What Does an AI Product Owner Do? Roles and Responsibilities Explained

The role of a Product Owner has always existed, but it was only recently, with the increased use of AI technologies, that this role evolved into what we call an AI Product Owner. 

Because of advances in AI technology, teams needed someone who could help define not only users' needs but also the data, models, and AI decisions that affect the outcome. 

With these shifting responsibilities, new roles and responsibilities were created in the form of an AI Product Owner.

A traditional Product Owner only needs to know what features to build. As an AI Product Owner, you will also be required to think about how AI will affect the outcome of your product. 

You will evaluate data, make predictions about what your AI models will do, and ensure that your end product is fair, meaningful, and valuable to end users. 

Essentially, everything that was done as a Product Owner before the advent of AI remains relevant, but now has additional areas of responsibility that did not previously exist.

As AI technologies are increasingly used in product development, many people are seeking information on how AI is changing Product Owners' careers and where they fit into the evolving market for the future of product development. 

In this blog post, we will outline the roles and responsibilities of this newly emerging position and provide insight into its importance as AI becomes the norm in product development.

What is an AI product owner?

An AI Product Owner is defined as a person who helps create AI-based products.

While performing traditional Product Owner functions, they also take on additional tasks associated with the intelligence of artificial intelligence systems, such as data management, model training and testing, ethics associated with the use of AI solutions, the impact of using AI solutions, etc. 

In short, a Product Owner typically decides on what to build in a product, while an AI Product Owner helps determine how AI should operate, how AI should develop skills, and how AI will interact within the confines of the product. 

An AI Product Owner is responsible for developing a useful AI solution and ensuring it aligns with the company's goals.

What are AI Product Owner Roles And Responsibilities?

The AI product owner is responsible to determine where AI can add the largest value, maintain an inventory of AI related tasks, collaborate closely with both the data and development team, ensure that AI is utilized responsibly and ethically, as well as monitor the performance of the AI technology post launch, and translate complex AI concepts into accessible written instructions that communicate what AI technology will deliver. 

Each responsibility will be covered in greater detail below and will impact all phases of an AI product's lifecycle.

1. Define how AI will add value to the product

The key responsibilities of an AI Product Owner are to identify user needs and understand the company's needs. They should not simply implement AI because it is "popular." 

An AI product owner will identify the specific application where AI will provide the greatest value to the end-user. 

They will then evaluate multiple options for applying AI while considering the benefits against the effort, and consider the applications that will deliver the most value with minimal effort. 

If an AI product owner has a clear understanding of what their users need and how their organization will benefit from an AI adoption, they will minimize wasted time building unnecessary features, allowing them to focus on creating valuable AI features for their end-users.

2. Work closely with data and development teams

Data is also a critical component of AI products, so an AI PO collaborates extensively with Data Experts, Engineering Teams, and Analyst Groups. 

The PO assists the Data Team in defining the necessary Data, explaining the level of Cleaning and/or Completeness required for the Data, and identifying potential challenges for the Development Team when developing it. 

The AI PO will also work to provide the Development Team with information about how the AI features will work (e.g., the Model Predictions, required levels of Accuracy, and User Interactions with the AI features). 

The PO's job is to assist in making it as simple as possible for the Development Teams to execute on the objectives without confusion.

3. Prioritize AI features in the product backlog

Instead of focusing on a single feature, as with many traditional products, the process of creating an AI product typically involves multiple activities across multiple iterations (including raw data collection, developing and training the model, and testing and improving the model). 

An AI Product Owner creates a backlog that includes these tasks and a priority list of tasks they will develop next, based on their overall value to customers or business units (to avoid wasted time on time-consuming, high-risk tasks). 

They understand how AI experimentation works and therefore build flexibility into their schedules for iterative improvement and learning. 

Such flexibility in prioritizing allows the team to make steady progress without experiencing bottlenecks.

4. Explain AI concepts to non-technical stakeholders

AI has the potential to change the way we do business, but not all stakeholders understand the technology behind it. 

As a result, the AI Product Owner serves as a bridge between stakeholders and AI developers.

By translating complex technology concepts into plain language, they help clients and other departments understand what is realistically possible with the use of AI as well as establish realistic expectations about what an AI model can deliver. 

This helps avoid unrealistic demands such as “Make this model 100% accurate.” They also provide a clear view of the risks and limitations associated with using an AI model, enabling the organization to make more informed decisions regarding budgets, timelines, and strategies.

5. Monitor AI performance after launch

Artificial Intelligence (AI) is not static; it continually evolves and improves as you carefully test, evaluate, and optimize your AI system throughout its entire lifecycle. 

As such, the behaviour of an AI system must always be checked for accuracy, value to users, and the extent to which the output (i.e., model predictions) becomes increasingly misaligned with reality over time to ensure that its users continue to receive value from it. 

Regular checks also allow AI Product Owners to identify when it's necessary to retrain and/or retune the AI model.

By continuously monitoring your AI systems, you can help ensure that these features remain high-value and reliable, thus providing users with continued confidence in their use.

6. Ensure ethical and responsible use of AI

AI can unintentionally create biased results if it learns from incomplete or unfair data.

The AI Product Owner takes responsibility for preventing this. They work with teams to review how data is collected, how the model makes decisions, and whether the outputs are fair to all user groups. They make sure privacy rules are followed and that automated decisions do not harm users. They also document the reasoning behind AI choices so the product remains transparent and accountable.

7. Change the Product Directions via Insight

AI products can provide insights into a user's behaviour and a model's operation. Based on the data analysis, the AI product owner uses the insights to determine the next step for the product.

When the AI product performs better than expected, the AI product owner may consider adding features or extending the model's use cases.

On the other hand, if the AI product isn't working well, the owner changes the product's requirements, goes back to the original dataset, or changes the AI design.  This is important because making an AI product is rarely a straight line; it needs constant learning, discovery, and improvement.

How to become an AI Product Owner?

To become an AI Product Owner, it is important to have a combination of product management and AI knowledge, along with an understanding of how technical aspects of software development relate to user needs

You do not have to be a data scientist; however, you must understand what an AI model is, how data influences the operation of AI models, and how to support the development of ethically responsible AI capabilities within development teams.

1. Begin with strong fundamental skills in product management

Before you can work with AI, you need to know how to build and manage products well. This means learning how to conduct user research, manage a backlog, plan a roadmap, and work with teams across departments. 

You're halfway there if you've worked as a Product Owner or in a product-associated job before. If you don't know these basics, you should start learning them as AI product ownership relies on them.

2. Understand the fundamentals of how AI and machine learning work.

You don't have to know how to code structures, yet you must understand enough to make choices. This means knowing:

• What kind of information does AI need?

• How models get better and learn

• What accuracy, bias, and model drift mean

• How AI features work differently from regular features

You can talk to data scientists and engineers rather than getting confused if you know this. You don't need much more than short training programs, beginner-friendly videos, and AI product books to get started.

3. Learn how data affects AI products

AI depends a lot on the quality, quantity, and relevance of data. You should learn the following to become an AI Product Owner:

• How data is gathered

• How complete or clean are the information requirements to be

• How data changes how well a model works

• What ethical and privacy issues should to be aware of

This lets you decide whether an AI concept is possible.

4. Learning about AI applications by getting hands-on experience

If you're new to artificial intelligence (AI), start small. Find a couple of easy-to-understand applications of AI, such as recommendations, sentiment analysis, and chatbots, and learn how these applications work. 

Get started using free resources, review case studies, or use no-code performance platforms to experiment with various applications of AI. 

Understanding the different categories and types of AI through experience will enable you to make well-informed product decisions.

5. Enhance your communication and teamwork abilities

AI development is a team effort involving data scientists, engineers, designers, and business professionals, with the AI Product Owner serving as a bridge among them. 

Effective communication, the ability to clarify concepts and provide user-friendly explanations, and the ability to translate technical information into terms that users understand are critical abilities for the AI Product Owner. 

These skills may be even more valuable than having a comprehensive understanding of AI.

6. Understanding and Implementing Responsible and Ethical Practices

With Fairness, Privacy, and Transparency being the three biggest priorities for companies working with AI, you must learn:

• Ways of Minimizing Bias in AI Decisions

• How to Safeguard User Privacy

• How to Clearly Explain AI Outputs to Users

• Ways to Assess Risk Before Releasing New AI Features

By educating yourself in this area, you'll be able to take a leading position within the organisation's AI initiatives.

7. Create a Portfolio to Clearly Demonstrate AI-Based Thinking

You may not currently work within AI-focused products; however, it is possible to create a basic portfolio that includes the following:

1. Proposed Features for AI products

2. Problem Statements that can be addressed by AI

3. User Experience Journey Maps for an AI-enabled experience

4. Roadmaps or breakdown of Features

This could help prospective employers see how an individual thinks like an AI Product Owner, even if they have not yet had industry experience.

8. Consider Applying for Positions That Will Support Your Development in This Area

When applying for jobs in this sector, you do not necessarily have to start with an AI Product Owner position. Positions that could help one build their experience before this are:

1. Associate Product Owner

2. Product Analyst

3. AI Project Coordination

4. Product Manager of AI features

5. Technical Product Owner

Each of these job functions can help build one's skill set while being in close proximity to AI development teams.

9. Keep up with how AI is changing over time

AI changes a lot. To stay up to date, you should read reports, follow industry news, and learn about new tools. As time goes on, this information helps you make better choices about products.

AI Product Manager vs Product Owner

Although these two jobs often work together, they don't have the same duties.  You may think of each of them as two people looking at the same product from different heights: one from a long-term, strategic view and the other from a short-term, execution view.

1. Point of Focus

• AI PMs, or AI Product Managers, look at the big picture. Their job is to figure out why the AI product should be made, who it will help, and what long-term value it will bring to the business. They look at trends in the market, what other companies are selling, what users need, and what business opportunities are out there.

• AI Product Owner (AI PO): They are in charge of making things happen. Their job is to make the strategy into real features. They are in charge of the backlog, lead the development team, and make sure that each AI feature is built correctly and safely.

2. How much power do you have to make decisions

• AI PM: Makes big decisions about what to build in the next quarter, which AI opportunities are worth investing in, and how the product should change over time.

• AI PO: Makes decisions every day about what to work on this week, how the AI feature should work, and what changes need to be made based on feedback from the team.

3. Working with Teams

• AI PM: Works with business leaders, marketing, sales, and customers to make the product's overall direction better.

• AI PO: Works closely with testers, developers, data scientists, and designers to make sure that the product vision becomes a working AI feature.

4. Responsibility to AI Strategy

• AI PM: Sets the direction for AI. They explain how AI works into the product and the way it helps the company do better in the market.

• AI PO: Carries out the AI plan. They divide the AI vision into smaller duties that the team can work on and test.

5. How to Measure Success

• AI PM: Long-term results like market acceptance, revenue, competitive advantage, and total product success are used to measure success.

• AI PO: Based on delivery, this includes the quality of AI features, how smoothly the sprint goes, how clear the backlog is, and how well each feature solves the problem it was meant to.

What are the Essential Skills for AI Product Owners?

Essential skills for AI Product Owners combine strong product management abilities with a solid understanding of AI and data. 

This includes understanding how AI models learn, interpreting data quality, considering ethical considerations, and guiding teams through AI-driven decision-making. 

They must also communicate clearly across technical and non-technical groups to bridge user needs, business goals, and model behaviour. 

Core skill areas include product strategy, AI and data literacy, technical awareness, business understanding, ethical AI practices, and effective cross-functional collaboration. We will explore each of these essential skills in detail in the section below.

1. Strong Product Management Foundation

When working as an AI product owner, understanding the processes involved in planning, building, and improving a product is critical. The fundamental skills needed are the same as those necessary for all products: prioritising product features, creating user stories, creating a product roadmap for future development, and working collaboratively with multi-disciplinary teams. The fundamentals will provide the basis for all future decisions regarding an AI-based product.

2. Basic Knowledge of AI and Machine Learning

There is no requirement to become a data scientist; however, at a minimum, you must understand how AI models learn, what types of data they require to learn, and some basic terminology such as accuracy, bias, and model drift. This knowledge will ensure that you make informed decisions, ask the correct questions, and liaise effectively with technical team members.

3. Ability to Work with Data

AI solutions depend on large amounts of data to provide insights into user experience; therefore, when working as an AI product owner, you should be able to clearly communicate about data quality and sources, and address any limitations they may pose. When a team has access to high-quality data on model performance, they can determine whether the initial idea for an AI product is feasible and how to develop an ideal version of the product through model training.

4. Excellent Communicative & Simplifying Skills

Many individuals may struggle to understand AI. An AI PO has to break down how features function, their capabilities, and limitations into easily digestible formats for all stakeholders. Conveying technical information in layman's terms is a key part of this job role.

5. Awareness of Ethical and Responsible AI

AI products must be equitable, transparent, and secure for their users. To properly develop AI products, you must be able to recognize possible pitfalls, take precautions to avoid biased outputs, and adhere to privacy regulations. This enables you to create AI features that your end-users will feel comfortable using.

What are AI Product Owner Certifications?

Getting certified can help you feel more confident, learn more about AI products in a structured way, and show employers you're serious about working in this field. 

You don't have to be very technical, but getting the right certifications will help you learn about AI, product thinking, and accountable development. Here are some good choices to think about.

1. Agilemania's AI Product Owner Training (Great for Hands-On Learning)

This program is for professionals who want to learn how AI works in real-world product workflows by doing. It covers the duties of an AI Product Owner, working with data teams, deciding which AI features are most important, and using AI responsibly. The training is easy to understand and focuses on applying the skills in the real world, making it an excellent choice for both new and experienced product professionals.

2. Product School offers a certification in AI for product managers.

This certification will teach you a lot about how AI affects the products we use today. It covers the basics of AI, how to think about user experience, and how to link AI's capabilities to business goals. People who intend to establish a strong strategic base will find it helpful.

3. Certified Scrum Product Owner (CSPO) – Scrum Alliance

CSPO is not specific to AI, but it does improve your basic Product Owner skills. You'll learn how to handle backlogs, write user stories, and work well with development teams. This certification helps you build the right foundational discipline because an AI Product Owner still has all the duties of a regular PO.

5. Andrew Ng's "AI for Everyone" on Coursera

This is for beginners who want to learn what AI can and can't do. It breaks down ideas into simple terms, helps you set realistic goals, and shows you how to find real-world AI use cases. It's a great place to start for anyone who wants to work with AI products.

Which AI Certification is required For Beginners?

AI for Everyone is a good place to start for people who are new to the field. Once you know the basics, you can move on to role-based training such as Agilemania's AI Product Owner Training. After that, you can get specialized certifications, such as DSPM or Product School's program. This sequential method allows you to develop new skills and build confidence at a suitable pace.

Conclusion

As Artificial Intelligence advances, it is rapidly reshaping the way products are created. 

The person or group responsible for managing an organisation's AI Projects-the AI Product Owner will be at the forefront of this transformative process. 

Although traditional skills related to managing and developing products still remain of utmost importance, there is now an additional requirement, which is to possess a broader understanding of both data analytics and how AI operates. 

Therefore, for AI Product Owners, it is important that they not only have a basic understanding of product management but also an appreciation (or insight) into how AI changes the way that products are being designed and developed. 

But more importantly, as AI becomes integrated within the development of all types of products, the capabilities and expertise of an AI Product Owner will play a central role in creating "intelligent" and "meaningful" products moving forward. 

Ultimately, if you wish to pursue this career path as a Professional AI Product Owner, concentrate on developing solid foundational training, gaining practical knowledge of AI, and implementing certification programs that will lead to furthering your education and skill sets.