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Demand is growing for cost-effective, current, and spatially detailed data on forest attributes— including species composition, growing stock, disturbances, and mortality—driven by management requirements, the multifunctional roles of forests, and their sensitivity to climate change. Advances in high-resolution remote sensing, deep learning, and rapid data processing now enable reliable, reproducible, wall-to-wall forest products that complement traditional inventories with regularly updated, spatially explicit information essential for sustainable, multifunctional, climate-adapted forest management.Despite four decades of developing remote sensing–based forest products, their adoption by forestry practitioners remains slow and often incorrect or limited (e.g., Barrett et al., 2016; Waser and Ginzler, 2023; Fassnacht et al., 2024; Waser et al., 2025). In some cases, products fail to meet user expectations for accuracy or update frequency, revealing a mismatch between development and practical needs. This gap stems largely from poor knowledge exchange between researchers and practitioners, leading to differing expectations and misunderstandings of product content. Misalignment arises from differing expectations, limited understanding of practical needs, and technical challenges. While datasets like canopy height models are widely and effectively used, more complex products such as tree species or disturbance maps remain challenging and prone to misinterpretation. Adoption is further hindered by technical terminology, the need to integrate products into existing workflows, and the time, cost, and complexity of adapting decision-making processes.In this study we show how to bridge the gap between remote sensing research and stakeholders, including forest industries, service providers, practitioners, and forest owners. We identify core challenges limiting the adoption, accuracy, and utility of forest products and propose a collaborative framework emphasizing cooperation between researchers and practitioners. We present examples of active user involvement to further improve the quality of remote sensing–based forest products by incorporating additional training data, adjusting model settings, and retraining iteratively based on new feedback. Active user involvement benefits both sides: it helps develop user-friendly products and provides supplementary reference data essential for machine learning, thereby advancing remote sensing research.We tackle the key challenges and opportunities for integrating remote sensing research into forestry practice and propose strategies to improve utilization and acceptance of these products. We focus on five critical components:Enhancing collaboration between researchers and forestry stakeholders to ensure product development matches user requirements and fosters technological progress.Engaging applied research initiatives, engineering firms, and start-ups to translate discoveries into practical products.Tailoring methods and products to practical, real-world applications, while maintaining relevance in informational content, accuracy, spatial resolution, and alignment with existing datasets.Integrating user feedback through quality checks, validation, and iterative improvements.Promoting clear communication and documentation, including intended use, interpretation guidance, and transparency regarding accuracy and uncertainty.In summary, we show that addressing these issues requires active engagement of stakeholders in product development, iterative quality assessments, and alignment of methods with real-world use cases.