Supply Chain Analysis and Optimisation Across Industries

Duration: 2 days

This masterclass provides a comprehensive approach to monetising artificial intelligence (AI) initiatives, focusing on both improving productivity and creating new revenue streams. Participants will learn how to harness AI to not only optimise organisational operations but also generate actionable insights that can drive client-facing business opportunities.

This includes exploring Answer Engine Optimisation (AEO) and Generative Engine Optimisation (GEO), where AI search and generative platforms are transforming digital visibility into new monetisation opportunities.

The course covers how to build a data-driven culture, create new datasets, govern data effectively, and develop AI-powered products that turn data into valuable assets. By the end, you will understand how to build a money-making factory powered by AI.

By the end of this programme, you will learn to:

Monetise your data assets using advanced analytics and AI.
Construct “quick wins” AI-powered monetisation business cases that can be implemented immediately.

Programme Outline

The Artificial Intelligence landscape: An introduction to AI technology and applications.
Organisational big data analytics and AI maturity: The stages of analytics and AI maturity and how they are related to business strategy, culture, people, process, and technologies.
The AI ecosystem: Understanding the AI-powered analytics environment from Data Ingestion, ETL, and Analytics, to Reports, Actionable Insights, and Organisational Transformation.
In addition, learn how structured data pipelines support AI-first search ecosystems, enabling AEO and GEO strategies that monetise content through higher visibility and AI citations.
The operations and money-making data scientist: The roles of different data scientists, what they do, and the value they create for the organisation.
Setting up a data factory to create your blue ocean of data variety and competitive advantage.
Building a data-driven culture, handling cultural conflict, and dealing with people and machines.
AI and data monetisation: Driving value from raw data to insights.
Ethical AI considerations: The role of big data in AI development and its moral and ethical dilemmas surrounding businesses, individuals, and governments.
Data governance: How to ensure better and cleaner data for superior AI-driven analytics and better business results.
Creating AI-powered data products: From standalone black boxes to end-to-end solutions. Explore how AI-optimised products also integrate AEO/GEO considerations, making them not only functional but also discoverable and revenue-generating in AI-driven marketplaces.
A systematic approach to starting an AI-driven project: Introduction to the CRISP-DM Methodology.
AI-driven industry use cases: Examining specific AI monetisation use cases and success stories that are relevant to the participants.
Group activity: AI-driven monetisation business case brainstorming and prioritisation: A logical process of analysis and prioritisation to identify “quick wins” AI business cases for the participants’ organisation. The identified “quick wins” business cases will justify the AI proofs-of-concept and projects.

Training Methodology

This workshop blends interactive lectures, group discussions, practical exercises, case studies, and sharing of real-world experiences to ensure concepts are immediately applicable.

Real-world case studies will also demonstrate how AEO and GEO directly link to monetisation, showing how organisations can convert AI-driven visibility into measurable business impact.

Who Should Attend

This programme is designed for middle- to senior-level executives from all divisions seeking to lead AI adoption and data monetisation initiatives.