Associate Professor of Technology and Operations, Michael R. and Mary Kay Hallman Fellow
GenAI in Business: Planning Framework for Implementation
What You'll Learn
- Identify the right problems in your business where generative AI can deliver the highest value
- Align your problems with the right generative AI solution types
- Determine the right data in your business to integrate into your generative AI solution
3 Modules
3 Hours
1 hr per module (approx.)
Rating
About GenAI in Business: Planning Framework for Implementation
In this course, you’ll learn how to identify the right business problems where generative AI can deliver the greatest value. We’ll guide you through the process of articulating these problems in terms of pain points and value levers, ensuring clarity for all stakeholders. Throughout each lesson, you’ll gain the main skills needed to “plan” your artificial intelligence acquisition, the second phase of the “See, Plan, Act” framework introduced in the “Generative AI in Business” series. With the problem defined, we’ll align it with the specific abilities your generative AI solution needs and help you choose the right technology and solution type.
Finally, you’ll develop a three-step roadmap to organize your data, evaluate its quality and readiness, and select the best approach to integrate it into your AI solution. By the end of the course, you’ll have a detailed blueprint for your generative AI solution, a clear understanding of the data you need, and a strategy for integrating it effectively into your business.
This is the second course in the “Generative AI in Business,” a course series for business professionals interested in using generative AI to support, enhance, and amplify the work of their organizations.
Skills You'll Gain
- AI Innovation
- Brand Strategy
- Decision Making
- Generative AI Agents
- Generative Artificial Intelligence
- Generative Model Architectures
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to GenAI in Business: Planning Framework for Implementation, the second course in the GenAI in Business series. This course focuses on the “Plan” phase of the See–Plan–Act framework, introducing the PAD (Problem–Abilities–Data) decision model for GenAI adoption. You will learn how to identify high-value use cases, align GenAI capabilities to business needs, and assess data readiness to support successful implementation.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: PAD Framework of GenAI Adoption: The Problem
- Video: Welcome to the Course
- Video: The PAD Framework
- Reading: Course Syllabus
- Reading: Pre-Course Survey
- Video: Problem Step: Main Decision Flow
- Video: Problem Search: The Top-Down Process
- Video: Problem Search: Pain Points and Value Levers
- Reading: Finding the Right Problem for GenAI
- Reading: Estimating the value and difficulty of GenAI solutions
- Video: Problem Articulation
- Reading: Defining Your Problem using the Pain-to-Gain Approach
Module 2: PAD Framework of GenAI Adoption: The Ability
- Video: Ability Step: Main Decision Flow
- Video: What Can GenAI Do?
- Video: GenAI Ability Spectrum
- Video: GenAI Solution Spectrum
- Reading: Align Needed Abilities with the Right Solution Type
Module 3: PAD Framework of GenAI Adoption: The Data
- Video: Data Step: Main Decision Flow
- Video: Data Catalog
- Video: Data Readiness
- Reading: Using the Right Data to Power Your GenAI Solution
- Video: Data Ingestion
- Reading: Assignment Introduction
- Video: Course Wrap-Up
- Reading: Glossary & Additional Resources
- Reading: End-of-Course Survey
Grading Policy
To pass the course and earn a certificate, learners must earn an overall grade of 80% or higher. The final grade is based on completion of a single graded assessment, “GenAI at ZStyle,” worth 100%.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
No prior experience required
