AI Leadership and Organizational Transformation

Name: AI Leadership and Organizational Transformation
Rating: 5.0 (6 reviews)

Lead responsible AI adoption through strategy, work redesign, governance, employee trust, and measurable organizational

Created byDr. Law Payne

Last updated 10/2026

English

English [Auto],

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.

Course content

1 section • 5 lectures • 50m total length

AI Is Not a Technology Project9:40
Objective:
Explain why AI transformation depends on strategy, work design, leadership, capability, governance, and accountability—not technology installation alone.
Description:
Students examine why technically successful AI projects can still fail organizationally. The SummitWorks case introduces the difference between installing a tool and transforming how an organization creates value. Students learn to evaluate AI initiatives through strategic value, operating design, human capability, governance, and measurable outcomes.
Redesigning Work for Human–AI Collaboration12:01
Objective:
Redesign workflows by separating automation, augmentation, human judgment, decision rights, escalation, and learning.
Description:
Students learn how to decompose work into tasks, decisions, handoffs, and accountability. The lecture introduces four modes of human–AI work and explains why persuasive output cannot replace professional judgment. Students apply a work-design framework that protects expertise while using AI to expand organizational capacity.
The Psychology of AI Resistance10:11
Objective:
Diagnose AI resistance as organizational information about identity, competence, autonomy, fairness, surveillance, status, and trust.
Description:
Students explore the psychological foundations of employee resistance to artificial intelligence. Rather than dismissing resistance as fear or negativity, leaders learn to identify what employees believe they may lose. The lecture presents a listening process that transforms legitimate concerns into stronger implementation, governance, and organizational learning.
AI Governance, Ethics, and Responsible Leadership9:17
Objective:
Design proportional governance using risk domains, review tiers, decision rights, human accountability, transparency, and redress.
Description:
Students move beyond abstract ethical principles and examine governance as an operating system for decisions. The lecture covers reliability, fairness, privacy, security, transparency, accountability, and redress. Students learn how to classify AI uses by risk and establish legitimate approval, monitoring, challenge, and repair processes.
From AI Experimentation to Organizational Adoption9:18
Objective:
Build a sustainable adoption system connecting strategy, workflow integration, capability, participation, governance, reinforcement, and evidence.
Description:
The final lecture explains why a successful pilot is not the same as organizational transformation. Students construct an adoption architecture, balanced measurement dashboard, and 90-day implementation roadmap. The course concludes with a leadership challenge requiring students to integrate strategy, work redesign, resistance, governance, and responsible impact.

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.

Who this course is for:

Business leaders and executives responsible for AI strategy, transformation, innovation, or organizational performance.
Managers preparing teams and workflows for human–AI collaboration.
Doctoral and graduate students studying leadership, management, organizational psychology, or digital transformation.
Consultants, change leaders, HR professionals, and learning professionals supporting AI adoption.
Technology professionals who want to understand the organizational and human dimensions of AI implementation.
1. Professionals concerned with responsible AI, employee trust, governance, accountability, and sustainable adoption.