Training course

Overview

Strategic AI-Powered Analytics is a professional training course designed to help senior managers, executives, analysts, strategy professionals, and organisational leaders integrate artificial intelligence, generative AI, automation, and advanced analytics into strategic planning and organisational decision-making. The course focuses on using AI-powered analytical capabilities to evaluate strategic priorities, monitor organisational performance, identify opportunities and risks, allocate resources, assess market and operational conditions, and translate complex evidence into actionable strategic insight. Participants develop the ability to connect AI analytics with organisational objectives while maintaining rigorous human judgement, governance, and accountability.

The programme provides practical tools and strategic frameworks for developing AI-enabled analytical workflows, including Microsoft Excel, AI assistants, natural-language analytics, advanced dashboards, KPI frameworks, scorecards, scenario analysis, sensitivity analysis, forecasting, risk registers, decision matrices, and strategic decision briefs. Participants apply frameworks such as CRISP-DM, Balanced Scorecard, Results-Based Management, Theory of Change, SMART objectives, PDCA, risk-based decision-making, root-cause analysis, evidence-to-action, and continuous-improvement principles. These approaches help organisations connect AI-powered analytics with strategic objectives, measurable outcomes, performance management, resource allocation, transformation initiatives, and strategic risk oversight.

Strategic AI analytics requires careful evaluation of evidence because advanced AI systems can produce inaccurate, biased, incomplete, or unsupported outputs. Participants therefore learn to assess data provenance, definitions, assumptions, quality, representativeness, analytical methodology, and model performance. The course addresses descriptive and multidimensional analysis, trends, ratios, benchmarks, variances, correlation, regression, probability, confidence intervals, forecasting, predictive analytics, scenario modelling, uncertainty, and sensitivity analysis. Particular attention is given to correlation versus causation, statistical versus strategic significance, selection bias, missing data, outliers, measurement errors, data leakage, overfitting, model drift, algorithmic bias, hallucinations, automation bias, and other risks that can affect strategic evidence and decision quality.

Through strategic case studies, real-world scenarios, analytical exercises, decision simulations, group challenges, and an applied capstone, participants progressively develop an end-to-end strategic AI analytics capability. The programme advances from foundational AI and strategic analytics concepts to advanced data preparation, performance intelligence, predictive analytics, scenario planning, automation, responsible AI, strategic decision support, governance, and implementation. By the end of the five-day course, participants will be able to identify high-value strategic AI analytics opportunities, evaluate the credibility and limitations of AI-generated evidence, develop strategic analytical outputs, assess risks and alternative scenarios, communicate insights to decision-makers, and establish responsible AI analytics practices that support long-term organisational performance.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:
• Senior managers, department heads, and organisational leaders
• Strategy, planning, transformation, and organisational development professionals
• Executives responsible for strategic performance and organisational decision-making
• Data, business intelligence, analytics, and management information professionals
• Senior researchers, research managers, and analytical professionals
• Monitoring, Evaluation, Research and Learning (MERL/MEL) professionals
• Programme, portfolio, and project managers
• Performance management and organisational improvement professionals
• Finance, accounting, audit, risk, and management reporting professionals
• Marketing, customer intelligence, sales, and market research professionals
• Operations and service-delivery leaders
• Human resources, workforce analytics, and organisational effectiveness professionals
• Supply chain, procurement, logistics, and resource planning professionals
• Policy, planning, development, NGO, government, and public-sector professionals
• Consultants, advisers, and professional services practitioners
• Executives and managers responsible for strategic KPIs, dashboards, scorecards, and performance reviews
• Professionals involved in strategic planning, budgeting, investment, resource allocation, and transformation
• Professionals responsible for research, reports, forecasts, models, and strategic evidence
• Leaders supervising analysts, researchers, consultants, or strategic planning teams
• Professionals responsible for organisational risk, governance, digital transformation, and continuous improvement

Course Objectives

By the end of the training, participants will be able to:
• Explain the strategic capabilities, applications, limitations, and risks of AI-powered analytics
• Distinguish between traditional analytics, AI-assisted analytics, machine learning, predictive analytics, generative AI, and decision intelligence
• Identify strategic problems and organisational opportunities suitable for AI-powered analytical solutions
• Align AI analytics initiatives with organisational strategy, strategic priorities, objectives, outcomes, and performance measures
• Define strategic questions, analytical objectives, stakeholders, constraints, alternatives, decision criteria, assumptions, and expected outcomes
• Evaluate data sources, provenance, metadata, definitions, quality, context, representativeness, and analytical requirements
• Use Microsoft Excel and AI assistants to support strategic analysis, modelling, reporting, and decision preparation
• Analyse KPIs, targets, benchmarks, ratios, trends, variances, material changes, exceptions, risks, and strategic performance gaps
• Develop strategic dashboards, scorecards, analytical reports, decision briefs, and evidence-based narratives
• Apply descriptive statistics, correlation, regression, probability, confidence intervals, forecasting, predictive analytics, and uncertainty appropriately
• Explain machine learning concepts including classification, regression, clustering, prediction, training, testing, and validation
• Evaluate predictive models, forecasts, assumptions, performance measures, limitations, and strategic relevance
• Identify bias, missing data, outliers, measurement errors, selection effects, data leakage, overfitting, model drift, and other analytical risks
• Evaluate AI-generated insights and recommendations for accuracy, relevance, consistency, unsupported claims, hallucinations, and strategic applicability
• Distinguish correlation from causation and recognise when additional evidence is required
• Assess statistical significance, practical significance, strategic significance, uncertainty, and decision relevance
• Apply Balanced Scorecard, Results-Based Management, Theory of Change, SMART, PDCA, risk-based decision-making, and evidence-to-action frameworks
• Apply structured decision analysis, weighted criteria, prioritisation, scenario analysis, sensitivity analysis, and strategic risk assessment
• Evaluate strategic alternatives, trade-offs, resource implications, assumptions, uncertainty, and potential consequences
• Apply responsible AI principles covering privacy, confidentiality, fairness, transparency, accountability, security, and human oversight
• Establish AI analytics governance, validation, monitoring, documentation, approval, and accountability mechanisms
• Communicate AI-powered strategic evidence, assumptions, limitations, risks, uncertainty, and recommendations effectively
• Establish continuous-improvement processes for data quality, AI analytics, model performance, and strategic decision support
• Complete an applied capstone demonstrating an end-to-end strategic AI-powered analytics and decision-support solution

Course Content

Day 1: Foundations of Strategic AI-Powered Analytics

Module 1: Foundations of Strategic AI-Powered Analytics

1.      Understanding Strategic AI-Powered Analytics and Its Role in Organisational Strategy

2.      Artificial Intelligence, Machine Learning, Generative AI, and Strategic Decision Intelligence

3.      Traditional Analytics Versus AI-Assisted, Automated, and Predictive Strategic Analytics

4.      Identifying Strategic Problems, Opportunities, and High-Value AI Analytics Use Cases

5.      Aligning AI Analytics with Organisational Strategy, Objectives, Outcomes, and Performance Measures

6.      Defining Strategic Questions, Decision Criteria, Stakeholders, Constraints, and Expected Outcomes

7.      Data Sources, Provenance, Metadata, Definitions, Context, and Strategic Information Requirements

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

9.      Case Study: Evaluating Strategic AI Analytics Opportunities Across an Organisation

10.  Strategic Exercise: Developing an AI-Powered Analytics Roadmap for an Organisational Priority

Day 2: Strategic Data Analysis, Performance Intelligence, and Visualisation

Module 2: Strategic Data Analysis, Performance Intelligence, and Visualisation

1.      AI-Assisted Data Preparation, Quality Assessment, and Strategic Analytical Review

2.      Using AI Assistants and Excel for Strategic Calculations, Modelling, and Data Analysis

3.      KPI Frameworks, Targets, Benchmarks, Ratios, Variances, and Strategic Performance Gaps

4.      Trend Analysis, Pattern Recognition, Material Changes, Exceptions, and Emerging Strategic Signals

5.      AI-Assisted Descriptive and Multidimensional Analysis for Strategic Performance

6.      Strategic Dashboards, Scorecards, Management Information, and Executive Visualisation

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

8.      Strategic Reports, Decision Briefs, Analytical Narratives, and Evidence-to-Action Communication

9.      Case Study: Using AI-Powered Performance Intelligence to Support Strategic Planning

10.  Practical Exercise: Building an AI-Assisted Strategic Performance Dashboard and Decision Brief

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

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

1.      Foundations of Predictive Analytics and Machine Learning for Strategic Decision-Making

2.      Classification, Regression, Clustering, Forecasting, and Strategic Business Applications

3.      Training, Testing, Validation, Model Performance, and Predictive Reliability

4.      Correlation, Regression, Relationships, and Interpretation of Strategic Analytical Evidence

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

6.      Forecasting Outputs, Predictive Scenarios, and Strategic Planning Implications

7.      Statistical Significance, Practical Significance, Strategic Significance, and Decision Relevance

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

9.      Case Study: Reviewing an AI-Generated Forecast, Predictive Model, or Strategic Recommendation

10.  Strategic Exercise: Challenging, Validating, and Interpreting AI-Powered Analytical Evidence

Day 4: Advanced Strategic AI Analytics, Scenario Planning, and Responsible AI

Module 4: Advanced Strategic AI Analytics, Scenario Planning, and Responsible AI

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

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

3.      AI-Assisted Automation of Strategic Reporting, Monitoring, and Management Information

4.      Integrating AI, Dashboards, Predictive Models, Forecasts, Risk Registers, and Decision Tools

5.      Advanced Scenario Planning, Sensitivity Analysis, Stress Testing, and Strategic Resilience

6.      Model Assumptions, Explainability, Transparency, Validation, and Strategic Challenge

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

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

9.      Case Study: Governing AI Analytics During a High-Impact Strategic Decision

10.  Practical Exercise: Designing a Responsible, Governed, and Automated Strategic AI Analytics Framework

Day 5: Strategic AI Decision Intelligence, Governance, and Capstone

Module 5: Strategic AI Decision Intelligence, Governance, and Capstone

1.      Integrating AI Analytics, Strategic Objectives, Organisational Context, and Human Judgement

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

3.      Applying Balanced Scorecard, Results-Based Management, Theory of Change, SMART, and PDCA Frameworks

4.      Structured Strategic Decision Analysis, Weighted Criteria, Prioritisation, Trade-Offs, and Options

5.      Evaluating AI-Powered Recommendations, Strategic Risks, Consequences, Resource Requirements, and Uncertainty

6.      Communicating AI-Generated Findings, Assumptions, Limitations, Risks, and Strategic Implications

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

8.      Monitoring Data Quality, Model Performance, AI Outputs, Strategic Outcomes, and Continuous Improvement

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

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