AI and Generative AI for Leaders: Empowering Digital Transformation and Strategic Decision Making

This will be a short, scene-setting session on leading AI-driven change, how to secure buy-in, build trust and model the behaviours that make adoption stick, equipping leaders and managers to lead from the front, make practical progress, and bring their teams with them.

This module introduces core concepts in deep learning and generative AI.

• Overview of neural network types, including Feedforward, Recurring Neural Networks, and Convolutional Neural Networks
• Distinctions between machine learning, deep learning, and reinforcement learning
• Introduction to generative AI and its applications in large language models
• Use of generative AI tools for market research and strategy development
• Application of generative tools in narrative construction and visual storytelling

This module explores the critical challenges of bias in financial services AI systems, focusing on strategies for ensuring fairness, accountability, and effective data management.

• An in-depth examination of bias and causal modelling in financial decision-making, equipping leaders with tools to identify and address AI-driven biases
• Practical approaches to assessing and mitigating AI bias, drawing on frameworks like fairness monitoring and strategic planning for responsible AI deployment
• Engagement with real-world examples, including successful AI implementations and notable failures, to highlight the impact of bias on customer trust, regulatory compliance, and reputational risk
• Review of key data risks and the duty to protect consumers, emphasising actionable strategies to safeguard against AI bias and ensure consumer trust

This module covers key aspects of AI regulation and AI governance, both nationally and internationally. We will discuss case studies from “compliance technology” and legal tech to examine some of the legal risks associated with the use of AI systems and explore strategies for mitigating them. We will cover some of the most important legal regimes, but also go beyond legal compliance to discuss questions of AI ethics, industry standards and design-based compliance approaches.

• Understanding the emerging regulatory framework in the UK, the EU and globally (UK AI White Paper; EU AI Act)
• AI and data protection law (UK Data Protection Act 2018
• AI harms, fault attribution, and liability
• Relevance and state of standards relating to bias and other risk controls
• Ethical AI: Impact Assessments, tools and policies (AI4People ethics toolkit; Montreal Declaration on ethical AI; UK gov. Responsible AI toolkit)
• Understanding monitoring and oversight throughout the AI lifecycle and expectations at each stage

This module covers IP aspects of AI systems, including ownership and licensing and managing risks of infringement. We will consider typical ways in which IP risks are allocated in contract and residual risks and mitigation measures. This week, we then bring considerations from AI regulation, governance and IP together in developing AI Policies for the development and use of AI.

• Intellectual property considerations:
  • Ownership and infringement issues: use of training data, inputs and outputs in the light of recent UK AI case law
  • Regulatory disclosure requirements for using copyright training material in developing AI (UK and EU)
• Intellectual property risk strategies:
  • Considering approaches for the development and use of AI systems and ensuring IP and confidential information are protected
  • Reviewing typical contractual approaches for ownership, licensing and allocation of IP risks
  • Developing AI Policies for the development and use of AI, considering IP, AI regulation, governance and risks

This module explores the impact of agentic AI on booking tourism services, automating back-office operations in hospitality, transforming the discovery of tourism products, and identifying the beneficiaries of AI-driven marketing changes in the tourism industry.

• The way that agentic AI is reshaping the booking of tourism products, including hotels and restaurants.
• Back-office process automation for operational organisations in hospitality and tourism.
• How AI is changing the discovery of tourism products?
• Who wins when AI changes marketing?

Please note: This is a mid-course break week. No online study materials will be offered during this week, and only the live online session will take place.

This module is a comprehensive introduction to AI fundamentals, covering machine learning types, data handling, prediction methods and model evaluation metrics.

• Fundamentals of AI and machine learning
• Types of machine learning: supervised (from examples), reinforcement (through trial and rewards) and unsupervised learning (on its own)
• Working with data: representing data, categorical vs real valued attributes, features
• Brief overview of prediction methods and examples of how to use them in a business context
• Measuring success of models: accuracy, precision and recall

This module provides an in-depth analysis of the crucial elements to the reliability and trustworthiness of AI systems across various industries.

• Review of key risks relating to the reliability of AI systems, statistical accuracy, data accuracy, and the validity of data inputs and generated outputs
• Understanding bias risks and concepts, as well as unintended outcomes for vulnerable customers
• Understanding transparency: A deep dive into transparency in context (audiences as clients, financial consumers, regulators, suppliers, and other stakeholders)
• Best practice approaches to explainability and a consideration of explainability frameworks

During this session, we will consolidate participants’ learning, provide assessment guidance, and focus on next steps.

Hosted in Edinburgh, event details will be confirmed with participants directly.