AI and Data PM

AI PM Vs Data PM Vs Generalist PM

In this post, we will discuss how the skills required from a Generalist PM differ from those of a Data PM and an AI PM. In our Product Management series, we covered the skills needed by a Generalist PM. These skills are also expected from AI and Data PMs. AI and Data PMs are still required to perform customer discovery to identify for whom they are building the products. What are the problems and pain points we need to solve for the target customer segments and user personas? We still need to set a product vision, create product roadmaps, develop product strategy and design, and detail everything in a PRD. We are still required to work with multiple stakeholders and be involved in multiple cross-functional teams to launch the product and then iterate on it. However, there are some additional skills needed for a Data PM and even more for an AI PM. Let's jump into listing the differences: 

Additional Skills for a Data Product Manager:

1. Data Management and technical skills

• Data Governance: Understanding of data governance principles, including data quality, data security, and compliance.

• Data Genealogy: Maintaining documentation to track the origins, movements, and transformations of data.

• Data Architecture: Knowledge of data architecture, databases, data warehousing, and ETL processes.

• Big Data Technologies: Familiarity with big data technologies such as Hadoop, Spark, and cloud-based data platforms (e.g., AWS, Google Cloud, Azure).

• Data Pipelines: Identifying and managing data creation and collection pipelines for ingestion and curation.

2. Data Product Management Skills

• Product Roadmapping: Ability to create and manage a product roadmap that integrates data initiatives.

• PRD Development: Writing Product Requirement Documents (PRDs) that include data sources, data points, data transformations, and business logic.

Delta of skills for an AI Product Manager: 

1. AI/ML Knowledge

   • Understanding AI Technologies: Knowledge of machine learning, natural language processing, data science, AI model training, data preprocessing, and evaluation metrics.

   • AI Limitations and Ethics: Awareness of AI limitations, ethical considerations, bias, and fairness in AI models.

2. Translation Across Disciplines

   • Bridging Technical and Non-Technical Teams: Ability to explain AI concepts and implications to non-technical stakeholders.

   • Articulating AI Capabilities: Setting expectations for AI performance and capabilities.

3. AI Product Design and Business Case Preparation

   • AI/ML Problem Framing: Exploring if AI/ML solution is the best solution for the problem.

   • Implementing AI/ML Solution: Implementing an AI/ML model and metrics to be measured

   • Continuous Model Refinement: Iterating AI models with fresh data to improve accuracy and performance over time.

4. Collaboration with More Stakeholders

   • Working with ML Engineers: Collaborating with machine learning engineers to develop, train, test, and deploy AI models.

   • Engaging with Legal and Compliance Teams: Interacting with legal, privacy, and compliance teams to mitigate risks and stay updated on AI regulations and data privacy issues.