Digital Transformation with Data and AI

Data Analytics and Business Intelligence October 25, 2025
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Introduction

This advanced strategic course examines how data and Artificial Intelligence (AI) are the foundational pillars of successful Digital Transformation (DX). Participants will learn to identify business processes ripe for automation, define an AI strategy, and manage the organizational change required to implement these technologies successfully. We cover the entire lifecycle, from building data infrastructure and adopting cloud solutions to deploying machine learning models at scale. This program is designed to equip senior leaders with the vision and framework necessary to drive significant, measurable business model reinvention.

Objectives

Upon completion of this course, participants will be able to:

  • Formulate a comprehensive Digital Transformation strategy centered on data, cloud, and AI.
  • Identify high-value business processes suitable for automation and machine learning deployment.
  • Evaluate the capabilities and limitations of key AI technologies, including Generative AI and Machine Learning.
  • Design the modern data architecture (Data Mesh, Data Fabric) necessary to power enterprise AI.
  • Manage the organizational change, cultural shifts, and skills development required for DX adoption.
  • Develop clear governance frameworks for ethical AI deployment and risk mitigation.
  • Quantify the financial benefits (ROI) and metrics for measuring DX program success.
  • Structure and lead cross-functional teams responsible for executing digital initiatives.

Target Audience

  • Chief Digital Officers (CDOs) and CIOs
  • Senior Directors of Strategy and Innovation
  • Executive Vice Presidents of Business Units
  • Heads of Enterprise Architecture
  • Heads of Research and Development (R&D)
  • Transformation Program Managers

Methodology

The methodology employs a top-down, strategic focus. **Case studies** involve analyzing the transformation journey of a traditional industry player that successfully integrated AI (e.g., using predictive maintenance or cognitive automation). **Group activities** require participants to audit their own organization's readiness for AI and draft a high-level data governance charter. **Scenarios** involve creating an AI application roadmap for a core business function, including risk and ROI projections. **Syndicate discussions** focus on managing the ethical dilemma of automation and the necessary leadership communication to address employee concerns about job displacement.

Personal Impact

  • Gain the strategic framework to confidently lead and govern complex AI and data initiatives.
  • Enhance ability to quantify the financial and operational benefits of digital investment.
  • Acquire expertise in building resilient data architectures that scale with AI needs.
  • Transition from traditional management to agile, data-empowered leadership.
  • Develop a nuanced understanding of AI ethics and compliance for future-proofing strategy.

Organizational Impact

  • Achieve significant competitive advantage through business model reinvention and process automation.
  • Ensure AI and data projects are strategically aligned, minimizing wasted investment and maximizing ROI.
  • Accelerate time-to-market for new digital products and services.
  • Future-proof the organization by establishing a scalable, resilient, and ethical data foundation.
  • Successfully manage and mitigate the organizational change risk inherent in transformation.

Course Outline

UNIT 1: Defining the Digital Transformation Landscape

Strategy and Vision
  • Understanding DX as Business Model Reinvention, not just IT Upgrade
  • Identifying the Foundational Role of Data, Cloud, and AI in DX
  • Assessing Organizational Digital Maturity and Capability Gaps
  • Developing a North Star Metric and Strategic Objectives for Transformation
  • The Economics of Data and Network Effects in the Digital Age

UNIT 2: The AI Strategy and Application Roadmap

Finding Value in Automation
  • Distinguishing AI, Machine Learning (ML), and Deep Learning (DL)
  • Identifying High-Value AI Use Cases Across Functions (HR, Operations, Customer Service)
  • Introduction to Generative AI and its potential for process optimization
  • The Build vs. Buy vs. Partner Decision for AI Solutions
  • Structuring an MLOps framework for deploying and managing AI models at scale

UNIT 3: Data Architecture for Transformation

Building the Foundation
  • Designing the Modern Data Stack (Cloud-Native Data Lakes and Warehouses)
  • Principles of Data Mesh and Data Fabric Architectures
  • Strategies for Data Migration and Legacy System Integration
  • Master Data Management (MDM) and ensuring data consistency for AI
  • Utilizing APIs and Microservices for real-time data flow

UNIT 4: Managing Organizational Change and Culture

The Human Element of DX
  • Overcoming Resistance to Change and Addressing Employee Anxiety about AI
  • Strategies for Upskilling and Reskilling the Workforce for Digital Roles
  • Leading Cross-Functional Digital Transformation Teams and Governance Bodies
  • Fostering a Culture of Experimentation, Agility, and Continuous Improvement
  • Communicating the Value Proposition of DX internally and externally

UNIT 5: Governance, Risk, and Measuring Success

Sustaining Transformation
  • Developing a robust Ethical AI Governance Framework
  • Mitigating AI Bias, Fairness, and Transparency Risks
  • Identifying and Managing Cyber Security Risks in New Digital Ecosystems
  • Defining Key Metrics (Non-Financial and Financial) to Track DX Success
  • Establishing a sustainable roadmap for continuous innovation post-initial rollout

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