Created for IWU Workshop 1

Artifact 1 (Workshop 1): AI/ML + Change Leadership Self-Assessment Reflection

Audience

This is for my instructor and classmates. It also doubles as a short snapshot for anyone evaluating how I lead AI delivery and organizational change in real projects.

Artifact

Two comprehensive self-assessments measuring baseline competencies: AI/ML Integration Leadership Skills Self-Assessment and Change Management Skills Self-Assessment.

Why it matters

I build and ship AI systems in real orgs, so leadership is not just model performance. These assessments help me name what I’m already strong at and what I need to level up to lead responsible deployments and team adoption.

Reflection

On the AI/ML leadership assessment, my strengths are Continuous Improvement (20/20), AI/ML Project Management (18/20), and AI/ML Adoption Strategies (18/20). This matches how I work day-to-day: shipping, measuring impact, iterating, and scaling systems like EveryParent (RAG) and client implementations.

My gaps show up in the parts of responsible AI that require more formal process: ethical considerations (3), data requirements (3), training programs (3), bias/fairness (3), and ethical guidelines (3). I’m comfortable building the system, but I need tighter guardrails and repeatable checks.
On the change management assessment, I scored highest in Problem-Solving (23), Culture Understanding (20), and Critical Thinking (19). My lowest areas were Delegation (2), adapting leadership style (2), comfort with ambiguity (2), and managing relationships (2). That is accurate: I tend to rely on structured analysis and personal execution. My next step is building better delegation and people-leadership habits so change does not depend on me carrying everything.

Revisions to the assessments

I would make both assessments more scenario-based.

  • For AI/ML: add items on evaluation plans, drift monitoring, permissions, audit logs, and rollback procedures.
  • For change management: add items on leading change asynchronously across GitHub/Jira/Slack, and managing change in distributed technical teams.
I’d also add a few questions that connect the two, like leading adoption when AI introduces fear, trust issues, or workflow disruption.