Artificial Intelligence for Project Monitoring, Evaluation and Performance Reporting Training Workshop
5 Days3 sessions available Certificate on completion
The Artificial Intelligence for Project Monitoring, Evaluation and Performance Reporting Training Workshop equips professionals with practical skills to use AI for project performance tracking, indicator analysis, predictive risk assessment, qualitative data analysis and automated reporting. Participants learn to apply generative AI, Microsoft Excel, Power BI and project management tools to analyse project data, identify implementation gaps, develop monitoring dashboards and prepare evidence-based reports. The course also covers AI-assisted results frameworks, schedule monitoring, early-warning systems, responsible AI governance and human verification. It is designed for project managers, M&E professionals, programme coordinators, consultants and development practitioners seeking to improve project accountability, reporting efficiency and evidence-based decision-making.
The course explores how generative AI, predictive analytics, machine learning and AI-enabled business intelligence tools can improve project performance tracking, identify implementation risks, analyse quantitative and qualitative monitoring data, automate routine reporting tasks and support evidence-based decision-making. Participants learn to combine AI tools with Microsoft Excel, Power BI and project management software to produce actionable insights, identify performance gaps, strengthen accountability and communicate results to stakeholders.
The workshop emphasises practical applications, responsible AI use, data privacy, human verification and the limitations of AI-generated findings.
Course objectives
Apply AI tools to develop project monitoring frameworks, indicators, targets and performance measurement plans.
Evaluate AI outputs for accuracy, bias, confidentiality and reliability, incorporating human oversight into project monitoring and evaluation processes.
Generate evidence-based project reports, dashboards, performance summaries and management recommendations using AI-enabled tools.
Apply AI techniques to quantitative and qualitative M&E data, including trend analysis, thematic analysis and identification of recurring issues.
Use AI and data analytics to analyse project implementation progress, identify variances and detect emerging performance risks.
Who should attend
Project managers, monitoring and evaluation officers, MEAL professionals, programme managers, project coordinators, development practitioners, NGO and non-profit professionals, donor-funded project teams, results-based management specialists, project planning officers, research officers, policy analysts, programme directors, data analysts, business intelligence professionals, consultants, grant managers, project controls officers, institutional performance managers and professionals responsible for project reporting and accountability.
Course content
Module 1: Foundations of AI in Project Monitoring and Evaluation Artificial intelligence concepts and terminology, generative AI and predictive analytics, machine learning fundamentals, AI applications across the project lifecycle, traditional versus AI-e
Module 2: AI Tools and Digital Workflows for Project Professionals Introduction to ChatGPT and Microsoft Copilot, AI assistants for project planning and reporting, Microsoft Excel and AI-supported analysis, Power BI and AI-enabled analytics, integration w
Module 3: AI-Assisted Project Planning and Results Framework Development Using AI to analyse project documents, drafting project objectives and deliverables, developing work breakdown structures, generating theories of change, developing logical framework
Module 4: Prompt Engineering for Monitoring and Evaluation Fundamentals of effective prompts, providing context and role instructions, structured prompting for indicator development, generating monitoring questions, analysing project performance narrative
Module 5: AI for Project Data Collection and Data Quality Designing digital data collection questions with AI assistance, developing survey and monitoring forms, identifying missing and inconsistent records, detecting duplicates and outliers, standardisin
Module 6: AI-Powered Quantitative Data Analysis Descriptive statistics, indicator achievement calculations, trend analysis, comparisons between targets and actual results, variance analysis, identifying unusual performance patterns, basic forecasting, cor
Module 7: AI for Project Progress and Schedule Monitoring Analysing planned versus actual progress, reviewing Microsoft Project schedules, identifying overdue activities and delayed milestones, summarising critical-path risks, examining resource constrain
Module 8: Predictive Analytics and Early-Warning Systems Introduction to predictive modelling, selecting relevant historical project data, defining project performance outcomes, identifying factors associated with delays and underperformance, risk scoring
Module 9: AI for Monitoring Indicators and Results-Based Management AI-assisted indicator reference sheets, indicator definitions and calculation methods, baseline and target analysis, disaggregation of results, indicator data validation, tracking output
Module 10: AI for Qualitative Data Analysis and Evaluation Research Preparing interview and focus group transcripts, AI-assisted transcription and summarisation, thematic coding, identifying recurring themes and emerging issues, sentiment analysis and its
Module 11: AI for Project Risk Analysis and Corrective Action Planning AI-assisted risk identification, risk registers and classification, probability and impact assessment, identifying dependencies between risks and activities, analysing implementation c
Module 12: AI-Enabled Dashboards and Performance Visualisation Preparing structured project datasets, selecting meaningful key performance indicators, designing monitoring dashboards in Power BI, using Excel charts and PivotTables, presenting target-versu
Module 13: Automated Project Reporting and Management Briefs Drafting weekly and monthly project updates, generating donor and executive reporting outlines, summarising achievements and implementation challenges, developing indicator narratives, explainin
Module 14: Responsible AI, Governance and Quality Assurance AI hallucinations and factual inaccuracies, data confidentiality and privacy, personal and sensitive information protection, bias in project datasets, responsible use of AI in evaluation, consent
Module 15: Integrated AI-Powered Project Monitoring and Evaluation Capstone Developing an AI-assisted project monitoring plan, analysing a sample project dataset, evaluating indicator achievement, identifying implementation delays, summarising qualitative
General notes
Software and AI Tools - ChatGPT: Project planning support, prompt engineering, data interpretation, report drafting and summarisation. - Microsoft Copilot: AI assistance within supported Microsoft applications, depending on licence and organisational configuration. - Microsoft Excel: Data cleaning, indicator calculations, PivotTables, variance analysis and performance tracking. - Microsoft Power BI: Interactive dashboards, performance visualisation and business intelligence. - Microsoft Project: Project scheduling, task dependencies, baselines, milestones and progress tracking. - Power Query: Repeatable data preparation and transformation. - Digital data collection tools: KoboToolbox, ODK or equivalent platforms for survey and monitoring data. - Optional analytical tools: Python with pandas and scikit-learn for participants who require more advanced analytics and predictive modelling.
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Frequently asked questions
The workshop is suitable for project managers, M&E and MEAL officers, programme coordinators, development professionals, research teams, consultants, data analysts and anyone responsible for project monitoring, evaluation or reporting.
No. The core workshop focuses on practical, no-code AI tools, Microsoft Excel, Power BI and project management workflows. Advanced predictive modelling can be introduced as an optional extension.
AI can assist with data preparation, analysis, summarisation, visualisation and report drafting. However, professionals must validate the evidence, verify calculations, interpret context and approve conclusions. AI should not independently determine whether a project has achieved meaningful outcomes or impact.
Yes. Participants are introduced to performance forecasting, risk scoring, early-warning indicators and predictive modelling concepts. The depth of implementation depends on available data, participant experience and whether the optional programming component is included.