Navigating the Differences: AI Product Managers vs. Traditional Product Managers
The Core Similarities
Whether you’re managing AI-powered products or traditional ones, the foundation of the role remains largely the same. Both AI PMs and traditional PMs focus on solving real customer problems, orchestrating across various stakeholders, and guiding the product through its entire lifecycle, from ideation to launch and beyond.
Key Similarities Include:
- Customer-Centric Focus: Both roles require a deep understanding of customer needs to create products that resonate.
- Stakeholder Management: Effective communication and collaboration with different departments are essential.
- Product Lifecycle Management: Overseeing the product’s development from concept to market launch is a shared responsibility.
- Defining Minimum Lovable Product (MLP): Both AI and traditional PMs need to create a product that users will love, iterating on it based on feedback.
Despite the overlaps, AI Product Managers require a distinct skill set and face unique challenges that set them apart from their traditional counterparts.
1. Deep AI/ML Knowledge AI PMs need a solid understanding of machine learning, natural language processing, data science, and other AI technologies. This knowledge helps them to communicate effectively with technical teams and make informed decisions about AI capabilities and limitations.
2. Bridging Technical and Non-Technical Disciplines AI PMs act as a bridge between highly technical teams (like data scientists and ML engineers) and non-technical stakeholders. They must translate complex AI concepts into understandable language and manage expectations regarding AI’s performance and potential.
3. Data Preparation and Management Accessing and cleaning data can be a lengthy process. AI PMs must collaborate closely with data scientists and engineers to ensure the availability of high-quality, unbiased data. This is crucial for training accurate AI models.
4. Managing More Stakeholders AI PMs work with a broader range of stakeholders, including legal, privacy, and compliance teams, to address the unique risks and regulations associated with AI, such as GDPR compliance and ethical considerations.
5. Continuous Iteration AI products are never truly “finished.” AI PMs must oversee continuous model iterations, requiring fresh data and ongoing adjustments to improve model accuracy and performance.
6. Different Metrics Traditional PMs focus on user engagement, retention, and revenue metrics. In contrast, AI PMs are concerned with model accuracy, precision, recall, and the risks of confident errors. Understanding these unique metrics is critical to evaluating an AI product's success.
Having led AI projects in both unicorn startups like Credit Karma and large tech companies like Meta and Amazon, I’ve observed notable differences in how AI product management is approached in these environments.
Pros at Startups:
- Faster Movement: With fewer stakeholders and less bureaucratic oversight, startups can iterate and launch AI products more quickly.
- Flexibility: Startups often allow for more experimental approaches and rapid pivoting when needed.
Cons at Startups:
- Limited Resources: Startups may lack extensive data science talent, which can slow down the development process. Engineers might have to take on tasks like data labeling, which would typically fall to specialized teams in larger companies.
Conclusion
While all Product Managers will likely use AI tools in the future, becoming an AI Product Manager requires a unique set of skills and a different approach to product development. The role is more challenging but also offers the opportunity to work at the cutting edge of technology, driving innovation in ways traditional product management cannot.
