Consumers are expecting increasingly more personalised shopping experience, especially when it comes to online shopping. If they don’t see something relevant for them immediately, chances are they’re going to leave. Machine Learning based recommendations systems can be used to tailor and personalise the shopping experience for each individual based on the consumers’ past interests and actions. This enables intelligent targeting not only within web but also through different marketing campaigns.
When making investment decisions, financial experts need to look for risks and opportunities by reading hundred-page documents, a time-consuming process prone to human error. An Natural Language Processing based AI model can learn the semantics behind key events and facts, and automatically extract the key risk-indicators for financial experts to evaluate. The AI generates a summarisation of its findings to be validated, improving process throughput time and gathering the entire department’s input into a single learning system.
Industrial production is a prime target for AI from a variety of perspectives. A production line AI brings value to the core production processes by intelligently combining data from different sources, optimising the production capacity and reducing the number of defects. With production line AI, you can identify common error patterns and scenarios in order to decrease line downtime, material losses and quality variation, but also to analyse idle time and optimise usage of production line machines with predictivity. The AI system continuously improves its level of automation and accuracy as it learns from the human’s feedback on its output.
In large-scale recruiting AI can be useful both to scout out new potential employees and to help retain the good hires. Large-scale recruiting generally follows a structured, multi-staged process, which can be improved in many ways.
AI can predict employee success based on historical data from the previously hired employees, increasing the efficiency of discovering and selecting high-quality applicants. AI recruiter adds an additional level of getting to know the candidates, leading to less time spent in the interviews. In addition, AI recruiters can bring out biases in the human recruiters, that would otherwise go unnoticed.
In an environment where constant monitoring of a vital system is required, a AI can help spot possible problems.For example, sewage pipes can be monitored by video cameras, and this video data can then be processed through a Computer Vision model.
The AI model recognises probable pipe defects, anomalous objects and blockages in the video and flags them. Human experts give the model feedback by either confirming or rejecting the potential problems, thus further improving the accuracy of the model and data quality for future use. The defect alerts can be then used to guide human activities such as maintenance visits.
In the legal industry the biggest asset of any company is the expertise of its employees. This expertise is captured in countless number of documents and metadata related to them. Capturing the collective experience of what has been developed by the company in the past for various cases forms the core of AI-assisted legal work. Law firms can significantly cut down the time used for getting started with a document by making better use of what’s already known to be a good approach and even clause level formulation for a specific situation. This speeds up new lawyer on-boarding and allows tapping into the knowledge often available only through discussion with senior lawyers.