AI-Powered Project Management Training Course

Duration 10 days 20 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends Project Managers, Programme Managers and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-PM-051

Before you join: Participants should have practical experience working on projects and understand core terms such as scope, milestones, risks, stakeholders, budgets and status reports.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Projects now generate large volumes of schedules, estimates, meeting records, risks, requirements and supplier information. Generative AI can reduce the time needed to structure this material, identify omissions and produce first drafts. It can also introduce serious errors: invented facts, weak assumptions, disclosure of confidential information and outputs that appear credible but cannot be defended. Project managers need a disciplined way to use AI while retaining ownership of decisions, controls and stakeholder commitments.

This course teaches participants how to apply generative AI throughout the project lifecycle: initiation, planning, delivery, monitoring, reporting, change control and closure. Participants use PMBOK® Guide concepts, PRINCE2® management products, work breakdown structures, critical path analysis, RAID logs, earned value management and stakeholder mapping alongside AI prompting and output-review methods. They learn to frame project questions, prepare safe source material, validate AI outputs, document assumptions and maintain human accountability.

Teaching combines short instructor briefings, realistic cases, guided group work, simulations and individual application planning. Cases reflect common assignments in public programmes, banking change portfolios, technology implementations, infrastructure projects and service transformation. Participants build a project management pack progressively during the course, then complete a capstone review and presentation. They leave with reusable prompts, templates, governance checks and an AI-assisted project plan adapted to their own work.

The course suits project professionals who already understand the basics of project delivery and need practical methods for introducing AI into established project controls without weakening assurance, privacy or decision quality.

What you will be able to do

  • Assess project tasks for appropriate AI assistance, human review and escalation
  • Write structured prompts that produce traceable project planning outputs
  • Build an AI-assisted project charter, scope statement and work breakdown structure
  • Develop schedules using dependency mapping, critical path analysis and resource assumptions
  • Create AI-supported RAID logs, stakeholder communications and status reporting packs
  • Validate AI-generated project content using source checks, assumption logs and quality criteria
  • Apply governance controls for confidential data, model use, approvals and audit trails
  • Present a complete AI-assisted project management pack for stakeholder review

Benefits of attending

For you

  • Produce defensible first drafts of project documents in less time
  • Recognise unreliable AI outputs before they enter project decisions
  • Use structured prompts for plans, risks, reports and stakeholder communications
  • Strengthen project assurance skills through documented assumptions and source checking
  • Present an AI-assisted project pack as evidence of practical capability

For your organisation

  • Reduce time spent preparing routine plans, reports and meeting outputs
  • Improve consistency of project documentation across teams and programmes
  • Introduce clear controls for confidential information used with AI tools
  • Identify schedule, risk and dependency gaps earlier through structured analysis
  • Create repeatable AI working standards for PMO and delivery teams

Course modules

20 modules over 10 days

Participants can identify suitable project management uses for AI and define human accountability.

  • Generative AI capabilities across project planning, reporting and knowledge management
  • Human accountability for decisions, approvals, commitments and stakeholder communications
  • Automation suitability matrix for repetitive, analytical and judgment-based project tasks
  • AI failure modes: hallucination, bias, stale knowledge and missing context
  • RACI assignments for AI-assisted project work and approval responsibilities
  • Project manager responsibilities under organisational AI use policies

Participants can apply practical controls to protect information and maintain auditable AI use.

  • Data classification rules for project documents, supplier records and personal data
  • Confidentiality checks before entering project material into AI tools
  • Prompt redaction techniques for commercially sensitive project information
  • Audit trail requirements for AI-generated recommendations and approved decisions
  • Model output retention rules within project document management systems
  • Escalation criteria for legal, ethical, security and compliance concerns

Participants can write structured prompts that generate usable, reviewable project outputs.

  • Prompt structure: role, task, context, constraints, format and validation
  • Few-shot prompting using approved examples of project management documents
  • Source-grounded prompts that restrict outputs to supplied project evidence
  • Iterative prompt refinement using acceptance criteria and reviewer feedback
  • Output formats for tables, registers, work packages and executive summaries
  • Prompt libraries organised by lifecycle phase and management product

Participants can test AI-generated project content before it informs actions or commitments.

  • Fact checking against approved baselines, contracts, requirements and meeting records
  • Assumption logs capturing source, owner, validation date and decision impact
  • Triangulation method comparing AI outputs with subject matter expert evidence
  • Quality criteria for accuracy, completeness, relevance, traceability and tone
  • Confidence statements distinguishing verified facts from provisional project assumptions
  • Review checklists for plans, reports, risks and stakeholder messages

Participants can create an evidence-based project charter with AI-supported drafting and review.

  • Project charter components: purpose, objectives, scope, benefits and governance
  • Problem statements framed through current state, target state and constraints
  • SMART objectives linked to measurable outcomes and accountable owners
  • AI-assisted synthesis of business cases, mandates and discovery notes
  • Project assumptions, dependencies and exclusions recorded within initiation documentation
  • Approval criteria for moving from concept to project planning

Participants can use AI to structure requirements while preserving scope traceability.

  • Scope statements separating deliverables, exclusions, constraints and acceptance criteria
  • Requirements categorisation using functional, non-functional and regulatory classifications
  • MoSCoW prioritisation for competing requirements and delivery constraints
  • Traceability matrix linking requirements, deliverables, tests and acceptance owners
  • AI-assisted identification of ambiguous, duplicated and missing requirements
  • Change boundaries established through approved baseline scope documentation

Participants can develop a deliverable-based work breakdown structure using AI-supported analysis.

  • Deliverable-oriented work breakdown structures using the 100 percent rule
  • Decomposition of major deliverables into manageable work packages
  • Work package definitions covering owner, output, effort and acceptance criteria
  • WBS dictionary entries generated from approved scope and requirements
  • AI prompts for identifying omitted activities and interface dependencies
  • Control accounts connecting work packages to budget and reporting structures

Participants can create a logical project schedule and test it for AI-generated planning gaps.

  • Precedence diagramming method for finish-to-start and overlapping activity relationships
  • Duration estimates using analogous, parametric and three-point estimating techniques
  • Critical path method identifying schedule drivers and float exposure
  • Milestone schedules distinguishing decision gates, deliveries and external commitments
  • AI-assisted schedule reviews for missing dependencies and unrealistic sequencing
  • Schedule assumptions documented for resources, approvals and supplier lead times

Participants can produce resource and cost assumptions that support realistic project commitments.

  • Responsibility assignment matrix matching work packages with accountable delivery roles
  • Resource loading assumptions for specialist availability, utilisation and peak demand
  • Bottom-up cost estimates built from labour, materials, services and contingency
  • Cost baseline components: approved budget, management reserve and control thresholds
  • Make-buy analysis for internal delivery, suppliers and managed services
  • AI-supported review of estimate gaps, duplicated costs and procurement dependencies

Participants can maintain an AI-assisted RAID log with clear ownership and response actions.

  • RAID log structure for risks, assumptions, issues and dependencies
  • Risk identification using cause-event-effect statements and category prompts
  • Probability-impact matrix with defined scoring scales and escalation thresholds
  • Risk response strategies: avoid, mitigate, transfer, accept and exploit
  • Issue management workflow from detection through ownership, action and closure
  • AI-assisted risk reviews tested against evidence, uncertainty and project context

Participants can develop targeted stakeholder communications using AI while protecting trust and accuracy.

  • Power-interest grid for stakeholder segmentation and engagement strategy selection
  • Stakeholder registers recording influence, expectations, concerns and communication preferences
  • Communication matrix defining audience, purpose, channel, frequency and owner
  • AI-assisted drafting of sponsor updates, team briefings and decision requests
  • Message testing for clarity, commitment, risk disclosure and appropriate tone
  • Escalation routes for contested decisions, stakeholder resistance and delayed approvals

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

  • Hybrid delivery models combining predictive governance with iterative product development
  • Product backlog refinement using user stories, acceptance criteria and prioritisation
  • Definition of Ready and Definition of Done for planned work
  • Sprint planning inputs: capacity, velocity, dependencies and delivery risks
  • AI-assisted synthesis of retrospective themes and impediment patterns
  • Scrum artefacts aligned with programme milestones and governance reporting

Participants can convert project discussions into accurate actions, decisions and controlled follow-up.

  • Meeting agenda design linked to decisions, risks, dependencies and milestones
  • Action logs with owner, due date, status, evidence and escalation route
  • Decision logs recording options, criteria, approver, rationale and implementation effects
  • AI-generated meeting summaries checked against recordings, notes and attendee confirmation
  • Follow-up prompts that identify overdue actions and unresolved decision dependencies
  • Information management rules for meeting transcripts and sensitive discussions

Participants can create concise, evidence-based reports tailored to project governance audiences.

  • RAG status criteria defined through measurable tolerances and supporting evidence
  • Executive status reports covering progress, forecast, decisions, risks and actions
  • Narrative variance analysis explaining causes, impacts, recovery actions and owners
  • Dashboard measures for scope, schedule, cost, risk, quality and benefits
  • AI-assisted report drafting constrained by approved metrics and source data
  • Board-level reporting that distinguishes facts, forecasts, assumptions and requests

Participants can interpret project performance data and use AI to explain material variances.

  • Earned value measures: planned value, earned value and actual cost
  • Schedule performance index and cost performance index interpretation
  • Estimate at completion calculations using approved forecasting assumptions
  • Variance thresholds triggering corrective action, escalation and management attention
  • AI-assisted explanations tested against timesheets, invoices and delivery evidence
  • Forecast narratives separating current performance from future delivery assumptions

Participants can assess and document project changes using controlled AI-supported analysis.

  • Integrated change control across scope, schedule, cost, risk and benefits
  • Change request forms documenting rationale, impact, options and recommendation
  • Impact assessment across requirements, dependencies, resources and stakeholder commitments
  • Configuration management for controlled documents, versions and approved baselines
  • Change authority levels defined through governance thresholds and delegated powers
  • AI-assisted change summaries checked against baseline and source evidence

Participants can establish governance arrangements that control AI-assisted project delivery.

  • Stage-gate reviews using defined evidence, tolerances and approval criteria
  • Three lines model applied to delivery ownership, oversight and assurance
  • Project board roles covering direction, business ownership and supplier interests
  • Exception reporting for breaches of agreed scope, time, cost or risk tolerances
  • AI use register recording approved tools, purposes, data classes and reviewers
  • Assurance reviews examining traceability, decisions, controls and delivery confidence

Participants can connect project outputs to benefits and close AI-assisted work with usable learning.

  • Benefits realisation plan linking outputs, outcomes, measures and benefit owners
  • ADKAR model for adoption planning, readiness assessment and resistance responses
  • Operational handover criteria covering ownership, training, support and documentation
  • Lessons learned methods capturing observations, causes, recommendations and accountable actions
  • Closure reports documenting acceptance, residual risks, finances and outstanding commitments
  • AI-assisted knowledge capture reviewed by project owners and operational teams

Participants can design repeatable AI working practices for a project team or PMO.

  • Use-case inventory prioritised by value, risk, data sensitivity and implementation effort
  • Standard prompt packs for initiation, planning, reporting and closure activities
  • Human-in-the-loop review points embedded within project management workflows
  • Quality assurance sampling for recurring AI-generated project documentation
  • Skills matrix identifying project team capability and targeted development needs
  • Implementation roadmap covering pilots, controls, ownership and success measures

Participants can build, review and present a complete AI-assisted project management pack for stakeholder approval.

  • Project charter, scope statement and benefits case assembled from approved evidence
  • Work breakdown structure, schedule and resource assumptions integrated into one plan
  • RAID log, stakeholder plan and communication matrix completed with clear ownership
  • Status dashboard, governance calendar and escalation routes prepared for project control
  • AI prompt record, source register and validation checklist included for assurance
  • Stakeholder presentation defending assumptions, recommendations, controls and delivery priorities

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. AI-assisted project charter template
  2. Structured project prompt library
  3. Project AI use and validation checklist
  4. Integrated schedule and RAID log template
  5. Executive project status report template
  6. AI-assisted project management pack

Who should attend

  • Project Managers — need practical AI methods without losing control of delivery decisions
  • Programme Managers — need consistent AI-supported reporting across multiple workstreams
  • PMO Managers — need standards, templates and controls for responsible project AI use
  • Business Analysts — need faster methods for structuring requirements and project evidence
  • Project Coordinators — need efficient ways to maintain plans, actions and status information
  • Transformation Leads — need AI-supported planning for complex change initiatives

Skills and tools covered

AI prompt designProject planning automationSchedule analysisRisk and issue controlAI output validationProject governance

Tools and standards

PMBOK® Guide, PRINCE2®, Critical Path Method, Earned Value Management

How the course runs

Training methodology

The course uses short briefings to establish methods, followed by cases, group exercises, simulations and individual application planning. Participants work with realistic project materials including charters, requirements, schedules, RAID logs, meeting notes and status reports. The instructor demonstrates prompt structures and output-review techniques, then participants apply them to project management decisions. Facilitated reviews compare AI-generated drafts against source evidence, project controls and stakeholder expectations. Each participant develops an AI-assisted project management pack for later use at work.

Requirements and prerequisites

Participants should have practical experience working on projects and understand core terms such as scope, milestones, risks, stakeholders, budgets and status reports. Familiarity with a project method such as PMBOK®, PRINCE2®, Agile or an internal delivery framework is useful. Participants should be comfortable reviewing project documents and using standard office software. No programming, data science, machine learning or prior generative AI experience is required. The course focuses on applying AI to project management work, not building AI models or selecting enterprise technology platforms.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

You should understand basic project management concepts and have worked with project documents such as plans, risks, milestones or reports. You do not need programming, data science or previous experience using generative AI.

Bring a laptop if attending in person, along with a non-confidential project example you can use for application planning. Do not bring restricted, personal, client-confidential or commercially sensitive information into course AI activities.

It is designed for project managers, programme managers, PMO staff, business analysts and transformation professionals. It is most useful for people who already contribute to project planning, governance, reporting or delivery decisions.

Standard project management courses focus primarily on delivery methods, while general AI courses often focus on tools or technical concepts. This course connects AI use directly to project controls, documents, governance decisions and assurance responsibilities.

Participants leave with prompt structures, validation checks and templates for common project tasks. These can be applied to charter drafting, risk reviews, meeting actions, status reports and change assessments within existing organisational policies.

The first week establishes responsible AI use and applies it to initiation, scope, planning, scheduling and risk management. The second week covers delivery controls, reporting, governance, benefits and a capstone project pack; each day runs from 08:30 to 16:30 with about 6.5 contact hours.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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