Leading Change in the Age of Generative AI

Name: Leading Change in the Age of Generative AI
Rating: 3.9 (18 reviews)

Change Management for Generative AI: Navigate resistance, align goals, and foster adaptability in AI adoption.

Created byMana Dowlatshahi Yeganeh

Last updated 3/2026

English

What you'll learn

Change Management for Generative AI
Navigating Organizational Change in the Generative AI Era
Navigating Resistance Aligning AI Driven Initiatives with Business Goals
Fostering a Culture of Adaptability & Continuous Learning During AI Integration

Course content

9 sections • 129 lectures • 81h 58m total length

01 Fundamentals of Change Management in AI and Automation10:19
Guide AI and automation adoption through change management, focusing on employee engagement, training, clear communication, and leadership alignment to drive adoption and ROI.
02 Foundations of Change Management Core Concepts5:08
03 Evolution of Change Management From Tradition to AI Driven Continuous Change5:12
04 Types of Organizational Change Navigating Transformation in Todays Workplace5:21
Navigate five dimensions of organizational change—structural, process, technological, cultural, and workforce shifts—to drive holistic transformation, engage people, and sustain competitive advantage.
05 The AI and Automation Shift Understanding the New Reality5:33
06 How AI Changes Organizations Accelerating Decisions Shifting Power and5:15
07 AI as a Change Multiplier Accelerating Transformation in 20265:19
Accelerate organizational transformation by leveraging AI as a change multiplier that speeds adoption, scales impact, and redesigns workflows for continuous, AI-enabled growth.
08 The Human Side of AI Driven Change5:14
09 Resistance to AI and Automation Understanding the Human Factor4:59
Explore why people resist AI and automation by examining the human factor, fear of job loss, disrupted workflows, and trust, and learn strategies to build transparency, accessibility, and tailored training.
10 Employee Mindset Shifts for the Future of Work5:01
Discover the ten employee mindset shifts for thriving in an AI-driven future, from problem solving and learning agility to collaborating with AI and fostering psychological safety.
11 Change Management Models Classic Foundations and AI Driven Evolution10:47
Examine Lewin's, Kotter's, ACRO, ADCR, and McKinsey 7S alongside AI driven evolution to guide people through continuous change and ethical, trusted AI adoption.
12 Why Classic Models Are Not Enough for AI10:28
13 AI Adapted Change Frameworks9:30
14 Change Sponsorship and Ownership Driving Successful Transformation11:12
Examine how change sponsorship and clear ownership drive successful AI transformation, with champions, governance, and alignment between business and IT.
15 Leadership and Governance in AI Change Navigating the Future10:30
Lead with vision and governance to balance ai innovation with transparency, accountability, and privacy. Develop oversight, ethics boards, and ai literacy to bridge the governance gap and build trust.
16 AI Governance and Ethics as Change Levers10:47
17 Workforce Transformation and Skills Navigating the Future of Work10:06
18 Reskilling and Upskilling for AI Empowering the Workforce of Tomorrow10:23
Develop AI literacy and reskill your workforce through role-specific, personalized learning pathways, protected learning time, and continuous, strategic upskilling to empower tomorrow's AI-enabled organization.
19 Talent Strategy in the Age of AI Change10:40
20 Communication and Engagement Strategy Mastering Connection in 202610:12
21 AI Change Management Mastering Stakeholder Engagement for Success10:41
Engage executives, managers, frontline employees, and unions with data-driven AI change strategies, mapping stakeholder needs to drive adoption, alignment, and responsible governance across the organization.
22 Building Trust in AI Systems9:48
Build trust in AI by using explainable AI, transparency, and robust human overrides; integrate feedback loops and active listening to enable safe, transparent, collaborative decision-making in high-stakes domains.
23 AI Implementation and Adoption Roadmapping Change for Impact11:00
24 AI Change Management and Adoption Metrics Measuring What Matters10:20
25 Embedding AI into Daily Work Redesigning for the Future10:37
Embed AI into daily work by redesigning workflows, enabling AI to augment routines and accelerate tasks while preserving human oversight.
26 Risk Failure and Recovery Navigating the New Reality of AI and Beyond10:38
27 Managing AI Failures Navigating Risks in the Age of Intelligent Machines10:41
28 Sustaining Change Building Lasting Impact in the Age of AI10:47
Master sustaining change in the ai era by implementing continuous improvement loops, strong governance, and a culture that embraces learning, rapid feedback, and enterprise ai maturity.
29 The Future of Change Management From Projects to Core Capability10:20
Explore how organizations shift from episodic projects to permanent change capabilities, embedding change agility with flexible leaders, learning culture, and AI-enabled real-time insights.
30 Human AI Collaboration Models Unlocking New Synergies10:40
Explore how human plus AI collaboration transforms work through a co-pilot culture that augments decision making, creativity, and productivity, with trusted, transparent human-in-the-loop and hybrid collaboration.
31 Preparing for Continuous Disruption Thriving in an AI Driven World10:24
32 AI Transformations Successes Failures and Hands On Learning10:47

Understanding The Circular Economy2:48:19
Lead change in the age of generative AI by understanding the circular economy and its implications for sustainable business models and innovation.
Fundamentals of SAP Modern Cloud & AI Platform1:45:10
AI in Operations Management Transforming Business Operations2:48:51
Sales and Customer Service AI ML NLP and Sentiment Analysis15:27
Explore how ai, ml, and nlp power sales and customer service to automate, personalize, and scale interactions using sentiment analysis.
TOGAF Enterprise Architecture The Framework Transforming Organizations32:29
Discover how to use TOGAF to align business strategy with technology through the ADM cycle, architecture domains, and the enterprise continuum, enabling governance, cost optimization, and sustainable digital transformation.
Six Sigma White Belt1:28:17
Explore the Six Sigma white belt concepts to prepare leaders for change in the age of generative AI.
Six Sigma White Belt What To Do and What Not To Do29:26
Develop foundational Six Sigma knowledge with the white belt, focusing on DMAIC, data-driven decisions, and frontline collaboration to lead process improvements and customer value.
Fundamentals of SLA in IT Service Management2:35:21
Explore the fundamentals of service level agreements in IT service management and their role in leading change in the age of generative AI.
Building a Personal Brand for Career Success!!2:13:26
Develop a personal brand for career success in the age of generative ai and learn to lead change.
Body Language Secrets for Effective Leadership37:34
Body Language Secrets for Effective Leadership What To Do and What Not To Do36:52

AI & GenAI Primer Fundamentals for All Employees Everyone!!3:07:24
Explore how traditional ai differs from generative ai and how gen ai transforms enterprises through automated workflows and content creation, while emphasizing responsible ai, governance, and stakeholder trust.
AI GenAI Ethics & Responsible Use Fundamentals for Everyone4:31:48
Explore how generative AI reshapes work, learning, and creativity, and learn to design ethical, transparent, and accountable systems with a focus on fairness and privacy.
Critical Thinking for Leadership & Management Basics!!2:48:02
Develop critical thinking to improve leadership and decision making through analyzing information and challenging assumptions. Apply bias awareness, metacognition, and structured problem solving to enable ethical leadership and resilient organizations.
Critical Thinking with AI & GenAI Fundamentals!!1:31:02
AI Literacy for All Employees - Fundamentals for Everyone!!1:29:07
Why AI ML NLP and Sentiment Analysis Matter in Sales and Customer Support38:27
AI Agents in Customer Support Sales and Marketing Use Cases Best Practices38:38
AI Agents in IT Network Systems and Cloud39:26
AI Agents in Security and IT Cybersecurity Protecting Businesses and Infra40:02
AI Cybersecurity What to Learn Why It Matters and the Fundamentals41:09
AI Cybersecurity Limitations and What Not to Do39:57
AI Cybersecurity Problems Solutions and What to Consider in 202639:55
AI Cybersecurity Defending the Future of Digital Trust38:41
Securing Generative AI Why It Matters and What We Must Solve41:04
Securing GenAI Systems39:58
Fundamentals of Cybersecurity in AI and Generative AI41:22
What NOT to Do When Securing Generative AI40:07
FinOps for GenAI & Cloud Computing4:16:15
TOGAF 92:45:13
Six Sigma Black Belt1:56:30
Cisco Data Center AI Infrastructure1:58:37
CEO Playbook Strategically Integrating Generative AI2:08:10
GIAC Certified Incident Handler (GCIH)1:39:04
AB-100 Microsoft Certified Agentic AI Business Solutions Architect1:57:59
Achieving Better WorkLife Balance for Productivity36:09
Reskilling Your Organization for Future Ready Leadership38:04
Foundations of Customer Service with ChatGPT39:08
Skills First Leadership Future Proof Your Organization38:04
ChatGPT for Product Management and Innovation38:42

Introduction Change Management for Generative AI26:30
Navigating Organizational Change in the Generative AI Era25:07
Navigate organizational change in the generative AI era by embracing AI driven innovation and retraining the workforce. Implement governance, ethical AI practices, data security, and continuous learning to sustain competitiveness.
Navigating Resistance Aligning AI Driven Initiatives with Business Goals30:37
Align AI driven initiatives with business goals by communicating value, addressing concerns, and demonstrating tangible benefits through pilots, while fostering leadership, upskilling, and continuous learning to sustain adoption.
Fostering a Culture of Adaptability & Continuous Learning During AI Integration30:53
QAs29:36
Examples8:34

The History and Evolution of AI38:59
AI Definitions and Key Concepts36:45
Explore core ai concepts, including machine learning, deep learning, neural networks, and natural language processing. Understand ai ethics, bias, safety, and explainable ai across applications in finance, education, and transportation.
Symbolic AI vs Machine Learning vs Generative AI30:54
Explore how symbolic AI, machine learning, and generative AI differ, how they hybridize, and how these paradigms enable explainable, scalable, and creative AI across domains.
AI and Machine Learning31:03
AI Neural Networks and Deep Learning Revolutionizing Machine Intelligence20:00
Learn how artificial neural networks and deep learning revolutionize machine intelligence. Explore CNNs, RNNs, and training with forward propagation, weights, and biases for image, text, and time series tasks.
Unlocking the Potential of Generative AI12:31
Unlock the potential of generative ai by leveraging deep learning, neural networks, transformers, and GANs and VAEs to generate text, images, audio, and video, while addressing ethical and governance implications.
The Rise of Large Language Models LLMs14:56
Explore how large language models transform industries and human–computer interaction, enabling text generation, multimodal processing, and reasoning while addressing bias, privacy, and employment concerns.
AI Image and Video Generation DALLE Midjourney and Stable Diffusion15:18
Explore Dall-E, Midjourney, and Stable Diffusion as text-to-image and video generation tools reshaping design and storytelling. Examine ethics, authorship, bias, and responsible use across creative industries.
The Revolution of Audio Speech AI9:58
Explore the revolution of audio speech AI, including whisper and 11 labs, enabling speech recognition, synthesis, translation, and real time multilingual transcription for accessible human computer interaction.
AI in Healthcare Revolutionizing Diagnostics and Drug Discovery8:06
AI in Finance Revolutionizing Fraud Detection and Algorithmic Trading9:58
AI Powered Content Generation and Sentiment Analysis in Marketing8:49
Leverage ai powered content generation and sentiment analysis to automate content production, personalize customer experiences, and deliver real-time insights while addressing ethical concerns.
AI in Manufacturing Revolutionizing Predictive Maintenance with Robotics9:22
The Environmental Impact of Large AI Models10:24
Explore the environmental footprint of large AI models, from data center energy use and carbon emissions to e-waste, and learn sustainable practices to reduce impact.
Common AI Implementation Challenges14:48
Explore common AI implementation challenges such as data quality, talent gaps, legacy integration, ethics, privacy, cost, and governance, and learn strategies to enable successful adoption.

Understanding Artificial Intelligence_ Concepts, Applications, and Future5:47
The Evolution of Artificial Intelligence5:20
Trace the evolution of artificial intelligence from Turing's 1950 paper to today's generative AI, highlighting milestones like the Dartmouth conference, logic theorist, Shakey, deep learning, and ethical considerations.
Narrow Versus General Artificial Intelligence5:53
Contrast narrow AI and general AI, highlighting their domains, transfer learning, ethics, and future implications, while recognizing current reliance on narrow AI for specialized tasks.
Real-World Applications of Artificial Intelligence7:37
The Intelligence Revolution_ An Introduction to Deep Learning9:23
Understanding AI, Machine Learning, and Deep Learning5:32
Explore the differences between artificial intelligence, machine learning, and deep learning, their real-world applications from chatbots to self-driving cars, and future trends shaping human-AI collaboration.
Introduction to Machine Learning Approaches22:02
Explore supervised, unsupervised, and reinforcement learning through real-world examples like image recognition, spam filtering, price forecasting, customer segmentation, anomaly detection, and self-driving cars, highlighting when to use each approach.
Data and Features in Machine Learning_ A Primer4:39
Machine Learning Model Training and Testing_ A Concise Overview5:40
Explore how machine learning models train, validate, and test using data splits, tune hyperparameters, and evaluate performance with metrics like accuracy, precision, recall, and F1 score, while avoiding overfitting.
Popular AI Tools and Frameworks_ TensorFlow, PyTorch, Scikit-learn6:28
Natural Language Processing_ Core Concepts and Applications10:48
AI in Computer Vision and Speech Recognition6:38
Explore how ai powers computer vision and speech recognition through deep learning, neural networks, and techniques like object detection, image segmentation, and 3D understanding.
Ethical AI Development_ Principles and Considerations6:15
Explore the four core pillars of ethical AI—transparency, fairness, privacy, and accountability—alongside bias, privacy safeguards, and accountable governance to shape responsible generative AI.
The Importance of Transparency in Artificial Intelligence6:54
AI Implementation Challenges5:15
Navigate ai implementation challenges by addressing transparency, data quality, legacy system integration, talent gaps, and ethics, with explainable ai and a strategic phased roadmap.
AI Data Types_ A Comprehensive Overview5:50
AI Data Preprocessing_ Cleaning, Formatting, and Transformation5:56
Learn data pre-processing to turn messy real-world data into clean, structured input for AI, covering cleaning, formatting, transformation, and techniques like imputation, normalization, and feature engineering.
Big Data's Foundational Role in Artificial Intelligence5:57
See how big data fuels artificial intelligence through collection, cleansing, and training for real-time insights. Learn how data quality, privacy, and ethics shape AI across healthcare, finance, and manufacturing.
Machine Learning Algorithms_ Decision Trees, Linear Regression, KNN5:53
Deep Learning & Neural Networks_ Power, Challenges, Future5:33
Machine Learning_ Data Splitting for Reliable Models5:36
Split data into training, validation, and test sets to ensure the model generalizes to unseen data, prevent overfitting, and provide an unbiased, trustworthy evaluation of performance.
AI Automation_ Transforming Tasks and Processes5:48
Discover how AI automation, powered by machine learning, NLP, and RPA, learns and transforms tasks across finance, HR, customer service, and manufacturing, improving decisions, data driven insights, and productivity.
RPA and AI_ Transforming Business Processes5:03
AI in Daily Life_ Assistants, Recommendations, and Beyond4:50
AI Revolution in Business and Consumer Products5:33
Discover how AI reshapes commerce, consumer products, and operations—from personalized shopping and voice assistants to predictive design, smart manufacturing, and smarter supply chains and demand forecasting.
AI Bias_ Origins, Types, Impact, and Mitigation6:30
Explore how AI bias arises from data, algorithms, and outputs, revealing its types, real-world harms, and practical strategies to mitigate unfair, discriminatory outcomes.
AI Data Privacy and Security_ Issues and Solutions13:05

Generative AI_ Implementation, Challenges, and Strategic Change14:29
Udemy MANA Generative AI_ Implementation, Challenges, and Strategic Change13:23
Navigating Organizational Change in the Generative AI Era14:06
Navigate organizational change in the generative ai era by aligning leadership, data infrastructure, and upskilling with ethical, collaborative, and iterative practices to unlock value.
Udemy MANA Navigating Organizational Change in the Generative AI Era13:16
Navigating AI_ Strategy, Implementation, and Business Alignment28:15
Udemy MANA Navigating AI_ Strategy, Implementation, and Business Alignment14:20
Udemy MANA Adapting and Learning in the Age of AI12:33

Requirements

This course is designed for professionals from diverse backgrounds who are looking to lead or support the integration of generative AI in their organizations. No prior technical expertise in AI is required, making the course accessible to individuals without a deep understanding of technology. However, a foundational awareness of organizational structures, business operations, and change management principles is recommended, as it provides helpful context for the strategies covered. Participants in leadership or managerial roles will find their experience particularly valuable, as the course delves into overcoming resistance, fostering collaboration, and aligning AI initiatives with organizational goals. Professionals in HR, IT, or strategic planning roles will also benefit, especially if they have some familiarity with how emerging technologies impact workforce dynamics and operations. While the course does not require prior hands-on experience with generative AI, a curiosity about its applications and potential can enhance the learning experience. Those who have had exposure to AI tools or technologies, whether professionally or through self-learning, may find it easier to relate to some of the concepts discussed, though this is not mandatory. The course focuses on the human, organizational, and strategic aspects of AI adoption rather than technical implementation, making it highly relevant for those in non-technical roles. To succeed in the course, participants should have an open and collaborative mindset, a willingness to reflect on real-world challenges, and an ability to engage in discussions and activities. Access to a computer or device with internet connectivity is necessary to participate in the modules, explore interactive content, and utilize supplementary resources provided. Whether you’re a leader, manager, HR professional, or strategist, this course equips you with practical tools and strategies to effectively navigate the complexities of generative AI integration and drive meaningful change in your organization.

Description

The rapid adoption of generative AI technologies is transforming industries by enabling new levels of efficiency, creativity, and innovation. However, implementing these tools requires more than just technical expertise—it demands thoughtful change management to align AI-driven initiatives with organizational goals. This course offers a comprehensive framework for leading change during the integration of generative AI, emphasizing the need for strong leadership, clear communication, and a culture of continuous learning.

While the potential of generative AI is immense, it comes with challenges that leaders must navigate carefully. Resistance to change, ethical concerns, and skill gaps are common hurdles organizations face during AI adoption. This course addresses these challenges by providing practical strategies to overcome resistance, mitigate risks, and ensure a smooth transition. Participants will learn how to balance technical considerations with human factors to maximize the advantages of generative AI.

Generative AI’s potential to revolutionize workflows and unlock new opportunities makes its successful integration crucial for staying competitive. By aligning AI initiatives with business goals, fostering employee buy-in, and preparing for ongoing technological advancements, organizations can thrive in an AI-driven world. This course highlights why effective change management is the key to unlocking generative AI’s transformative potential.

This course is designed for professionals who play a role in leading or supporting change within their organizations. Leaders, managers, HR professionals, project managers, and innovation strategists will benefit from the insights and tools provided. Whether you’re spearheading AI adoption or supporting its integration, this course equips you with the knowledge and strategies to lead your organization into a future shaped by generative AI.

Who this course is for:

This course is ideal for leaders, managers, HR professionals, IT specialists, and strategists who are directly or indirectly involved in driving organizational change, especially during the adoption of generative AI technologies. It is particularly suited for those who aim to align AI-driven initiatives with business goals, navigate resistance, and foster a culture of adaptability. As generative AI reshapes industries, professionals equipped with the knowledge and tools to manage this transition will be invaluable in ensuring its successful integration. By understanding the strategic, human, and operational challenges of AI adoption, participants will gain the ability to lead their organizations through change, mitigate risks, and unlock the transformative potential of AI technologies. This course empowers professionals to confidently navigate the complexities of AI-driven transformation and maintain a competitive edge in an increasingly AI-powered world.

Leading Change in the Age of Generative AI

What you'll learn

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Course content

Updated JAN 2026 >> Leading Change in the Age of Generative AI32 lectures • 4hr 49min

Bonus: Additional Training Resources11 lectures • 16hr 11min

Bonus: Additional Training Resources29 lectures • 41hr 18min

Previous Version >> Leading Change in the Age of Generative AI6 lectures • 2hr 31min

More Info >> AI Fundamentals: Additional Resources15 lectures • 4hr 32min

Further Learning Podcast: TechCast on Key Concepts & FAQs (Fundamentals/Basics)27 lectures • 3hr 10min

Support Materials: Briefings, Docs, FAQs & Glossary1 lecture • 5hr 18min

Need More Help? Watch, Read & Listen (Podcasts + Key Concepts + FAQs)7 lectures • 1hr 50min

Further Learning: SLA, ITSM, CM, IR, ISO 20000, COBIT 2019, ITIL 41 lecture • 2hr 19min

Requirements

Description

Who this course is for: