Summary of content

This module introduces AI applications in business contexts. It is specifically designed for students from diverse academic backgrounds without requiring extensive mathematical or programming expertise. The module emphasises practical application over theoretical complexity, teaching students to evaluate, select, and implement AI solutions using business-friendly, no-code/low-code platforms. Students develop critical skills in AI tool assessment, business problem-solving, and stakeholder communication while integrating Gen-AI workflows to accelerate analytical work and create effective prompts for business insights.

The learning journey begins with a foundational overview of AI's role in business. Students explore categories of business problems where AI adds distinct value compared to traditional analytics and industry case studies that illustrate successful and failed implementations. Key topics include return-on-investment analysis for AI projects and organisational readiness assessments. Building on this, the module emphasises the data foundations essential for effective AI. Learners examine customer, operational, financial, and external business data types and develop core data cleaning and preparation skills using Excel and SPSS. Visualisation techniques using KNIME and SPSS support exploratory analysis, with attention given to GDPR and data governance practices.

Students are introduced to predictive analytics through supervised learning techniques for business problems such as churn prediction, customer segmentation, demand forecasting, and price optimisation. Business-friendly platforms like KNIME and SPSS enable hands-on modelling, with evaluation methods balancing statistical and business performance metrics. Unsupervised learning techniques, particularly clustering, are explored in the context of customer and behavioural segmentation. Students apply Recency, Frequency, and Monetary (RFM) analysis and learn to group customers based on behavioural patterns, including website int

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