Day 1 – Master the Fundamentals of AI and Its Business Impact

Build a clear, strategic understanding of Artificial Intelligence, from its evolution and core concepts to its role in shaping today’s competitive markets. Learn how machines learn through supervised, unsupervised, and reinforcement methods, and examine real-world case studies of successful AI integration. Understand the key phases of the AI project lifecycle and how to classify machine learning techniques for different applications. Survey the current AI ecosystem, including tools for text, image, and code generation, and gain insight into the frameworks behind generative AI. Evaluate the strengths and limitations of these technologies across domains, and develop strategies for integrating them into business operations with measurable results.

Day 2 – Machine Learning, Neural Networks and Language Intelligence in Action

Gain applied mastery in the AI techniques that underpin today’s most powerful business applications. Develop a clear understanding of machine learning algorithms and neural network architectures, and learn when and where each delivers the greatest return. Build and evaluate a computer vision model, and explore generative approaches such as GANs and autoencoders for practical innovation. Then turn to language intelligence, unpacking Natural Language Processing, attention mechanisms, and Transformer architectures, the core of large-scale language models, and connect them directly to high value real-world business applications.

Day 3 – Deploy Large Language Models and AI Solutions with Precision

Gain the insight and practical skill to move large language models from experimentation to enterprise deployment. Understand the lifecycle of LLMs, from pre-training and fine-tuning to business integration, and apply prompt engineering techniques to deliver reliable outputs. Explore Retrieval Augmented Generation and the role of autonomous AI agents in creating scalable, high value applications. Learn proven strategies for deploying AI solutions, applying evaluation frameworks to measure both model performance and business impact. Anticipate the organisational challenges of adoption, and develop approaches for scaling AI capability across the enterprise.

Day 4 – From Data to Decisions: Building an Evidence-Driven Enterprise

Learn to connect strategic decisions to the right data, ensuring every choice is informed by robust evidence rather than assumption or bias. Apply structured decision-making processes and data-driven initiatives to identify, reduce, and counter cognitive distortions. Master the principles of experimental design to identify key performance drivers, test alternative hypotheses, and generate actionable insights. Build capability in predictive modelling using linear and logistic regression, decision trees, random forests, and neural networks, and gain hands-on experience applying these models to forecast outcomes and evaluate performance in real-world business contexts.

Day 5 – Generative AI and Building the Organisational Capability for AI Success

Develop a strategic understanding of generative AI, from how large language models learn to where they create the most value in enterprise contexts. Assess strengths such as creativity, scalability, and speed, alongside limitations including bias, hallucinations, and data dependency. Translate this knowledge into practical applications across industries, from automating content creation to accelerating product design and decision-making.

Advance into causal analytics to establish cause-and-effect relationships that prove business impact. Apply methods including matching, control variables, and difference-in-differences to evaluate initiatives with precision. Conclude by focusing on the organisational capability required for success, aligning AI projects with business goals, building a data-driven culture, and overcoming the challenges that prevent analytics from delivering results at scale.