AI for Executives

Abstract

This course will enable participants to shape the AI-driven future of their company. It focuses on building a roadmap of company-specific AI use cases roadmap, innovating with AI business models, and on the capabilities non-AI companies need to build to organically deploy AI. While acquiring the skills to drive an AI Transformation participants will draft an AI strategy for their own organization

Objective

The primary aim of this course is to equip participants with the knowledge and skills to drive Data & AI value creation within their organization. This requires mastery of three major pillars of AI for business: operational capabilities, technology, and strategy. Each pillar entails a set of submodules, examples of which are listed in the ‘Content’ section below. Under the ‘actionable learning’ approach taken in this course, the submodules discussed in class will be structured around a short theory part, including case studies, followed by a guided exercise or group session, during which participants will apply what they have just learned to the context of their own company or organization. Taken as a whole and consolidated, the output of these submodule exercises can form the basis for a complete AI strategy and transformation roadmap for an organization. The second aim of this course is therefore for participants to build a draft, actionable AI strategy for their own business. The graded project to be delivered at the end of the course consists of a minimum number of completed submodule exercises, articulated into a coherent AI strategy relevant to a target organization.

Content

1st Pillar – AI Operational Capabilities (aka Enterprise AI) + Ideate, structure, and prioritize AI use cases for your company using a systematic cost-impact assessment framework + Build AI/ML/Data Product skills and teams in your organization + Spread AI innovation and value realization across your company’s organization, including operational, commercial, and business functions (Note: establishing values and processes for the ethical and legally compliant handling of data and AI is a key component of Enterprise AI and is covered in a dedicated course in the 3rd semester.) 2nd Pillar – Technology of Data & AI: + Understand which of your company’s business challenges AI/ML algorithms could possibly solve + Understand the technologies, processes and organization required to manage the data that feeds these algorithms, and identify where your company has data maturity gaps + Understand how to develop and operate AI-powered products and business applications + Integrate real-time machine data into your AI applications: digital twins and the basics of scalable IoT cloud architectures 3rd Pillar – AI Strategy + Understand the different business models that companies can apply to create economic value with AI, and identify those that are suitable to your company’s context and market + Understand the business environment implications of putting data & AI at the core of an organization’s strategy, then map the data ecosystem and data partnerships relevant to your company + Articulate a coherent AI strategy for your company, including value drivers and a roadmap for use cases deployment and capabilities build-up Important note: Participants to this course must have a basic, yet solid, understanding of the main classes of Machine Learning algorithms: linear and logistic regressions, neural networks, decision trees, clustering methods, and NLP. This course will not address these topics, as they are covered in the ‘Fundamentals of Machine Learning for Executives’ course. Participants who take the ‘AI for Executives’ course but don’t have working knowledge of ML, nor practical experience with developing ML algorithms, are strongly advised to take the ‘Fundamentals of ML’ course first.

Resources