Certified Professional in Managing AI (PMI-CPMAI)
About This Course
This is a four-day, practitioner-focused programme that equips learners to manage Artificial Intelligence (AI) initiatives from business problem identification through development, evaluation, deployment and ongoing operational governance.
Using Project Management Institute (PMI)’s vendor-agnostic, data-centric and iterative Certification for Professionals Managing AI (CPMAI) methodology, learners examine how to align AI solutions with business needs, assess organisational and technical feasibility, determine data requirements, oversee data preparation, coordinate AI model development, evaluate model performance and operationalise AI solutions responsibly.
The programme integrates responsible and trustworthy AI throughout the AI project lifecycle, including privacy, security, transparency, explainability, fairness, accountability, regulatory compliance, auditability and human oversight. Through realistic scenarios, case discussions, practical exercises and examination preparation activities, learners develop the managerial judgement required to support AI projects without needing to become data scientists or software developers.
What You'll Learn
b. Develop an AI project business case incorporating scope, stakeholders, success criteria, expected benefits, risks, return on investment and total cost of ownership.
c. Determine the data types, sources, ownership, access permissions, infrastructure and expertise required for an AI initiative.
d. Evaluate data readiness based on quality, completeness, accuracy, consistency, representativeness, privacy, security, licensing and regulatory requirements.
e. Coordinate governed data preparation and iterative AI model development, including data preprocessing, model selection, testing, version control and experiment tracking.
f. Evaluate AI systems against business, technical and responsible-AI criteria, including performance, robustness, explainability, fairness, reliability and generalisation.
g. Formulate evidence-based go/no-go recommendations at critical stages of the AI project lifecycle.
h. Develop an AI deployment and operationalisation plan covering system integration, transition, monitoring, model drift, retraining, incident response, business continuity and continuous improvement.
i. Apply responsible and trustworthy AI principles throughout the project lifecycle through appropriate governance, accountability, documentation, stakeholder approval and human oversight.
Entry Requirements
No programming, model-development or data-science experience is required, however should have:
a. Basic understanding of project, product, technology, data, business-process or digital-transformation work.
b. Foundational awareness of Artificial Intelligence (AI) concepts or be prepared to complete the prescribed pre-course learning materials.
c. Basic computer and digital-literacy skills, including using web-based learning platforms and productivity applications.