In a recent LinkedIn post, Teresa Torres discusses the persistent challenge product teams face in conducting thorough synthesis of customer interview data, and proposes a novel solution involving technology to bridge the gap.
Torres begins by expressing a desire for product teams to consistently engage in careful synthesis following customer interviews, but acknowledges the practical difficulties that often lead to teams abandoning this crucial step. She observes that without dedicated effort, teams tend to rely on the most easily recalled fragments of information rather than a comprehensive understanding.
“We’re not competing with making good synthesis faster. We’re competing with making shallow synthesis better.”
This candid observation, as articulated by Torres, highlights a fundamental reality in many product development environments: the inherent difficulty and time commitment required for deep synthesis often outweigh the perceived immediate benefits, leading to superficial outcomes. The post suggests that this common pitfall is not due to a lack of desire for good synthesis, but rather the practical hurdles in achieving it consistently.
Rethinking Synthesis: A Technology-Forward Approach
Torres’s realization that rigorous manual synthesis is frequently bypassed has led her to advocate for a shift in strategy. Instead of solely focusing on improving the speed of manual processes, she proposes that the focus should be on enhancing the quality of the synthesis that is actually being done, even if it’s initially shallow. This perspective is a significant departure from traditional advice, which often emphasizes diligence and time allocation for manual analysis.
The core of Torres’s argument is that if product teams are demonstrably struggling to perform in-depth manual synthesis, then the responsibility should be shared with, or even offloaded to, technological tools. This suggests a future where software plays a more active role in processing and structuring qualitative data, thereby augmenting the capabilities of product teams.
The Role of Software in Augmenting Synthesis
According to Torres, the implication of this challenge is clear: the software itself should be designed to facilitate better synthesis. This could involve AI-powered tools that can help identify themes, patterns, and key insights from interview transcripts or recordings, presenting them in a digestible format for the product team. By offloading some of the heavy lifting of data processing and initial analysis, software can empower teams to focus on higher-level strategic thinking and decision-making, rather than getting bogged down in the mechanics of data organization.
Torres points out that this approach doesn’t aim to replace human analysis entirely but rather to make it more effective and efficient. The goal is to democratize good synthesis, making it accessible even when time and resources are constrained. As she implies, the true innovation lies not just in collecting data, but in reliably extracting meaningful value from it.
Moving Beyond Memory-Based Insights
In essence, Teresa Torres is challenging the status quo of product development practices. Her insights, shared via LinkedIn, underscore the need for practical solutions that acknowledge the realities of team workloads and cognitive load. By suggesting that software can be a partner in the synthesis process, Torres offers a forward-thinking perspective on how to ensure that valuable customer feedback translates into actionable product improvements.
The full discussion, including links to a podcast episode, can be found via the resources shared by Torres, indicating a broader exploration of this topic beyond her initial LinkedIn post.
📝 About This Content
This article is based on insights shared by Teresa Torres on LinkedIn.
📅 Originally posted on October 2, 2026 | View original post on LinkedIn →