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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 more detail, when focusing on Inventory Optimisation, Machine Learning can identify top-performing SKUs by market segment, replaces under-performing SKUs, and improve SKU mixes at a more detailed level than with ad-hoc analysis.

In addition to using Machine Learning solutions to optimise retail inventories, a large number of leading retailers are implementing hyper-personalised recommendation systems through unsupervised learning techniques and deep learning. For example, implementing a Machine Learning Recommendation Engine modernises engine capabilities for customer-relevant product and service offerings, which can lead to incremental revenue generation. Machine Learning can ensure more personalisation for each customer by offering them relevant information which, in turn, provides retailers with improved data about the customer’s brand engagement experience.




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.

When it comes to the application of Artificial Intelligence in the Automotive Industry, Cloud Platforms ensure that data is available when needed. Unlike conventional vehicles, connected vehicles can do more than alert us with check-engine lights, oil lights, and low-battery indicators. AI monitors hundreds of sensors and is able to detect problems before they affect vehicle operation. By monitoring thousands of data points per second, AI can spot minute changes that may indicate a pending component failure — often long before the failure could leave you stranded – saving you time and money in the long run.




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.

Another use case for Artificial Intelligence in the Education Sector is that the technology offers the potential to decrease grading bias. This is to say, any attitudes and prejudices of teachers will no longer affect the students’ scores and grades. Using ML will help teachers mark and assess assignments given to their students with reduced human interference.

Artificial Intelligence in education can also be used to identify students that need assistance and identify teachers that are available to help. Those students will be able to get personalised schedules with teachers as well as personalised “suggested work” that the technology highlights in areas that need improvement or extra attention.





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. In terms of diagnostics, one of the chief ML applications in healthcare is the identification of hard-to-diagnose diseases. This can include anything from cancers -which are tough to catch during the initial stages – to other genetic diseases. Business problems in healthcare services include workforce productivity and efficiency, predictive modelling, and fraud analytics. Moreover, solutions that target marketing budget allocation, channel management, product feature optimisation, and demand forecasting generate the biggest value proposition for pharmaceutical and medical product companies.

Machine Learning has several potential applications in the field of health or medical research. As anybody in the industry would tell you, clinical trials cost a lot of time and money and can take years to complete in many cases. Applying ML-based predictive analytics to identify potential clinical trial candidates can help researchers draw a pool from a wide variety of data points, such as previous doctor visits, social media, etc. Machine learning has also found usage in ensuring real-time monitoring and data access of the trial participants, finding the best sample size to be tested, and leveraging the power of electronic records to reduce data-based errors.




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.

“Underwriting” could be described as a perfect job for machine learning in finance. Especially at large companies (big banks and publicly traded insurance firms), Machine Learning algorithms can be trained on millions of examples of consumer data (age, job, marital status) and financial lending or insurance results, such as whether or not a person defaulted or paid back their loans on time. The underlying trends that can be assessed with algorithms, and continuously analysed to detect trends that might influence lending and ensuring into the future. These results have a tremendous tangible yield for companies – but at present are primarily reserved for larger companies with the resources to hire data scientists and the massive volumes of past and present data to train their algorithms.




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.

Machine Learning technology can constantly monitors the inner workings of machines and at the touch of a button provide a precise picture of their condition. If values deviate from the normal state, the ML technology can warn of possible future malfunction or failure. This means that machines can be repaired before an emergency occurs. In addition, data analysis reveals how a machine should be configured – right down to the smallest components. The result is a reduction in manual reworking and an overall improvement in product quality.

In terms of improving manufacturing quality, Machine Learning technology can predict the quality of a product right from the early stages of production – with millimetre precision. This makes it possible to detect minuscule cavities and check the porosity of castings during production. However, what is even more interesting is that self-learning algorithms not only report predefined sources of error, but also detect those that were previously unknown.