Business problem, AI fit, feasibility and ROI.
Managing AI
PMI-CPMAI™
PMI Certified Professional in Managing AI
A phase-based path for professionals who want to manage AI initiatives with measurable business value, fit-for-purpose data and Responsible AI checkpoints.
27total hours
9live sessions
21hcore learning
6hexam readiness
Why this programme
Official structure. Added practice. Real judgement.
The RUNOTECH PMI-CPMAI™ Exam Prep Course is a 27-hour instructor-led programme. Its 21 Core Learning hours organise the available PMI Sections 0 and 2–7 into an end-to-end methodology: Business Understanding, Data Understanding, Data Preparation, Model Development, Model Evaluation and Operationalisation. A further six hours focus on an integrated AI case, responsible-AI decisions, mixed scenarios and exam strategy.
Learning outcomes
From knowledge to applicable professional judgement.
The programme connects the PMI framework with decisions, trade-offs and realistic professional scenarios.
Data needs, quality, governance and readiness.
Model iteration, performance, bias and explainability.
Operationalisation, monitoring and continuous AI improvement.
Programme structure
Core Learning + a dedicated Exam Readiness layer.
21hCore Learning
21 hours of Core Learning across seven live sessions, from the business problem and data through the model lifecycle and operationalisation.
6hExam Readiness
Six hours across two sessions for an end-to-end AI case, responsible-AI checkpoints, a mixed mock and exam strategy.
Exam domain alignment
Detailed curriculum
9 live sessions. A clear objective for every session.
Open each session to view its topics and participant outcomes.
01
Core Learning
7 sessions · 21h
01Section 0 + 2 | Introduction → Phase I Business Understanding3h · Core Learning+
Topics
AI basics, Trustworthy AI, the PMI-CPMAI methodology, business problem, AI vs non-AI alternatives and initial criteria.
Participant outcome
Distinguish a valid AI use case from a technology-first idea and define a measurable problem and outcome.
02Section 2 + 3 | Phase I → Phase II Data Understanding3h · Core Learning+
Topics
AI patterns, feasibility, Go/No-Go, ROI, scope, roles, AI-specific risks, data needs, sources and access.
Participant outcome
Connect an AI pattern with the business need and bring data and risk into the decision before build.
03Section 3 + 4 | Phase II → Phase III Data Preparation3h · Core Learning+
Topics
Data role, quantity, quality, datasets, privacy, compliance, access, infrastructure and data cleansing.
Participant outcome
Assess data fitness for purpose and identify governance, privacy and readiness constraints.
04Section 4 + 5 | Phase III → Phase IV Model Development3h · Core Learning+
Topics
Data pipelines, verification, transformation, synthetic data, augmentation, labelling, GenAI data and model-development foundations.
Participant outcome
Connect data preparation with model quality, reproducibility and governed model choices.
05Section 5 + 6 | Phase IV → Phase V Model Evaluation3h · Core Learning+
Topics
Model development and validation, pretrained/foundation models, GenAI, evaluation metrics and validation logic.
Participant outcome
Assess model choices against business and technical criteria and define an evaluation approach.
06Section 6 + 7 | Phase V → Phase VI Operationalization3h · Core Learning+
Topics
Real-world performance, bias, Trustworthy AI evidence, monitoring, platforms, deployment and integration.
Participant outcome
Connect acceptance with business criteria and prepare deployment and integration.
07Section 7 | Operationalization & Next Iteration3h · Core Learning+
Topics
Operationalising GenAI, model lifecycle management, governance, monitoring, limits, adoption and the next iteration.
Participant outcome
Turn a model into an operational solution and organise monitoring, support and iteration.
02
Exam Readiness
2 sessions · 6h
08Phase Review & End-to-End AI Case3h · Exam Readiness+
Topics
Business → Data → Model → Evaluation → Operationalisation case, Go/No-Go and Responsible AI decision points.
Participant outcome
Integrate the methodology and exam domains and justify phase-gate decisions.
09Integrated Mock & Exam Strategy3h · Exam Readiness+
Topics
Mixed-domain scenarios, methodology sequence, dependencies, error analysis and final exam strategy.
Participant outcome
Stabilise end-to-end AI judgement, identify weak domains and build an exam routine.
Included
A complete training-to-readiness experience.
An official / PMI-aligned learning structure with RUNOTECH's added practice layer.
Review→Practice Questions→Mock Exams→Gap Analysis→Targeted Revision→Exam
01What is the PMI-CPMAI™ exam like?
It focuses on structured, iterative and responsible management of AI initiatives, from business need and data through evaluation and operationalisation.
02When am I genuinely exam-ready?
When your performance is stable, time management is sound, mistakes are understood and PMI logic can be applied across different scenarios.
03What are mock exams and why do they matter?
They simulate the exam, reveal weak areas, improve time management and reduce examination stress.
04What happens after the course ends?
Review → Practice Questions → Mock Exams → Gap Analysis → Targeted Revision → Exam. Preparation continues until there is a clear readiness map.
Next step
Talk to the Academy about whether PMI-CPMAI™ fits your path.
Basis: PMI-CPMAI™ Instructor-Led materials, Sections 0 and 2–7, and the corresponding Examination Content Outline. The 27-hour allocation is a RUNOTECH delivery design.
PMI eligibility requirements, exam structure, procedures and fees may change. Current official PMI guidance should be checked before every application.