The AI industry is moving beyond the simple strategy of increasing model size and data. As we hit limits in performance gains, research is shifting toward more innovative approaches, such as test-time compute and synthetic data generation. This transition will change product development dynamics, emphasizing efficiency and thoughtful application over just larger models.

This article critiques the role of UX strategists who often delay decisions with vague responses like "it depends." It highlights how this approach leads to wasted time and money, and contrasts it with more effective, action-oriented strategies.

Impact is a crucial yet often neglected aspect of product development, and the Total Impact Matrix serves as a tool to better understand and evaluate it. By assessing the value created for both customers and the business, teams can identify various types of impact, from incremental improvements to breakthrough innovations, while also recognizing the risks of pursuing low or negative-impact ideas. Engaging in thorough research and product discovery can enhance success rates and lead to more informed decision-making in product strategy.

A research toolkit is essential during the discovery phase of product development, helping teams gather insights and validate ideas. The article outlines various methods and tools available for effective research, emphasizing the importance of structured approaches to inform decision-making and design processes.