WHAT IS THE PMI-CPMAI
Is your organization thinking about implementing AI initiatives
to streamline business processes and increase efficiency?
Our Project Manager, Kimberly Eaton, is a Certified Professional
in Managing AI (PMI-CPMAI). But what does that mean?
About the PMI-CPMAI
With PMI-CPMAI, Project Managers can:
Translate ambitious AI ideas into actionable, achievable plans
Adapt confidently to evolving technologies
Align diverse, cross-functional teams through a common delivery framework
Produce results that are responsible, measurable, and strong enough to meet rigorous business expectations
CPMAI equips project managers with a purpose-built framework for planning, managing, and delivering AI initiatives.
Grounded in a results-driven approach, PMI-CPMAI demonstrates that a project manager can handle complexity, bring varied stakeholders together, and deliver solutions that combine technical strength with meaningful business value.
CPMAI-certified professionals recognize the typical pitfalls that derail AI projects and apply proven best practices, Agile methods, and data-focused principles to drive successful outcomes.
Recognized globally and supported by PMI’s high standards, the certification carries credibility across industries.
Why You Need a PMI-CPMAI
AI projects are significantly more likely to succeed and deliver real value when formal project management practices are applied.
Without Project Management, AI Projects have an overall success rate of roughly 20%. But CPMAI Project Management can increase that to 70%. For completed projects, both ROI and on-time delivery
Below, you can find PMI’s details of the five domains of skill that a CPMAI can bring to your project. Avoid wasting time and money, and protect the trust and support of your stakeholders by providing your AI endeavors with the structure of AI-specific project management.
Success Rate for AI Projects with and without
CPMAI Project Management
A Certified Professional in Managing AI will…
1. Support Responsible and Trustworthy AI Efforts
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Establish data governance protocols for personally identifiable information (PII)
Implement encryption and access controls for AI training data
Conduct privacy impact assessments for AI model deployment
Ensure compliance with GDPR, CCPA, and other data protection regulations
Design secure data handling procedures throughout the AI lifecycle
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Document model selection criteria and decision rationale
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Analyze training data for demographic and representation imbalances
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Track evolving AI regulations and industry standards
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Create comprehensive records of AI model development decisions
Content for the five skills domains was taken from PMI’s CPMAI Outline. Click HERE to review the full document.
2. Identify Business Needs and Solutions
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Conduct stakeholder interviews to understand business pain points
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Assess technical viability of proposed AI solutions
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Identify potential failure modes and safety implications
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Define project boundaries and deliverables for AI initiatives
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Calculate expected benefits from AI solution implementation
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Assess organizational change management requirements
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Create high-level architecture for AI system design
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Establish measurable performances indicators for AI models
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Gather financial data and projected benefits for business case
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Assess skill requirements for AI project team composition
3. Identify Data Needs
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Specify data types and formats needed for AI model training
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Locate domain experts with knowledge of relevant data sources
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Map internal databases and data warehouses containing relevant information
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Provision computing resources for data processing and model training
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Execute data extraction from identified sources and systems
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Verify data usage rights and licensing agreements
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Assess data quality dimensions including accuracy, completeness, and consistency
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Compare available data against defined requirements and specifications
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Prepare executive summaries of data assessment findings
4. Manage AI Model Development and Evaluation
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Research and evaluate appropriate algorithms for specific use cases
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Establish model testing protocols and quality assurance procedures
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Plan training schedules and resource allocation for model development
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Oversee data cleaning and preprocessing workflows
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Conduct final data quality assessments before model training
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Evaluate model performance against established success criteria
5. Operationalize AI Solutions
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Develop comprehensive deployment strategy and timeline
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Coordinate deployment activities across technical teams
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Establish model lifecycle management procedures
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Implement monitoring dashboards for business and technical metrics
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Document project outcomes and achievement of objectives
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Plan transition from project ream to operational support
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Develop incident response procedures for AI system failures
Content for the five skills domains was taken from PMI’s CPMAI Outline