Digital Transformation 2.0 with Generative AI¶

Title: SEIS 666: Digital Transformation 2.0 with Generative AI - Revolutionizing Business with ChatGPT and GAI

Target Audience: Graduate students in software engineering, information systems, business analytics, and technology management

Prerequisites: No technical programming knowledge required. Students should have a high-level understanding of the Internet, web technologies, cloud services, and personal computer/mobile phone applications. Students must create free accounts with ChatGPT, Claude, Perplexity, and Gemini before the course begins.

Course Overview¶

Digital transformation has become a strategic imperative across every sector—from enterprise B2B operations and consumer experiences to government services and civic engagement. Yet the transformation journey has entered a fundamentally new phase. While foundational digital initiatives focused on cloud migration, automation, and data infrastructure, we are now witnessing an unprecedented acceleration driven by generative AI technologies that are reshaping how organizations create value, make decisions, and compete.

Research consistently shows that organizations achieving digital maturity outperform their peers—delivering products and services at twice the speed, reducing operational expenditures by 25-40%, and realizing significant gains in brand equity and customer satisfaction. However, fewer than one in four organizations successfully execute their digital transformation strategies. The differentiator? The ability to act with speed and intelligence, embedding AI-driven capabilities into the fabric of business operations.

Digital Transformation 2.0 represents the convergence of mature digital infrastructure with the transformative power of large language models (LLMs), multimodal AI, and autonomous agents. This graduate-level course examines this new paradigm, exploring how technologies like OpenAI's GPT models, Anthropic's Claude, Google's Gemini, and emerging platforms such as Perplexity AI and xAI's Grok are fundamentally altering business models, workforce dynamics, and competitive landscapes.

Main Topics Covered¶

Foundational Concepts¶

Introduction to digital transformation and its evolution
Digital maturity models and organizational readiness assessment
Digital capability frameworks and benchmarking
Business drivers for transformation initiatives
Value creation in the digital economy

Generative AI Fundamentals¶

Large Language Model (LLM) architecture and how they work
Transformer architecture and attention mechanisms
Training methodologies: pre-training, fine-tuning, RLHF
Token economics and context windows
Model parameters and their business implications

AI Platform Landscape¶

OpenAI GPT models (GPT-4, GPT-4o, GPT-4 Turbo)
Anthropic Claude (Claude 3 family, Claude 3.5 Sonnet)
Google Gemini (Gemini Pro, Gemini Ultra)
Perplexity AI and search-augmented generation
xAI Grok and emerging platforms
Open-source models (Llama, Mistral, Mixtral)

Prompt Engineering¶

Zero-shot prompting techniques
Few-shot learning and in-context learning
Chain-of-thought reasoning
System prompts and persona design
Output formatting and structured responses
Advanced techniques: tree-of-thought, self-consistency
Prompt optimization and iteration strategies

Custom AI Solutions¶

Custom GPT development and configuration
AI agents and autonomous systems
No-code AI tools and platforms
Workflow automation with AI
Retrieval-Augmented Generation (RAG)

Technical Integration¶

LLM API fundamentals (REST APIs, SDKs)
OpenAI API architecture and endpoints
Anthropic API integration patterns
API parameters: temperature, top-p, max tokens
Embeddings and vector representations
Rate limiting and cost optimization

Multimodal AI¶

Text-to-image generation (DALL-E, Midjourney, Stable Diffusion)
Diffusion models and how they work
Vision capabilities and image analysis
Text-to-video emerging technologies
Audio and speech AI applications
Multimodal business applications

Organizational Excellence¶

Generative AI Center of Excellence (GAICoE) design
AI governance frameworks and policies
AI strategy development and roadmapping
Change management for AI adoption
Scaling AI initiatives across the enterprise

Ethics and Responsibility¶

AI bias detection and mitigation
Hallucination management and factual accuracy
Data privacy and security considerations
Intellectual property and AI-generated content
Regulatory landscape and compliance
Responsible AI deployment principles
Red-teaming and adversarial testing

Future of Work¶

AI-augmented workforce models
Skill transformation and reskilling strategies
Role evolution in the AI era
Human-AI collaboration patterns
Organizational structure changes
Productivity and creativity enhancement

Business Applications and Case Studies¶

AI use case identification methodologies
Value mapping and ROI estimation
Prioritization frameworks for AI initiatives
Industry-specific transformation examples
Success factors and failure patterns
Converging technologies and emerging trends

Topics Not Covered¶

This course does not cover:

Deep technical machine learning implementation (neural network coding)
Model training from scratch or fine-tuning at scale
Data engineering and MLOps infrastructure
Statistical foundations of machine learning
Computer vision algorithm development
Natural language processing research methods
Reinforcement learning mathematics
Hardware optimization for AI workloads
Academic research paper writing
Specific programming languages (Python, JavaScript) in depth

Learning Outcomes¶

After completing this course, students will be able to:

Remember¶

Retrieving, recognizing, and recalling relevant knowledge from long-term memory.

Define digital transformation and distinguish it from digitization and digitalization
List the key components of digital maturity models
Identify the major generative AI platforms (ChatGPT, Claude, Gemini, Perplexity, Grok)
Recall the basic architecture components of large language models
Name the six levels of Bloom's Taxonomy and their application to AI learning
List common prompt engineering techniques (zero-shot, few-shot, chain-of-thought)
Identify the key API parameters used in LLM integrations (temperature, top-p, max tokens)
Recall the components of a Generative AI Center of Excellence
List common AI ethical concerns (bias, hallucination, privacy, IP)
Name the primary text-to-image generation platforms (DALL-E, Midjourney, Stable Diffusion)
Identify key workforce transformation trends driven by AI
Recall the metrics used to measure digital transformation success

Understand¶

Constructing meaning from instructional messages, including oral, written, and graphic communication.

Explain how large language models generate text through next-token prediction
Describe the transformer architecture and the role of attention mechanisms
Summarize the differences between GPT, Claude, Gemini, and other major LLM platforms
Explain the concept of context windows and their business implications
Describe the training process for LLMs including pre-training and RLHF
Interpret digital maturity assessment results and their organizational implications
Explain how prompt engineering affects model outputs and response quality
Describe the purpose and structure of system prompts in AI applications
Summarize the ethical considerations in deploying generative AI at scale
Explain the concept of embeddings and their use in semantic search
Describe how RAG (Retrieval-Augmented Generation) improves AI accuracy
Interpret AI governance frameworks and their organizational purpose
Explain the relationship between temperature settings and output creativity/determinism
Describe multimodal AI capabilities and cross-modal applications
Summarize the business case for establishing a GAI Center of Excellence

Apply¶

Carrying out or using a procedure in a given situation.

Use ChatGPT, Claude, and Gemini to solve business problems
Apply zero-shot prompting techniques to generate useful outputs
Implement few-shot learning by providing examples in prompts
Use chain-of-thought prompting to improve reasoning in complex tasks
Apply system prompts to establish consistent AI personas
Use structured output formatting to generate JSON, tables, and lists
Implement the OpenAI and Anthropic APIs for basic text generation
Apply API parameters appropriately to control output characteristics
Use digital maturity assessment frameworks to evaluate organizations
Apply use case prioritization matrices to rank AI opportunities
Build custom GPTs for specific business applications
Use text-to-image tools to generate visual content for business needs
Apply red-teaming techniques to identify AI implementation risks
Use prompt iteration strategies to improve response quality
Implement basic RAG patterns for knowledge-augmented applications

Analyze¶

Breaking material into constituent parts and determining how the parts relate to one another and to an overall structure or purpose.

Compare and contrast the capabilities of major LLM platforms
Analyze organizational readiness for AI adoption using capability models
Differentiate between AI use cases based on value and feasibility
Examine the trade-offs between different prompt engineering approaches
Analyze the cost-benefit implications of various API parameter settings
Compare custom GPT approaches versus API integration strategies
Differentiate between various text-to-image generation techniques
Analyze the components of effective AI governance structures
Examine the relationship between AI ethics principles and implementation practices
Compare workforce transformation strategies across industries
Analyze case studies to identify success factors and failure patterns
Differentiate between hype and genuine business value in AI applications
Examine the interplay between technical capabilities and business strategy
Analyze the impact of context window limitations on application design
Compare open-source and proprietary AI model trade-offs

Evaluate¶

Making judgments based on criteria and standards through checking and critiquing.

Assess organizational digital maturity levels against industry benchmarks
Evaluate AI use cases based on strategic alignment and feasibility
Judge the quality and appropriateness of AI-generated outputs
Critique prompt engineering approaches for effectiveness and efficiency
Evaluate the suitability of different LLM platforms for specific use cases
Assess the ethical implications of AI deployment decisions
Judge the effectiveness of AI governance frameworks
Evaluate the business case for AI investments using ROI frameworks
Critique AI implementation strategies for scalability and sustainability
Assess the risks associated with specific AI applications
Evaluate vendor claims about AI capabilities against real-world performance
Judge the readiness of emerging AI technologies for business adoption
Critique case study organizations' transformation approaches
Evaluate the alignment between AI initiatives and business objectives
Assess the quality of custom GPT implementations against requirements

Create¶

Putting elements together to form a coherent or functional whole; reorganizing elements into a new pattern or structure.

Design a comprehensive digital transformation roadmap incorporating AI
Develop custom GPTs tailored to specific organizational needs
Create effective prompt libraries for recurring business tasks
Design an AI use case prioritization framework for an organization
Develop a GAI Center of Excellence charter and governance structure
Create AI-augmented workflows that enhance human productivity
Design ethical AI guidelines appropriate for specific industry contexts
Develop training programs for AI literacy across organizational roles
Create business cases for AI investments with clear value propositions
Design API integration architectures for enterprise AI applications
Develop change management plans for AI adoption initiatives
Create multimodal content strategies leveraging text, image, and other AI capabilities
Design AI risk assessment frameworks and mitigation strategies
Develop AI-powered solutions addressing real organizational challenges
Create comprehensive AI strategy documents aligning technology with business goals

Capstone Project: Students will design and present a comprehensive AI transformation strategy for a real organization, incorporating digital maturity assessment, use case prioritization, governance framework, implementation roadmap, and change management plan. The project demonstrates the integration of all course concepts into a coherent, actionable strategy.

Course Structure¶

The course consists of 14 weeks of instruction:

Week	Topic	Lab Component
1	Introduction to Digital Transformation	AI landscape exploration—compare platforms
2	Digital Maturity & Capability Models	Self-assessment using capability frameworks
3	AI Use Case Identification & Prioritization	Build a use case prioritization matrix
4	Understanding LLMs: Architecture & Applications	Prompt engineering fundamentals
5	Advanced Prompt Engineering	Complex prompting techniques
6	Custom GPTs, Agents & No-Code AI	Build a custom GPT
7	Midterm Exam
8	LLM APIs & Integration	Hands-on with OpenAI/Anthropic APIs
9	Multimodal AI	Experiment with image generation
10	GAI Center of Excellence	Draft a GAICoE charter
11	Ethics & Responsible AI	Red-teaming exercise
12	Future of Work	AI-augmented workflow redesign
13	Case Studies & Converging Technologies	Analyze real-world cases
14	Project Presentations	Final presentations

References¶

Gale, M. & Aarons, C. The Digital Helix: Transforming Your Organization's DNA to Thrive in the Digital Age
Rogers, D. The Digital Transformation Playbook: Rethink Your Business for the Digital Age
An, J. Digital Capability Model
OpenAI Documentation and API References
Anthropic Claude Documentation
Google Gemini Documentation