MASSIVUE
    AI Project Management
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    AI & Digital TransformationIntermediateMicrocredential

    AI Project Management

    The failure modes in AI delivery are managerial, not mathematical. This course teaches you to catch them.

    About 80 minutes·Self-paced online·Lifetime access·Verified digital credential
    Enrol nowTalk to an advisor
    Microcredential Credential 4 of 4Part of Certified Practitioner in AI & Digital TransformationSee the pathway ↓
    Why it matters

    What most courses get wrong, and what this one does differently

    From

    AI literacy with a project management sticker: you learn what ML is and leave with no changed behaviour

    To

    Treating data readiness as a gate, not a task, before a single sprint starts

    From

    Task-based sprints applied to AI, where velocity looks like failure even when the team is working correctly

    To

    Experiment-based sprint planning with a framework for pivot, extend, or escalate decisions

    From

    Declaring success at ship, when the model is actually starting to decay

    To

    Planning for closure and handover: retraining triggers, monitoring ownership, and ROI measurement

    Outcomes

    What you'll be able to do

    • Explain why AI projects behave differently from traditional software projects, and why probabilistic outcomes break conventional progress reporting
    • Navigate the AI project lifecycle from ideation through pilot, production, and continuous optimisation
    • Assess AI feasibility across technical, business, organisational, and ethical dimensions
    • Build an AI project charter with defensible success criteria
    • Run a data readiness gate before development starts, and decide when a project should not proceed
    • Adapt agile delivery for experimentation: experiment-based sprints, pivots, and escalation
    • Use MLOps concepts (experiment tracking, model versioning, registries, monitoring) to govern delivery
    • Identify and manage AI-specific technical debt, including data debt and model debt
    • Build an AI risk register and run governance gates across the lifecycle
    • Evaluate model quality, including bias and fairness testing, and decide when not to deploy
    • Close an AI project properly: operational handover, retraining triggers, and ROI measurement
    Skills

    Skills you'll gain

    AI project lifecycle managementFeasibility and opportunity assessmentData readiness assessmentAI project charteringExperiment-based sprint planningMLOps literacy for governanceAI technical debt managementAI risk registers and escalationModel evaluation and bias testingResponsible AI governance gatesOperational handover and model monitoring
    Curriculum

    4 modules · 20 lessons · About 80 minutes

    About 80 minutes, module by module

    Explain why AI projects behave differently from traditional software delivery, understand the AI project lifecycle, and define the project manager role and competencies for AI initiatives

    Assess AI feasibility, build an AI project charter with defensible success criteria, run a data readiness gate, and set up the team RACI for AI delivery

    Run experiment-based sprints, make pivot, extend, or escalate decisions, use MLOps concepts to govern delivery, manage AI-specific technical debt, and maintain an AI risk register

    Evaluate model quality including bias and fairness, decide when not to deploy, close an AI project with a proper operational handover, set retraining triggers, and measure ROI

    The credential

    The credential you earn

    A verified digital credential you can share publicly, and that stacks toward a full certification.

    • Publicly verifiable via a unique credential link
    • One-click add to your LinkedIn profile
    • Verified digital credential, CPD recognition in progress
    How it's earned · Final Assessment (10 minutes): Scenario-based questions testing AI feasibility assessment, project charter design, experiment-based sprint governance, and model evaluation judgment including bias and fairness, plus a capstone: define the governance gates, risk register entries, and operational handover checklist for a real or example AI initiative.
    The MASSIVUE pathway

    Complete all 4 to earn Certified Practitioner in AI & Digital Transformation.

    Self-paced microcredentials, about 5 hours 50 min of learning in total. Each one stands alone; together they earn the full certification.

    1
    Generative AI & Prompt EngineeringAbout 100 minutesBuild practical fluency with generative AI: how it works, how to prompt it well, how to design AI workflows, and how to use it safely at work. No coding required.
    2
    Building AI Projects & Real-World AI SolutionsAbout 80 minutesUnderstand how real AI systems are designed, built, and shipped: the decisions and trade-offs behind model selection, RAG, agents, production, and safety. No coding required.
    3
    AI Business ConsultingAbout 80 minutesA practical introduction to how AI transformation is diagnosed, scoped, and delivered inside organisations: readiness, opportunities, business cases, and responsible delivery.
    4
    AI Project ManagementAbout 90 minYou are hereThe failure modes in AI delivery are managerial, not mathematical. This course teaches you to catch them.
    See the full certification →Enrol in the full certification
    How MASSIVUE credentials work:1Take a microcredential→4Stack all 4→★Earn the certification
    Who it's for

    Built for the people accountable for delivery

    Project managers and delivery leads moving into AI initiatives
    Programme managers overseeing AI portfolios
    Product managers responsible for AI features
    PMO leads defining AI delivery governance
    Business analysts and transformation leads on AI projects
    Technical leads who have inherited project accountability
    Not for: Data scientists or ML engineers who want to build models. This course does not teach you to develop AI. It teaches you to govern its delivery. It is also not a substitute for a recognised project management certification such as PMP; it assumes you already know how to run a project, and teaches what changes when the project is probabilistic. Prerequisites: Project or delivery experience. No technical or coding background required.
    What's included

    Everything in the credential

    4 modules of focused video lessons
    About 80 minutes covering the full AI delivery governance lifecycle from foundations through operational handover
    Six embedded scenarios from real AI failure modes
    A fraud-detection model missing its accuracy threshold, a demand-forecasting project blocked by fragmented data, a healthcare triage model with training data gaps, a recommendation engine whose experiments all fail, and a financial-services model with demographic bias
    Practical governance frameworks
    The AI project charter, the data readiness gate, RACI for AI teams, CRISP-DM, the AI risk taxonomy and risk register, and lifecycle governance gates
    MLOps concepts for project governance
    Experiment tracking, model versioning, model registries, data versioning, and production monitoring: enough to govern delivery without building the pipeline
    Module-end quizzes, 10-question final assessment, and a capstone project
    Knowledge checks plus a capstone that produces a deliverable you can use in your actual work
    Lifetime access
    Learn at your own pace and revisit as AI delivery practices evolve
    Verified digital badge and certificate
    A publicly verifiable credential you can share on LinkedIn
    For organisations

    Bring this to your team

    For teams

    • Volume pricing and central billing
    • Team progress reporting
    • Optional tailored examples for your sector
    Talk to us about team access

    Deliver under your brand

    • Co-branded or fully white-label delivery
    • Your LMS or ours
    • Revenue-share partnership options
    Become a partner
    FAQ

    Questions, answered honestly

    No, and it does not pretend to be. Those are established, exam-based project management credentials and if that is what you need, get one. This course assumes you can already run a project and teaches what changes when outcomes are probabilistic, progress is experimental, and success depends on data you may not have.

    No. That is the point. The documented causes of AI project failure are managerial, not mathematical: undefined success criteria, unready data, missing integration, weak sponsorship, no handover plan. You need enough MLOps literacy to ask the right questions, not to build the pipeline.

    The headline figures vary a lot by study and by how failure is defined, so treat any single number carefully. MIT's Project NANDA reported that roughly 95 percent of generative-AI pilots showed no measurable P&L return; RAND has put AI project failure above 80 percent. What is consistent across the research is not the number, it is the cause: the technology is rarely why projects fail.

    That one is about technical architecture decisions: when to use RAG, agents, or a simpler approach. This one is about delivery governance: charters, data gates, experiment sprints, risk registers, and handover. Different job, different course.

    It is a verified digital credential you can share and verify online. It is not an accredited or government-recognised qualification. CPD recognition is in progress.

    Yes. Team access with volume pricing and central billing is available on request. This works well as a shared vocabulary for a PMO that is starting to take on AI delivery.

    Keep stacking

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    AI project failure is a management problem, not a model problem. The failure modes are documented, the causes are known, and they are exactly the things a project manager is trained to catch.

    Verified digital credential

    Enrol nowSee the full certification