Applied Generative AI for Digital Transformation

• What large language models are, explained without technical jargon

• How inputs, outputs, and context windows shape model behavior

• Why AI hallucinations occur and their impact on organizational use

• How tokens, cost, and model size affect budgeting and deployment decisions

• What multimodal AI enables across text, images, audio, and code

• How leading models differ at a high level and what matters for leadership decisions

• Key warning signs to watch for when evaluating AI vendor claims

• What prompt engineering is and why it matters

• Core techniques: zero‑shot, few‑shot, chain‑of‑thought, role prompting

• Prompting for executive tasks such as briefings, strategy memos, and stakeholder communication

• Prompting for team tasks including documentation, summaries, and reporting

• Automating routine workflows with reusable prompt templates

• Prompt security and what to avoid sharing in public AI tools

• Hands‑on practice to build your personal prompt library

• Marketing and CX: personalization, content generation, service automation

• Operations: workflow automation, documentation, procurement, supply chain

• HR and Talent: recruiting, onboarding, learning and development, performance

• Finance: reporting, forecasting, compliance summarization

• Product and Innovation: ideation, prototyping, market research

• Leadership and Strategy: competitive intelligence, board communication, scenario planning

• Case Studies: Klarna, Coca‑Cola, JPMorgan and others

• Use‑Case Evaluation: assessing value, risk and organizational readiness

• The ethics landscape and what is at stake when AI makes decisions

• Where bias in AI comes from and how leaders can mitigate it

• Privacy, data sovereignty, and guidelines for employee use of AI tools

• When transparency and explainability are required for auditable AI

• The regulatory environment, including the EU AI Act, U.S. frameworks, and sector rules

• How Responsible AI frameworks from Google, Microsoft, and NIST translate into practice

• Disinformation, deepfakes, and content integrity challenges

• How to begin building an internal AI policy for your organization

• What AI readiness means across technology, data, people, and culture

• Common failure patterns in AI adoption and how to avoid them

• Communicating about AI amid uncertainty and rapid change

• Upskilling your workforce and identifying essential future skills

• Addressing AI‑related anxiety and supporting psychological safety

• Roles of the CDO, CAIO, and AI Center of Excellence

• Applying change‑management frameworks such as Kotter and ADKAR to AI initiatives

• Building internal AI champions and governance structures

• The economic impact of generative AI across productivity, displacement, and new value creation

• Which jobs and tasks are most exposed to automation and which remain resilient

• AI augmentation versus replacement and how leaders can apply this distinction

• How AI is reshaping competitive dynamics through first‑mover advantage and differentiation

• What AI‑native competitors reveal and what incumbents can learn from them

• Global AI investment trends and their implications for sector strategy

• How leadership is evolving and which human capabilities grow in importance

• Reviewing the strategic AI framework and bringing all concepts together

• Using the AI opportunity canvas to map use cases to business value

• Applying an effort‑impact‑risk matrix to prioritize AI initiatives

• Building an AI roadmap across quick wins, mid‑term initiatives, and long‑term transformation

• Crafting the business case for AI investment and making the ask effectively

• Presenting your AI strategy to boards, investors, and skeptical stakeholders

• Capstone workshop to develop and peer‑review your organizational AI strategy

• Final reflection on the kind of AI leader you aim to become

The modules follow a strategic progression: foundations → how GenAI works → prompt engineering → organizational applications → ethics/governance → AI‑ready culture → economic implications → final AI strategy capstone. Each module builds toward leadership capability, not technical mastery.

The course integrates cross‑industry case studies (e.g., marketing, operations, HR, finance, innovation) and real examples from companies like Klarna, Coca‑Cola, JPMorgan, Microsoft, and others. These are used as supporting examples—not full case studies—to illustrate practical applications and emerging patterns.

Applicable taxes will be calculated and added at checkout in accordance with country/state regulations.

Yes. The course concludes with a capstone where learners synthesize all concepts into an actionable AI strategy. You will create an opportunity assessment, a prioritized roadmap, ownership and timeline plans, risk and dependency mapping, and a clear set of success criteria.

You will build three core artifacts:

• A Prompt Library for Leaders

• A Responsible AI Governance Checklist

• An AI Adoption Framework tailored to your organizational context

These resources are designed for immediate real‑world use.

Each module includes guided activities, practical analysis, a formative quiz, and applied exercises. Supplemental “for the technically curious” resources offer deeper exploration but are optional.

All key assignments include rubrics aligned with updated, measurable learning outcomes. These clarify expectations and ensure consistent evaluation across learners.

The redesign updates outdated tools, references, and demos; emphasizes evergreen concepts; and moves fast‑changing examples to lighter-touch modules, live sessions, or optional content. Regulatory and governance content is aligned with the EU AI Act, U.S. frameworks, and emerging global standards.

Yes. Instructor-led sessions are incorporated to clarify complex topics, provide updated context, and deepen engagement. Faculty sessions may vary by cohort based on availability.

Learners typically spend 4–6 hours per module, including videos, activities, readings, practice exercises, and the capstone. The course is designed for working professionals.

Yes. The redesign includes multimodal AI (text, image, audio, code) and introduces future-forward concepts like agents, long-context models, and advanced prompting—presented with non-technical clarity.

A dedicated module covers bias, transparency, risk management, disinformation, and regulatory requirements. Learners also build a practical governance checklist aligned with recognized frameworks (NIST, Microsoft, Google, EU AI Act).

Business leaders, functional heads, cross‑functional collaborators, non‑technical professionals, product and operations leaders, and project managers overseeing AI‑related work—especially within engineering or scientific environments.