Gen AI for Complex Problem-Solving & Root Cause Analysis
Leverage Generative AI to analyze complex problems, uncover root causes, and develop innovative, data-driven solutions for smarter decision-making.
February
11,
2026
12:00 PM - 02:00 PM (Eastern Time)
Duration: 1 Day
Hours: 2 Hours
Training Level: All Levels
About the Course:
As systems grow more interconnected, leaders are increasingly confronted with ambiguous problems, hidden dependencies, and unclear failure patterns. Generative AI can accelerate how teams explore hypotheses, identify causal drivers, surface risks, and synthesize data, without replacing critical thinking.
This workshop teaches participants how to combine classic problem-solving frameworks with AI prompting patterns to investigate issues more thoroughly, reduce bias, and develop clearer recommendations.
Course Objective
By the end of this course, participants will be able to:
- Frame complex problems using structured prompting.
- Generate, compare, and evaluate hypotheses efficiently.
- Use AI to support root cause analysis (RCA) with proven frameworks.
- Identify hidden dependencies, constraints, and second-order effects.
- Synthesize qualitative data into themes and signals.
- Create more effective action plans and mitigation paths.
- Reduce cognitive bias using multi-lens prompting.
- Validate assumptions with scenario testing and counterfactual analysis.
Who is the Target Audience?
- Product, Project, and Program Managers.
- Engineering Managers and Tech Leads.
- Business Analysts and Operations Leads.
- Change Managers and Strategy roles.
Basic Knowledge:
- Ideal for anyone tackling ambiguous issues with unclear root causes.
Curriculum
Total Duration: 2 Hours
Kickoff & Problem-Solving Realities
- Why complex problems get misdiagnosed
- Symptoms vs. causes vs. contributing factors
- Where human bias creeps in
Prompting for Problem Framing
- Clarifying scope and success criteria
- Identifying missing context
- Role-based perspective prompts
Hypothesis Generation
- Divergent prompts for option expansion
- Convergent prompts for narrowing
- Premortems and assumption surfacing
Root Cause Analysis Frameworks
- 5 Whys
- Fishbone (Ishikawa) diagram categories
- Fault tree analysis
- Causal loop prompts
Multi-Perspective Analysis
- Technical, operational, customer, risk, compliance, change lenses
- Prompting different personas
- Contradiction surfacing
Theme Extraction from Qualitative Data
- Summarizing feedback
- Clustering pain points
- Identifying leading indicators
Dependency Mapping
- Upstream vs. downstream impacts
- Cross-team constraints
- Bottleneck prompts
Second-Order Effects
- Scenario mapping
- Risk propagation analysis
- Time-delayed consequences
Counterfactual & Scenario Testing
- “What if we were wrong?”
- Alternative histories
- Stress tests
Bias Reduction Techniques
- Confirmation bias checks
- Overfitting to recent events
- Single-cause fallacy
Solution Evaluation & Prioritization
- Impact vs. effort grids
- Risk reduction scoring
- Mitigation alignment
Recommendations & Communication
- Turning analysis into crisp recommendations
- Executive framing
- Actionable next steps
Monitoring & Feedback Loops
- Leading vs. lagging indicators
- Early signal detection
- Trigger conditions