Learn to use AI tools for user story generation, customer feedback analysis and market research, while keeping product judgement where it belongs. Self-paced, exam included, valid for two years.
Holders of the Product Owner AI Plus certification receive a certificate and a digital badge that anyone can verify on LiveCertification.
Learning paths
Choose the Scrum Intelligence certification on its own, or add a partner certification in the same enrolment.
Product Owner AI Plus
- Scrum Intelligence certification
- Training and exam included
What you will learn
- Apply AI tools to backlog refinement and user story generation
- Use AI to structure and stress-test prioritisation decisions
- Analyse customer feedback at scale to surface themes and sentiment shifts
- Summarise market and competitor signals into roadmap-ready insight
- Draft roadmap narratives and stakeholder updates with AI support
- Evaluate AI tools against your organisation's security and data constraints
- Apply responsible AI practices covering customer data privacy and bias
- Keep product judgement and value decisions with the human, not the tool
Syllabus
Module 1: The AI-Augmented Product Owner
Where artificial intelligence fits inside product ownership and where it must not. Separating information gathering, which AI does well, from value judgement and stakeholder negotiation, which remain yours. Establishing the boundaries before introducing tools to your team.
Module 2: AI-Assisted Backlog Refinement
Turning raw requests, support tickets and stakeholder emails into well-formed user stories with clear acceptance criteria. Prompt techniques that produce usable drafts, gap analysis to find missing edge cases, and how to keep the team involved so refinement does not become a solo exercise.
Module 3: Prioritisation with AI Support
Using AI to structure prioritisation frameworks such as MoSCoW, WSJF and cost-of-delay against real backlog data. Stress-testing your own ordering by asking AI to argue the opposite case, and recognising when AI recommendations reflect your prompt rather than genuine analysis.
Module 4: Customer Feedback and Market Analysis
Clustering large volumes of qualitative feedback into themes, tracking sentiment shifts across releases, and summarising competitor and market signals. Covers sample bias, how to avoid over-weighting the loudest customers, and validating AI-surfaced themes before they reach the roadmap.
Module 5: Roadmaps and Stakeholder Communication
Using AI to draft roadmap narratives for different audiences, summarise long stakeholder threads into decisions and open questions, and prepare for review meetings. Keeping the commitments yours while letting AI handle the drafting.
Module 6: Responsible AI in Product Ownership
Customer data privacy when feedback passes through third-party tools, governance around approved tooling, bias in AI-generated insight, and the conversations you will need with security, legal and leadership before rolling anything out.
Why this certification
Product ownership has always been a judgement role: deciding what matters most, saying no to good ideas, and defending priority under pressure. AI does not remove that judgement, but it changes how much information you can bring to it.
Product Owner AI Plus is built around that shift. Instead of spending hours reading through support tickets and survey responses, you learn to have AI cluster them into themes and surface what is actually being asked for. Instead of writing every user story from scratch, you learn prompt techniques that produce solid first drafts you then refine. Instead of guessing at market direction, you learn to use AI to summarise competitor moves and industry signals into something you can act on.
The course is deliberately practical. Every module maps to something a Product Owner does in a normal week, and each one covers where AI genuinely helps and where trusting it would be a mistake. You will also cover the governance side — what customer data can safely pass through third-party tools, and how to answer security questions from your organisation.
For employers, an AI Product Owner certification signals that you can work at pace without losing rigour. As product teams adopt AI across discovery and delivery, that combination becomes the baseline expectation rather than a differentiator.
Who should take this certification
This certification is best suited for:
- Practising Product Owners who want to modernise how they manage a backlog
- Product Managers introducing AI tooling into discovery and delivery
- Business Analysts moving towards product ownership responsibilities
- Product Owners preparing for roles at organisations already using AI in product work
- Founders and solo product people who need to cover more ground with less time
- Anyone holding a Product Owner credential who wants a recognised AI specialisation
- No coding background is required. If you have written a user story and defended a priority call, you have enough context to start.
The exam
The exam checks your understanding of the course topics and how to apply them to real team situations. You take it online on ExamVault once you have completed the course material.
The exam is taken online on ExamVault by CertExpert. Three attempts are included in the fee.
Career outcomes
AI fluency is becoming a standard expectation in product roles rather than a bonus. This certification supports progression into:
- AI-Enabled Product Owner and Product Manager roles at technology-forward organisations
- Senior Product Owner positions where tooling and process decisions sit with you
- Product Lead and Head of Product roles requiring both delivery pace and analytical rigour
- Product Operations roles focused on tooling, data and process across multiple teams
- Consulting roles helping product organisations adopt AI-assisted ways of working
- Product job descriptions increasingly list AI tooling experience. A named credential makes that easy to evidence on a CV, on LinkedIn, and in interviews.
Frequently asked questions
What is an AI Product Owner certification?
An AI Product Owner certification validates that you can apply artificial intelligence tools to real product work — backlog refinement, prioritisation, customer feedback analysis and roadmap planning — while keeping value decisions with the human. Product Owner AI Plus from Scrum Intelligence covers which tools genuinely help, where they introduce risk, and how to use them without outsourcing your product judgement.
Do I need technical or coding skills for this course?
No. Product Owner AI Plus is written for product practitioners, not engineers. You will not build models or write code. If you have written a user story and defended a priority decision, you have enough background. Every AI technique is introduced through a product scenario.
How do Product Owners actually use AI day to day?
The highest-value uses are clustering large volumes of customer feedback into themes, drafting user stories and acceptance criteria from raw requests, stress-testing a prioritisation order by asking AI to argue against it, summarising long stakeholder threads into decisions, and preparing roadmap narratives for different audiences. In each case AI handles volume and drafting while you keep the judgement.
Will AI replace the Product Owner role?
No. AI is strong at summarising, drafting and finding patterns in data. It is weak at deciding what matters, negotiating with stakeholders, saying no, and taking accountability for an outcome — which is most of the Product Owner role. What changes is the balance: less time reading and writing, more time deciding and communicating. This course is built around that shift.
How long does the certification take to complete?
The course carries roughly twelve hours of material and is entirely self-paced. Most people finish within two to three weeks working evenings or weekends, though there is no deadline. There are no fixed class timings and no need to take leave from work.
Is the training live or self-paced?
Entirely self-paced. Your material is available online as soon as you enrol, and you work through the modules whenever it suits you. The assessment is also taken online at a time you choose.
What does the exam involve and is the fee included?
The CertiPlus assessment is scenario-based and open-book, taken online. It presents realistic product situations and asks how you would apply AI-assisted techniques, rather than testing definitions. Your enrolment fee covers the exam and three attempts are included.
How much does the AI Product Owner certification cost?
Product Owner AI Plus is US $175, which includes the full course material, the assessment fee, your three attempts, a digital certificate and a shareable digital badge. There are no separate exam charges.
How long is the certification valid?
Your credential is valid for two years from the date of issue and can be renewed. We send renewal reminders as the date approaches, and renewal keeps the same credential ID so anything you have already shared stays accurate.
Should I take Product Owner AI Plus or a traditional Product Owner certification first?
If you are new to product ownership, start with a foundational credential such as Product Owner Plus to build the core skills. If you already own a backlog and want to work faster without losing rigour, Product Owner AI Plus is the better next step — it assumes you know the role and focuses on applying AI within it.
Is customer data safe when using AI tools for feedback analysis?
That depends entirely on the tool and how you configure it, which is why the course covers it directly. Module 6 deals with what customer data can safely pass through third-party services, how to anonymise feedback before analysis, and how to answer the security and legal questions your organisation will raise before approving a tool.
What AI tools does the course cover?
The course focuses on categories and techniques rather than a single vendor, because the tooling landscape changes quickly. You will work through general-purpose generative assistants, backlog tools with built-in AI features, and feedback analysis approaches — along with a framework for evaluating any new tool against your context and constraints.
