18/06/2024
Hello Fam!,
So, if we are friends or you are following me and you are interested in becoming an or an (ML Engineer, Data Engineer, or Data Scientist), the below article on my AI Product Manager Journey will be insightful.
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My AI Product Manager Learning Journey!
Recently, I have focused on developing the right skills and knowledge to lead AI initiatives in product development, learning from one of the best in the AI industry right now—IBM. As someone who has been deeply involved in building enterprise, SaaS, B2B, and B2C products over the past 7 years, I am finding the journey of what it takes to build AI products and be an AI product manager both refreshing and interesting.
So, in my , permit me to share with you what I have learned about the stages of AI product development. While many aspects are similar to traditional software/digital product development stages, there are unique differences in activities and team compositions.
Here’s a detailed breakdown:
Stage 1: Ideation or Innovation Stage
Activities to Expect:
• Identify the talents required to develop the product, including data scientists, ML engineers, domain experts, designers, business analysts, and of course yourself as the AI product manager.
• Define the unique problem, such as customer pain points, and how AI can address them.
• Conduct extensive market research that validates the need for your product and develop a value proposition explaining how your AI product addresses that need.
• Define the product by outlining the critical features and functionalities, and prioritize them as you would in traditional software product development.
• Conduct a cost-benefit analysis and share Return on Investment (ROI) with business stakeholders.
• Develop mockups and wireframes.
• Define the MVP (Minimum Viable Product) that will satisfy customer needs.
Expected Deliverables at this Stage:
• Formation of the development team.
• Finalized product features.
• Defined activities for subsequent stages.
• Calculated costs and benefits.
• Have a clear understanding of what to build.
Stage 2: Data Management Stage
Activities to Expect:
• Establish infrastructure to support data management efforts.
• Identify required data and ensure its accuracy and cleanliness.
• Select the best AI model to create the required features and functionalities.
• Establish data pipelines to support all required data feeds.
Expected Deliverables:
A well-defined requirement for:
-Data collection
-Data preparation
-Data storage
-A data pipeline in place.
Stage 3: Research and Development (R&D) Stage
Activities to Expect:
• Use management skills to assemble a development team and establish expectations for the product.
• Conduct experiments with AI models and select the most suitable one for your product.
• Adhere to the product development schedule, ensuring the model follows guidelines to develop the MVP while avoiding scope creep.
• Develop a functional prototype to demonstrate core functionalities and solicit feedback from both internal and external sources.
Expected Deliverables:
• Selection of the most suitable model.
• Creation of an MVP prototype.
Stage 4: Deployment Stage
Activities to Expect:
• Ensure launch readiness by testing the product to meet all defined MVP requirements.
• Conduct alpha testing to identify bugs.
• Conduct beta testing to allow potential users to experience the product and provide feedback.
• Determine your launch strategy.
Expected Deliverables:
• Completed testing.
• Validated product for full-scale launch and deployment.
Summary
1. The AI product development process consists of four stages: ideation or innovation, data management, R&D, and deployment.
2. At the ideation/innovation stage, the AI product manager forms the team that determines the product features, associated costs, and benefits.
3. At the data management stage, the product manager defines the type of data to use, how to use it, and the expected results.
4. At the R&D stage, the AI process team, comprising data scientists, data engineers, process specialists, domain experts, and ML engineers, selects the model to meet the product requirements.
5. At the deployment stage, the product manager ensures that the infrastructure supporting the planned features and functionalities of the product is properly set up and operational.
My advice to product managers who are interested in entering the AI era as AI product managers: arm yourself with product management skill, get technical, or your product will fail right in front of you. As an AI PM, you are the heart and soul of your product's success, and you need to be knowledgeable enough to guide your team through these four stages.
&D
