Diagnose organizational AI maturity and readiness by evaluating evidence across five dimensions (organizational positioning, governance, people, processes, and technology), identifying critical gaps that prevent advancement, and determining realistic timelines for achieving strategic alignment based on proven phase transition frameworks
Navigate competing stakeholder pressures by balancing executive urgency for rapid AI deployment against foundational requirements, managing expectations with evidence-based timelines, addressing team concerns about job security and bias, and reconciling competitive pressure with actual organizational capacity
Design appropriate governance frameworks and risk management strategies for organizations with current governance gaps, respond to real incidents like bias complaints and data privacy issues with immediate and strategic solutions, and articulate business risks in terms executives understand while enabling controlled innovation
Build strategic AI portfolios and business cases by evaluating which initiatives align with organizational maturity (saying "yes" to appropriate use cases and "not yet" to premature ones), applying award-winning benchmarks to create realistic projections, allocating constrained budgets across competing priorities, and demonstrating ROI beyond simple cost reduction
Develop comprehensive implementation roadmaps that sequence foundational work (data quality, system integration, governance) alongside AI initiatives, design phased approaches managing risk while demonstrating progress, apply proven change management practices to improve adoption, and create measurement frameworks that track both leading and lagging indicators
Synthesize complex analysis into clear strategic recommendations by connecting AI strategy to broader business objectives, identifying the single most critical success factor for organizational context, communicating what to pursue now versus defer, and presenting actionable plans that balance ambition with realism for stakeholder decision-making
Course Curriculum
PHASE 2 CASE STUDY EXAMPLE AND TEMPLATE
PHASE 2 INSTRUCTIONS
00:00
PHASE 2 FREQUENTLY ASKED QUESTIONS AND HELPFUL SUGGESTIONS
BE SURE TO CHECK OUT THE RELATED RESOURCES FOR THIS COURSE
Industry Perspective: AI Progression Model for Empowering HR Excellence – A Five-Phase Framework for Advancing Human Resources Capabilities through AI Readiness and Integration
Playbook: The HR Leader’s Guide to AI Transformation – A Strategic Playbook for Building AI-First Human Resources Functions
Case Study: EPAM Systems, Inc.’s AI-Powered English Assessment for a Global Workforce
Case Study: Foxconn Achieves 98% Job Satisfaction Among Employees with Disabilities
Case Study: NTT DATA Saves 24 Workdays Annually Through CYPHER AI-Driven Learning
Case Study: TELUS Digital AI Training Cuts Attrition 4%, Saves $34,000 in Facilitator Costs
REQUIREMENTS
In Phase 2, you will analyze the Case Study of a fictitious company, TechMark Solutions, a mid-sized technology services company navigating AI transformation challenges. This case study reflects real-world complexities including executive pressure for rapid deployment, governance gaps, data quality issues, team concerns, and competing priorities.
In Phase 2, you will apply the frameworks, benchmarks, and best practices from all 10 course modules to develop a comprehensive AI strategy for TechMark Solutions. Your analysis will demonstrate your ability to balance competing pressures while creating realistic, actionable recommendations.
More detailed instructions are set forth in the "Instructions" for Phase 2.
CERTIFIED AI TRANSFORMATION STRATEGIST PROGRAM Phase 1
