Machine Learning

Machine Learning (ML), a subset of Artificial Intelligence is an important aspect of modern business and research. The primary aim is for computers to learn automatically without human intervention or assistance and adjust actions accordingly. It was born to enhance human-centric decisions with data-driven predications for greater outcomes, in any business function or industry. Organisations are using machine learning for greater horsepower. The capabilities are proven across a number of use-cases and allows companies to gain competitive advantage, increase their ROI, streamline their systems and other business needs. 

By Business Function



Customer Service

Sales is an area in which machine learning and AI solutions can be readily applied. Demand forecasting enables the optimisation of inventory to minimise storage and provisioning costs and the opportunity costs of lost sales and profits. In addition, customer segmentation models and recommendation engines facilitate hyper-personalisation of targeted marketing for cross-selling, up-selling, and enhanced customer engagement. Read more to find out the clear roll Machine learning has on automating or enhancing sales business processes.

The Marketing business function contains ample opportunities for machine learning systems. Algorithmic marketing enables the optimisation of pricing strategies in accordance to market fluctuations and the expectation of competitor products and actions. People want brands to care about them. So much so, that 52% of customers are likely to switch brands if they don’t feel a company is making enough effort to personalise their messaging. Read more about the effects on brands survival. 

Machine learning and AI systems can be effectively applied to the customer service business function. Natural language programming models are applied to monitor customer satisfaction across call centres and survey responses. Emotion and sentiment analyses are able to capture changes in customer mood throughout an interaction with a customer service representative. Call classification can also be applied to automatically delegate incoming calls to staff based on their expertise and strengths. Read more to learn about social listening and ticketing vendors.




The Operations business function is currently being disrupted by Machine Learning and AI. Inventory and supply chain optimisation algorithms are able to automate and optimise the inventory management process to minimise loss of profits and storage costs. In addition, predictive maintenance models are able to forecast unscheduled equipment downtime based on historical patterns – in effect reducing downtime, maintenance costs, and increasing operational efficiency. Read more to amplify your operations.

Machine Learning and AI systems are most commonly used in the Finance business function in order to forecast future expenditure for budget allocation and prediction. Fast Fourier transformations are able to generate stable approximations of even the most complex seasonality patterns while recurrent neural networks capture non-linear asymmetric cyclic patterns. Read more to learn about sophisticated algorithms.

The Human Resources space has large potential for application of AI and machine learning techniques to better improve search and operational efficiencies. Performance and satisfaction management systems are able to monitor employee engagement and satisfaction, minimising churn while providing quantifiable performance metrics. Moreover, attrition models are able to forecast expected employee turnover by department and seniority. Read more to learn about natural language programming models. 

By Industry





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.

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.

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.





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.

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.

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.

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