Dynamic Problem Solving
Training Course
Strengthen complex problem solving through AI-augmented root cause analysis, collaborative insight, decision tools, creative options, and accountable action.
Dynamic Problem Solving helps organizations develop leaders, managers, analysts, project contributors, team members, and cross-functional professionals who need to approach complex challenges with clear structure, stronger collaboration, better evidence, and disciplined action. Anchored in AMS Academy’s 4x4 Training Design Model℠ and AI-Powered Diagnostic & Customization Framework℠, the course meets participants at the right level and guides practical skill application. It strengthens problem framing, root cause analysis, evidence review, decision discipline, creative thinking, and accountable follow-through.
Participants will build capability in 5 Whys, cause-and-effect diagrams, SWOT, decision trees, cost-benefit analysis, assumption testing, data visualization, brainstorming, SCAMPER, option evaluation, implementation planning, and outcome monitoring. AI supports the work through information synthesis, pattern review, root cause prompts, scenario comparison, option generation, risk visibility, and decision support. The emphasis remains on human judgment, curiosity, collaboration, context, creativity, and accountability. Depending on selected modular content, customization needs, delivery modality, and client purpose, the experience can range from a focused 90-minute session to a multi-day program of up to three full days. This course can also be stacked within a solution set to support AMS Advisory consulting solutions.
Course Description
Learning Themes
This course is built around selected learning themes informed by current problem-solving, decision-making, collaboration, and business execution trends:
- Problem Framing, Boundary Definition, Desired Outcomes, Assumption Awareness, and Decision Context
- Root Cause Analysis, 5 Whys, Cause-and-Effect Diagrams, SWOT, Pattern Recognition, and Data Visualization
- AI-Augmented Information Synthesis, Root Cause Prompting, Scenario Comparison, Option Generation, and Risk Visibility
- Collaborative Decision-Making, Active Listening, Stakeholder Input, Creative Thinking, SCAMPER, and Brainstorming
- Decision Trees, Cost-Benefit Analysis, Option Evaluation, Implementation Planning, Monitoring, and Accountable Action
Course Customization
Applying the AI-Powered Diagnostic & Customization Framework℠ and 4x4 Training Design Model℠ calibrates the course for:
- Pre-Training Survey Insights, Cohort Readiness Patterns, Problem-Solving Confidence, and Current Decision Pain Points
- Problem-Solving Experience Level, Role Mix, Business Context, Decision Scope, and Collaboration Requirements
- Client Operating Challenges, Data Environment, Process Constraints, Stakeholder Expectations, and Execution Priorities
- Existing AI Tool Access, Approved Use Boundaries, Source Material, Data Quality Limits, and Human Review Expectations
- Selected Modular Content, Duration, Delivery Modality, Practice Depth, and Applied Problem-Solving-AI Practice Emphasis
- Scenario, Exercise, Case Example, Problem Map, Root Cause Review, Decision Tool, and Action Plan Focus
Applied Learning Outcomes
Converting dynamic problem-solving concepts into practical workplace behaviors enables participants to:
- Frame Problems Clearly by Defining Boundaries, Root Questions, Success Measures, Assumptions, and Desired Outcomes
- Use Root Cause Analysis, 5 Whys, Cause-and-Effect Diagrams, SWOT, and Data Review to Understand Contributing Factors
- Use AI-Supported Prompts and Summaries to Synthesize Inputs, Compare Scenarios, Surface Risks, and Generate Practical Options
- Apply Decision Trees, Cost-Benefit Analysis, Stakeholder Input, and Trade-Off Logic to Evaluate Solutions
- Use Creative Thinking, Brainstorming, and SCAMPER to Expand Options Without Losing Analytical Discipline
- Implement Decisions with Clear Owners, Milestones, Monitoring Practices, Feedback Loops, and Human Accountability
Activity Design
Engaging participants through problem-solving simulations, scenario-driven practice, and action-learning activities may include:
- Problem Framing, Boundary Definition, Assumption Awareness, and Desired Outcome Mapping Exercise
- Root Cause Analysis, 5 Whys, Cause-and-Effect Diagram, and SWOT Practice
- AI-Augmented Information Synthesis, Pattern Review, Scenario Comparison, and Risk Visibility Activity
- Collaborative Decision-Making, Active Listening, Stakeholder Input, and Option Development Simulation
- Decision Tree, Cost-Benefit, Trade-Off, and Solution Evaluation Workshop
- SCAMPER, Brainstorming, Creative Option Generation, and Practical Solution Refinement Activity
- Implementation Plan, Monitoring Routine, Feedback Loop, and Dynamic Problem-Solving Action Plan
Skills in Practice
Applying dynamic problem-solving skills enables participants to:
- Clarify Complex Problems Before Jumping to Solutions, Decisions, or Assumptions
- Identify Root Causes, Constraints, Patterns, Risks, Data Gaps, and Decision Drivers More Effectively
- Use AI as an Augmentation Layer for Source Synthesis, Scenario Review, Root Cause Exploration, and Option Generation
- Strengthen Collaboration Through Active Listening, Shared Framing, Stakeholder Input, and Open Communication
- Compare Solution Options Using Evidence, Cost-Benefit Logic, Decision Trees, Trade-Offs, and Business Fit
- Develop Creative, Practical Solutions that Balance Innovation, Feasibility, Risk, and Execution Reality
- Monitor Decision Impact and Adjust Actions Based on Evidence, Feedback, Learning, and Follow-Through Measures
Collaborative Engagement Model
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