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From Fragmented Insights to Continuous Intelligence: As the first AI Operating Layer for insight teams, InsightGig turns disconnected data, static reports, and siloed tools into continuous, integrated intelligence. This is why we built it. This is the first article in our series on the Operating Layer. Read the others here: Why do Insight Teams need […]
This is the second article in our series on the Operating Layer. Read the others here: InsightGig: The Operating Layer for Insights Operating Layer vs. Platform vs. Tool Today, for the first time, we’re genuinely positioned at the threshold of a transformative shift in how consumer insights can inform business decisions. Historically, despite organizations’ best […]
This is the third article in our series on the Operating Layer. Read the others here: InsightGig: The Operating Layer for Insights Why do Insight Teams need an Operating Layer Now? What’s the Real Difference? To maximize the value from your technology investments, it’s critical to understand what exactly InsightGig, the Operating Layer for AI-led […]
AI is already being used across research workflows often in small, scattered ways. But real scale will come when your team is using AI with clarity, consistency, and quality and whether your systems can support that use at scale. These six markers will help you assess if your insight practice is truly ready to work […]
Most teams adding AI to qualitative work worry specifically about three critical areas of failure: undefined roles, false confidence, untraceable claims. We don’t see them as purely model problems; they’re in fact operating-model problems. Clearly defining the “Human in the loop” within the Qualitative process when adding AI fixes this by making the division of […]
Key Takeaways AI transformation is not just about tools — it’s about systems The “messy middle” of actually integrating AI is where most teams struggle AI adoption feels like regression before progress The true value comes from revisiting and clarifying work Success comes to teams who expect the pain — and work through it We […]
Key Takeaways GPT is useful for quick summaries but lacks precision, scalability, and research-specific structuring. GPT struggles with context, data pre-processing, accuracy, and large-scale analysis. Security and compliance risks make GPT unsuitable for handling sensitive research data. AI for research should be embedded into workflows, supporting structured insights and multi-step analysis. Can’t I Just Use […]
As researchers building tech, it’s impossible for us not to see the parallels between Insight Debt and Tech Debt. In software, Tech Debt happens when quick fixes and short-term decisions pile up, making future development slower and more complex. Insight Debt is the research equivalent. The thing about Insight Debt is that it doesn’t announce […]
Every research team knows they should be storing, organizing, and reusing insights better. Yet, despite best intentions, Insight Debt keeps growing: valuable insights disappear, research gets repeated, and decision-makers grow skeptical of past findings. The issue isn’t just about storing data; it’s about how research is designed, conducted, and applied. Insight Debt comes in many […]
There’s a certain romance to the grind of market research—those long nights spent analyzing messy datasets, cross-referencing insights, and uncovering hidden patterns. The best researchers thrive in this challenge, developing skills like skepticism, storytelling, and strategic thinking. But today, many insight teams are overwhelmed by logistics, admin work, and inefficient workflows, leaving little time for […]
