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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.
1. Ambiguity of outcomes
2. Explainability of outcomes
3. Fairness, bias and data imbalance
4. New infrastructure/processes/tools
5. Identifying the right problems to solve creating intelligent experiences
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.
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 Narain – Founder, RealWordAI
