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.

Artificial Intelligence

2 Hours

Description

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 Objectives

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.

Target Audience

Product, Project, and Program Managers.

Engineering Managers and Tech Leads.

Business Analysts and Operations Leads.

Change Managers and Strategy roles.

Basic Understanding

Ideal for anyone tackling ambiguous issues with unclear root causes.

Course Content

No sessions available.

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Gen AI for Complex Problem-Solving & Root Cause Analysis

Session 1: Kickoff & Problem-Solving Realities

  1. Why complex problems get misdiagnosed
  2. Symptoms vs. causes vs. contributing factors
  3. Where human bias creeps in

Session 2: Prompting for Problem Framing

  1. Clarifying scope and success criteria
  2. Identifying missing context
  3. Role-based perspective prompts

Session 3: Hypothesis Generation

  1. Divergent prompts for option expansion
  2. Convergent prompts for narrowing
  3. Premortems and assumption surfacing

Session 4: Root Cause Analysis Frameworks

  1. 5 Whys
  2. Fishbone (Ishikawa) diagram categories
  3. Fault tree analysis
  4. Causal loop prompts

Session 5: Multi-Perspective Analysis

  1. Technical, operational, customer, risk, compliance, change lenses
  2. Prompting different personas
  3. Contradiction surfacing

Session 6: Theme Extraction from Qualitative Data

  1. Summarizing feedback
  2. Clustering pain points
  3. Identifying leading indicators

Session 7: Dependency Mapping

  1. Upstream vs. downstream impacts
  2. Cross-team constraints
  3. Bottleneck prompts

Session 8: Second-Order Effects

  1. Scenario mapping
  2. Risk propagation analysis
  3. Time-delayed consequences

Session 9: Counterfactual & Scenario Testing

  1. “What if we were wrong?”
  2. Alternative histories
  3. Stress tests

Session 10: Bias Reduction Techniques

  1. Confirmation bias checks
  2. Overfitting to recent events
  3. Single-cause fallacy

Session 11: Solution Evaluation & Prioritization

  1. Impact vs. effort grids
  2. Risk reduction scoring
  3. Mitigation alignment

Session 12: Recommendations & Communication

  1. Turning analysis into crisp recommendations
  2. Executive framing
  3. Actionable next steps

Session 13: Monitoring & Feedback Loops

  1. Leading vs. lagging indicators
  2. Early signal detection
  3. Trigger conditions

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