David Cronshaw

AI/ML product management – combines both human and machine intelligence to create better products and experiences.

AI/ML product management is about finding the right balance between automation and human intervention, depending on the use case, the customer needs, and the ethical implications of the product.

• AI/ML products require a multidisciplinary approach that involves collaboration with data scientists, engineers, designers, researchers, domain experts, and customers throughout the product lifecycle.
• AI/ML products should be designed with transparency, explainability, fairness, accountability, and privacy in mind, as well as with clear metrics and feedback loops to measure and improve the product performance and user satisfaction.
• AI/ML products should also be adaptable and resilient to changing data, environments, and user behaviors, as well as to potential failures or errors that may occur in the system.
• AI/ML products should leverage the strengths of both human and machine intelligence, such as creativity, intuition, empathy, and domain knowledge for humans, and speed, scalability, accuracy, and consistency for machines.
• AI/ML products should aim to augment rather than replace human capabilities, and empower users to achieve their goals more efficiently and effectively.
• AI/ML products should be aligned with the core value proposition and business objectives of the product, and deliver tangible benefits to the customers and stakeholders.
• AI/ML products should be validated and tested with real users and data, as well as with synthetic or simulated data to cover edge cases and scenarios that may not be captured by real data.
• AI/ML products should be iterated and refined based on user feedback, data analysis, experimentation, and continuous learning.
• AI/ML products should be communicated and marketed clearly and honestly to the customers and stakeholders, highlighting the benefits and limitations of the product, as well as the expectations and responsibilities of the users.