Product Management for AI/ML Products
The role / title of a Product Manager came into existence relatively late— I’d say ~25 years ago. AI Product Management is focused on using AI, Deep Learning and/or Machine Learning to enhance, improve, create and shape products. AI Product Management is certainly a very recent role.
If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). You already know the game and how it is played: you’re the coordinator who ties everything together, from the developers and designers to the executives. You’re responsible for the design, the product-market fit, and ultimately for getting the product out the door. But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. A lot to learn, but worthwhile to access the unique and special value AI can create in the product space.
While AI product management shares the same responsibilities and skills as traditional product management, the AI PM role expands to include the following aspects:
- Additional input: As an AI PM, you work with data scientists and data engineers, in addition to the traditional product team and stakeholders. AI PMs need to be able to deliver AI-powered specifications to data science teams effectively.
- Problem mapping: Yes, this is also a key component of traditional product management. It’s essential, however, for AI PMs to remember to remain customer-centric in the face of AI. While the potential of AI is fascinating, an AI PM’s goal is still very much about solving a customer’s problem. AI is not a cure-all; AI PMs must keep customer focus front and center.
- Data literacy: Knowing the right questions to ask about customer data is an entirely new PM skillset. Having hands-on experience or working knowledge of data and models as an AI PM is considered to be an essential skill by AI experts.
- Communication: Like problem mapping, this is not a skill unique to an AI PM. To be successful, all product managers must be excellent communicators. Effective communication continues to be a key component of AI-focused product management. Some AI experts describe AI PMs as translators because they must bridge the language of data science with the language of product development.
- Acceptance criteria: An AI PM’s new metric to pay attention to is AI accuracy (vs. open bugs, for example).
- Explainability: Trust plays a crucial role in customer satisfaction and retention and overall business success. Explainable AI gives customers insight into AI decision-making. AI PMs are responsible for explainability and ensuring trust in a product.
- Ethics: For AI PMs, the ethical application of AI must be an ongoing consideration and deep concern. AI and ML are powerful tools; it’s up to an AI PM to remember that with great power comes great responsibility.
- Evangelism: PMs must evangelize more than just the product. They must also understand and promote the merits of AI adoption to help their companies stay competitive in a brave, new AI world.
Closing Thoughts:
Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Unfortunately, many ML-oriented ideas bubbled up from the business side aren’t feasible. As a result, product leaders managing ML projects need to have good intuition for what is and isn’t possible to have hard pragmatic conversations about prioritizations. Good product managers “know the difference between easy, hard, and impossible problems”.
References:
- https://www.oreilly.com/radar/practical-skills-for-the-ai-product-manager/
- https://blog.dominodatalab.com/product-management-for-ai/
- https://medium.com/swlh/essential-guide-to-ai-product-management-9483688d38d0
- https://www.infoq.com/presentations/product-management-ai/
- https://www.productplan.com/ai-product-management/