AI-curated insights from 1000+ daily updates, delivered as an audio briefing of new capabilities, real-world cases, and product tools that matter.
Stay ahead with AI-curated insights from 1000+ daily and weekly updates, delivered as a 7-minute briefing of new capabilities, real-world cases, and product tools that matter.
Join The GenAI PMDive deeper into the topics covered in today's brief with these AI PM insights.
As of November 2025, Pomelli—introduced by Josh Woodward from the GoogleLabs team—is designed to help Product Managers automatically generate and manage marketing campaigns for small and medium-sized businesses. This tool simplifies the process of setting up and tracking marketing efforts by utilizing AI to handle campaign creation and adjustments. Here’s how PMs can take advantage of Pomelli: 1. Configure Campaign Parameters: Start by inputting your SMB’s target market demographics, campaign goals, and budget constraints into the Pomelli platform. This allows the tool to tailor campaigns to specific business needs. 2. Leverage Auto-Generation Features: Pomelli uses AI to automatically generate ad creatives, select optimal channels, and schedule campaign launches. This reduces manual effort and allows PMs to focus on strategy rather than day-to-day execution. 3. Monitor Real-Time Performance: Use Pomelli’s dashboard to track campaign metrics such as engagement rates and conversion statistics in real time. Early users have reported more agile campaign adjustments based on live data. 4. Iterate Based on Feedback: Based on performance analytics, continually refine campaign parameters. This iterative approach helps improve campaign effectiveness over time. While detailed performance metrics are emerging, these actionable steps offer a clear path for PMs to automate SMB marketing workflows using Pomelli. It represents a practical example of how AI product management tools from GoogleLabs are aligning with SMB needs, reducing manual overhead and enhancing overall marketing effectiveness.
As of November 2025, the File Search Tool integrated into the Gemini API is a robust solution for AI Product Managers looking to streamline document management and retrieval workflows. This tool, announced by Philipp Schmid, uses a RAG-based approach that supports the creation of file stores, concurrent uploads, and advanced retrieval techniques. Here’s how PMs can implement this tool: 1. Integrate the API: Begin by incorporating the Gemini API into your existing document management system. Review the API documentation to ensure you correctly establish file stores and configure access controls. 2. Set Up Concurrent Uploads: Configure the system to allow simultaneous uploads, ensuring that large volumes of documents are indexed efficiently. This can significantly reduce downtime during batch operations. 3. Utilize RAG-Based Retrieval: Leverage the advanced retrieval mechanism to conduct thorough searches across different file types and formats. This feature is particularly useful for tasks such as cross-referencing data and extracting insights from large datasets. 4. Test and Iterate: Conduct early pilot tests with a subset of documents to benchmark performance. As of November 2025, initial reports suggest that early adopters may see improved retrieval times, though detailed case studies are still emerging. By following these actionable steps, PMs can enhance document management systems and reduce the manual workload in data-heavy environments, setting the stage for more efficient AI-driven workflows.
In 2025, Kimi K2 Thinking has emerged as the first open-source model that outpaces some proprietary APIs in agent capabilities, according to insights shared by Clement Delangue. This development provides AI Product Managers with an opportunity to assess a powerful tool that could redefine agent performance benchmarks. Here are specific steps PMs should consider when evaluating Kimi K2 Thinking: 1. Benchmark Testing: Create a set of tasks that replicate your current use cases, ranging from customer support agent interactions to internal workflow automations. Compare Kimi K2’s performance against established proprietary APIs to measure responsiveness, accuracy, and overall efficiency. 2. Evaluate Flexibility and Customization: Given its open-source nature, assess how easily the model can be tailored to your specific needs. Look into documentation and community support that can help customize prompts and agent behaviors. 3. Analyze Integration Efforts: Determine the ease of integrating Kimi K2 Thinking into your existing platforms. This includes reviewing available APIs, SDKs, and potential compatibility with current systems. 4. Monitor Early Adoption Metrics: While concrete case studies are still emerging as of November 2025, gather data from pilot programs and early users in the community. Metrics such as task completion rate improvements and reductions in processing time will be key indicators of performance. By following these steps, PMs can make informed decisions about deploying Kimi K2 Thinking as part of their agent strategy. This evaluation process not only helps ensure that the tool meets performance criteria but also aids in planning for potential scalability and customization needs.