GenAI in Business: Planning Framework for Implementation
Learn to spot high-value business problems for generative AI, match them with the right solutions, and integrate data for successful implementation.
Learn to spot high-value business problems for generative AI, match them with the right solutions, and integrate data for successful implementation.
This comprehensive course teaches business professionals how to identify and articulate the right business problems where generative AI can deliver maximum value. Following the "See, Plan, Act" framework in the "Generative AI in Business" series, this course focuses on the "Plan" phase of AI acquisition. Students learn to articulate business problems in terms of pain points and value levers, ensuring clarity for all stakeholders. The course guides participants through aligning these problems with specific generative AI solution types and capabilities, helping them choose the appropriate technology. Additionally, students develop a three-step roadmap to organize business data, evaluate its quality and readiness, and select the best approach for integration into their AI solution. By course completion, participants will have a detailed blueprint for implementing generative AI in their organization, with a clear understanding of the problem, required capabilities, necessary data, and integration strategy.
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What you'll learn
Identify high-value business problems where generative AI can deliver maximum impact
Articulate business problems using pain points and value levers for stakeholder clarity
Align specific business problems with appropriate generative AI solution types
Develop a systematic approach to evaluating which problems are best suited for AI
Create a data roadmap to organize and integrate your business data with AI solutions
Assess data quality and readiness for AI implementation
Skills you'll gain
This course includes:
1.7 Hours PreRecorded video
1 assignment
Access on Mobile, Tablet, Desktop
Batch access
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There are 3 modules in this course
This course provides a structured approach to planning generative AI implementation in business contexts. The curriculum follows a three-part framework: Problem identification, Ability alignment, and Data integration (PAD). Students first learn to identify and articulate high-value business problems suitable for generative AI solutions, including techniques for evaluating potential value and difficulty. Next, they discover how to match business problems with appropriate generative AI capabilities and solution types across the AI spectrum. Finally, students develop skills to catalog organizational data, assess its readiness, and determine the best methods for integrating it into generative AI solutions. Throughout the course, practical examples and frameworks help translate theoretical concepts into actionable business strategies.
PAD Framework of GenAI Adoption: The Problem
Module 1 · 1 Hours to complete
PAD Framework of GenAI Adoption: The Ability
Module 2 · 41 Minutes to complete
PAD Framework of GenAI Adoption: The Data
Module 3 · 1 Hours to complete
Fee Structure
Instructor
Pioneering Research in FinTech and Blockchain
Andrew Wu is a prominent researcher in fintech, specializing in blockchain, cryptocurrencies, and robo-advisors. He leverages machine learning and automated text analysis to examine large-scale, unstructured data, with his findings published in the Journal of Financial Economics and featured in op-eds for The Hill. Dr. Wu teaches courses on FinTech Innovations and Global Business Field Projects in FinTech, and he has conducted an Executive Education program on Smart Banking in the Age of FinTech for the Industrial and Commercial Bank of China. He earned his PhD in finance from the Wharton School at the University of Pennsylvania and holds a BA in mathematics and economics from Yale University.
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4.8 course rating
10 ratings
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