Artificial Intelligence in Project Management Training Course

Participants can identify, score and prioritise AI opportunities within a project or PMO.

• Use-case discovery from recurring project pain points and manual tasks
• Value-feasibility-risk matrix for ranking proposed AI applications
• Data readiness assessed through availability, quality, ownership and access
• Prioritising quick wins against longer-term project control improvements
• Defining measurable problem statements before selecting an AI solution
• Use-case canvas covering users, inputs, outputs, controls and benefits

Participants can assess project data sources and prepare a data readiness profile.

• Project data sources: schedules, RAID logs, costs, documents and messages
• Data quality dimensions: completeness, accuracy, timeliness, consistency and validity
• Classifying confidential, personal, commercial and public project information
• Data lineage records for AI-generated project management outputs
• Reconciling conflicting data between schedules, finance systems and status reports
• Minimum viable dataset defined for each proposed AI use case

Participants can establish governance controls for responsible AI use in project delivery.

• NIST AI RMF functions: Govern, Map, Measure and Manage
• AI governance roles using RACI for project-level accountability
• Approval thresholds for AI-assisted recommendations and external communications
• Confidentiality controls for prompts, attachments, outputs and retained records
• Bias, fairness and accessibility checks for project decision support
• Escalation paths for harmful, inaccurate or noncompliant AI outputs

Participants can create controlled prompts that generate useful project-management outputs.

• Prompt structure using role, task, context, constraints and format
• Few-shot prompting with approved examples from project documentation
• Separating instructions, source evidence and requested output fields
• Asking AI to identify assumptions, uncertainties and missing information
• Structured output schemas for tables, RAID entries and action lists
• Prompt version control and reusable prompt library management

Participants can use AI to improve early project definition while retaining sponsor ownership.

• Drafting project charters from approved business case evidence
• SMART objectives tested against benefits, constraints and strategic alignment
• Stakeholder identification from organisational charts, contracts and meeting records
• Assumption logs generated from incomplete initiation documentation
• Problem framing using five whys and cause-and-effect analysis
• Sponsor review checkpoints for AI-assisted charter content

Participants can apply AI-assisted techniques to analyse requirements and control scope definition.

• Extracting candidate requirements from interviews, policies and source documents
• MoSCoW prioritisation supported by traceable business and technical criteria
• Requirements traceability matrix linking needs, deliverables, tests and owners
• Detecting ambiguous requirements through acceptance-criteria quality checks
• Work breakdown structures drafted from approved scope statements
• Human validation of requirements before baselining scope commitments

Participants can use AI-supported analysis to improve schedule planning and forecast scrutiny.

• Critical path method inputs checked for missing dependencies and constraints
• Precedence diagrams reviewed for logic gaps and parallel work opportunities
• Three-point estimates using optimistic, most likely and pessimistic durations
• Schedule risk indicators derived from milestone slippage and dependency trends
• AI-generated recovery options tested against resource and scope constraints
• Baseline protection when AI proposes schedule changes or resequencing

Participants can assess AI-supported cost, resource and procurement information for project decisions.

• Earned value management metrics interpreted using planned and actual performance
• Estimate at completion scenarios generated from cost performance evidence
• Resource capacity analysis using skills, availability, demand and constraints
• Procurement document summaries checked against contractual obligations and exclusions
• Supplier risk signals extracted from performance reports and correspondence
• Financial approval controls applied before accepting AI-generated forecasts

Participants can build an AI-assisted process for identifying, analysing and escalating project risks.

• Risk identification from RAID logs, lessons learned and project correspondence
• Probability-impact scoring calibrated through defined rating criteria
• Bow-tie analysis linking causes, preventive controls and recovery actions
• Issue triage rules distinguishing incidents, risks, decisions and changes
• Risk response actions assigned with owners, dates and trigger conditions
• AI risk suggestions validated against current project evidence

Participants can produce AI-assisted reports that remain evidence-based, concise and decision-focused.

• Executive status reports structured around progress, exceptions and decisions
• RAG status criteria defined with objective project control thresholds
• Narrative summaries grounded in approved schedule, cost and RAID data
• Decision papers presenting options, implications, recommendations and required approvals
• Stakeholder messages tailored using influence-interest analysis and communication needs
• Fact-checking protocol applied before distributing AI-generated project communications

Participants can apply AI within Agile and hybrid delivery without weakening team ownership.

• Product backlog refinement supported by user-story quality analysis
• INVEST criteria applied to AI-generated user stories and acceptance criteria
• Sprint planning inputs checked for capacity, dependencies and definition-of-ready
• Burndown and velocity trends interpreted without misleading productivity assumptions
• Retrospective themes clustered from team feedback and impediment records
• Hybrid governance connecting Agile evidence with stage-gate reporting requirements

Participants can define AI applications that support portfolio oversight and benefits management.

• Portfolio prioritisation using strategic alignment, value, risk and capacity
• PMO data standards supporting consistent cross-project AI analysis
• Benefits maps linking outputs, outcomes, measures and accountable owners
• Portfolio heat maps tested for source-data completeness and comparability
• Cross-project dependency analysis identifying concentration and sequencing risks
• Management dashboards designed around decisions rather than data volume

Participants can evaluate AI tools and suppliers against project, technical and governance requirements.

• Tool selection criteria covering functionality, integration, security and usability
• Vendor due diligence questions for models, data handling and support
• Proof-of-concept success criteria tied to measurable project outcomes
• Testing AI outputs against representative project scenarios and edge cases
• Service-level requirements for availability, incident reporting and change notification
• Procurement evaluation matrix documenting evidence, scores and selection rationale

Participants can specify project controls for AI security, privacy, compliance and auditability.

• Threat modelling using assets, threats, vulnerabilities and mitigations
• Prompt injection risks identified within project document workflows
• Access controls based on least privilege and approved user roles
• Records retention requirements for prompts, outputs and approval evidence
• Privacy impact assessments for personal data used by AI tools
• Audit trails linking AI outputs to source evidence and reviewers

Participants can test AI-generated project outputs against defined quality and acceptance criteria.

• Accuracy testing against approved project records and known answers
• Hallucination detection using citation checks and source-evidence comparison
• Bias testing through representative stakeholder and scenario sampling
• Red-team prompts designed to expose unsafe or misleading outputs
• Acceptance criteria for AI-assisted reports, plans and recommendations
• Quality assurance log recording defects, corrections, owners and decisions

Participants can design an operating model for sustained AI use by project teams.

• Target operating model defining processes, roles, technology and governance
• Change impact assessment covering tasks, skills, controls and stakeholder concerns
• Training needs analysis for project managers, sponsors and PMO analysts
• Communities of practice supporting shared prompts, lessons and standards
• Adoption metrics tracking usage, quality, time saved and user confidence
• Communications plan addressing benefits, limitations and responsible use expectations

Participants can measure AI value and improve project-management use cases through evidence.

• Benefits realisation measures defined before AI deployment begins
• Baseline metrics for reporting effort, forecast accuracy and issue resolution
• Control charts used to monitor process variation after implementation
• User feedback loops capturing defects, usefulness and unmet needs
• Cost-benefit analysis including licences, training, controls and maintenance
• Retirement criteria for ineffective, unsafe or low-value AI use cases

Participants can create a phased roadmap for introducing approved AI use cases into project work.

• Roadmap phases covering discovery, pilot, scale, assurance and improvement
• Pilot scope defined through users, workflows, data and measurable outcomes
• Dependency mapping for systems, approvals, training and supplier inputs
• Implementation risks logged with mitigations, contingencies and escalation owners
• Milestone plan aligned to governance gates and benefit review dates
• Business case narrative linking investment to delivery performance measures

Participants can build, review and present a complete AI-enabled project management implementation pack for a selected project or PMO.

• Use-case portfolio prioritised through value, feasibility, risk and data readiness
• Governance model defining RACI, approvals, records and escalation controls
• Prompt library containing tested templates for selected project workflows
• AI-assisted RAID process supported by validation and audit requirements
• Phased implementation roadmap with milestones, measures, costs and dependencies
• Executive presentation defending recommendations, controls, benefits and next actions