Unlocking the Future: The Game-Changing Differences Between Product Managers and AI Wizards
The origin story of each product manager is unique. Each product manager has followed a different path to be a product manager; they come from all walks of life. They were business analysts, software engineers, or marketing managers in their previous life. With the explosive popularity of OpenAI’s Large Language Models, the role of a product manager is also evolving, especially if they are onboarding the ubiquitous AI technologies.
Product Managers and AI Product Managers share the same foundational roles and responsibilities. Both roles require a blend of interdisciplinary skills. Product managers may benefit from a basic understanding of AI, and AI Product Managers find value in broader product management skills beyond the AI domain. Both roles require extensive communication across diverse teams, where AI product managers may also communicate complex technical aspects to non-technical stakeholders. AI product managers are also responsible for understanding AI models and data science. There are six fundamental differences.
1. Data & Analytics
Product managers must have a good grasp of the analytics and reporting on the KPIs of their products to make decisions on optimizations, feature prioritization, and enhancements. AI product managers must also understand data modeling, model training, and model evaluation, in addition to standard data and analytics required to maintain a product.
2. Technical understanding
The Product manager and AI product manager must bridge the gap between technical teams (developers) and non-technical stakeholders. While a product manager needs to understand the technical aspect of the product, having deep technical expertise can deepen the understanding of the business, customer, and technical aspects of their products. AI Product managers must understand deep learning frameworks, AI technologies, algorithms, and data science concepts.
3. Product development lifecycle
A product manager focuses on the entire lifecycle of a product, from discovery, ideation, and elaboration to development, testing, deployment, and maintenance.
In addition, an AI Product manager must also manage the lifecycle of AI models, which includes data collection, model training and tuning, deployment, and continuous monitoring for performance and improvements.
4. Risks and ethical considerations
Product managers need to address standard risks related to the product, including legal, compliance, market fit, and user experience.
AI Product managers must also consider the ethical implications of AI, including biases in data, hallucinations, transparency in algorithmic decision-making, misinformation, and impact on users and society at large.
5. Cross-functional collaboration
Product managers work closely with engineering, business, and marketing stakeholders.
AI Product managers also work closely with machine learning engineers, data scientists, AI researchers, and ethicists.
6. Continuous Learning
Product managers must stay up-to-date with market trends, competition, and the latest technologies to ensure the product roadmap stays efficient and competitive.
AI Product managers must also learn about advancements in AI, data science, and AI modeling technologies to make informed cost-effective decisions for their products.
Conclusion
In the face of rapid evolution, both roles require adaptability and the ability to embrace change. Whether delving into the intricacies of AI or steering a traditional product, a holistic approach is essential. Recognizing and understanding these nuanced differences ensures that both Product Managers and AI Product Managers navigate the ever-changing landscape competently and efficiently.
