[Free] Ai Leadership And Organizational Transformation
Lead responsible AI adoption through strategy, work redesign, governance, employee trust, and measurable organizational
What you’ll learn
- Diagnose why technically successful AI initiatives frequently fail to produce organizational transformation.
- Redesign workflows by deciding what AI should automate, augment, or leave to protected human judgment.
- Interpret employee resistance through trust, identity, autonomy, fairness, competence, and organizational power.
- Develop proportional AI governance using risk tiers, decision rights, human oversight, accountability, and redress.
- Build an adoption architecture connecting strategy, workflow, capability, participation, governance, and reinforcement.
- Measure AI adoption through readiness, behavior, performance, value creation, and responsible organizational impact.
- 1. Create a practical 30-, 60-, and 90-day roadmap for responsible AI implementation and organizational learning.
Requirements
- No programming, coding, or technical AI experience is required.
- A basic understanding of organizations, leadership, management, or workplace decision-making is helpful but not required.
- Students should be prepared to evaluate AI as a leadership and organizational transformation challenge—not merely a technology tool.
- Professionals may apply the frameworks to an existing AI initiative, workflow, or organizational challenge.
Description
This course contains the use of artificial intelligence.
Artificial intelligence is not simply a technology initiative. It is an organizational transformation that changes work, decision-making, authority, accountability, professional identity, and the relationship between people and technology.
This advanced course examines how leaders can move beyond isolated AI experiments and build responsible, sustainable organizational adoption.
Rather than focusing on software features or technical implementation, the course explores the leadership systems required to translate artificial intelligence into meaningful organizational value. Students will learn how to connect AI initiatives to strategy, redesign work for human–AI collaboration, understand employee resistance, establish responsible governance, and measure adoption through behavior, performance, and impact.
Throughout the course, students follow SummitWorks, a fictional organization navigating AI disruption, employee concerns, executive pressure, operational risk, and the challenge of scaling a successful pilot. This continuing case helps students apply leadership frameworks to realistic organizational decisions.
The course addresses critical questions such as:
Why do technically successful AI projects still fail organizationally?
Which tasks should AI automate, augment, or leave to human judgment?
What does employee resistance reveal about trust, identity, and power?
How should leaders govern high-risk AI uses?
How can organizations scale adoption without losing accountability?
Which measures demonstrate genuine value instead of superficial activity?
Students will leave with a practical framework for leading AI transformation responsibly, including an adoption architecture, governance model, measurement dashboard, and 90-day leadership roadmap.
The course is taught by Professor Law, who holds a bachelor’s degree in Business Administration and Marketing, a master’s degree in Educational Psychology, a master’s degree in Business, and a Doctor of Business Administration specializing in Organizational Leadership and Development.
No technical or programming background is required.
Author(s): Dr. Law Payne
