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What is AI product management?

By Mamta Narain – Founder, RealWordAI

AI product management involves solving customer problems using data enabled by artificial intelligence and machine learning. In the past, data was mostly used for analytics, where once the product is launched, you would run some data reports, analytics and then find some insights based on that information. Based on these insights, you would go back and program something and create products. 

In the world of AI we actually start with data. With the given set of data, we try to find out what kind of problems it can solve. 

Product managers usually focus on an existing problem and try to understand whether AI or ML can solve it. They need to know if they even have the required data to solve it. This domain of using data analysis to solve product challenges comes with its own set of problems. This is generally overlooked and product managers often do not pause to focus on the challenges that AI and ML is bringing in the product management world. Hence,this quite often results in these challenges becoming a showstopper for getting funding or getting the resources or getting the talent.

Key Takeaways:

  • AI and ML based product management involves utilizing these intelligence systems to find and solve product problems.
  • AI product management is different from traditional product management where product behavior is usually binary and predetermined.
  • AI product management has become more relevant in the last few years.
In this article
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    Challenges posed by AI product management

    1. Ambiguity of outcomes

    • In a traditional product management system, product behavior is usually binary, with predetermined results.
    • For example, a simple website’s submit button is either blue or not blue. Or when the customer launches on an e-commerce website, and if they are logged in the computer would say “Hello Tom”. 
    • But now with ML, the outcomes can no longer be clearly determined. Product managers have to deal with probabilities. For example, AI and ML might predict that the probability of a customer buying a product is, let’s say, 80%. 
    • Let us take a real life example. Cancer and other healthcare industries heavily use AI and ML. Looking at an X-ray film, AI and ML can make a prediction that there is a 95% likelihood that the customer may have Tuberculosis. This is answered only in terms of a percentage and not a final yes or no. These variable amounts generated by using AI have to be interpreted and navigated by the product managers.

    2. Explainability of outcomes

    • ML algorithms often become black boxes, often making it difficult for product managers to understand the reasoning behind their predictions. 
    • Let us take another example. When a credit card transaction is declined, it is quite difficult to understand the rationale behind this outcome. This turns the situation into a data science problem for product managers. 
    • This later on becomes even more difficult for them to justify the AI-based product decisions to stakeholders.

    3. Fairness, bias and data imbalance

    • The data that you use may not be very balanced. It may be skewing towards one segment of people. Machine learning models trained on such data are usually biased towards the majority class.
    • This usually happens when models try to minimize the overall error rate and, in the process, tend to ignore the minority class.

    4. New infrastructure/processes/tools

    • In the AI and ML world we live in, the AI ops and infrastructure, processes and tools that we use are very different from traditional product management.
    • This requires product managers to become experts in understanding and utilizing the new AI based tools to solve product problems.

    5. Identifying the right problems to solve creating intelligent experiences

    • This still remains a very big issue for the product managers and hence they have become so much more relevant. 
    • They should know how to find the right ML problem from the business problem that they have, and also know how to translate that into a ML problem so that the data scientists exactly know what to model for.
    • These problems are not easy to go to the stakeholders and pitch the return of investments because AI products take a long time to materialize.

    Role of Product Managers in AI Product Development

    1. Integration of AI into product development

    Product managers play a very important role in integrating AI into their product. They are responsible for embedding AI capabilities, making their involvement crucial in the process.

    2. Identifying the right ML problem, aligned to the business needs

    Product managers need to understand where the data is within their company, how to form a ML problem from this data, who is bringing the data and identifying the right set of stakeholders and groups who have the control of data. 

    3. Demonstrating a realistic ROI from AI products/ investments to the business

    One of the most important roles of a product manager is to bridge the gap between business problems and AI solutions. They should be able to embed AI in their business problems, which helps data scientists to model effectively. They must navigate the complex process of conveying realistic returns on AI investments to stakeholders, recognizing that AI products often take a longer time to materialize.

    4. Understanding the company dynamics, and identifying stakeholders who are in control of the data

    Product managers need to have a good understanding of their organization’s dynamics, particularly regarding data control. It is essential to identify and establish connections with the stakeholders who are responsible for data governance, in order to ensure data accessibility and accuracy.

    5. Having a good operational understanding of machine learning process, who does what, and when

    Product Managers are expected to have a good understanding of the machine learning process. This entails knowing who performs what tasks and when throughout the entire machine learning workflow, enhancing their effectiveness in orchestrating AI initiatives.

    Product managers are becoming more important than ever in utilizing AI to build products. They are responsible for embedding AI into products, identifying strategic problems, demonstrating a clear ROI, navigating organizational dynamics, and mastering the operational intricacies of machine learning. Their prominence in these roles is greater than ever before.

    Frequently Asked Questions

    In traditional product management, product behavior is usually binary and predetermined. AI product management deals with probabilistic outcomes.

    Challenges associated with AI product management include ambiguity of outcomes, difficulty in explaining the rationale behind the outcomes, addressing fairness and bias concerns, adapting to new infrastructure and tools, and selecting the right problems to solve with AI.

    Product managers help in integrating intelligence in their products, identifying the right AI and ML problems from given data, demonstrating a realistic ROI from AI products/ investments to the business, understanding the company dynamics and identifying stakeholders who are in control of the data.

    About the Author:

    Mamta NarainFounder, RealWordAI

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