Why We Need More AI Product Owners, Not Data Scientists

Jan Zawadzki Jan Zawadzki
March 16, 2021 AI & Machine Learning

About a crucial, yet underutilized role in AI Product development

If you think Artificial Intelligence (AI) is a short-lived hype, think again. Stanfords 2021 AI Index shows that global corporate AI investment amounted to a record-high $67 bn. Last year’s AI investments topped even 2019’s all-time high comfortably by 40%. According to PWCs 22nd Global CEO Survey, 77% of Fortune 500 CEO’s plan to start or already employ AI initiatives. Given the steadily increasing investment in AI, Data Science comes under pressure to deliver tangible results.

State Of AI in 10 Charts
Source: State of AI in 10 Charts.

The potential rewards of integrating AI into your business are immense. According to the 2020 McKinsey Global AI Survey, AI is contributing more than 20% of EBIT for an elite group of AI practitioners. Additionally, these AI High Performers spend more of their budget on AI initiatives than competitors. They also have the capabilities to develop AI solutions in-house, instead of depending on external suppliers.

Why We Need More AI Product Owners, Not Data Scientists
Photo by Andy Kelly on Unsplash

As Data Science matures, so does the pressure for the field to actually live up to the hype. The field needs to deliver tangible business benefits. A vital, yet underutilized role for the success of AI Products is the AI Product Owner (AI PO).

In this article, I share my perspective on the importance and required skills of AI POs. Instead of staffing AI Product development teams only with Data Scientists, AI POs increase the chances of successfully developing AI Products. The article describes how to build successful AI Product Teams, the role of an AI PO, and study resources to become one.

First, let’s analyze the roles of a traditional Product Owner and an AI Product Owner.

Product Owner vs. AI Product Owner

The role of the Product Owner is defined in the Scrum framework. Scrum is a popular agile development method. The framework requires the roles of a Product Owner, Scrum Master, and Developer.

Scrum pays meticulous attention to divide the product and people responsibilities between the Scrum Master and Product Owner. The Product Owner is responsible for maximizing the value of the Scrum team. She creates a product vision, communicates with stakeholders, and prioritizes the Product Backlog. Scrum alliance states that a PO needs business, user experience, technical, and communication skills.

Product Owner
Image by author

The AI PO role is an extended, specialized version of the general PO role. AI POs inherit the responsibilities of a general PO. They extend them to maximize the impact of AI-based Products.

Image By Author
Image By Author

So, what skills do AI POs need? First and foremost, AI POs need to know about the potentials and pitfalls of AI-based applications. What is AI good at, where does it struggle? Which business problems could an AI-based solution solve, where is it misplaced?

Next, AI POs need to pay special attention to monitoring the predictions of AI models. AI is based on statistical assumptions, so the predictions always carry a degree of uncertainty. Depending on the context, a wrong prediction can cause severe consequences. AI POs should be able to design AI applications to include human decision-making when required.

Products having ML in their core in a production setting is still a nascent discipline, so there are many learnings being made by both PMs and entrepreneurs operating in this space. — Sahar Mor

AI Entrepreneur

Additionally, AI-based Products are dynamic. They measure how customers react to their predictions. AI Products also need to account for ongoing adjustments to data. When designing AI-based Products, AI POs need to keep the virtuous cycle of AI in mind. The true power of intelligent products comes from their ability to continuously improve based on new data.

The AI Flywheel Allows AI-based Products To Continuously Improve
The AI Flywheel allows AI-based Products to continuously improve. Image by author

On the technical side, the more technical knowledge AI POs have, the better. AI POs neither need to be former developers nor have a Master’s degree in Computer Science. Yet, it is hard to understand what is easy and what is virtually impossible to implement with AI. The feasibility of an AI Product can be hard to evaluate for AI POs without the proper technical background. Please note that opinions differ here and others believe that technical knowledge is neither required for a PO nor AI PO.

Last but not least, AI POs understand that AI development is different from the software development workflow. AI development tests different hypotheses and iterates quickly. Traditional software development can follow a more modular and structured approach. Understanding that ML development isn’t as linear as traditional software development is crucial to communicating expectations with stakeholders and delivering value on time.

“Since the development lifecycle of AI projects is based on “searching” rather than “planning”, companies need professionals who are trained to look at products as optimization problems rather than a programming problems.” — Adnan Boz

Founder AI Product Management Institute

Focus Area Of General PO vs. AI POs
Focus areas of general PO vs. AI POs. Image by author

After understanding the role of the AI PO, let’s analyze how the AI PO works in an AI Product team.

Structuring data product teams

Delivering AI-based Products depends on forming cross-functional teams. Cross-functional teams are staffed with experts with complementary skills and roles. Too often, I have seen single-minded Data Science Teams struggling to operationalize their models. Requiring Data Scientists to fulfill all roles required to bring an AI-based Product to customers is a recipe for failure.

Product Development Team
Exemplary AI Product Development Team. Image by author

Instead, the AI PO supports Data Scientists by maximizing business value. The graphic above shows a prototypical AI Product development team with cross-functional experts. Don’t get hung up on the roles, they can change from one product to another. Some products might need AI UI/UX designers, some might not. However, I do believe that all AI Product teams should at least have an AI PO. You can also see that Data Scientist is just one role in the team, among many others.

Now that you know what an AI PO does, read on to see where you can learn to skills to become one.

Resources for AI POs

As you’ve seen, AI POs can play a significant role in productionizing AI Products. Luckily, there are plenty of resources to teach you to become an AI PO:

  • AI Solution Product Management by Adnan Boz
  • AI Product Manager Nanodegree by Udacity
  • AI for Everyone by deeplearning.ai

First, I’d like to recommend the AI Product Management Institute by Adnan Boz. Adnan is doing great work in teaching at Stanford and working at Nvidia. He also started the AI Product Management Institute to develop AI POs. Adnan helps aspiring product owners break into the field of AI and has received thoroughly positive student feedback.

Above Mentioned PO Resources
Above mentioned AI PO resources. Image by author

The Udacity nanodegree looks comprehensive and valuable. I heard good feedback about the program. The program takes two-months and covers a variety of AI PO skills to follow along with dataset creation, model building, and AI Product monitoring.

Lastly, I am an avid promoter of the deeplearning.ai content and have reviewed the “AI for Everyone” course myself before it was released. It’s not exactly targeted at developing AI Product Owners, but the course helps you understand the inner workings, benefits, and limitations of AI Products. For AI POs it is essential to understand the non-technical fundamentals of AI, which this course teaches you.

Please note that this is not a sponsored post, but my personal recommendation.

Key Takeaways

After reading this article, I hope you understand that we don’t need more Data Scientists, but more AI POs. Instead of requiring Data Scientists to cover all product development stages, the AI PO supports AI Product teams. The AI PO focuses on creating value through AI-based Products.

  • AI POs are crucial for successfully developing AI-based applications
  • AI POs understand the benefits and pitfalls of AI, design products to use the AI flywheel, and understand the AI development workflow
  • AI Product teams need a variety of skills, and not only Data Scientists

After finishing this post, you have a better understanding of the need for AI POs and what they do.

Sources

Daniel Zhang, Saurabh Mishra, Erik Brynjolfsson, John Etchemendy, Deep Ganguli, Barbara Grosz, Terah Lyons, James Manyika, Juan Carlos Niebles, Michael Sellitto, Yoav Shoham, Jack Clark, and Raymond Perrault, “The AI Index 2021 Annual Report,” AI Index Steering Committee, Human-Centered AI Institute, Stanford University, Stanford, CA, March 2021.

The AI Index 2021 Annual Report by Stanford University is licensed under Attribution-NoDerivatives 4.0 International. To view a copy of this license, visit http://creativecommons.org/licenses/by-nd/4.0/.

Tara Balakrishnan, Michael Chui, Bryce Hall, Nicolaus Henke, “Global survey: The state of AI in 2020”, McKinsey Analytics, Retrieved from McKinsey.com, March 2021.

Originally published on Medium.

  • Experfy Insights

    Top articles, research, podcasts, webinars and more delivered to you monthly.

  • Jan Zawadzki

    Tags
    Artificial IntelligenceData ScienceMachine LearningProduct Owner
    © 2021, Experfy Inc. All rights reserved.
    Leave a Comment
    Next Post
    Explainable AI: Do We Trust AI Enough To Make Decisions For Us?

    Explainable AI: Do We Trust AI Enough To Make Decisions For Us?

    Leave a Reply Cancel reply

    Your email address will not be published. Required fields are marked *

    More in AI & Machine Learning
    AI & Machine Learning,Future of Work
    AI’s Role in the Future of Work

    Artificial intelligence is shaping the future of work around the world in virtually every field. The role AI will play in employment in the years ahead is dynamic and collaborative. Rather than eliminating jobs altogether, AI will augment the capabilities and resources of employees and businesses, allowing them to do more with less. In more

    5 MINUTES READ Continue Reading »
    AI & Machine Learning
    How Can AI Help Improve Legal Services Delivery?

    Everybody is discussing Artificial Intelligence (AI) and machine learning, and some legal professionals are already leveraging these technological capabilities.  AI is not the future expectation; it is the present reality.  Aside from law, AI is widely used in various fields such as transportation and manufacturing, education, employment, defense, health care, business intelligence, robotics, and so

    5 MINUTES READ Continue Reading »
    AI & Machine Learning
    5 AI Applications Changing the Energy Industry

    The energy industry faces some significant challenges, but AI applications could help. Increasing demand, population expansion, and climate change necessitate creative solutions that could fundamentally alter how businesses generate and utilize electricity. Industry researchers looking for ways to solve these problems have turned to data and new data-processing technology. Artificial intelligence, in particular — and

    3 MINUTES READ Continue Reading »

    About Us

    Incubated in Harvard Innovation Lab, Experfy specializes in pipelining and deploying the world's best AI and engineering talent at breakneck speed, with exceptional focus on quality and compliance. Enterprises and governments also leverage our award-winning SaaS platform to build their own customized future of work solutions such as talent clouds.

    Join Us At

    Contact Us

    1700 West Park Drive, Suite 190
    Westborough, MA 01581

    Email: support@experfy.com

    Toll Free: (844) EXPERFY or
    (844) 397-3739

    © 2025, Experfy Inc. All rights reserved.