Transforming Your Business with AI Programme Onboarding

In the introductory week of the programme, we will provide an overview of why using AI for business transformation can be challenging and explain how the course design aims to addresses these challenges.

Additionally, you will be introduced to the programme faculty and the key topics covered throughout the course. Guidance will be provided on how to make the most of the course, including details on programme completion, certification requirements, and an overview of your 8-week learning journey. You’ll also learn how to navigate the platform and explore its various functionalities.

Furthermore, you will be introduced to the “Action Learning Project” (ALP), a core component of the online programme. The ALP offers you the opportunity to apply what you’ve learned in TBAI to create a strategic proposal for AI-driven business transformation. The objective of the ALP is to reinforce your learning and provide a tangible space to apply concepts learned to your own real, relevant organisational context.

This week, we will start by emphasising a key principle: understanding AI today requires expertise across multiple domains. We will explore the foundational scientific principles of AI, as well as the emerging "science of intelligence." We will explore the major breakthroughs in AI history, acknowledging that although the term "artificial intelligence" is relatively new, many of its core principles and technologies have existed for centuries and have been applied in business far longer than most people realise. Furthermore, we will examine what makes AI unique compared to other technologies, while also considering the ways in which it shares similarities with them. In addition, we will examine AI’s profound economic impact and the social and geopolitical implications of this technology. 

Following this, we will shift our focus to AI in business, focusing on how to identify value creation opportunities for your business and build an AI portfolio. In particular we will discuss how to develop and implement your AI strategy and learn concepts that are useful when it comes to quantifying the potential business impact of AI. 

Learning Objectives:

• Identify and correct common misconceptions about AI to develop a well-rounded understanding of its current capabilities.
• Explore the key domains of AI knowledge essential for navigating the evolving AI landscape effectively.
• Understand AI value creation opportunities and how to build and manage an AI portfolio.
• Learn how to design an AI strategy aligned with business goals, including methods for quantifying its business value.

Understanding AI’s organisational impact requires a strategic approach that encompasses alignment, governance, and execution. We will explore three key areas: (1) executing an AI strategy effectively, including the implementation of specific AI use cases; (2) navigating a typical AI maturity journey of an organisation and the types of decisions one needs to make during such a transformation; and (3) understanding AI governance, its relationship to AI risks, regulations, and opportunities, and how it connects to an organisation’s broader AI strategy. Along the way, we will examine real-world examples of AI maturity journeys in organisations, explore the key capabilities that individuals, teams, and businesses need to develop to maximise the potential of AI, and consider critical risks and regulations that must be taken into account.

Learning Objectives:

• Analyse the organisational challenges involved in implementing and adopting AI solutions.
• Explore the stages of AI maturity and the key opportunities and obstacles businesses may encounter along the way.
• Understand the role of AI governance in managing risks, supporting strategy, and enabling responsible AI growth.
• Identify practical levers, tools, and real-world insights to guide an organisation’s AI journey and decision-making.

Generative AI is built on decades of progress in machine learning, with deep learning at its core. This session begins with an overview of how AI systems learn, covering key machine learning principles and the evolution of deep learning, including neural networks and their role in modern AI. We will then explore the mechanics of Generative AI, including large language models (LLMs) and their reliance on word-by-word prediction, embeddings, and attention. The session will also examine the product lifecycle of GenAI, the startup ecosystem driving its growth, and the growing role of agentic AI and synthetic personas. Finally, we will discuss key challenges such as hallucinations, and techniques like RAG and RLHF that enhance AI safety and reliability.

Learning Objectives:

• Explain the fundamentals of deep learning and how they enable Generative AI, including the architecture and training of neural networks, as well as the roles of embeddings and attention in large language models (LLMs).
• Analyse the lifecycle of Generative AI products and their unique development pathways, comparing them with traditional AI approaches and exploring the startup ecosystem driving innovation.
• Evaluate key risks and safety considerations in Generative AI, such as hallucinations and security vulnerabilities, along with mitigation strategies like guardrails, RAG, and RLHF.
• Examine emerging applications of GenAI, including agentic AI and synthetic personas.

Designing AI solutions involves balancing competing priorities such as accuracy, interpretability, fairness, and privacy. This session begins with the essential steps for developing an AI solution, from defining a business case to ensuring high-quality data. We will explore major trade-offs in AI design, including accuracy vs. interpretability, fairness vs. accuracy, and privacy vs. accuracy, using real-world case studies like fraud detection and the COMPAS algorithm. The discussion will highlight how bias can emerge in AI systems, the challenges of achieving fairness, and the critical role of high-quality data in AI success. Finally, we will explore AI implementation strategies, the role of competition-based AI development (e.g., Kaggle), 

Learning Objectives:

• Develop a clear business case for AI initiatives.
• Identify and navigate key trade-offs in AI system design, such as accuracy vs. interpretability, fairness vs. accuracy, and privacy vs. accuracy.
• Analyse fairness and privacy challenges in AI, using real-world case studies and exploring techniques like differential privacy.
• Compare AI implementation strategies—such as in-house development vs outsourcing, and competition-based approaches—while understanding the role of privacy-preserving techniques like differential privacy in system design.

This session will take a holistic view on how AI affects organisations. As the AI change agent in your organisation , you will need to develop a point of view on the management challenges that the advancement of AI poses. Accordingly, we cover issues around how AI changes general management, and organisation design. 

Learning Objectives:

• How AI transforms management.
• Improving managerial decision making with zero-coding analytics.
• AI impact on human skills.
• AI and organisational hierarchy.
• What do human-centric organisations look like? 

These two weeks are for you to synthesise your learning from the past few weeks into a compelling ALP Final Assignment submission. It will also offer an opportunity for you to give feedback to, and receive feedback from, your peers for the ALP Final Assignment. No new course content will be released in these weeks.