Generative AI Applications for Leaders Bootcamp (in collaboration with Michigan Engineering & Microsoft)

Generative AI Applications for Leaders Bootcamp

In collaboration with Michigan Engineering Professional Education, University of Michigan Online & Microsoft

• Certificate issued by Michigan Eng Pro-Ed, affiliated with a U.S. university ranked #9 for artificial intelligence.
• Apply GenAI to a wide range of business scenarios (Sales, Marketing, Engineering, Data Analytics, Customer Support, Product Research & Development, Operations)
• 12 Weeks length program live classroom (5-6 hours / Week, weekend classes)
• Connect with a cohort of senior leaders to share insights, strategies, and best practices.
• Ask us for the next cohort and schedule details!

Gain the skills to create and execute AI-driven strategies that foster innovation and strengthen competitive advantage! The Generative AI for Business Transformation program with Michigan Engineering Professional Education Online equips leaders with the expertise to integrate AI into business operations, boost efficiency, and strengthen competitive advantage.

Tailored for senior executives, managers, consultants, business, and technology leaders, the program offers a practical, business-oriented approach to AI adoption. This program equips leaders to lead GenAI transformation at scale. You’ll gain strategic insight into practical use cases across sales, marketing, analytics, engineering, and beyond, and learn how to assess and apply GenAI to speed up innovation, optimize operations, and create measurable business value.

Graduates earn a prestigious certificate from Michigan Engineering Professional Education, positioning them to accelerate their careers in AI-driven leadership roles.

Key Features

• Course and material are in English
• in collaboration with Michigan Engineering Professional Education Online
• Beginner to intermediate level
• 12 Weeks program (5-6 hours/week weekend classes)
• 55+ hours of live classes supported by 63 guided practices and demos
• 200+ hours of study time and practice recommended
• 1 year course access & session recordings
• 16+ cutting-edge AI tools, including OpenAI, ChatGPT, and Microsoft Copilot.
• Work on 12 hands-on, industry-focused projects across various business functions
• Get a Microsoft course completion certificate hosted on the MS Learn portal
• Program completion certificate from Michigan Engineering Professional Education Online.
• Secure a Michigan Eng Pro-Ed digital badge

Engaging Learning Experience

• Peer Interaction
• Enjoy a true classroom-like environment by connecting with fellow learners and engaging with mentors in real time through Slack.
• Flexible Learning
• Never fall behind—access recorded sessions anytime to catch up and stay aligned with your cohort.
• Mentorship Sessions
• Receive expert support from mentors to resolve doubts, get project guidance, and enhance your learning journey.
• Dedicated Support
• Benefit from a Cohort Manager who provides personalized assistance and ensures you stay on track toward success.

About University of Michigan

The University of Michigan is a leading public research university in the United States, globally recognised for academic excellence, innovation, and leadership in science and engineering. Michigan Engineering Professional Education, part of the university’s College of Engineering, delivers industry-focused programs led by expert U.S.-based faculty, helping professionals and leaders apply cutting-edge research and engineering practices to real-world business and technology challenges.

What added value does University of Michigan contribute to the program?

The program curriculum is reviewed and approved by Michigan Engineering Professional Education, Please be aware that the live classes are not held by actual University faculty staff but by many experienced Industry experts. While the instructors themselves are not employees of the University of Michigan. Beyond content approval, Michigan Engineering Professional Education also oversees instructor evaluation, quality assurance, learner satisfaction, and overall program outcomes which gives the program quality legitimacy and a co-branded certificate of completion.

Learning Objective

• Strategic AI Adoption: Learn to implement AI across business functions to strengthen decision-making.
• AI Leadership: Lead generative AI initiatives that enhance sales, marketing, customer service, and product development.
• Digital Transformation: Automate workflows to improve efficiency and drive organizational change
• Data-Driven Insights: Use AI-powered analytics to extract meaningful insights for smarter business strategies.
• Customer Experience: Boost engagement with AI-driven personalization, chatbots, and virtual assistants.
• Product Innovation: Apply AI to market research, prototyping, and continuous product improvement.
• Responsible AI: Understand AI economics and ethics to ensure compliance, maximize ROI, and manage costs effectively.
• Practical Application: Leverage Microsoft Copilot to streamline operations across sales, HR, IT, finance, and more.

13+ Skills Covered

• Scaling AI Initiatives
• Conversational AI
• AI Risk Assessment
• Predictive Analytics
• Workflow Automation
• Ethical AI and Compliance
• Chatbot Implementation
• AI Economics
• ROI Analysis
• Model Evaluation and Optimization
• LLMOps and AI Deployment
• Sentiment Analysis
• Scenario Simulation

Target Audience:

This program is designed for professionals aiming to harness generative AI to accelerate business transformation, boost innovation, and improve efficiency. Whether you are leading an organization, managing strategic initiatives, or driving technology adoption, this program equips you with the skills to leverage AI effectively.

• Business Leaders
• Consultants
• C-Suite Executives
• Senior Managers
• Entrepreneurs
• Functional Heads
• Technology Leaders

Prerequisites:

To qualify for the Generative AI for Business Transformation program, candidates are expected to have:

• At least 5 years of professional experience (preferred)
• A solid awareness of AI’s rising impact on business
• A strong motivation to apply GenAI for solving real-world business challenges

Learning Path

1. AI Literacy for Business Leaders
2. Generative AI Tech Stack
3. Workflow Automation
4. Generative AI Use Cases: Sales & Marketing
5. Generative AI Use Cases: Customer Service Operations
6. Generative AI Use Cases: Software Engineering
7. Generative AI Use Cases: Data Analytics
8. Generative AI Use Cases: Product Research and Development
9. Ethics and Economics of Generative AI
10. Microsoft Copilot for Business

Electives

• Academic Masterclass - Agentic AI for Product Innovation & Enterprise Automation
• Microsoft 365 Copilot - How to drive enablement in your organization

COURSE CONTENT DETAILS

Course 1: AI Literacy for Business Leaders

This module introduces the core concepts of AI, ML, and generative AI, giving business leaders the knowledge to navigate AI-driven environments with confidence and make data-informed strategic decisions.

Learning Outcomes

• Build a strong foundation in AI, machine learning (ML), and generative AI for smarter business decisions.
• Distinguish between AI and GenAI to uncover strategic business applications.
• Understand key learning models: supervised, unsupervised, and reinforcement learning.
• Explore how neural networks, GANs, transformers, and LLMs enable intelligent AI solutions.
• Assess the development and business potential of intelligent chatbots.
• Improve AI-generated results using temperature settings and sampling techniques.

Course curriculum

• Introduction to AI and ML
• Fundamentals of Generative AI
• AI vs. Generative AI
• Supervised, Unsupervised, and Reinforcement Learning
• Large Language Models (LLMs)
• Neural Networks and Deep Learning
• Intelligent Chatbots and Their Business Applications
• AI Output Optimization: Temperature Settings & Sampling Techniques

Course 2: Generative AI Tech Stack

Building on the fundamentals from AI Literacy for Leaders, this module focuses on the generative AI technology stack. Participants will explore the infrastructure, tools, and deployment practices needed to implement and scale AI solutions effectively across the enterprise.

Learning Outcomes

• Understand generative AI infrastructure and its impact on business operations.
• Compare different foundation models to identify the best applications.
• Assess AI models in terms of cost, flexibility, and scalability.
• Apply LLMOps practices to ensure monitoring, security, and ethical deployment.
• Use AI development tools to design and implement AI-driven solutions.
• Evaluate fine-tuning vs. retrieval-augmented generation (RAGs) for optimal cost-performance balance.
• Harness cloud and compute resources to efficiently scale AI initiatives.

Course curriculum

• Generative AI Infrastructure
• Foundation Models and Applications
• Open-Source vs. Proprietary Models
• LLMOps for AI Deployment
• AI Development Tools
• Fine-Tuning vs. RAGs
• Cloud and Compute Resources
• End-to-End Generative AI Solution Design

Course 3: Workflow Automation

This course equips you with the knowledge and skills to confidently work with SQL databases and leverage them effectively in your applications. Learn fundamental SQL statements, conditional statements, commands, joins, subqueries, and functions to manage databases and support scalable growth.

Learning Outcomes

• Grasp the principles of workflow automation and its role in boosting efficiency.
• Use Zapier to design AI-powered workflows with triggers and Zaps.
• Integrate OpenAI’s API with automation tools for intelligent task execution.
• Build and troubleshoot automated workflows for content creation, email, and social media.
• Apply advanced automation techniques with multistep Zaps, conditional logic, and integrations.
• Monitor, optimize, and scale AI-powered workflows for long-term performance.

Topics Covered

• Workflow Automation Basics
• Zapier Fundamentals
• OpenAI API Integration
• Building & Debugging Workflows
• Advanced Automation Techniques
• Scaling AI-Powered Automation

Course 4: Generative AI Use Cases — Sales & Marketing

After mastering workflow automation, this module applies generative AI to core business functions, beginning with sales and marketing. Through hands-on learning and case studies, participants will discover how to deploy AI effectively while overcoming common challenges to maximize business impact.

Learning Outcomes

• Apply AI-driven strategies to streamline and enhance sales and marketing operations.
• Harness AI for lead generation, customer segmentation, and hyper-personalized campaigns.
• Use predictive analytics to improve sales forecasting and strengthen customer retention.
• Automate content creation with AI-generated text, visuals, and videos.
• Boost customer engagement with AI-powered chatbots and virtual assistants.
• Examine real-world AI applications in sales and marketing to gain strategic insights.
• Address critical challenges including data privacy, brand consistency, and implementation barriers.

Topics Covered

• AI in Sales & Marketing
• AI-Powered Lead Generation
• AI-Generated Content
• Predictive Analytics: Forecasting, Pipelines, and Retention
• Conversational AI
• Case Studies
• Implementation Challenges

Course 5: Generative AI Use Cases — Customer Service Operations

AI-driven automation is reshaping customer support by enabling personalization, faster resolutions, and more responsive interactions. This module equips learners to design and implement intelligent, scalable customer service solutions using generative AI.

Learning Outcomes

• Evaluate the role of generative AI in improving efficiency and personalization in customer service.
• Deploy AI chatbots and virtual assistants for automated, 24/7 customer interactions.
• Automate issue resolution and generate dynamic, AI-powered responses.
• Apply sentiment analysis to understand customer emotions and behavior.
• Examine real-world AI applications in virtual assistance and outage management.
• Tackle challenges such as data privacy, service quality, and balancing human-AI collaboration.

Topics Covered

• AI in Customer Service
• Personalized AI Interactions
• Automated Problem Resolution
• Sentiment Analysis & Insights
• Case Studies
• Implementation Challenges

Course 6: Generative AI Use Cases — Software Engineering

After exploring generative AI applications in core business functions, this module shifts focus to technical domains. Participants will learn how to apply AI to software engineering, enabling greater efficiency, automation, and quality in development. By leveraging AI-driven tools and practices, engineers and leaders can accelerate software delivery, enhance user experience, and stay ahead of emerging trends in AI-powered development.

Learning Outcomes

• Automate code generation, completion, and translation with AI-driven tools.
• Apply AI for code reviews, test case generation, and security checks.
• Generate, refine, and customize software documentation using AI.
• Improve code performance and readability with AI-assisted refactoring.
• Enhance UI/UX design through AI insights on user behavior and accessibility.
• Integrate AI into development workflows, IDEs, and prototyping tools.
• Evaluate future trends in AI-powered and no-code software engineering.

Topics Covered

• AI in Software Development
• AI-Powered Code Generation
• Automated Testing & Security
• AI-Generated Documentation
• Code Optimization & Refactoring
• AI in UI/UX Design
• AI-Integrated Development Tools
• Future Trends

Course 7: Generative AI Use Cases — Data Analytics

Building on the applications of generative AI in software engineering, this module turns to data analytics. Participants will learn how to leverage AI for deeper insights, stronger predictive capabilities, and more efficient decision-making. Through real-world use cases, they will also explore the opportunities and challenges of applying AI to modern data analysis workflows.

Learning Outcomes

• Use generative AI for data exploration, trend discovery, and hypothesis generation.
• Detect anomalies and outliers with AI-powered analysis methods.
• Create interactive visualizations to uncover insights and support decisions.
• Develop predictive models and AI-driven forecasts for business trends.
• Simulate risks and opportunities through AI-based scenario modeling.
• Examine real-world AI applications in optimizing analytics workflows.
• Address issues around computational limits, synthetic data ethics, and model stability.

Topics Covered

• AI in Data Analytics
• AI-Powered Data Exploration
• Anomaly & Outlier Detection
• AI-Driven Predictive Modeling
• Scenario Simulation
• Case Studies
• Challenges & Ethics

Course 8: Generative AI Use Cases — Product Research & Development

Expanding on generative AI applications in technical functions, this module focuses on how Product R&D teams can leverage AI to accelerate innovation. Participants will explore how AI automates market research, predicts customer needs, optimizes product design, and aligns product strategy with brand values. The goal is to enable data-driven, customer-centric, and future-ready product development while addressing ethical and privacy concerns.

Learning Outcomes

• Apply AI for automated market research, idea generation, and product validation.
• Use predictive analytics to map customer journeys and anticipate future needs.
• Create design variants and virtual prototypes for rapid iteration.
• Conduct AI-driven SWOT analysis and risk assessments to guide strategic decisions.
• Enhance user experiences with adaptive AI-powered features and predictive behavior modeling.
• Leverage real-time data for performance tracking and feature prioritization.
• Address ethical considerations, data privacy, and AI’s alignment with brand strategy.

Topics Covered

• AI in Product Development
• Automated Market Research
• Customer Journey Mapping
• AI-Driven Prototyping
• Strategic Analysis & Risk Assessment
• AI-Powered UX Optimization
• Data-Driven Product Management
• Ethical & Brand Considerations

Course 9: Ethics and Economics of Generative AI

After exploring generative AI applications across business and technical functions, this module addresses the critical aspects of ethics and economics in AI adoption. Participants will learn how to ensure AI solutions are both cost-effective and ethically sound, enabling sustainable implementation that respects legal, social, and regulatory boundaries.

Learning Outcomes

• Assess initial setup and ongoing costs of AI implementation.
• Compare AI-driven processes with manual approaches to evaluate scalability and efficiency.
• Calculate ROI for AI projects and analyze trade-offs in adoption.
• Identify and mitigate ethical risks such as bias, fairness, and accountability.
• Examine data privacy implications and ensure compliance with GDPR, CCPA, and other regulations.
• Explore future directions in AI ethics and the role of governments and stakeholders in responsible AI use.

Topics Covered

• Initial AI Costs
• Ongoing AI Costs
• Unit Economics
• ROI and Trade-Offs in AI Projects
• Ethical Challenges in AI Decision-Making
• Data Privacy Regulations
• Future Directions: AI Ethics with Governments & Stakeholders

Course 10: Microsoft Copilot for Business

Following the exploration of AI ethics and economics, this module focuses on the practical application of Microsoft Copilot to enhance productivity across business functions. Participants will learn how to integrate Copilot into daily workflows to optimize operations, improve decision-making, and drive efficiency—while applying best practices for responsible AI adoption.

Learning Outcomes

• Apply Microsoft Copilot to streamline business operations.
• Improve sales processes with AI-assisted personalized proposals.
• Optimize IT operations using Copilot for troubleshooting and issue resolution.
• Generate marketing insights and impactful campaign reports with Copilot.
• Enhance financial planning and create AI-generated budget summaries.
• Strengthen HR processes through AI-driven onboarding checklists.
• Monitor and manage operations with AI-generated inventory dashboards.
• Implement integration best practices for Copilot across business functions.

Topics Covered

• Overview & Features of Microsoft Copilot
• Using Copilot for Sales Optimization
• Using Copilot for IT Security
• Using Copilot for Web Browsing
• Using Copilot for Marketing Insights
• Using Copilot for Financial Planning
• Integration Best Practices

Course 11: Ethics and Economics of Generative AI

After exploring generative AI applications across business and technical functions, this module addresses the critical aspects of ethics and economics in AI adoption. Participants will learn how to ensure AI solutions are both cost-effective and ethically sound, enabling sustainable implementation that respects legal, social, and regulatory boundaries.

Learning Outcomes

• Assess initial setup and ongoing costs of AI implementation.
• Compare AI-driven processes with manual approaches to evaluate scalability and efficiency.
• Calculate ROI for AI projects and analyze trade-offs in adoption.
• Identify and mitigate ethical risks such as bias, fairness, and accountability.
• Examine data privacy implications and ensure compliance with GDPR, CCPA, and other regulations.
• Explore future directions in AI ethics and the role of governments and stakeholders in responsible AI use.

Topics Covered

• Initial AI Costs
• Ongoing AI Costs
• Unit Economics
• ROI and Trade-Offs in AI Projects
• Ethical Challenges in AI Decision-Making
• Data Privacy Regulations
• Future Directions: AI Ethics with Governments & Stakeholders

Elective Courses:

Elective 1: Academic Masterclass

This masterclass covers the essential building blocks of Agentic AI—planning, memory, tool integration, and multi-agent coordination—then moves into real-world product use cases and enterprise deployment models. Participants will take part in hands-on walkthroughs with LangChain, Lovable, and CrewAI, and conclude with strategic guidance on roadmaps, go-to-market strategies, and team capability frameworks

Elective 2: Microsoft 365 Copilot Enablement

This Microsoft course trains participants to lead user enablement and AI adoption within their organizations. It introduces a people-first strategy through the Microsoft 365 Copilot Enablement Framework, structured into four key phases: Get Ready (laying a strong foundation), Onboard & Engage (empowering employees), Deliver Impact (driving measurable value), and Extend & Optimize (broadening capabilities).

Industry Case Studies and Projects

• Project 1 – ChatGPT-Based Storytelling
• Project 2 – Leveraging GenAI for Business Insights and Decision-Making
• Project 3 – Automating Blog Creation and Email Notifications Using Zapier
• Project 4 – Managing Brand Consistency with Generative AI
• Project 5 – Creating an AI-Driven Content Campaign
• Project 6 – Implementing and Managing a GenAI-Powered Customer Support Agent
• Project 7 – Integrating LeadSuccessBot with FairJob Platform
• Project 8 – Enhancing Data Analytics Processes with Generative AI
• Project 9 – Driving Business Innovation with Generative AI in Data Analytics
• Project 10 – Managing the Fit Smart Product Lifecycle with ChatGPT
• Project 11 – Automating Feature Scaling Decisions with Generative AI
• Project 12 – Enhancing Product Launches with AI-Powered Microsoft Edge Copilot

FREQUENTLY ASKED QUESTIONS

How is the program delivered?

The course is delivered entirely online through live virtual classes, offering an 80:20 blend of experiential training and theoretical learning. You'll engage in hands-on projects, case studies, and interactive sessions led by industry experts.

How is the class schedule looks like? Is there recordings?

The course typically spans about 12 weeks with an estimated 5–6 hours of weekly weekend live sessions with a variety of schedule. In between courses, there will be a lot of hands-on project to complete. Please email us to get the details of the schedule of the program. If you miss a class, you can always watch the recording.

NOTE:

Attendance cannot be marked by simply watching the session recordings. Attendance is recorded only when a learner joins the live session. Since these are university-affiliated programs, the criteria are more stringent, as they are set by the universities themselves. However recordings will be available . Learners can view the specific certificate criteria for each course directly on their LMS

Can I work full-time while enrolled in this program?

Yes, you can! The program schedule is designed to help busy professionals with full-time work. You can attend live instructor-led sessions which are mostly held on weekends at the designated time according to your schedule and then complete assignments/projects during your free time.

Can I change my cohort after enrolling in the program?

Yes. You’re entitled to one free cohort change within the first 60 days of enrollment. If you’re unable to continue with your current cohort after using this option, you may request an additional transfer for a fee. For guidance on the process or assistance with your request, please reach out to our support team.

How Is This Program Different from Other Online AI Courses or MOOCs?

Unlike typical self-paced courses, this program offers a university-backed, interactive learning model with over 55 hours of live sessions led by industry experts. Learners benefit from real-time engagement, dedicated mentorship, and peer collaboration, ensuring higher completion and deeper understanding. The curriculum emphasizes business-focused, applied AI use cases—not just technical skills—while providing a University co-branded certificate, adding credibility and long-term career value.

What is the Generative AI Applications for Leaders program?

This program prepares professionals to apply AI-powered solutions across business operations. With 55+ hours of live instruction, hands-on projects, and real-world tools, learners build practical experience in boosting efficiency, driving innovation, and scaling impact through Generative AI.

What Does a Generative AI Engineer Do?

A Generative AI Engineer plays a pivotal role in shaping AI-driven business transformation. They design and implement algorithms for generative models using advanced deep learning techniques, fine-tuning them for accuracy, scalability, and performance. Beyond the technical scope, these professionals collaborate with cross-functional teams to integrate AI into real-world workflows, streamline operations, and unlock new opportunities for growth.

Equipped with expertise from a business transformation training program, they also ensure ethical AI deployment, stay current with emerging tools and frameworks, and effectively translate complex technical concepts for non-technical stakeholders—bridging the gap between innovation and business impact.

How Qualified Are the Trainers?

The program is led by industry experts and seasoned professionals from the fields of data and AI, ensuring that participants gain both real-world insights and practical, hands-on knowledge. Each instructor undergoes a rigorous selection process, including profile assessment, technical evaluation, and a live training demonstration, to guarantee high-quality instruction.

How Can Generative AI Be Leveraged in an Organization’s Digital Transformation Strategy?

Generative AI can be a powerful catalyst in an organization’s digital transformation journey. By integrating advanced generative AI models and systems into core business functions, organizations can streamline operations, optimize supply chains, and create new business models that drive growth.

Beyond efficiency, generative AI enables innovation at scale, from personalized customer experiences to predictive analytics that inform smarter decision-making. When embedded into a broader transformation strategy, generative AI becomes a key enabler of sustainable competitiveness, agility, and long-term success in a rapidly evolving digital economy.

Who Is the Ideal Candidate for This Business Transformation Program?

This program is designed for professionals who want to leverage Generative AI strategically to drive business innovation, efficiency, and digital transformation. It is particularly suited for experienced individuals aiming to lead AI initiatives across business functions, rather than entry-level technical practitioners.

Target Professional Roles

• Business Leaders and C-suite Executives
• Senior Managers and Functional Heads
• Technology Leaders and Consultants
• Entrepreneurs driving innovation

Experience Prerequisite

• A minimum of 5 years of professional experience is preferred, enabling participants to better contextualize the program’s strategic applications of AI.

Mindset Requirement

• Participants should demonstrate a strong eagerness to apply Generative AI to real-world business problems and the ambition to lead AI-driven change within their organizations.

Industry Background

• Current cohorts include professionals from IT, software, manufacturing, pharma & healthcare, and BFSI, showcasing the program’s relevance across industries.

What Specific AI Tools and Platforms Will I Learn to Use?

This program offers hands-on experience with 16+ leading AI tools and platforms to ensure practical, real-world application.

• Core Generative AI Models & APIs: ChatGPT, OpenAI API, and Hugging Face.
• Automation & Content Creation: Zapier, Invideo.ai, Figma, and MIRO.
• Data & Development Tools: Julius.ai, MOSTLYAI, Visual Studio Code, Microsoft PowerApps, and Google Cloud.
• Microsoft Ecosystem: In-depth training with Microsoft Copilot to optimize sales, IT, HR, finance, and operations.

What Are the Biggest Risks of Ignoring Generative AI?

Companies that fail to adopt Generative AI risk losing competitiveness, efficiency, and market relevance as AI-first organizations pull ahead.

Key risks include:

• Operational inefficiency compared to competitors automating sales, marketing, and customer service.
• Slower innovation cycles in R&D and product development.
• Less accurate decision-making without AI-powered data analytics.
• Weaker customer engagement due to limited personalization and outdated campaigns.
• Talent challenges, as top professionals gravitate toward AI-driven organizations.

For a Business Leader, What’s the Difference Between Regular AI and Generative AI?

The main difference lies in analysis vs. creation. Regular AI analyzes past data to make predictions or classifications, while Generative AI creates new content, ideas, and solutions.

• Regular AI: Analytical—used for forecasting, trend detection, and classification.
• Generative AI: Creative—used to produce marketing content, product designs, prototypes, or automated workflows.

Generative AI isn’t a passing trend—it marks a fundamental shift in how businesses operate. By transforming decision-making, innovation, and scalability, it is set to have a lasting impact on the global economy. This program is designed on the principle that AI integration is now a strategic imperative for long-term growth.

I Don’t Have a Programming Background. Can I Still Lead AI Projects?

Yes. Leading AI projects is less about coding and more about strategy, use cases, and ethical implementation. This program is built for business and technology leaders, not just programmers.

• No coding prerequisite—eligibility emphasizes work experience and strategic thinking.
• Curriculum focus—AI literacy, business applications, and deployment strategies.
• Hands-on learning—practical training with low-code and no-code platforms to make AI accessible to non-programmers.

What’s More Important for AI Success: Technology or Business Strategy?

While advanced technology matters, a clear business strategy is the true driver of AI success. Without defined goals, ROI metrics, and ethical guidelines, even the best tools can fail. This program takes a strategy-first approach, starting with AI literacy before exploring technologies. You’ll learn how to:

• Integrate AI across business functions for smarter decision-making.
• Optimize areas like sales, marketing, and operations.
• Ensure responsible deployment with a focus on economics and ethics.

How Do You Measure the ROI of a Generative AI Project?

ROI is measured by comparing financial gains—such as revenue growth, cost savings from automation, and efficiency improvements—against the total costs of implementation, including software, development, and maintenance. In this program’s Ethics and Economics of Generative AI module, you’ll learn a structured framework to:

• Assess initial and ongoing costs.
• Quantify financial benefits from AI adoption.
• Evaluate trade-offs to ensure sustainable ROI.