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Foundations in AI Enablement
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1 students
Foundations in AI Enablement
A Case Study for Responsible AI Adoption
Last updated 9/2026
English
What you'll learn
- Develop and implement an AI adoption roadmap.
- Manage organizational change, culture, and inter-departmental collaboration during AI integration.
- Operationalize AI governance principles at various organizational levels.
- Move from responsible AI principles to organization-wide implementation.
- Apply lessons from the Mila case study to inform your own organizational AI enablement strategy.
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Course content
4 sections • 16 lectures • 1h 36m total length
Requirements
- No Technical Prerequisites: You do not need a background in coding, data science, or machine learning. This course focuses on organizational design, change management, and operational strategy—not software development. No prior experience is needed.
- Organizational Context: To get the most value out of this course, you should have an understanding of your organization's general structure, operational workflows, or team dynamics.
- No Specialized Tools Required: All templates and resources provided are adaptable using standard office software (e.g., PDF readers, spreadsheet software like Excel or Google Sheets, and basic presentation tools).
- Ideal Context (Optional): While not required, having taken Mila’s Foundations in Responsible AI & AI Ethics (or possessing a basic understanding of Responsible AI principles) provides helpful context, though this course is fully standalone for operational execution.
Description
Responsible AI adoption is not just about choosing the right technology. It’s about helping people and organizations successfully integrate it into the way they work.
Successful organizations recognize that AI adoption is ultimately a human journey. As the technology is only one part of the equation, lasting impact depends on how people engage with, adapt to, and integrate AI into their work.
This course provides a practical, real-world roadmap for turning AI strategy into responsible execution. It bridges the gap between high-level principles and day-to-day implementation, drawing on Mila’s own AI enablement journey to show what it takes to move from ambition to meaningful, sustainable adoption.
Through Mila’s experience as a practical case study, you’ll explore the full journey of organizational AI adoption and how:
Leadership framed the initiative
Organizational readiness was assessed
Governance and policy were translated into practice
Teams were trained and supported
Training can lead to experimentation and exploration
How adoption can be measured and sustained
As such, you’ll gain practical tools, templates, decision points, and implementation lessons that you can adapt to your own organization and context.
Whether you are leading an AI initiative, shaping organizational strategy, supporting teams through change, or in charge of AI governance and enablement, this course will help you move beyond principles and plans toward practical, human-centered execution.
This is not a course about AI hype. It’s about what it takes to make AI work in a real organization. Responsibly, strategically, and sustainably.
Who this course is for:
- C-Suite Leaders & Executive Directors: Executives who need to frame AI strategy, set ethical "red lines," drive institutional buy-in, and ensure AI adoption aligns with organizational values.
- AI Strategy Leads, Innovation Officers & Operations Leaders: Managers tasked with designing and executing a step-by-step AI enablement roadmap, measuring business KPIs, and managing the "Buy vs. Build" decision process.
- HR, L&D, and Change Management Professionals: Practitioners responsible for organizational upskilling, psychological safety, addressing staff fears/hesitations, and designing continuous learning journeys.
- Legal, Risk, and Compliance Officers: Professionals who need to operationalize abstract AI ethics principles into practical, auditable governance policies and safe sandbox environments without stifling innovation.
- Team Managers & Project Leaders: Department heads looking to integrate generative AI tools into their team's daily, documented workflows while maintaining human-in-the-loop accountability.