AI时代，你的PMF会“一夜过时”吗？

Group 1 - The core idea is that in the AI era, achieving Product-Market Fit (PMF) is no longer a static milestone but a dynamic process that requires continuous adaptation and expansion to meet rapidly changing user expectations [1][2][4] - AI tools are making it easier for users to integrate solutions into their workflows, leading to a steeper PMF threshold that companies must navigate to avoid losing market relevance [4][7] - User expectations are shifting from seeking tools to wanting fully automated solutions, which increases the risk of existing products becoming obsolete if they do not evolve accordingly [9][10] Group 2 - Companies must closely monitor changes in user expectations and adapt their research processes to be more agile and responsive, leveraging AI to gather and analyze user feedback in real-time [7][8] - Understanding how AI is transforming customer expectations across the tech product landscape is crucial for companies to remain competitive [9][10] - Companies should assess their PMF loss risk by evaluating factors such as product usage channels, frequency of use, and the integration of their products into users' core workflows [10][11][12][13][14][15] Group 3 - Companies need to adjust their product strategy based on the assessed risk of PMF loss, reallocating resources between different types of product work, such as PMF expansion and feature work [16][18] - There are five types of product work that companies should focus on: PMF Work, Feature Work, Growth Work, Scaling Work, and PMF Expansion, each serving a distinct purpose in maintaining and enhancing PMF [18]