Chief Artificial Intelligence Officer and Generative Artificial Intelligence Leadership 3: Enterprise Artificial Intelligence and Agentic Workflows
About This Course
This two-day course equips participants to move from individual Artificial Intelligence (AI) use to the design of enterprise AI systems, knowledge-grounded applications, and agentic workflows. Using the AI Factory concept, participants will examine the Infrastructure, Production, and Application layers, and compare model choices including closed, open, Retrieval-Augmented Generation (RAG)-based, multi-model, and agentic systems. They will also apply ISO/IEC 5338 lifecycle thinking to scope, design, validate, deploy, monitor, and retire AI systems responsibly. By the end of the course, participants will develop an Enterprise AI Project Blueprint and agentic workflow that is useful, trustworthy, scalable, and fit for purpose.
What You'll Learn
1. Explain how enterprise Artificial Intelligence (AI) differs from individual AI tool use, and why Chief Artificial Intelligence Officers (CAIOs) must understand technology, data, workflows, lifecycle, and governance implications before scaling AI.
2. Apply the AI Factory concept to describe the three layers of enterprise AI capability: Infrastructure Layer, Production Layer and Application Layer.
3. Apply lifecycle thinking aligned to ISO/IEC 5338 across the key AI system lifecycle stages such as Inception, Design and Development, Verification and Validation, Deployment and Monitoring, and finally, Disposal.
4. Identify key technology and data dependencies required for responsible enterprise AI implementation, including data quality, access controls, system integration, monitoring, logging, validation, and collaboration between the CAIO, Chief Technology Officer (CTO), Chief Information Officer (CIO), Chief Data Officer (CDO), security, governance, and business owners.
5. Develop an Enterprise AI Project Blueprint and agentic workflow using the ISO/IEC 5338 AI system lifecycle framework, reflecting the organisation's business needs, data and knowledge sources, model choices, workflow requirements, oversight mechanisms, validation needs, and scaling considerations.
Entry Requirements
Participants should preferably be a degree holder or a diploma holder with at least 3 years of relevant managerial or professional experience.