AI for Better Decision-Making in the Workplace Course
Formats⟩Live Virtual: 4 hrs./1 Day |In-Person: 6 hrs./1 Day
AI for Better Decision-Making in the Workplace equips leaders and analysts with practical strategies to enhance decision quality through AI and data-driven approaches. Participants will explore how AI supports each stage of the business decision lifecycle—from situational awareness and option analysis to risk assessment and forecasting—while ensuring decisions remain transparent, defensible, and aligned with regulatory expectations. The course also covers the use of generative AI for research, scenario modeling, and decision support, along with critical considerations such as bias, privacy, and governance. By the end, participants will be prepared to lead effective human-AI decision processes that improve outcomes and maintain public trust.
Learning Objectives »
Explore how AI enhances business decision-making.
Apply AI-supported decision frameworks.
Evaluate AI outputs for accuracy and transparency.
Identify and manage risks (bias, privacy, compliance).
Use generative AI for decision support and analysis.
Support effective human-AI decision partnerships.
Course Agenda
The Future of Decision-Making
Decision-Making in Workplace
Data-Driven/AI-Augmented Decisions
What “Good” Decisions Require
Where AI Improves Outcomes
Decision Intelligence
Decision Frameworks That Work with AI
The Decision Lifecycle
Structured Decision Models
Enhancing Situation Awareness with AI
Option Generation and Tradeoff
Risk Assessment and Forecasting
Decision Augmentation vs Automation
Defensible, Transparent, Audit-Ready
Defensibility and Traceability
Evaluating AI Outputs
Explainability and Transparency
Documentation and Justification
Generative AI for Decision Support
Decision Speed and Access to Insight
Research, Synthesis, and Briefings
Scenario Modeling and “What-If”
Prompting for Decision Support
Hallucinations and Weak Outputs
AI-Assisted Decision Workflow
AI-Supported Decision Scenario
Risk, Bias, and Responsible Decisions
AI Risk in Business Decisions
Bias in Data and Impact on Policy
Privacy, Security, and Compliance
Ethical Decision-Making and Red Flags
Building Trust in AI Decisions
Governance and Oversight
AI Guidance and Decision Governance
Roles and Accountability
Evaluating AI Tools and Vendors
Monitoring and Auditing AI Decisions
Decision Effectiveness and Risk
Human + AI Decision Leadership
The Role of Leaders and Analysts
Human Bias vs Algorithmic Bias
Human-AI Decision Partnerships
AI for Better Decision-Making in the Workplace Course
Formats⟩Live Virtual: 4 hrs./1 Day |In-Person: 6 hrs./1 Day
AI for Better Decision-Making in the Workplace equips leaders and analysts with practical strategies to enhance decision quality through AI and data-driven approaches. Participants will explore how AI supports each stage of the business decision lifecycle—from situational awareness and option analysis to risk assessment and forecasting—while ensuring decisions remain transparent, defensible, and aligned with regulatory expectations. The course also covers the use of generative AI for research, scenario modeling, and decision support, along with critical considerations such as bias, privacy, and governance. By the end, participants will be prepared to lead effective human-AI decision processes that improve outcomes and maintain public trust.
Learning Objectives »
Explore how AI enhances business decision-making.
Apply AI-supported decision frameworks.
Evaluate AI outputs for accuracy and transparency.
Identify and manage risks (bias, privacy, compliance).
Use generative AI for decision support and analysis.
Support effective human-AI decision partnerships.
Course Agenda
The Future of Decision-Making
Decision-Making in Workplace
Data-Driven/AI-Augmented Decisions
What “Good” Decisions Require
Where AI Improves Outcomes
Decision Intelligence
Decision Frameworks That Work with AI
The Decision Lifecycle
Structured Decision Models
Enhancing Situation Awareness with AI
Option Generation and Tradeoff
Risk Assessment and Forecasting
Decision Augmentation vs Automation
Defensible, Transparent, Audit-Ready
Defensibility and Traceability
Evaluating AI Outputs
Explainability and Transparency
Documentation and Justification
Generative AI for Decision Support
Decision Speed and Access to Insight
Research, Synthesis, and Briefings
Scenario Modeling and “What-If”
Prompting for Decision Support
Hallucinations and Weak Outputs
AI-Assisted Decision Workflow
AI-Supported Decision Scenario
Risk, Bias, and Responsible Decisions
AI Risk in Business Decisions
Bias in Data and Impact on Policy
Privacy, Security, and Compliance
Ethical Decision-Making and Red Flags
Building Trust in AI Decisions
Governance and Oversight
AI Guidance and Decision Governance
Roles and Accountability
Evaluating AI Tools and Vendors
Monitoring and Auditing AI Decisions
Decision Effectiveness and Risk
Human + AI Decision Leadership
The Role of Leaders and Analysts
Human Bias vs Algorithmic Bias
Human-AI Decision Partnerships
PTR Training uses 'cookies' to improve your website experience. View our Privacy Policy for details. By continuing, you agree to accept and adhere to our Terms of Use.
