- This course focuses on the methods and principles used to assess the value of AI products and systems, both from a technical and business perspective. Students will learn how to evaluate ROI, measure impact, account for costs, and align AI solutions with organisational objectives. The course also explores how to build business cases for AI projects and prioritise features based on their value propositions.
- Level: Intermediate
- Prerequisites: Basic understanding of AI and business concepts.
- Assessments: Weekly Case Studies, Final Valuation Report.
- Job Roles Applicability: AI Product Manager, Business Analyst, AI Strategist, AI Consultant, AI Investment Analyst, Investment Banking (AI), Venture Capital, Private Equity.
Week 1 - Foundations of AI Valuation
Learning OutcomeUnderstand the fundamentals of valuing AI products and systems.
1.1 The role of valuation in AI development and deployment.1.2 Key factors influencing AI product value (e.g., performance, scalability, business alignment).
1.3 Overview of valuation methods: qualitative, quantitative, and hybrid approaches.
Practical Component
Analyse AI Products from the Equipment for Valuation, consider various objectives.
Week 2 - Cost Assessment in AI Systems
Learning OutcomeLearn to calculate the direct and indirect costs of AI projects.
2.1 Identifying costs in AI development (e.g., data collection, model training, hardware).2.2 Evaluating operational costs (e.g., maintenance, scalability, compliance).
2.3 Understanding opportunity costs and risk factors in AI investments.
Practical Component
Create a cost breakdown for an AI project using a provided model.
Week 3 - Measuring ROI and Impact of AI Systems
Learning OutcomeDevelop the ability to calculate the return on investment (ROI) and overall impact of AI solutions.
3.1 Basics of ROI calculation for AI projects.3.2 Measuring intangible benefits (e.g., customer experience, brand value).
3.3 Long-term impact analysis (e.g., sustainability, scalability).
Practical Componenet
Evaluate the ROI of a hypothetical AI model shared through the ecosystem equipment.
Week 4 - Valuation Metrics and Business Alignment
Learning OutcomeUnderstand how to select valuation metrics that align with business goals.
4.1 Key performance indicators (KPIs) for AI systems.4.2 Linking valuation metrics to business objectives.
4.3 Relative Value Displaced Metrics.
Practical Componenet
Develop KPIs for an AI system demonstrated and create a presentation for stakeholders.
Week 5 - Use Cases for AI Products To assess Value
Learning OutcomeConstruct use case understanding of transfer value of technology to understand value to alternate organisations to create value analysis.
5.1 Complexity and Barrier to Building5.2 Competitive Landscape Accuracies.
5.3 Transferable use cases, and value of model and code.
Practical Component
Assess the value of algorithms, and compare and contrast with various peer models.
Week 6 - Final Project: End-to-End Valuation of an AI System
Learning OutcomeApply valuation principles to analyze the value and impact of an AI system.
6.1 Conduct a Value Assessment of a chosen AI product with your format.6.2 Present for peer review, and guide on higher value creation strategies.
6.3 Final Presentations and grading.
Practical Component
Complete an end-to-end valuation of an AI product, including costs, ROI, and impact analysis, and present findings.