Training course

Overview

AI-Powered Analytics for Managers is a practical professional training course designed to equip managers and organisational leaders with the knowledge and skills required to use artificial intelligence, generative AI, automation, and modern analytics to improve managerial decision-making and organisational performance. The course focuses on how managers can use AI-assisted analytics to examine operational, financial, customer, workforce, project, programme, and performance data, identify meaningful patterns, evaluate evidence, and translate analytical outputs into practical management actions. It emphasises managerial judgement, responsible AI use, and the ability to challenge and validate AI-generated insights rather than relying on automated outputs without professional review.

The course provides practical exposure to Microsoft Excel, AI assistants, natural-language analytics, formulas, pivot tables, charts, dashboards, KPI analysis, automated reporting, decision-support tools, scenario analysis, and AI-assisted data preparation. Managers learn how to apply structured frameworks such as CRISP-DM, Balanced Scorecard, Results-Based Management, SMART objectives, PDCA, root-cause analysis, risk-based decision-making, and evidence-to-action approaches within AI-enabled analytical workflows. Practical management scenarios demonstrate how AI can support performance monitoring, resource allocation, operational improvement, planning, forecasting, problem solving, and strategic decision support.

A major focus of the programme is managerial evaluation of analytical quality and AI-generated outputs. Participants learn how to assess data accuracy, completeness, consistency, relevance, timeliness, provenance, assumptions, and context before using AI-assisted analysis for management decisions. The course covers descriptive statistics, trends, variances, ratios, benchmarks, correlation, regression, probability, confidence intervals, uncertainty, forecasting concepts, anomaly detection, and predictive analytics. Managers also learn to recognise correlation-versus-causation problems, bias, missing data, outliers, measurement errors, data leakage, overfitting, misleading visualisations, hallucinations, unsupported recommendations, automation bias, and other risks that can affect AI-powered management analysis.

Through management case studies, practical datasets, group exercises, decision simulations, workplace scenarios, and an applied capstone, participants progressively build an end-to-end AI-powered management analytics capability. The programme progresses from foundational AI and analytics concepts to AI-assisted data preparation, performance analysis, predictive analytics, automation, responsible AI, risk management, decision intelligence, governance, and strategic implementation. By the end of the five-day course, managers will be able to identify appropriate AI analytics opportunities, evaluate analytical evidence, use AI tools effectively, challenge unreliable outputs, communicate insights to stakeholders, and establish practical governance and monitoring practices for responsible AI-powered management analytics.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:
• Managers, department heads, and organisational leaders
• Senior and middle managers responsible for operational or strategic decisions
• Programme and project managers
• Operations and service-delivery managers
• Finance, accounting, audit, risk, and management reporting managers
• Business intelligence, analytics, performance, and management information managers
• Marketing, sales, customer experience, and market research managers
• Human resources and workforce management professionals
• Strategy, planning, transformation, and organisational development managers
• Monitoring, Evaluation, Research and Learning (MERL/MEL) managers
• Supply chain, procurement, logistics, and resource planning managers
• Policy, planning, development, NGO, government, and public-sector managers
• Managers responsible for KPIs, dashboards, performance reports, and management information
• Managers supervising analysts, researchers, consultants, or data teams
• Business owners and operational leaders seeking AI-enabled analytical capabilities
• Managers involved in budgeting, resource allocation, performance improvement, and risk management
• Managers responsible for evaluating research, forecasts, models, reports, and analytical recommendations
• Consultants, advisers, and professional services managers
• Professionals preparing for management roles involving data-driven and AI-supported decision-making

Course Objectives

By the end of the training, participants will be able to:
• Explain the foundations, capabilities, limitations, and managerial applications of AI-powered analytics
• Distinguish between traditional analytics, AI-assisted analytics, machine learning, predictive analytics, and generative AI
• Identify managerial problems and organisational processes that can benefit from AI-powered analytics
• Define management questions, analytical objectives, decision criteria, expected outputs, and success measures
• Evaluate data sources, data quality, metadata, provenance, assumptions, context, and analytical requirements
• Use Microsoft Excel and AI assistants to support data preparation, analysis, calculations, reporting, and management information
• Develop effective prompts for managerial analysis and validate AI-generated formulas, calculations, interpretations, and recommendations
• Analyse KPIs, trends, variances, ratios, benchmarks, exceptions, and performance gaps using AI-assisted workflows
• Develop practical charts, dashboards, scorecards, management reports, and decision briefs
• Apply descriptive statistics, correlation, regression, probability, confidence intervals, forecasting concepts, and uncertainty appropriately
• Explain basic machine learning concepts including classification, regression, clustering, prediction, training, testing, and validation
• Interpret predictive analytics outputs and evaluate their relevance to managerial decisions
• Identify bias, missing data, outliers, measurement errors, data leakage, overfitting, model limitations, and other analytical risks
• Evaluate AI-generated insights for accuracy, consistency, relevance, unsupported claims, hallucinations, and inappropriate recommendations
• Distinguish correlation from causation and identify when additional evidence or investigation is required
• Apply structured management frameworks including Balanced Scorecard, Results-Based Management, SMART, PDCA, root-cause analysis, and evidence-to-action
• Apply scenario analysis, sensitivity analysis, risk assessment, prioritisation, and structured decision-support techniques
• Apply responsible AI principles covering privacy, confidentiality, fairness, transparency, accountability, security, and governance
• Establish human-in-the-loop controls for validating AI-assisted management analysis and recommendations
• Communicate AI-powered analytical findings, risks, assumptions, uncertainty, and recommendations effectively to management and stakeholders
• Develop practical monitoring, governance, documentation, and continuous-improvement processes for AI-powered analytics
• Complete an applied capstone demonstrating an end-to-end AI-powered management analytics and decision-support workflow

Course Content

Day 1: Foundations of AI-Powered Analytics and Managerial Decision-Making

Module 1: Foundations of AI-Powered Analytics and Managerial Decision-Making

1.      Understanding AI-Powered Analytics and Its Role in Modern Management

2.      Artificial Intelligence, Machine Learning, Generative AI, and Managerial Analytics

3.      Traditional Analytics Versus AI-Assisted and Automated Management Analytics

4.      Identifying Managerial Problems Suitable for AI-Powered Analytics

5.      Defining Management Questions, Analytical Objectives, and Decision Requirements

6.      Data Sources, Data Types, Metadata, Context, and Management Information

7.      Assessing Data Quality, Accuracy, Completeness, Consistency, Relevance, and Timeliness

8.      CRISP-DM, Human-in-the-Loop Validation, and the AI Analytics Lifecycle

9.      Case Study: Identifying AI Analytics Opportunities Across Organisational Functions

10.  Practical Exercise: Developing an AI-Powered Analytics Plan for a Management Problem

Day 2: AI-Assisted Data Analysis, Performance Monitoring, and Visualisation

Module 2: AI-Assisted Data Analysis, Performance Monitoring, and Visualisation

1.      AI-Assisted Data Preparation, Cleaning, Structuring, and Validation

2.      Handling Missing Data, Duplicates, Outliers, Errors, and Inconsistent Management Records

3.      Using AI Assistants with Excel Formulas, Sorting, Filtering, and Data Transformation

4.      Pivot Tables, Management Summaries, and AI-Assisted Analytical Exploration

5.      KPI Analysis, Targets, Benchmarks, Ratios, Variances, and Performance Gaps

6.      AI-Assisted Trend Analysis, Pattern Recognition, and Exception Detection

7.      Developing Management Charts, Dashboards, Scorecards, and Visual Reports

8.      Using AI for Management Reporting, Narrative Summaries, and Decision Briefs

9.      Case Study: AI-Assisted Performance Analysis for an Operational Management Team

10.  Practical Exercise: Building an AI-Powered Management Dashboard and Performance Analysis Workflow

Day 3: Statistical Evidence, Predictive Analytics, and Managerial Judgement

Module 3: Statistical Evidence, Predictive Analytics, and Managerial Judgement

1.      Foundations of Predictive Analytics and Machine Learning for Managers

2.      Classification, Regression, Clustering, Forecasting, and Practical Management Applications

3.      Training, Testing, Validation, and Basic Predictive Model Evaluation

4.      Descriptive Statistics, Correlation, Relationships, and Management Evidence

5.      Probability, Confidence Intervals, Uncertainty, and Risk in Management Analysis

6.      Forecasting Concepts and Interpreting Predictive Outputs for Management Planning

7.      Correlation Versus Causation and the Limits of AI-Generated Conclusions

8.      Evaluating AI-Generated Analysis, Recommendations, Reports, and Management Insights

9.      Case Study: Validating AI-Powered Analysis Before Making a Management Decision

10.  Practical Exercise: Reviewing and Challenging AI-Generated Analytical Outputs

Day 4: Advanced AI Analytics, Automation, Risk, and Responsible AI

Module 4: Advanced AI Analytics, Automation, Risk, and Responsible AI

1.      Generative AI for Management Analytics, Research, Reporting, and Decision Support

2.      Prompt Engineering for Management Analysis and AI-Assisted Problem Solving

3.      Automating Repetitive Management Reporting, Data Analysis, and Information Workflows

4.      Integrating AI, Excel, Dashboards, KPIs, Statistical Analysis, and Predictive Analytics

5.      AI-Assisted Scenario Analysis, Sensitivity Analysis, and What-If Management Modelling

6.      Analytical Assumptions, Explainability, Transparency, and Managerial Validation

7.      AI Risks: Hallucinations, Bias, Automation Bias, Data Leakage, Model Drift, and Unsupported Outputs

8.      Responsible AI, Privacy, Confidentiality, Fairness, Security, Accountability, and Data Governance

9.      Case Study: Managing AI Analytics Risk in a High-Impact Management Decision

10.  Practical Exercise: Designing a Responsible and Automated AI Management Analytics Workflow

Day 5: AI-Powered Management Decision Intelligence, Governance, and Capstone

Module 5: AI-Powered Management Decision Intelligence, Governance, and Capstone

1.      Integrating AI Analytics, Management Experience, Organisational Context, and Professional Judgement

2.      Developing AI-Powered Management Dashboards, Scorecards, Reports, and Decision Briefs

3.      Applying Balanced Scorecard, Results-Based Management, SMART, PDCA, and Evidence-to-Action Frameworks

4.      Evaluating AI-Powered Recommendations, Alternatives, Risks, Trade-Offs, and Consequences

5.      Applying Root-Cause Analysis, Prioritisation, Risk Assessment, and Structured Decision Support

6.      Communicating AI-Generated Findings, Assumptions, Limitations, and Uncertainty to Stakeholders

7.      Establishing AI Analytics Governance, Roles, Responsibilities, Validation Controls, and Approval Processes

8.      Monitoring AI Analytics Performance, Model Changes, Data Quality, Outputs, and Continuous Improvement

9.      Integrated Case Study: End-to-End AI-Powered Management Analytics and Decision Simulation

10.  Capstone Exercise: Develop, Validate, Communicate, Govern, and Implement an AI-Powered Analytics Solution for a Real-World Management Scenario