Chief Artificial Intelligence Officer and Generative Artificial Intelligence Leadership 4: Artificial Intelligence Economics and Control
About This Course
This two-day course equips participants to understand and manage the economics of Generative Artificial Intelligence (AI) and Agentic AI. Participants will examine AI through the practical lens of demand, supply, price, scarcity, and resource allocation, before exploring token economics, compute consumption, Large Language Model (LLM) benchmarks, model selection, cost-performance trade-offs, and agentic workflow costs. The course also focuses on measuring AI value and designing controls such as budget caps, model tiering, routing rules, approval gates, escalation triggers, and AI Factory dashboards. By the end of the course, participants will develop an AI Economics and Control Plan for sustainable AI scaling.
What You'll Learn
1. Explain how Artificial Intelligence (AI) economics differs from traditional Information Technology (IT) software, including the impact of usage-based consumption, compute demand, model choice, and workflow complexity.
2. Analyse token economics and compute consumption, including how prompts, context windows, retrieval, tool use, repeated calls, and agentic loops affect the cost of AI workflows.
3. Compare AI model and deployment options from an economic and performance perspective, including premium models, lower-cost models, open models, Retrieval-Augmented Generation (RAG)-based systems, multi-model routing, and build-buy-partner choices.
4. Interpret Large Language Model (LLM) benchmarks and performance indicators to support better decisions on model selection, cost-performance trade-offs, reliability, and fit-for-purpose deployment.
5. Evaluate AI initiatives using value, cost, productivity, quality, risk, scalability, and business impact rather than adoption volume or tool usage alone.
6. Design cost governance controls such as budget caps, usage thresholds, model tiering, model routing rules, approval gates, monitoring dashboards, and escalation triggers.
7. Develop an AI Economics and Control Plan that supports responsible allocation of resources, measurable value creation, cost discipline, and sustainable scaling.
Entry Requirements
Participants should preferably be a degree holder or a diploma holder with at least 3 years of relevant managerial or professional experience.