Program Details
This course begins with governance decisions that determine whether and how an AI initiative should proceed. Participants will learn to prioritize opportunities, define production scope, assign accountability, tier risk, and translate responsible-AI, legal, cybersecurity, regulatory, and third-party requirements into approval gates, audit evidence, human oversight, incident response, and retirement controls. They then assess production readiness; make sourcing, data, architecture, security, integration, testing, monitoring, and cost decisions; and design the operating model, portfolio discipline, adoption approach, shared capabilities, and learning loops required for sustainable enterprise scale.
Course Outcomes
- Prioritize enterprise AI opportunities, define production scope, and set staged investment gates
- Establish governance with clear accountability, decision rights, risk tiers, and deployment approvals
- Translate responsible-AI, legal, cybersecurity, regulatory, and third-party requirements into auditable controls
- Design human oversight, testing, monitoring, audit, incident-response, remediation, and retirement processes
- Assess production readiness and make executive decisions about sourcing, data, architecture, security, integration, and total cost
- Develop a governed and resourced 90/180/365-day roadmap for adoption, value realization, continuous learning, and scale
This course was built for:
- Executives accountable for AI strategy, governance, investment, risk, or transformation
- Leaders across technology, data, product, operations, finance, legal, compliance, cybersecurity, and responsible AI
- Program and portfolio leaders moving AI pilots into governed, sustainable operations
- No coding or data-science background is assumed or required.
