Non-AI Improvements
Even though this course centered on generative AI, one of the core competencies emphasized in CIDM 6096 is the ability to identify inefficiencies and apply low-tech, high-impact operational improvements. These improvements demonstrate disciplined thinking, clarity in identifying root causes, and attention to workflow design — skills that complement AI adoption.
This section highlights my Lean improvements and process-focused assignments completed during the course.
2-Second Lean Improvements
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This assignment focused on recognizing everyday inefficiencies and eliminating small but recurring sources of friction. Examples included reorganizing cluttered spaces, improving access to frequently used items, and creating simple systems for consistency.
Key Themes:
Eliminating wasted motion
Simplifying access
Creating home locations for items
Reducing time spent searching or reorganizing
Why It Matters: Lean improvements reduce cognitive load and increase efficiency — foundational skills that support process automation and AI integration.
Lean Morning Meetings Plan
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This improvement introduced a structured morning meeting plan based on Lean principles. The plan included concise updates, reflection points, and opportunities for team alignment.
Key Elements:
Daily accountability
Visibility into tasks and priorities
Empowering team communication
Creating a predictable, reliable structure
Why It Matters: A consistent morning rhythm enables teams to adopt and sustain GenAI workflows because expectations are clear and routines are stable.
Identify the 8 Wastes
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This assignment applied the Lean “TIMWOODS” framework to real examples, such as overproduction, defects, waiting time, excess inventory, and unused human potential.
Key Improvements:
Recognizing inefficiencies in home and work environments
Understanding how small problems accumulate into larger process failures
Applying Lean categorization to identify root causes
Why It Matters: This kind of analysis builds foundational operational thinking — a necessary precursor to AI-driven automation.
How These Improvements Support AI Adoption
Together, these improvements show:
disciplined operational thinking
Comfort evaluating workflows
the ability to identify waste
skill in clarifying processes before automation
readiness to integrate AI in a stable, organized environment
In CIDM 6096, this is a core learning outcome: AI is most effective when paired with strong operational foundations.