Lead AI initiatives: opportunity assessment, risk & governance, and implementation roadmap.

Identify use cases with highest **impact** and **feasibility**.
Mitigate **technical, ethical and business** risks with proven practices.
Define **realistic, measurable** implementation roadmaps.
Governance for **responsible use** and compliance.
Core AI concepts, ML/DL/GenAI types, use cases and trends. - What is AI today? - Types and scope - Enterprise success stories
Frameworks to identify and prioritize AI projects by impact/feasibility. - ICE/ROI model - Technical & data feasibility - Business-aligned prioritization
Data quality, privacy, security and dataset preparation for AI. - Data readiness - Security & privacy - Dataset preparation
Roles, structure and governance to run AI at scale. - Roles (PM, DS, Eng, MLOps) - Operating model & governance - Biz–tech collaboration
Technical, ethical and financial risks; mitigation. - Bias & hallucinations - Operational/financial risk - Mitigation plans
Governance and responsible AI policies. - Ethical principles - Traceability & audit - Compliance
KPIs, benefits and value sustainability. - Business KPIs - Adoption metrics - Value sustainability
Realistic roadmaps and progressive scale. - Phased plan - MLOps & automation - Run & improve
Trends, Copilot and emerging opportunities. - Copilots & agents - Multimodality - Business impact
This program equips business leaders to confidently run Artificial Intelligence initiatives—from opportunity assessment and risk management to a prioritized roadmap and a responsible governance framework. The modality is live online, with a practical approach and real cases adapted to your organization.
By the end, you will be able to:

10-hour course providing a foundation in cloud computing fundamentals and Azure.

Intensive executive format: apply Copilot (Word/Excel/PowerPoint/Teams) to real cases and measure time saved.
Let's talk about how AI for Managers can help your team.
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