AI-Assisted Decision Making and Business Performance Course

Overview

AI-Assisted Decision Making and Business Performance Course is a ten-day foundation course for business leaders, functional managers, strategy teams, performance managers, transformation leaders, and decision analysts, who leave with an AI-Assisted Decision and Business Performance System. Participants frame decisions, assess evidence, compare scenarios, evaluate options, define performance measures, design experiments, assign human review, monitor outcomes, and establish adoption routines. Agile Leaders Training Center provides training in AI-assisted decision making and business performance.

Who Should Attend

• Business leaders responsible for cross-functional decisions and results
• Functional managers responsible for priorities and performance
• Strategy teams responsible for options, initiatives, and alignment
• Performance teams responsible for objectives, measures, and reviews
• Transformation teams responsible for adoption and operating change
• Decision analysts responsible for evidence, scenarios, and recommendations

The course assumes participants contribute to managerial decisions or performance reviews and leaves out data-science programming, model development, technical forecasting, executive leadership theory, and process-engineering certification.

Departments and Industries

The course supports evidence-based managerial decisions and business-performance improvement across organizations.

• Corporate strategy and transformation functions
• Finance, planning, and performance-management teams
• Commercial, customer, and service operations
• Energy, manufacturing, and infrastructure organizations
• Financial, healthcare, and professional service organizations
• Government, education, and nonprofit organizations

Learning Objectives

By the end of this course, participants will be able to:

• Apply structured framing to managerial decisions
• Analyze evidence quality, uncertainty, and assumptions
• Compare scenarios, forecasts, and business options
• Build objectives, KPIs, and performance-review routines
• Evaluate experiments, outcomes, and corrective actions
• Build an AI-Assisted Decision and Business Performance System

Course Agenda

Day 1: Decision Context and Value

• Managerial Decision-Scope Canvas
• Stakeholder and Value-Outcome Map
• Decision Type and Frequency Matrix
• Constraint, Dependency, and Risk Register
• Decision Owner and Review-Rights Chart

Day 2: Evidence and Data Quality

• Decision Evidence Inventory
• Data Provenance and Quality Checklist
• Assumption and Uncertainty Register
• Bias and Missing-Evidence Review
• Evidence Sufficiency Decision Gate

Day 3: AI Use Cases and Human Oversight

• AI Decision-Support Use-Case Matrix
• Prediction, Recommendation, and Classification Map
• Human-in-the-Loop Design Sheet
• Explanation and Challenge Requirement Table
• Override, Escalation, and Accountability Protocol

Day 4: Scenarios and Forecasts

• Business Driver and Signal Map
• Baseline and Alternative Scenario Builder
• Forecast Assumption and Range Table
• Sensitivity and Stress-Test Matrix
• Scenario Implication Decision Log

Day 5: Options and Priorities

• Business Option Design Card
• Multi-Criteria Option Scorecard
• Benefit, Cost, Risk, and Feasibility Matrix
• Portfolio Priority and Resource Grid
• Decision Recommendation Record

Day 6: Objectives and Performance Measures

• Strategy Objective and Cause-Effect Map
• Outcome, Driver, and Guardrail Measure Set
• KPI Definition and Calculation Sheet
• Target, Threshold, and Tolerance Table
• Performance Data Ownership Register

Day 7: Experiments and Learning

• Decision Hypothesis and Test Card
• Pilot and Experiment Design Canvas
• Success, Stop, and Adapt Criteria
• Experiment Evidence and Learning Log
• Scale, Revise, or Stop Decision Gate

Day 8: Reviews and Corrective Action

• Performance Review Meeting Structure
• Variance and Root-Cause Analysis Sheet
• Initiative Benefit Realization Tracker
• Corrective Action and Owner Register
• Decision Outcome Feedback Loop

Day 9: Governance and Adoption

• AI Decision Governance and Control Map
• Performance Reporting and Communication Plan
• Manager Adoption and Capability Matrix
• Decision Routine and Workflow Integration Plan
• Monitoring, Audit, and Change-Control Schedule

Day 10: Decision and Performance Practice

• Suggested Exercise: Frame a Cross-Functional Decision
• Suggested Exercise: Compare Scenarios and Score Options
• Suggested Exercise: Build KPIs and an Experiment
• Suggested Exercise: Design Reviews, Oversight, and Adoption
• Capstone Exercise: AI-Assisted Decision and Business Performance System

Practical Exercises

The course uses suggested activities that connect AI-assisted evidence to managerial decisions and performance routines.

• Suggested activity: frame a decision, identify owners and stakeholders, inventory evidence, and document uncertainty and assumptions
• Suggested activity: create scenarios and forecasts, test sensitivities, score options, and record a recommendation
• Suggested activity: map objectives, define KPIs, set targets and guardrails, and design a decision experiment
• Suggested activity: establish reviews, corrective actions, human oversight, adoption routines, monitoring, and change control

FAQs

Who suits AI-assisted decision making and business-performance training?

AI-assisted decision and performance training suits leaders, managers, strategy, performance, transformation, and analysis teams. It assumes responsibility for business choices or results and requires no programming.

How does AI-assisted decision making differ from data-science training?

AI-assisted decision making focuses on framing, evidence, scenarios, options, human review, KPIs, experiments, and adoption. Data-science training focuses on data preparation, algorithms, coding, model building, and technical evaluation.

How can AI improve managerial decision making?

AI can organize evidence, identify patterns, generate forecasts, compare scenarios, and support option scoring. Managers remain responsible for objectives, context, uncertainty, tradeoffs, stakeholder effects, approval, and accountability.

How should business performance be connected to decisions?

Business performance should connect each decision to objectives, expected outcomes, driver and guardrail measures, targets, owners, review timing, experiments, corrective actions, and learning from actual results.

What is an AI-Assisted Decision and Business Performance System?

The system connects decision canvases, evidence standards, scenarios, option scores, human review, objectives, KPIs, experiments, performance reviews, corrective actions, governance, and adoption routines.

Conclusion

Participants take back an AI-Assisted Decision and Business Performance System connecting evidence, options, measures, experiments, oversight, and reviews. The system makes assumptions, tradeoffs, ownership, performance expectations, and learning visible across functions. It supports traceable managerial choices, coordinated performance routines, and timely adjustment when evidence or outcomes change.