Artificial Intelligence (AI) to Improve Decision-Making & Business Performance

These three analytics types are introduced on Day 1 and applied throughout the course descriptive analytics explains what has happened, predictive analytics forecasts what is likely to happen, and prescriptive analytics recommends what action should be taken to achieve the best outcome. Delegates develop a clear, practical understanding of how each analytics type is applied to different decision contexts — from understanding past performance and forecasting future outcomes to optimising resource allocation and automating decision recommendations.  

Day 3 covers a wide range of AI performance improvement applications — including strategic planning and competitive analysis, operational efficiency improvement, financial analysis and performance forecasting, customer experience enhancement, supply chain and procurement optimisation, and risk management and scenario analysis. Delegates develop the ability to evaluate AI application opportunities across their own functional areas and to make informed, evidence-based decisions about where AI investment will deliver the greatest performance impact.  

Decision automation uses AI to make decisions without human involvement appropriate for high-volume, rule-based, low-risk decisions. Decision augmentation uses AI to inform and improve human decisions by providing insights, options, and recommendations — more appropriate for complex, high-stakes, or contextually nuanced decisions. This course addresses the distinction directly helping delegates identify which decisions in their organisations are candidates for automation, which benefit from augmentation, and what governance is required for each.  

Day 2 covers data quality, governance, and readiness in full — examining why data quality is the foundation of every reliable AI decision support system, what data governance frameworks look like in practice, and how to assess organisational data readiness before implementing AI decision tools. Delegates leave with the understanding to evaluate their organisation's data foundations and identify the gaps that must be addressed before AI can deliver consistent, trustworthy decision support.  

Day 4 dedicates full focus to predictive and prescriptive analytics — covering how predictive models forecast business outcomes, how prescriptive tools generate optimised decision recommendations, and how AI-based scenario modelling and what-if analysis support better budgeting, investment, and cost optimisation decisions. Delegates develop the analytical confidence to work with AI-generated insights and to integrate them into their managerial judgement in a way that improves decisions rather than replacing the experience and context that only humans can provide.  

AI governance and ethics are addressed within Day 5 — covering how to ensure AI decision systems are fair, transparent, and accountable, how to manage AI bias in decision support tools, what governance structures are needed to oversee AI-driven decision-making, and how to maintain human accountability when AI recommendations influence consequential business outcomes. Delegates develop the governance awareness to deploy AI decision tools responsibly — meeting both regulatory expectations and the ethical standards that stakeholders increasingly demand.