PMI has released its AI project management methodology — Cognitive Project Management for AI

The first question is why?

Traditional project management and application development methodologies do not fully address the
complexity of AI projects. AI solutions are data-driven, not just software-driven, which requires a
systematic approach that ensures:
● Alignment with business objectives and ROI
● Proper data preparation and management
● Robust model evaluation and operationalization
● Iterative development to adapt to changing data and needs

CPMAI is data-centric and based on an iterative approach.

The methodology provides 7 patterns —

Conversational and human interaction
Recognition
Patterns and anomalies
Predictive analytics and decision support
Hyperpersonalization
Autonomous systems
Goal-driven systems

The methodology includes 6 main phases

Business understanding
Data understanding
Data preparation
Model development
Model assessment
Transfer to operational use
In the presented methodology, a separate section is devoted to the issues of trust in AI, and embedding trust in each phase of the project. (Note: For those interested in issues of trust and ethics in the development of AI systems, I refer to my presentations — 1 and 2)

Key roles in AI projects

Project Manager/AI Project Lead: oversees the entire AI initiative, ensuring its compliance with the CPMAI stages. Project Manager: Provides leadership support to the AI project, allocates resources, and helps align the AI initiative with the broader organizational strategy. This role coordinates timelines, manages risks, engages with stakeholders, and ensures the project aligns with business goals.
● Data Engineer: Creates the data pipelines and architecture that power the AI systems. They
perform tasks such as data ingestion, integration, cleansing, transformation, and ensure the reliability, scalability, and security of data flows, which is critical to smooth model development and deployment.
● Business Analyst/Subject Matter Expert: Ensures the AI solution aligns with real business goals and domain needs. They help define success criteria, interpret results, and translate AI insights into actionable actions for stakeholders and end users.
● MLOps/DevOps Engineer: Bridges the gap between model development and deployment to production by managing version control, continuous integration/continuous delivery, and ongoing performance monitoring.
● Data Scientist: If an ML model needs to be developed from scratch or requires fine-tuning to extend existing models, organizations may also need a data scientist on the team. This role focuses on building and validating models. The data scientist has expertise in statistics, machine learning algorithms, and model experimentation. They must translate business needs into technical requirements and make modeling decisions accordingly.
AI Soft Skills and Culture

● Critical Thinking and Problem Solving: AI projects are experimental by nature, so
teams must be able to conceptualize approaches, test solutions quickly, and iterate.
● Communication and Storytelling: Stakeholders need clear explanations of complex AI results, whether to justify resource allocation or solve user problems.
● Collaboration and cross-functionality: AI projects span data, engineering, and business domains. Strong teamwork and cross-functional alignment are essential.
● Adaptability and tolerance for uncertainty: As models evolve and data changes, teams must be prepared for iterative cycles and the ability to iterate.

SUMMARY

Use CPMAI’s iterative, data-driven, AI-specific approach
● CPMAI’s six stages: From understanding the business (stage I) to operationalizing the model (stage VI), each stage ensures that the right problems are being addressed, the right data is in place, the AI is being developed responsibly, and that real needs are being met.
● Iterative, data-driven approach: AI projects are not static. They require iterative cycles of data preparation, model refinement, and stakeholder feedback to stay relevant and avoid drift.
● Integration with organizational processes: CPMAI stages effectively complement conventional project management practices (including Agile and DevOps/MLOps),
providing the AI-specific conditions necessary for success.
● Trustworthy AI: Ethical, responsible, and transparent development practices are integral to long-term AI adoption. CPMAI places special emphasis on bias detection, governance, and stakeholder engagement at each stage.

● Team and culture: AI success depends on the right distribution of roles – data engineers, data scientists, analysts, subject matter experts, and project managers – working together using an agile, learn-as-you-go approach.