Great research doesn't always lead to great decisions. Most insight leaders already know this. They’ve seen how research, despite being thorough and relevant, can fail to make an impact when it matters most.

The problem is usually not the quality of the findings but the way the findings are shared, stored, and remembered.

If your organization lacks a structured way to surface and use customer insights at the moment of decision-making, that research is functionally useless. And unfortunately, this seems to be a common problem. According to various surveys, 80% of companies say data goes unused to some degree, and on average, a full third (33%) of data is never acted on. This leads to a full 40% of key decisions that are taken without reference to customer or market data.

This creates two problems:

So how do we fix it?

First we have to acknowledge that consumer understanding expires. The motivations, attitudes, and expectations that a study captured eight months ago may no longer reflect how your customers think today, especially in organizations moving quickly with new products, campaigns, or market entries.

In extreme cases, research may become irrelevant before it ever gets used. A 2020 study by Stravito found that two-thirds of UK businesses commissioned market research that year which was never acted on, rendered outdated by how fast conditions changed during the pandemic. Covid was an unusual accelerant, but it made visible something that was already true in calmer times: insight has a shelf life, and most organizations are working from a picture of the customer that is already aging.

Especially for qualitative research, the problem comes down to the time it takes to complete a project. The traditional insight cycle looks something like this:

The old way Commission a study → wait weeks for results → prepare a report → file it in a shared drive → hope the right people find it in time

By the time insight completes that journey, the decision it was meant to inform has often already been made.

The new way Research runs continuously → findings surface in real time → insight is searchable, shareable, and formatted for the moment it's needed → the customer is present in decisions as they happen

This shift from episodic research to continuous understanding is one of the most significant changes AI makes possible for insight teams. When research can be conducted at speed and at scale, it no longer has to be rationed around a handful of high-stakes moments.

"The medium is the message." Marshall McLuhan said that in 1964 to describe how a communication method (television, radio, the printed word) shapes the content it carries. His point was that it's not just what you say, it's how it reaches people that determines its effect.

When it comes to business insights, the medium has long been some kind of report. Customer data, both quantitative and qualitative, gets condensed into a presentation or a summary of findings. But the format insight gets delivered in shapes whether it gets used, and this is an underappreciated quality marker for customer insights.

A heavy PowerPoint deck or lengthy report is harder to use than it looks. It’s difficult to scan and search. It’s built around a specific question or hypothesis. And by design it only presents a sliver of the total findings. The full depth of the original research is still available, but mostly as original interview transcripts or videos that are even harder to review quickly for new insights.

Somewhat ironically, the curated value of a good report is also its greatest limitation. Wouldn’t it be great to have access to both the expert analysis and key findings as well as the original source material?

GetWhy was built around a belief that most insight platforms stop too early. Generating good data is necessary, but it isn't sufficient.

GetWhy Stories is designed to solve that by turning research into on-demand narratives. Teams can search through original source material to answer questions as their needs change. Now you can watch a video to get your customers’ own words, in context.

It’s also possible to package these insights in new ways. You can create a podcast that distill the key lessons from a customer segment or search across multiple studies, filtered by market or segment, and find the insights you need with citations back to the original source.

The GetWhy Research Agent makes it possible to chat directly with your data, ask follow-up questions, and turn findings into new stories, all without commissioning a new study. Insight that was produced six months ago becomes useful again when someone asks the right question.

This is what it means to make the customer present.

Remember the two problems we started with: research investment that goes to waste, and decisions that get made without it.

These are both problems with technology and research quality, but also something more. They're workflow and culture problems. They're what happens when organizations treat research as a deliverable instead of a resource.

Solving them takes more than a faster tool or a better methodology. It takes a different approach to the whole system: how insight is created, how it moves, and how it is referenced when decisions are actually being made.

If you're going to invest in understanding your customers (and we obviously think you should), then you also need to invest in the infrastructure that makes that understanding count.

Better research is only half the job. The other half is making sure it doesn't go to waste.