AI for Product Owners: A New Era of Productivity and Quality

What if you could double your impact as a Product Owner without adding extra strain?

Artificial Intelligence has become a key asset when it comes to digital product.

For Product Owners, this change means great opportunities: automate repetitive tasks, refine how information is synthesized, and focus on what truly matters—delivering value to the product.

We will explore how to use AI effectively at every stage, featuring tools and practical prompts to help you get started. Here’s how AI can become your trusted product co-pilot.

Important Note: The tools mentioned in this article have not all been extensively tested. AI can speed up certain processes and enhance your analysis, but it does not replace your critical thinking or business validation. Always review the generated content carefully, check its relevance, and ensure its accuracy before using it in subsequent stages or sharing it with your teams.

Discovery Phase: AI as a Driver for Smarter Exploration

Discovery is a structured process to understand user needs, explore possible solutions, and reduce uncertainty before building.

This critical phase typically involves several key steps.

Let us examine how AI can support the Product Owner at every stage to improve efficiency and focus on value.

1. Finding and Recruiting Target Users

Discovery often starts with identifying and engaging the most relevant user profiles.

How AI can help:

Generate personalized recruitment email scripts.
Suggest audience segmentation criteria.
Analyze CRM databases to identify the best profiles to contact.

Tool examples:

ChatGPT (for segmentation prompting)
Apollo IO (for prospecting automation)
Segment AI (for customer analysis and targeting)
[Jasper, Copy.ai)

Use case: Quickly generate personalized invitation messages.

Sample prompt:
“Write an email template to invite [Product] users to a user interview. Target: [user type]. Style: professional but engaging, maximum 150 words.”

Benefit: Create targeted emails or LinkedIn posts in minutes with minimal effort.

2. Preparing User Interviews and Questionnaires

A successful Discovery relies on well-crafted questions.

How AI can help:

Generate tailored interview guides.
Create clear, neutral questionnaires.
Suggest open-ended questions to capture richer qualitative insights.

Tool examples:

Notion AI (for drafting interview guides)
Typeform AI (for optimized questionnaires)

Sample prompt:
“Write a 10-question interview guide to understand how users manage their digital subscriptions. Goal: uncover main frustrations and existing alternative solutions.”

Benefit: Quickly produce structured, insightful questions that reveal user needs without bias.

3. Running Sessions and Capturing Insights

During interviews, capturing information accurately is essential, without drowning in notes.

How AI can help:

Automatic transcription of interviews.
Smart summaries of key exchanges.

Tool examples:

Fireflies.ai or Maze for transcription and summaries
Otter.ai for automated reporting

Example of automation:

Record your Zoom sessions directly through Fireflies.ai. The tool will automatically extract:

Questions asked

Key responses

Critical points to follow up

Benefit: Keep all insights organized and accessible, freeing you to focus on interpretation and next steps.

4. Synthesizing Collected Data

Once feedback is collected, the next step is synthesis: spotting patterns, prioritizing needs, and identifying major pain points.

How AI can help:

Automatically group feedback by theme.
Generate persona profiles based on the collected data.

Tool examples:

Dovetail for thematic analysis
Delve AI to create personas from qualitative data

Example synthesis prompt (ChatGPT):
“From this set of user interviews, identify:

3 recurring problems

2 product innovation opportunities

1 concise example persona”

Benefit: Quickly turn raw feedback into structured insights that guide your product decisions.

5. Brainstorming Solutions

Next comes the creative phase: generating ideas for solutions to explore. AI can help the Product Owner go beyond usual patterns and uncover new opportunities during discovery.

How AI can help:

Stimulate idea generation (“assisted ideation”).
Suggest functional or UX variations.

Tool examples:

Miro AI to enrich mind maps
ChatGPT for basic conceptual wireframes

Example prompts:
“Give me 10 feature ideas to help a user track their monthly subscriptions better. Rank them by potential impact (high/medium/low) on user retention.”

Or:
“Generate 20 open-ended questions to understand how [user type] manages [identified problem]. Neutral, unbiased questions only.”

Or:
“Propose 10 creative solutions a team could consider to address [user problem].”

Benefit: Rapidly generate a wide range of ideas and possibilities, giving your team more options to explore before committing to development.

6. Prioritizing Explored Options

Finally, it is essential to sort solutions based on value, feasibility, and potential impact.

How AI can help:

Rank solutions using matrices (e.g., effort vs. impact).
Suggest scoring models based on business criteria.

Tool examples:

Amplitude AI to predict product impact
GPT-4 / ChatGPT in “technical architect” mode (advanced prompt engineering)

Example prompts:
“Here are 8 feature ideas. Help me place them on a Business Impact vs Technical Complexity matrix, and recommend the 3 best candidates for an MVP.”
Or:
“I want to develop this feature: [describe functional need].
Can you outline the technical components needed to implement it (backend, frontend, storage, security, third-party services, etc.) in a [React / Node / etc.] stack?
Also, highlight any potential technical challenges with a relative complexity estimate (low/medium/high).”

Summary: AI-Augmented Discovery

Using AI during Discovery does not replace human judgment:

It accelerates data collection, organization, and analysis to free up time for strategic thinking.

By leveraging the right tools and prompts, the Product Owner can transform Discovery:
From weeks of intensive work ➔ to a few days of informed decision-making.

Writing Jira Tickets with AI: Myth or Reality?

For a long time, writing Jira tickets was seen as a tedious, manual task.

Now, AI can turn this into a real opportunity to speed up the process – as long as it is used thoughtfully.

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Creating Complete Tickets: Beyond Title and Description

Most AI tools can produce a basic ticket.

A high-quality ticket goes further:

A clear functional description
Detailed acceptance criteria (Given/When/Then or “Etant donné / Lorsque / Alors”)
Associated test cases, if needed

Automating Acceptance Criteria

How AI can help:

Turn vague requests into concrete acceptance scenarios
Suggest multiple variations to challenge and clarify scope

Example tools:

ChatGPT – for targeted prompts to generate acceptance scenarios
Linear AI Smart Create – to enrich and structure tickets
Jira AI Labs – to automatically populate key fields

Example prompt:
“From this user request: ‘I want to receive a notification 10 minutes before my scheduled appointment,’ generate:

3 acceptance criteria using Given/When/Then

1 negative criterion (what should not happen).”

Expected output:

Given a user has scheduled an appointment,
When the appointment time is 10 minutes away,
Then the user receives a push notification on their phone.

Going further: generating manual test cases

A clear ticket also helps QA/Testers.
Why not ask the AI to generate test scenarios directly?

Example prompt:
“Generate 5 manual test cases from this ticket:

Verify reception of push notification

Failure case if the internet connection is lost

Case where the reminder is disabled by the user”

Expected output:

Test 1: Schedule an appointment ➔ Check that the notification is received 10 minutes before.
Test 2: Enable airplane mode ➔ Verify that no notification is received.
Test 3: Disable reminders in settings ➔ Verify that no notification is sent.

Bonus: Generating automated test cases (pseudo-code)

For CI/CD teams, AI can also generate automated test scripts.

Example prompt (ChatGPT or GitHub Copilot):
“Write an automated test in JavaScript using Jest to verify that the user receives a push notification 10 minutes before a scheduled appointment.”

Optimizing Technical Documentation for Developers

Writing tickets is often slowed down by unclear information on:

The APIs involved
Expected input/output data
Technical dependencies

How AI can help:

Generate technical briefs based on functional requirements
Identify potential impacts (backend, APIs, security, performance)

Example tools:

ChatGPT / Claude.ai for generating technical documentation

Example prompt for technical documentation:
“From this user story:

Feature: Appointment reminder notification

Input data: appointment time, user time zone

Output data: push notification in standard format
Write a concise technical brief for backend and mobile developers. Include: API endpoints to create or modify, business logic, expected error cases.”

Expected outcome:

Backend: add a scheduled job based on the appointment time stored in UTC.
Mobile: listen for a new push event type reminder and handle localized display.

Direct benefits for the Product Owner

Write more complete tickets in half the time.
Standardize the quality of acceptance criteria.
Anticipate developers’ questions before they arise.
Simplify QA by providing test foundations right from the ticket creation stage.

The PO no longer just writes tickets: they increase product value and raise quality standards.

How to Build ‘Prompt Templates’ to Industrialize Ticket Writing with AI

The goal is to enable any Product Owner to standardize their requests to the AI and quickly produce tickets with consistent quality, without starting from scratch each time.

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Why Use Prompt Templates?

Using templates allows you to:

Save time by avoiding rewriting each request.
Ensure consistent quality across ticket structures.
Easily delegate to other POs, PMs, or junior team members.
Get the most out of AI by clearly framing your expectations.

How to Build a Good Prompt Template

A solid prompt template for tickets should include:

Functional context – What is it? For whom? Why?
Delivery expectations – Business description, acceptance criteria, and potential test cases.
Expected format – For example, Given/When/Then.
Style guidelines – Clear, concise, no unnecessary jargon.
Optimization request – Include edge cases or error scenarios.

Reusable Prompt Template Example

Prompt Type:

Context:

Product: [brief description]

Feature: [expected functionality]

Target User: [primary user type]

Business Goal: [problem solved or value delivered]

Your Mission:

Generate a clear functional description (max 5 lines)

Propose 3 to 5 acceptance criteria in Given/When/Then format

Generate 2 associated manual test cases

Generate 1 critical error scenario to test

If possible, write a mini technical brief for the developer (APIs impacted, input/output data)

Style Guidelines:

Stay neutral and factual

Use simple sentences

Avoid marketing language

Scale Further with “Prompt Libraries”

To take it even further:

Build a Notion or Confluence library with your standard prompt templates: “New Button,” “New Signup Flow,” “Add Notification,” etc.

Define variable “slots” in your templates: [Product], [User], [Action], [Expected Impact].

Use tools like Zapier to automate sending prompts to ChatGPT or an internal AI agent and automatically retrieve the formatted ticket.

Direct Benefits for the Product Owner

Less time lost structuring each ticket

Consistent quality even at high volumes

Simplified handover to developers and QA

Industrialization of Product Management

Will Product Owners Disappear Tomorrow?

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The automation of certain tasks raises a concern: will the PO role disappear?

Let us be clear: no — but it will evolve.

What AI cannot do:

Translate a complex business strategy into product priorities.
Mediate between stakeholders with conflicting interests.
Build a deep sense of empathy for the end user.

Example of a strategic prompt

For Amplitude AI / ChatGPT Business Analysis:
“From these three potential features for our mobile health app, estimate:

The user engagement potential

The attrition risk if not implemented

The 6-month ROI
Provide a prioritization recommendation based on these elements.”

Outcome: the PO grounds decisions on enriched data and strengthens their role as product decision-maker.

Conclusion

AI is a real opportunity for Product Owners who know how to use it:

Less cognitive load on mechanical tasks
More time to drive the vision, listen to users, and align teams
Stronger ability to make better product decisions, based on faster and richer insights

Tomorrow, the best PO will not be the one who writes the best tickets, but the one who knows how to use AI to become the strategist of the product.

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Bonus: Ready-to-Use Prompt Packs

To help you take action right away, here are 3 AI prompt template packs:

Discovery: Recruit, interview, synthesize
Tickets: Write, test, brief developers
Documentation: Describe flows, simplify architecture

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