{"id":203617,"date":"2021-03-16T18:00:34","date_gmt":"2021-03-16T15:00:34","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/we-need-more-ai-product-owners-not-data-scientists\/"},"modified":"2021-03-16T18:00:34","modified_gmt":"2021-03-16T15:00:34","slug":"we-need-more-ai-product-owners-not-data-scientists","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/we-need-more-ai-product-owners-not-data-scientists\/","title":{"rendered":"#We need more AI product owners, not data scientists"},"content":{"rendered":"

“#We need more AI product owners, not data scientists<\/strong>”<\/p>\n

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Scrum pays meticulous attention to divide the product and people responsibilities between the Scrum Master and Product Owner. The Product Owner is responsible for\u00a0<\/span>maximizing the value of the Scrum team<\/a>. She creates a product vision, communicates with stakeholders, and prioritizes the\u00a0<\/span>Product Backlog<\/a>.\u00a0<\/span>Scrum alliance<\/a>\u00a0states<\/span> that a PO needs business, user experience, technical, and communication skills.<\/p>\n

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<\/a>Image by author<\/figcaption><\/figure>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/figure>\n

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

AI-based Products differ from traditional software products. First, AI uses data to learn patterns implicitly, instead of developers implementing rules explicitly. Second, AI-based products have the chance to continuously improve with incoming data. Third, Machine Learning allows us to build products that were not possible before in this quality, like\u00a0<\/span>speech assistants<\/a>,\u00a0<\/span>automated driving<\/a>, or\u00a0<\/span>medical diagnostics<\/a>. Hence, POs need to adjust their skills to deliver AI-based Products.<\/p>\n

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<\/a>Image by author<\/figcaption><\/figure>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/figure>\n

So, what skills do AI POs need? First and foremost, AI POs need to know about the potentials and pitfalls of AI-based app<\/a>lications. What is AI good at, where does it struggle? Which business problems could an AI-based solution solve, where is it misplaced?<\/p>\n

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.<\/p>\n

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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 \u2014\u00a0<\/span>Sahar Mor, AI Entrepreneur<\/a><\/p>\n<\/blockquote>\n

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.<\/p>\n

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<\/a>The AI Flywheel allows AI-based Products to continuously improve. Image by author<\/figcaption><\/figure>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/figure>\n

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\u2019s degree in Computer Science<\/a>. 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.<\/p>\n

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\u2019t as linear as traditional software development is crucial to communicating expectations with stakeholders and delivering value on time.<\/p>\n

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\u201cSince the development lifecycle of AI projects is based on \u201csearching\u201d rather than \u201cplanning\u201d, companies need professionals who are trained to look at products as optimization problems rather than a programming problems.\u201d<\/em>\u00a0<\/span>\u2014\u00a0<\/span>Adnan Boz,\u00a0, Founder AI Product Management Institute<\/a><\/p>\n<\/blockquote>\n

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<\/a>Focus areas of general PO vs. AI POs. Image by author<\/figcaption><\/figure>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/figure>\n

After understanding the role of the AI PO, let\u2019s analyze how the AI PO works in an AI Product team.<\/p>\n<\/div>\n