should be stored for analysis and which deleted.

Data visualisation typically supports better analytics and decision making - collating and standardising data from potentially multiple sources. AI will enable faster and scalable data visualisation with the potential to respond to real time issues. This will be a set of tools increasingly embedded in other applications and use cases.

Monitor and analyse interactions with customers (potentially across all channels ranging from market research to social media to direct contact) to create insights to improve product offering. The aim is to ensure that there is a positive feedback loop in to product development.

Automate generation of articles - for example using national data sets and then producing local variant articles from this. The same can be done for press releases.