AI Project Management
Coaching Programme
A comprehensive, independently designed workshop to help professionals truly understand how to plan, lead, and deliver AI projects — and build the knowledge to confidently pursue the PMI-CPMAI™ certification.
5
Domains Covered
40
Tasks Explored
5 Days
Intensive Workshop
30+
Hours of Coaching
120Q
Exam Format Prep
About this programme
What DreamsPlus AI Project Management Coaching offers
This workshop is architected by DreamsPlus Institute as an independently developed coaching programme. It is not a reproduction of PMI’s official course materials. Instead, it draws on the publicly available PMI-CPMAI™ Exam Content Outline (September 2025) as a reference framework, and builds an original, practitioner-focused curriculum around it — covering real-world AI project scenarios, hands-on planning exercises, case studies, and exam strategy sessions.
The goal is twofold: first, to make participants genuinely capable of planning and running AI projects in their organisations; second, to help them build the conceptual mastery needed to approach the PMI-CPMAI™ certification exam with confidence.
Programme Name
AI Project Management Coaching
by DreamsPlus InstituteFormat
Live Instructor-led
Online & In-person (Chennai)Duration
5 Days · 30+ hrs
Intensive workshop formatCertification alignment
PMI-CPMAI™ ECO
by DreamsPlus InstituteProvider status
Independent
Not affiliated with PMI®Website
dreamsplus.in
www.dreamsplus.in
Important prerequisite: To sit the PMI-CPMAI™ certification exam, PMI requires all candidates to complete the official 21-hour PMI-CPMAI™ Exam Prep Course available directly at pmi.org. DreamsPlus coaching is supplementary — it does not fulfil this mandatory PMI requirement. We strongly encourage participants to enrol in both programmes for the best exam readiness outcome.
Learning outcomes
What you will be able to do after this workshop
By the end of the DreamsPlus AI Project Management Coaching programme, participants will be able to:
Design and oversee responsible AI governance frameworks including data privacy, PII protection, and regulatory compliance for AI systems
Identify genuine business problems suited to AI solutions, evaluate feasibility, develop scopes, and build a compelling AI business case
Assess, gather, evaluate, and communicate data readiness decisions — understanding when data is truly ready for AI model training
Oversee AI/ML model selection, training, quality assurance, and make informed go/no-go decisions on model deployment
Plan and manage the full deployment and operationalisation of AI solutions including monitoring, governance, and contingency planning
Apply the CPMAI™ methodology phases across the AI project lifecycle in realistic workplace scenarios
Navigate complex, scenario-based exam questions with the analytical confidence required for the PMI-CPMAI™ certification exam
Communicate AI project status, data readiness, model performance, and risk to business stakeholders in clear, non-technical language
Exam reference blueprint
PMI-CPMAI™ exam structure — our coaching coverage map
The PMI-CPMAI™ exam comprises 120 questions (100 scored + 20 unscored pre-test) with a time limit of 160 minutes. The five domains and their exam weightings are mapped below against DreamsPlus coaching intensity.
| # | Domain | Exam Weight | Tasks | DreamsPlus Focus |
|---|---|---|---|---|
| I | Support Responsible and Trustworthy AI Efforts | 15% | 5 tasks | Day 1 · AM session |
| II | Identify Business Needs and Solutions | 26% | 10 tasks | Day 1 PM + Day 2 AM |
| III | Identify Data Needs | 26% | 9 tasks | Day 2 PM + Day 3 AM |
| IV | Manage AI Model Development and Evaluation | 16% | 6 tasks | Day 3 PM + Day 4 AM |
| V | Operationalize AI Solution | 17% | 7 tasks | Day 4 PM + Day 5 AM |
DreamsPlus coaching coverage is aligned to — but not reproduced from — the PMI-CPMAI™ Examination Content Outline (September 2025). All workshop content, case studies, and exercises are independently developed by DreamsPlus Institute.
Detailed curriculum
Domain-by-domain coaching plan
Click any domain to expand. Each domain shows the tasks covered and the DreamsPlus original coaching approach — what we teach, how we teach it, and the real-world skills you will build.
I Support Responsible and Trustworthy AI Efforts
15% of exam 5 tasks · Day 1 Morning
DreamsPlus coaching approach: We open the workshop with AI ethics and governance because every AI project decision has legal, ethical, and reputational consequences. Participants work through realistic regulatory scenarios — from GDPR erasure requests on training data, to bias audits on production models — learning not just what compliance means but how a project manager operationalises it.
Key knowledge areas (from PMI-CPMAI™ ECO)
- Data governance protocols for personally identifiable information (PII)
- Encryption and access controls for AI training data
- Privacy impact assessments for AI model deployment
- Compliance with GDPR, CCPA, and data protection regulations
- Secure data handling procedures throughout the AI lifecycle
DreamsPlus original coaching content
- Workshop: “Map the PII risk” — participants audit a sample AI training dataset and build a data governance plan from scratch
- Case study: Healthcare AI — navigating HIPAA + GDPR + state law simultaneously on a patient outcome prediction model
- Practical: Writing a Privacy Impact Assessment (PIA) using our DreamsPlus PIA canvas template
- Exam strategy: Distinguishing encryption-at-rest vs. in-transit questions and what PMI-CPMAI™ expects as the “PM’s role” in security decisions
Key knowledge areas (from PMI-CPMAI™ ECO)
- Document model selection criteria and decision rationale
- Transparent reporting on data sources and preprocessing steps
- Explainability requirements for stakeholder communication
- Audit trails for algorithmic decision-making processes
- Model interpretability tools and techniques (LIME, SHAP)
DreamsPlus original coaching content
- Exercise: Create a Model Card for a loan approval AI — documenting data sources, model choice rationale, and known limitations
- Discussion: When does a regulator ask for explainability — and what does “explain this to a customer” actually require from a PM?
- Practical: Audit trail design — what to log, where, and how long to retain for regulatory defensibility
Key knowledge areas (from PMI-CPMAI™ ECO)
- Analyse training data for demographic and representation imbalances
- Fairness testing across different population groups
- Bias detection metrics and monitoring systems
- Review model outputs for discriminatory patterns
- Bias mitigation techniques during model development
DreamsPlus original coaching content
- Case study: Predictive hiring tool — identifying representation gaps in historical training data and PM’s role in mitigation
- Framework: The DreamsPlus Bias Review Checklist — 3 phases (data, model, output) a PM should oversee
- Exam focus: Understanding the PM’s role vs. the data scientist’s role in bias management — a common exam trap
Key knowledge areas (from PMI-CPMAI™ ECO)
- Track evolving AI regulations and industry standards
- Sector-specific compliance requirements
- Coordinate with legal and compliance teams on AI governance
- Compliance monitoring and reporting mechanisms
- Documentation for regulatory audits and reviews
DreamsPlus original coaching content
- Landscape briefing: EU AI Act, DPDP Act (India), GDPR, CCPA — what each means for an AI PM in 2025–2026
- Practical: Building a Regulatory Compliance Register for an AI project — what changes, who owns it, and when to escalate
Key knowledge areas (from PMI-CPMAI™ ECO)
- Comprehensive records of AI model development decisions
- Version control for models, data, and training processes
- Stakeholder approvals and go/no-go decision points
- Chain of custody records for training and test data
- Accountability reports for executive and regulatory review
DreamsPlus original coaching content
- Template: DreamsPlus AI Decision Log — version-controlled record of every major model and data decision
- Simulation: “The Regulator Knocks” — participants defend a previous AI decision using only their audit documentation
II Identify Business Needs and Solutions
26% of exam 10 tasks · Day 1 PM + Day 2 AM
DreamsPlus coaching approach: This is the highest-weighted domain and the one where most AI projects fail before they begin. We spend significant time on problem framing, feasibility thinking, and ROI construction — not as theory, but as live workshop exercises where participants pitch AI solutions, challenge each other’s assumptions, and build real scope statements and business cases.
Key knowledge areas (from PMI-CPMAI™ ECO)
- Stakeholder interviews to understand business pain points
- Analyse existing processes to identify automation opportunities
- Define target user personas and use cases for AI solutions
- Map business problems to appropriate AI patterns and approaches
- Validate problem statements with subject matter experts
DreamsPlus original coaching content
- Workshop: “Problem or solution?” — participants receive AI project briefs and must distinguish genuine problems from pre-decided technology choices
- The 7 AI Patterns framework — mapping real business scenarios to the right AI approach (classification, clustering, anomaly detection, NLP, etc.)
- Live exercise: Persona mapping for an AI-powered customer service bot
Key knowledge areas (from PMI-CPMAI™ ECO)
- Assess technical viability of proposed AI solutions
- Analyse data availability and quality for model training
- Evaluate computational resource requirements and constraints
- Review organisational readiness for AI implementation
- Compare AI approaches against traditional solution alternatives
DreamsPlus original coaching content
- The DreamsPlus AI Feasibility Scorecard — a structured tool for rapid go/no-go assessment across 5 dimensions
- Discussion: When is the right answer “don’t use AI”? Understanding when traditional automation or rules-based systems win
Key knowledge areas (from PMI-CPMAI™ ECO)
- Identify potential failure modes and safety implications
- Assess cybersecurity vulnerabilities in AI systems
- Evaluate ethical implications of AI decision-making
- Analyse reputational and business continuity risks
- Develop risk mitigation strategies and contingency plans
DreamsPlus original coaching content
- Risk mapping exercise: Participants complete a full AI Risk Register for a medical diagnosis AI system
- Ethics panel simulation: Group debate on autonomous AI decisions — where must humans remain in the loop?
Key knowledge areas (from PMI-CPMAI™ ECO)
- Define project boundaries, deliverables, and success criteria for AI initiatives
- Identify in-scope and out-of-scope functionality
- Calculate expected benefits and total cost of ownership
- Develop business case with financial justification and cost-benefit analysis
DreamsPlus original coaching content
- Participants draft a complete Scope Statement for a real-world AI use case of their choice
- ROI workshop: Build a simple but defensible AI business case — benefits quantification, TCO components, and payback period
- Peer review: Teams challenge each other’s scope and ROI assumptions
Key knowledge areas (from PMI-CPMAI™ ECO)
- Assess organisational change management and adoption barriers
- Create high-level AI system architecture and data flow diagrams
- Establish measurable KPIs and business impact success criteria
- Support business case creation with financial and technical evidence
- Identify and plan team composition, hardware, and contractor needs
DreamsPlus original coaching content
- Change management playbook for AI adoption: the 5 most common resistance patterns and how PMs resolve them
- Capstone (Day 2): Teams present a complete AI Project Charter — problem, solution design, KPIs, risks, ROI, and team plan
III Identify Data Needs
26% of exam 9 tasks · Day 2 PM + Day 3 AM
DreamsPlus coaching approach: Data is where most AI projects genuinely live or die. We immerse participants in the full data lifecycle — from defining what data you actually need, to evaluating whether what you have is good enough. Critically, we focus on the project manager’s role: not doing the data science, but asking the right questions, making governance decisions, and knowing when to call a stop.
Key knowledge areas (from PMI-CPMAI™ ECO)
- Specify data types, volumes, formats, and quality standards for AI model training
- Identify domain experts, data stewards, and governance owners
- Map internal databases, external providers, cloud repositories, and legacy systems
- Document data ownership and access permissions
DreamsPlus original coaching content
- Exercise: Data Requirements Canvas — participants define exactly what data a fraud detection model needs before any data is collected
- Workshop: Stakeholder mapping for data — who owns it, who knows it, and who can unlock access to it
- Practical: Data Source Inventory worksheet applied to a retail recommendation engine scenario
Key knowledge areas (from PMI-CPMAI™ ECO)
- Provision compute and storage for AI development environments
- Execute data extraction, migration, and validation procedures
- Verify data usage rights, licensing, and access controls
- Conduct privacy impact assessments and document data lineage
DreamsPlus original coaching content
- Infrastructure planning exercise: What a PM needs to know (and ask) about AI compute without being a cloud architect
- Data compliance checklist walkthrough — applying DPDP Act (India) + GDPR to a cross-border data collection scenario
Key knowledge areas (from PMI-CPMAI™ ECO)
- Assess data quality dimensions: accuracy, completeness, consistency, freshness
- Analyse distributions, biases, and schema compatibility
- Make go/no-go decisions on data readiness for model training
- Prepare executive summaries and visualisations of data readiness status
DreamsPlus original coaching content
- Simulation: Participants receive a flawed dataset report and must make a go/no-go recommendation with justification
- Communication exercise: Explaining “our data is not ready” to an impatient executive sponsor — messaging frameworks
- Capstone (Day 3 AM): Full data readiness report for a given AI scenario, presented to the “leadership team” (other participants)
IV Manage AI Model Development and Evaluation
16% of exam 6 tasks · Day 3 PM + Day 4 AM
DreamsPlus coaching approach: A project manager does not train models — but they must be able to ask the right questions, manage data scientists effectively, and make critical go/no-go decisions on model quality. We demystify model development for non-technical PMs using plain-language analogies, translated metrics, and structured oversight frameworks.
Key knowledge areas (from PMI-CPMAI™ ECO)
- Evaluate appropriate algorithms for specific use cases
- Guide choices between supervised, unsupervised, and reinforcement learning
- Assess trade-offs between model complexity, performance, and interpretability
- Establish testing protocols and configuration management for model versions
DreamsPlus original coaching content
- “Algorithm Plain English” session: What each model type does and when a PM should question the team’s choice
- QA/QC checklist for AI models: What questions a PM asks at each model review gate
- Exercise: Review a data scientist’s model selection memo and identify missing justification
Key knowledge areas (from PMI-CPMAI™ ECO)
- Plan training schedules and resource allocation
- Monitor training progress and computational resource utilisation
- Oversee data cleaning, feature engineering, and preprocessing workflows
- Ensure data transformation reproducibility and documentation
DreamsPlus original coaching content
- Planning a model training sprint: milestones, resources, experiment tracking, and re-planning triggers
- Discussion: What “experiment tracking” means practically for a PM — versioning, reproducibility, and why it matters for audits
Key knowledge areas (from PMI-CPMAI™ ECO)
- Final data quality assessments before model training begins
- Evaluate model performance against success criteria
- Assess model robustness, generalisation, and deployment readiness
- Make final approval decisions for model deployment
DreamsPlus original coaching content
- Go/No-Go simulation: Participants receive model evaluation reports and must decide whether to approve deployment — with accountability for their decision
- Reading an evaluation report: accuracy, precision, recall, F1 — what a PM needs to understand (and what to ask when the numbers look good but the business problem isn’t solved)
V Operationalize AI Solution
17% of exam 7 tasks · Day 4 PM + Day 5 AM
DreamsPlus coaching approach: Deployment is where AI projects become real — and where most governance failures happen. We focus on production monitoring, model drift, contingency planning, and the often-neglected transition from project team to operational support. Participants leave with practical deployment and handover plans they can adapt immediately.
Key knowledge areas (from PMI-CPMAI™ ECO)
- Develop comprehensive deployment strategy and timeline
- Plan infrastructure, rollback procedures, and contingency plans
- Coordinate deployment activities across technical teams
- Validate system functionality and performance in production
DreamsPlus original coaching content
- Deployment planning workshop: Build a deployment plan including rollback triggers, smoke test criteria, and go-live communication
- Blue/green deployment and canary releases — what a PM needs to know to ask the right questions of the engineering team
Key knowledge areas (from PMI-CPMAI™ ECO)
- Establish model lifecycle management and version control
- Monitor model performance and drift detection in production
- Implement monitoring dashboards for business and technical KPIs
- Generate regular performance reports and alerting systems
DreamsPlus original coaching content
- What is model drift? — plain-language explanation and the PM’s role in setting drift thresholds and retraining schedules
- Monitoring dashboard design exercise: What goes on a PM’s AI operations dashboard vs. a data scientist’s dashboard
- KPI mapping: From model metrics to business outcomes — connecting accuracy to revenue impact
Key knowledge areas (from PMI-CPMAI™ ECO)
- Document project outcomes, lessons learned, and best practices
- Plan transition from project team to operational support
- Coordinate knowledge transfer and create handover documentation
- Develop incident response, disaster recovery, and business continuity plans for AI systems
DreamsPlus original coaching content
- Capstone (Day 5): Each participant produces a complete AI Project Closure Package — final report, transition plan, and contingency playbook
- Lessons learned facilitation: How to run a retrospective on an AI project that captures what traditional project retrospectives miss
- AI Incident Response Simulation: What happens when the production model goes wrong — who is called, what is done, what is documented
Workshop schedule
5-day intensive programme structure
The PMI-CPMAI™ exam comprises 120 questions (100 scored + 20 unscored pre-test) with a time limit of 160 minutes. The five domains and their exam weightings are mapped below against DreamsPlus coaching intensity.
| Day | Session | Domain / Focus | Delivery |
|---|---|---|---|
| Day 1 | AM: Responsible AI Foundations | Domain I — All 5 Tasks | Lecture + PIA Workshop |
| PM: Business Needs — Part 1 | Domain II — Tasks 1–5 | Case Study + Scope Exercise | |
| Day 2 | AM: Business Needs — Part 2 | Domain II — Tasks 6–10 | AI Charter Capstone Presentation |
| PM: Data Needs — Part 1 | Domain III — Tasks 1–5 | Data Requirements Workshop | |
| Day 3 | AM: Data Needs — Part 2 | Domain III — Tasks 6–9 | Go/No-Go Simulation + Leadership Presentation |
| PM: Model Development — Part 1 | Domain IV — Tasks 1–3 | Algorithm Demystification + QA Review | |
| Day 4 | AM: Model Development — Part 2 | Domain IV — Tasks 4–6 | Deployment Readiness Simulation |
| PM: Operationalisation — Part 1 | Domain V — Tasks 1–4 | Deployment Planning Workshop | |
| Day 5 | AM: Operationalisation — Part 2 | Domain V — Tasks 5–7 | Closure Capstone + Incident Simulation |
| PM: Exam Readiness Session | All 5 Domains | Mock Exam + Strategy Debrief |
Day 5 afternoon: Full mock exam session — 60 scenario-based questions drawn from DreamsPlus’s independently developed question bank, structured proportionally to the PMI-CPMAI™ domain weightings. Individual performance reports and personalised study guidance provided to every participant.
Assessment and certification
How DreamsPlus measures your readiness
3
Domain Capstone exercises throughout the workshop
60Q
Mock exam on Day 5 — scenario-based, timed
70%
Target pass score on DreamsPlus mock exam
1:1
Post-workshop study plan for every participant
DreamsPlus does not award the PMI-CPMAI™ certification. The credential is issued solely by Project Management Institute, Inc. upon passing the official exam. DreamsPlus issues a Workshop Completion Certificate for the AI Project Management Coaching Programme — this is our own credential and is not endorsed by or affiliated with PMI®.
Legal notice and intellectual property
How DreamsPlus operates legally and ethically
DreamsPlus Institute has designed this programme as an independent coaching offering. The structure of this syllabus is informed by the publicly available PMI-CPMAI™ Examination Content Outline (September 2025, published by Project Management Institute, Inc.) which serves as a reference framework — in the same way any coaching institute references an examination board’s publicly published syllabus. All workshop content, exercises, case studies, templates, and frameworks are original intellectual property of DreamsPlus Institute and are not reproductions of PMI’s proprietary materials.
Not a PMI Authorized Training Partner
Not Endorsed by PMI®
Not Affiliated with PMI®
Original DreamsPlus Content
Independent Provider
PMI–CPMAI™ ECO Used as Reference Only
Full trademark attribution: PMI-CPMAI™, CPMAI™, PMI®, and the PMI logo are trademarks of Project Management Institute, Inc. All rights reserved. DreamsPlus Institute makes no claim to these marks. Their use in this document is solely for the purpose of identifying the certification examination for which this coaching programme provides independent preparation — consistent with nominative fair use principles under trademark law.