AI in Product Management Strategies

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

AI products don't work without frameworks. Data teams need to know how broad or narrow the use cases to build them. Teams often have clear strategic goals but fail to adequately define the tactical scope of the problem - which essential for developing good AI products. This leads to a cycle of developing, launching, and eventually abandoning AI product development. The data team is then often perceived as a cost sink. Define how narrow your solution needs to be. Narrowly focused AI products optimize engineering resources and cater to specific segments. This helps to focus the data team on a limited set of features and use cases. Define how broad it your solution needs to be. Broad AI products aim for wider reach with diverse applications. You’ll need to know this if you are working between multiple teams and business units. AI PMs and data teams must make tough choices about how they approach the scope of data products. Data teams and AI PMs that define these frameworks will be strong performers in the next 12 months. The reason why most ML/AI products fail isn't because of bad engineering. It's often because range of use cases for users wasn't explored. We need to approach products with a defined and solid use case framework. It's no longer enough to deploy a model and hope it's a product. #datalife360 #datastrategy #ai #productmanagement #datascience

🚀 Exciting News: I've just published an article that introduces the GSAIF framework, a new approach to prioritizing GenAI products! Drawing from my recent experience in AI-driven innovation, I've developed this structured method to overcome the common challenges faced with traditional prioritization frameworks. 🤖 If you've ever encountered issues with effort estimations, impact measurement, or scalability in AI initiatives, you'll find GSAIF particularly relevant. It's designed to ensure that your GenAI projects not only align with strategic goals but also deliver ethical and regulatory-compliant value. 🔍 Dive into the article to discover how GSAIF can transform your AI product management. I'm eager to hear your thoughts and experiences, so please feel free to share your feedback or ask questions in the comments section. #AI #Innovation #ProductManagement #GenAI #GSAIF #StrategicAlignment #EthicalAI