Faculty
Professor of Technology and Business, INSEAD
Professor of Strategy
Associate Professor of Technology and Operations Management
Syllabus
Artificial Intelligence (AI) is rapidly emerging as the most important and transformative technology of our time. Recent advances, particularly in machine learning – a computer’s ability to improve its performance without ongoing human instruction – have led to a rapid proliferation of new applications that are changing business models and strategies for companies in almost all industries.
This unique opportunity for transformation of your business also comes with risks – in conception, design and execution of your AI strategy. What makes confronting these challenges particularly difficult is that they require an ability to integrate business knowledge and technical skills.
This programme is aimed squarely at those with business knowledge seeking to use AI to transform their organisations. It provides an encapsulated and gentle introduction to the core technical principles that any manager in this space must know, as well as detailed examination of how technology and organisation must mesh to produce transformation.
It will support you in becoming an AI change agent in your organisation – giving you the vocabulary to bridge between data science, business and strategic stakeholders.
Overall Learning Objectives:
- To give you a functional understanding of AI technologies (i.e. what they can do) and a high-level understanding of how they do it.
- How to get the best out of your data and data scientists.
- How to prioritise and execute AI based projects that are likely to yield the best returns.
- How to maximise the success rate of your data and AI business projects.
- How to think about new risks these technologies can create for your business and society.
- Identify skill gaps for you and your organisation for your upskilling journey towards a data driven organisation.
-
Introduction
-
You will find guidance on how to make the most of the module, information on completion requirements, and an overview of the upcoming week’s journey.
-
Week 1: What AI is Doing Today
-
In this week we will first focus on what AI is doing today – the range of applications it already covers and what lies just beyond the horizon. We also discuss why AI is different from other technologies.
Next, we turn to some of the technical details of how AI works. We will distinguish machine learning from big data analytics and consider using data for perception (describing what is happening) vs. prediction (forecasting what will happen). Finally, we consider the elements of a prediction machine and evaluating the accuracy of your prediction machine.
Learning Objectives:
- “State of the Union”: A brief overview of changes that AI is bringing to business.
- Be exposed to the range of skills needed to be integrated to develop a successful AI project in your business.
- Understand the differences between perception, prediction and prototyping, and the opportunities and limitations in each.
- Build intuitions about what makes AI and Machine Learning different from other technologies, including other data related ones.
-
Week 2: Building AI Capabilities in an Organisation
-
A focus on value and successful organisational adoption and change provides a powerful compass for navigating the opportunities and business risks created by data and AI. The second week of the programme first closes the discussion on technical intuitions relevant for you, with which we ended Week 1. Then it discusses recent thinking on the use of AI to augment jobs and decision making, introduces key principles and examples of how to manage AI and data projects, identify AI business opportunities, and finally how to think about the journey of your organisation towards becoming more data driven and capable of leveraging data and AI to create value and innovate.
Learning Objectives:
- Understand key differences between “humans and machines” and how to leverage data and AI to augment jobs and decision making.
- Learn key principles of managing data and AI projects.
- Learn through examples (and a case) and frameworks what it takes for your organisation to progress in its journey towards becoming a more data-driven one and better leveraging data and AI to create value.
-
Week 3: Implementing AI with External Expertise
-
Having big data is not sufficient for solving a business problem. Week 3 explains why and provides ground rules for formulating a business question as “AI solvable”. A suitable formulation can then be handled in-house or outsourced. We discuss how outsourcing AI development has experienced major innovations through “gamification”. Such large-scale forecasting competitions have observed that, like humans, machines can often perform better in teams. We discuss different ways to build such “teams of models” and develop intuition on what types of teams can be expected to work well together.
Learning Objectives:
- Learn about outsourcing AI development.
- Understand ensemble learning and the role of diversity.
- Understand the tradeoff between accuracy and interpretability.
- Understand why Big Data is necessary but not sufficient: Lots of data does not mean actionable business insight. Data must come from a process that has structure, stability and is free of social biases.
-
Week 4: Deep Learning and Causal Thinking
-
Typical machine learning models construct a function that gives accurate predictions of the outcome of interest. These methods, however, understand the world only through association. As is well-known, however, correlation does not imply causation.
In this session we compare these two approaches, discuss important tools for each, and develop frameworks for when to use each. We will also sensitise you to the important limitation of all AI techniques today – they are basically correlational and suffer the risk of “hidden variables”. That’s why beyond perception and prediction, we need prototyping techniques for evaluating whether an intervention will be effective before using it.
Learning Objectives:
- Understand the mechanics of neural networks.
- Develop tools for causal thinking.
- Learn about A/B testing and Randomised Controlled Trials.
-
Week 5: Seeing the Big Picture
-
In this final week of the course we 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 challenges and risks, both logistical and ethical, that the advancement of AI poses. Accordingly, we cover issues around privacy and ethicality (fairness) and the implications for organisation designs, as well as evolving regulatory issues relevant for business.
Learning Objectives:
- Learn about processes that AI accelerates: Innovation, Automation, Flattening.
- Understand business and societal AI risks, upcoming regulations and what they mean for you and your business.
- Learn about planning for AI governance.
- Wrap up around skills for you and your teams and organisations to develop, linked to the overall course.
Learning Journey
Please take a moment to review the Learning Journey as it covers important steps of the module.
Reflection Assignment
This reflection exercise aim to help you develop an AI Strategy Proposal to address challenges or opportunities within your organisation. Through a structured process, you will define your business context, assess AI capabilities, and explore implementation strategies. You will also evaluate the effectiveness of your AI solution through testing and stakeholder engagement while considering governance and risk management.
The final proposal, in a 1-2 page document, should integrate insights from the course and outline a clear action plan. This exercise encourages strategic thinking to ensure AI adoption aligns with your organisation’s goals and long-term success.
