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Quest Training

AI for Project Managers: Planning, Scheduling & Control Training Course

Explores how AI tools can help project managers build work breakdown structures and schedules, forecast risks and strengthen monitoring and control of projects.

DPM · Digital & AI-Enabled Project ManagementAll Levels#2688ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Project Management & PMOCategories:Digital & AI-Enabled Project Management

Course Overview

The AI for Project Managers: Planning, Scheduling & Control Training Course equips project managers, project planners, project controls professionals, and programme leaders with the knowledge and practical skills required to apply artificial intelligence (AI) across project planning, scheduling, monitoring, and control activities. The programme explores how AI-enabled tools and data-driven techniques can enhance planning accuracy, improve visibility into project performance, support risk-based decisions, and strengthen the ability to anticipate and respond to changing project conditions. Projects frequently operate under tight deadlines, resource constraints, budget pressures, and evolving stakeholder requirements. Traditional planning and control methods remain essential, but managing complex projects increasingly requires faster data analysis, more responsive forecasting, and improved early-warning capabilities. This course examines how AI can complement established project management practices by supporting schedule analysis, identifying emerging patterns, automating selected reporting activities, and providing insights that help project teams make better-informed decisions. Participants explore practical applications of AI in work breakdown structures, activity definition, schedule development, resource planning, progress monitoring, cost forecasting, risk assessment, and performance reporting. The programme introduces generative AI, predictive analytics, machine learning concepts, and intelligent automation, with emphasis on their appropriate use in project environments. Participants also examine how AI tools can support the interpretation of project documentation, summarize progress information, identify potential schedule conflicts, and improve the preparation of management reports. A key focus of the course is the integration of AI into project controls without compromising professional judgment, data integrity, or established governance procedures. Participants learn to assess the reliability of AI-generated outputs, validate forecasts against actual project information, recognize data limitations, and maintain human oversight over critical decisions. The programme also addresses confidentiality, cybersecurity, responsible AI use, and the risks associated with inaccurate or unsupported recommendations. Through practical exercises, project-based case studies, and simulated planning scenarios, participants develop an actionable approach to integrating AI into project planning, scheduling, and control workflows. The course is suitable for organizations seeking to improve project predictability, strengthen schedule and cost control, reduce repetitive administrative work, and build the capabilities needed for increasingly data-driven project delivery.

Learning Objectives

  • Explain the principles of artificial intelligence and its relevance to modern project management and project controls.
  • Identify suitable AI applications across project initiation, planning, scheduling, monitoring, and reporting.
  • Use AI-assisted techniques to develop work breakdown structures, define activities, and organize project planning information.
  • Apply AI tools to support schedule development, activity sequencing, dependency analysis, and milestone planning.
  • Interpret project data to identify schedule deviations, emerging risks, and potential performance issues.
  • Explore predictive analytics for forecasting completion dates, resource requirements, and project cost trends.
  • Apply AI-assisted methods to analyse project progress and improve the preparation of status reports.
  • Use generative AI to support documentation, meeting summaries, action tracking, and stakeholder communications.
  • Evaluate AI-generated recommendations using project records, professional standards, and established control procedures.
  • Integrate AI-supported risk identification and scenario analysis into project planning and decision-making.
  • Establish appropriate governance, data protection, and human review practices for AI-enabled project workflows.
  • Develop a practical implementation roadmap for using AI to improve project planning, scheduling, and control performance.

Who Should Attend

This course is designed for professionals responsible for project planning, scheduling, monitoring, control, reporting, and delivery across technical, commercial, and organizational environments. Project managers and senior project managers seeking to improve planning and control effectiveness through AI. Project planners and planning engineers responsible for developing and maintaining project schedules. Project controls managers and specialists involved in schedule performance, cost control, forecasting, and reporting. Programme and portfolio managers overseeing multiple projects, dependencies, and resource priorities. Project management office (PMO) professionals responsible for governance, performance standards, and portfolio reporting. Cost engineers and project cost controllers involved in budget monitoring and financial forecasting. Risk managers and project risk specialists assessing uncertainty, mitigation strategies, and delivery exposure. Construction, engineering, infrastructure, energy, and industrial project professionals managing complex project schedules. Project coordinators and project administrators responsible for documentation, progress tracking, and reporting activities. Digital transformation professionals supporting the adoption of AI and automation in project delivery. Consultants and project management advisers helping organizations improve project planning and control systems. Government and public-sector project leaders responsible for programme delivery, reporting, and resource accountability.

Learning Outcomes

  • Explain how AI, machine learning, generative AI, and predictive analytics can support project management activities.
  • Identify project planning and control processes that are suitable for AI assistance or automation.
  • Develop structured project planning inputs, including work packages, activities, dependencies, and milestones.
  • Use AI-assisted analysis to review schedules and identify potential sequencing issues and inconsistencies.
  • Interpret project progress data to recognize trends, deviations, and emerging delivery risks.
  • Evaluate the use of predictive techniques for completion forecasting, resource planning, and cost monitoring.
  • Generate draft project reports, meeting summaries, risk registers, and action logs using AI-assisted workflows.
  • Apply scenario analysis to evaluate alternative schedules, resource allocations, and mitigation strategies.
  • Validate AI-generated outputs against reliable project data and established project controls practices.
  • Identify limitations, biases, and reliability risks associated with AI-generated analysis and recommendations.
  • Apply responsible AI practices that protect confidential project information and maintain human accountability.
  • Prepare an implementation plan with defined use cases, performance indicators, governance measures, and improvement priorities.

Course Outline

Day 1

AI Fundamentals and Applications in Project Management

  • Understanding artificial intelligence, machine learning, generative AI, and predictive analytics.
  • The evolving role of AI in project management, planning, scheduling, and control.
  • Identifying opportunities for AI across the project lifecycle.
  • Understanding the relationship between project management methodologies, structured data, and AI-enabled tools.
  • Reviewing project information sources, including schedules, budgets, progress reports, risk registers, and correspondence.
  • Understanding the importance of data quality, consistency, completeness, and traceability.
  • Using generative AI to summarize project documents and extract relevant planning information.
  • Developing effective prompts for project planning, analysis, and management reporting.
  • Recognizing AI limitations, unsupported outputs, and the need for professional verification.
  • Establishing appropriate boundaries between AI assistance and project manager accountability.
  • Practical Exercise: Review a sample project brief and use AI-assisted techniques to extract objectives, deliverables, milestones, constraints, assumptions, and initial planning considerations.
Day 2

AI-Assisted Project Planning and Schedule Development

  • Structuring project scope and developing a work breakdown structure.
  • Using AI to assist with activity identification, work package descriptions, and planning documentation.
  • Developing activity lists, duration assumptions, dependencies, and milestone definitions.
  • Understanding logical relationships between activities and their effects on project sequencing.
  • Reviewing baseline schedules for completeness, consistency, and potential omissions.
  • Applying AI-assisted analysis to identify missing dependencies and scheduling inconsistencies.
  • Exploring resource planning and workload assessment using structured project data.
  • Evaluating schedule constraints, resource limitations, and competing project priorities.
  • Using AI to compare alternative planning assumptions and scheduling scenarios.
  • Maintaining planner oversight and validating AI-assisted schedule recommendations.
  • Practical Exercise: Develop a preliminary project schedule from a sample scope statement, generate an activity structure with dependencies and milestones, and review the proposed schedule for logical and resource-related issues.
Day 3

AI for Progress Monitoring, Forecasting, and Project Controls

  • Establishing a reliable baseline for schedule and performance monitoring.
  • Integrating actual progress, remaining durations, milestones, and forecast completion dates.
  • Using analytical techniques to identify schedule deviations and emerging performance trends.
  • Exploring predictive analytics for estimating completion dates and identifying potential slippage.
  • Understanding earned value management concepts and their relationship to project performance data.
  • Analysing cost trends, budget variances, and forecast expenditure.
  • Using AI-assisted techniques to prepare progress summaries and management reports.
  • Identifying early-warning indicators for schedule, cost, resource, and delivery problems.
  • Comparing planned performance, actual results, and revised forecasts.
  • Validating analytical findings against source records and approved project control procedures.
  • Practical Exercise: Analyse a simulated project progress dataset, identify schedule and cost deviations, review projected completion trends, and prepare an AI-assisted status report with verified findings and recommended actions.
Day 4

AI for Project Risk Management, Scenario Analysis, and Decision Support

  • Identifying potential project risks from schedules, progress reports, issue logs, and project correspondence.
  • Using AI to support risk categorization, description, and preliminary assessment.
  • Reviewing probability, impact, exposure, and mitigation planning principles.
  • Exploring data-driven indicators of schedule slippage, resource shortages, and cost escalation.
  • Developing alternative project scenarios based on changes in scope, resources, or delivery assumptions.
  • Evaluating the potential effects of delays on critical activities and major milestones.
  • Comparing mitigation strategies, resequencing options, and resource allocation alternatives.
  • Using AI to support risk register updates, action tracking, and issue summarization.
  • Applying sensitivity analysis to identify assumptions that have the greatest influence on project outcomes.
  • Understanding uncertainty, model limitations, and the risks of treating AI predictions as guaranteed outcomes.
  • Establishing human review and approval requirements for significant project decisions.
  • Practical Exercise: Use a simulated project scenario to identify emerging risks, compare alternative mitigation options, assess likely schedule implications, and prepare a prioritized response plan.
Day 5

AI Integration, Governance, and Project Performance Improvement

  • Integrating AI-assisted planning, scheduling, monitoring, and reporting into existing project workflows.
  • Identifying repetitive project administration tasks suitable for automation.
  • Designing AI-supported reporting processes with clear data inputs, review responsibilities, and approval points.
  • Establishing governance requirements for data access, confidentiality, cybersecurity, and responsible AI use.
  • Defining quality assurance procedures for AI-generated schedules, forecasts, reports, and risk assessments.
  • Developing performance indicators to evaluate the effectiveness of AI-enabled project processes.
  • Assessing implementation costs, expected benefits, operational constraints, and adoption risks.
  • Managing stakeholder expectations and supporting project team adoption of AI tools.
  • Establishing training requirements and capability development priorities for project professionals.
  • Developing an implementation roadmap with milestones, responsibilities, and measurable objectives.
  • Reviewing opportunities for continuous improvement as project data and AI capabilities evolve.
  • Final Workshop: Participants work with an integrated project case containing a scope statement, baseline schedule, progress updates, budget information, risk register, and stakeholder communications. Each team applies AI-assisted techniques to refine planning information, examine schedule dependencies, analyse progress, develop completion forecasts, identify risks, and prepare a management report. Teams then propose an AI integration roadmap that identifies priority use cases, data requirements, validation procedures, governance controls, and performance indicators. The workshop concludes with presentations and a review of the expected benefits, implementation challenges, and measures required to maintain reliable, accountable project control.

Upcoming Dates

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Dubai · 14 – 18 Feb 2027 · Classroom

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16 – 20 Nov 2026€5,400—
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23 – 27 Nov 2026€4,200€3,780until 2 Nov
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7 – 11 Dec 2026€5,800€5,220until 16 Nov
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