The retail industry presents itself as one of the largest value opportunities for AI and machine learning. Major areas in which AI can be best implemented include pricing optimisation, inventory optimisation, promotional forecasting, customer acquisition, and customer service management. In addition, a large number of leading retailers are implementing hyper-personalised recommendation systems through unsupervised learning techniques and deep learning.
A lot of attention has been given to computer vision and spatial temporal models in autonomous vehicles; however, the application for AI and machine learning in the automotive industry is far wider. In supply chain management, machine learning models can be used to optimise energy usage, yield, procurement, and inventory while considering throughput targets and other constraints. Advanced AI systems can be further applied to customer service management, churn prediction, and customer acquisitions in sales and marketing operations.
The education industry presents a wide array of powerful applications for AI and machine learning. One use case is for AI to deliver personalised curricula and content to the individual strengths and challenges of each student, which can further be refined by learning from the student themselves. Machine learning techniques can be used to examine student performance and identify key opportunities in which learning can be supplemented. Further, improvements in recurrent neural networks and other sequence processing models can be used to develop unbiased and consistent grading systems that scale beyond the typical classroom.
Opportunities in which AI and machine learning can be applied are prevalent in healthcare, from operational and organisational systems, diagnostics and testing, devices and pharmaceuticals. Business problems in healthcare services include workforce productivity and efficiency, predictive modelling, and fraud analytics. Moreover, solutions targeting marketing budget allocation, channel management, product feature optimisation, and demand forecasting generate the biggest value proposition for pharmaceutical and medical product companies.
Early adopters in the finance industry are at the forefront of applying state-of-the-art AI and machine learning solutions to generate competitive advantage. Given the range of functions which finance encompasses, these solutions can optimise value across business areas. Back office functions of fraud detection, underwriting valuation, insurance, risk management and hedging, and asset management can benefit from a scalable AI data-driven approach. Front office functions such as lead generation, customer interaction, personalised advice, and budgeting are ready for disruptive models based on AI and machine learning solutions. Recent examples include major banks’ interest in adopting sequence-to-sequence models to drive customer engagement through chatbots and robot-advisors.
Given the prevalence of sophisticated robotics and mechanisation, AI and machine learning are natural extensions of an overall trend to greater automation in the manufacturing industry. Leading predictive models are easily applied to yield, energy, and manufacturing throughput analysis. Likewise, these models can be subsequently reframed for inventory and resource optimisation. In addition, large leaps in computer vision through convolutional neural networks has enabled accuracy rates that surpass humans in object detection and identification – leading to new areas of application such as the automated monitoring of safety gear usage and working conditions.