Technology & security
AI Product Management
Build an evidence-led AI Product Management foundation you can explain in a real workplace or interview.
Plan responsible AI-enabled products around people, value and risk.
About this course
Learn problem framing, data needs, human oversight, evaluation, rollout and stakeholder communication.
Career relevance
Where this can take you
Designed for learners exploring technology careers and building a practical, honest portfolio.
Portfolio project: Create a AI Product Management capstone pack that documents your purpose, method, evidence, quality check and next action.
Skills you will gain
Practical skills, not just theory
What you will learn
Understand the core workflow behind ai product management.
Create practical evidence using controlled, workplace-ready methods.
Make evidence-led decisions, communicate limitations and escalate appropriately.
Present a portfolio-ready capstone and development plan.
Objective map
A numbered, measurable syllabus
Every lesson, knowledge check and practical activity maps to the objectives below. Domain weights show how much of the programme each area represents.
Domain 1
AI Product Management: role and foundations to Data needs
- 1.1Explain and distinguish ai product management: role and foundations in a realistic ai product management context: establish the purpose, professional boundaries and workplace context for ai product management.
- 1.2Demonstrate problem framing in a realistic ai product management context: apply problem framing to a realistic ai product management brief, documenting the decision, evidence and next action.
- 1.3Demonstrate data needs in a realistic ai product management context: apply data needs to a realistic ai product management brief, documenting the decision, evidence and next action.
Domain 2
Human oversight to Rollout
- 2.1Demonstrate human oversight in a realistic ai product management context: apply human oversight to a realistic ai product management brief, documenting the decision, evidence and next action.
- 2.2Demonstrate evaluation in a realistic ai product management context: apply evaluation to a realistic ai product management brief, documenting the decision, evidence and next action.
- 2.3Demonstrate rollout in a realistic ai product management context: apply rollout to a realistic ai product management brief, documenting the decision, evidence and next action.
Domain 3
Stakeholder communication to Portfolio lab: AI Product Management case
- 3.1Demonstrate stakeholder communication in a realistic ai product management context: apply stakeholder communication to a realistic ai product management brief, documenting the decision, evidence and next action.
- 3.2Apply and control integrated ai product management workflow in a realistic ai product management context: connect the programme topics into one controlled workflow with clear inputs, checks, handover points and escalation boundaries.
- 3.3Complete and defend portfolio lab: ai product management case in a realistic ai product management context: create a portfolio-safe ai product management artefact from a realistic workplace scenario and explain the choices made.
Domain 4
Final AI Product Management capstone
- 4.1Complete and defend final ai product management capstone in a realistic ai product management context: complete and review an end-to-end ai product management case with evidence, quality checks, limitations and a professional recommendation.
How the programme works
Learn, practise, explain and prove it
This is a long-form, eight-week guided programme rather than a stack of short articles. Each module contains six substantial lessons that move from concepts and methods into a worked case, a quality clinic, a workplace lab and assessed reflection.
1. Guided learning
Study the purpose, process, risks and professional standard through structured readings and active notes.
2. Worked cases
Compare plausible options, expose missing evidence and defend a decision under realistic constraints.
3. Quality clinics and labs
Diagnose weak work, revise it against a rubric and create portfolio evidence with a full audit trail.
4. Hard assessment
Pass applied checks and the final assessment at 85%, with no unresolved critical-error questions.
Curriculum
A complete programme, module by module
The curriculum below shows the actual learning pathway, objective and evidence for every module. Full lessons remain locked until enrolment.
Build the base
Standards, vocabulary and the core method you will use throughout the programme.
01AI Product Management: role and foundationsEstablish the purpose, professional boundaries and workplace context for ai product management.+
Measured objectiveExplain and distinguish ai product management: role and foundations in a realistic ai product management context: establish the purpose, professional boundaries and workplace context for ai product management.
Evidence you will produceA technical ai product management: role and foundations brief with requirements, safe method, sample output or architecture, validation evidence and handover notes.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
02Problem framingApply problem framing to a realistic ai product management brief, documenting the decision, evidence and next action.+
Measured objectiveDemonstrate problem framing in a realistic ai product management context: apply problem framing to a realistic ai product management brief, documenting the decision, evidence and next action.
Evidence you will produceA technical problem framing brief with requirements, safe method, sample output or architecture, validation evidence and handover notes.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
03Data needsApply data needs to a realistic ai product management brief, documenting the decision, evidence and next action.+
Measured objectiveDemonstrate data needs in a realistic ai product management context: apply data needs to a realistic ai product management brief, documenting the decision, evidence and next action.
Evidence you will produceA checked working paper, analysis or report for data needs, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You can explain the fundamentals and complete a first guided task.
Develop core skills
The working skills and tools of the role, practised one at a time.
04Human oversightApply human oversight to a realistic ai product management brief, documenting the decision, evidence and next action.+
Measured objectiveDemonstrate human oversight in a realistic ai product management context: apply human oversight to a realistic ai product management brief, documenting the decision, evidence and next action.
Evidence you will produceA technical human oversight brief with requirements, safe method, sample output or architecture, validation evidence and handover notes.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
05EvaluationApply evaluation to a realistic ai product management brief, documenting the decision, evidence and next action.+
Measured objectiveDemonstrate evaluation in a realistic ai product management context: apply evaluation to a realistic ai product management brief, documenting the decision, evidence and next action.
Evidence you will produceA technical evaluation brief with requirements, safe method, sample output or architecture, validation evidence and handover notes.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
06RolloutApply rollout to a realistic ai product management brief, documenting the decision, evidence and next action.+
Measured objectiveDemonstrate rollout in a realistic ai product management context: apply rollout to a realistic ai product management brief, documenting the decision, evidence and next action.
Evidence you will produceA technical rollout brief with requirements, safe method, sample output or architecture, validation evidence and handover notes.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You can carry out routine work independently and check it properly.
Apply it in realistic scenarios
Case studies and simulations with competing priorities and incomplete information.
07Stakeholder communicationApply stakeholder communication to a realistic ai product management brief, documenting the decision, evidence and next action.+
Measured objectiveDemonstrate stakeholder communication in a realistic ai product management context: apply stakeholder communication to a realistic ai product management brief, documenting the decision, evidence and next action.
Evidence you will produceA technical stakeholder communication brief with requirements, safe method, sample output or architecture, validation evidence and handover notes.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
08Integrated AI Product Management workflowConnect the programme topics into one controlled workflow with clear inputs, checks, handover points and escalation boundaries.+
Measured objectiveApply and control integrated ai product management workflow in a realistic ai product management context: connect the programme topics into one controlled workflow with clear inputs, checks, handover points and escalation boundaries.
Evidence you will produceA technical integrated ai product management workflow brief with requirements, safe method, sample output or architecture, validation evidence and handover notes.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
09Portfolio lab: AI Product Management caseCreate a portfolio-safe ai product management artefact from a realistic workplace scenario and explain the choices made.+
Measured objectiveComplete and defend portfolio lab: ai product management case in a realistic ai product management context: create a portfolio-safe ai product management artefact from a realistic workplace scenario and explain the choices made.
Evidence you will produceA technical portfolio lab: ai product management case brief with requirements, safe method, sample output or architecture, validation evidence and handover notes.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You can make and justify decisions in realistic situations.
Prove it and plan your next step
Portfolio evidence, final review and a development plan you can show an employer.
10Final AI Product Management capstoneComplete and review an end-to-end ai product management case with evidence, quality checks, limitations and a professional recommendation.+
Measured objectiveComplete and defend final ai product management capstone in a realistic ai product management context: complete and review an end-to-end ai product management case with evidence, quality checks, limitations and a professional recommendation.
Evidence you will produceA technical final ai product management capstone brief with requirements, safe method, sample output or architecture, validation evidence and handover notes.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You have portfolio-ready evidence and a clear next step.
Suggested pace
Eight-week study plan
Plan for roughly 5–9 hours each week across readings, worked cases, practice, portfolio production and assessment remediation. Repeat a week when the knowledge checks show a gap.
Week 1
AI Product Management: role and foundations to Problem framing
- •Complete the six guided lessons for AI Product Management: role and foundations and Problem framing and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the ai product management: role and foundations and problem framing portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 2
Data needs
- •Complete the six guided lessons for Data needs and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the data needs portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 3
Human oversight
- •Complete the six guided lessons for Human oversight and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the human oversight portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 4
Evaluation
- •Complete the six guided lessons for Evaluation and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the evaluation portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 5
Rollout to Stakeholder communication
- •Complete the six guided lessons for Rollout and Stakeholder communication and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the rollout and stakeholder communication portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 6
Integrated AI Product Management workflow
- •Complete the six guided lessons for Integrated AI Product Management workflow and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the integrated ai product management workflow portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 7
Portfolio lab: AI Product Management case
- •Complete the six guided lessons for Portfolio lab: AI Product Management case and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the portfolio lab: ai product management case portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 8
Final AI Product Management capstone
- •Complete the six guided lessons for Final AI Product Management capstone and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the final ai product management capstone portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Submit the capstone evidence pack, defend the key decision, remediate every weak objective and pass the final assessment at 85% with no critical misses.
Technology & security
Continue your learning path
AI for Work & Prompting
Use generative AI responsibly to research, write, analyse and improve everyday work.
60–72 hours
API & Integration Foundations
Understand how systems exchange information safely and reliably.
55–66 hours
C# & .NET Foundations
Build foundational skills for modern business-application development.
45–54 hours
Assessment and certificate
A scenario-led ai product management case, practical portfolio review and final knowledge assessment. It is a UGEN ACADEMY completion record, not an external professional credential.
Who this is for
- ✓Learners exploring technology careers.
- ✓Job seekers building practical ai product management evidence.
- ✓Professionals developing a structured new capability.