The evidence-to-decision gap in product teams

Product teams accumulate a lot of information.

There are interviews, support tickets, research studies, dashboards, customer requests, competitor moves, usage data, internal documents and stakeholder opinions.

Yet when a decision needs to be made, one question often remains surprisingly difficult to answer:

What actually supports this direction?

Turning information into usable evidence requires interpretation, connection and judgment. This is where many product decisions lose traceability.

Collecting feedback does not guarantee it reaches the decision

Atlassian’s State of Product 2027 shows the distance between collection and use.

64% of teams say the feedback they collect is not consistently used in decisions. Only 4% say every roadmap decision can be traced back to customer feedback or evidence.

That suggests a large part of the problem appears after collection.

A customer request is a signal that needs context.

A drop in activation shows that something happened, while leaving the explanation open.

An interview can weaken an important hypothesis without representing the behavior of the entire market.

Each source contributes in a different way. The work is understanding what it supports, what it challenges and which questions remain open.

That is why assumptions, sources and evidence need to stay distinct.

AI expanded research capacity and increased the amount that needs interpretation

AI is already part of the research workflow for many teams.

A Lyssna survey of 428 Product, Design, Engineering and Research professionals found that 94% use AI somewhere in the path from research to decision.

The sample was self-selected and particularly engaged with AI, so these figures are useful for understanding an emerging behavior without treating them as a benchmark for every product team.

The reported bottlenecks are still relevant.

After research is completed, 32% identified manual synthesis as the main slowdown. Another 26% pointed to pulling data together across multiple tools.

81% said they would find value in an AI-generated, decision-ready report based on their research data.

Research can move faster while the need to connect sources, preserve context and assess confidence remains.

Where the flow tends to break

The evidence-to-decision gap appears in several ways:

• a conclusion reaches the roadmap without a clear record of the evidence behind it;
• qualitative research stays disconnected from quantitative data;
• a summary removes provenance from the original information;
• contradictory signals get simplified too early;
• a hypothesis starts to look like a fact after being repeated across meetings;
• new information appears without a clear way to tell whether it changes the previous reading.

When this happens, more dashboards, documents or summaries can increase the amount of available information without improving the connection between signals.

A decision needs a reasoning trail

Every decision carries some path of interpretation, even when that path is not documented.

One possible structure is:

Problem or signal -> Hypotheses -> Evidence -> Contradictions -> Gaps -> Current reading -> Decision

This structure helps a team discuss more useful questions:

What does this evidence change in our current reading?

What points to another explanation?

What do we still not know?

What new information could change our direction?

This also helps answer another difficult question: how much evidence is enough for a product decision?.

More research can help, depending on what remains unresolved

Some decisions still have important gaps and need further investigation.

In other cases, the material already available may be enough to move forward once the signals are organized and challenged explicitly.

The amount of research needed depends on the uncertainty that still matters to that decision, the risk involved and the cost of being wrong.

The number of interviews, links or documents says little on its own.

Evidence needs to stay connected to the decision

A well-supported decision makes it possible to understand what the team believes at that point in time, which signals support that reading, what challenges it and which gaps still matter.

That context remains useful after the decision enters the roadmap.

New data can appear. A hypothesis can weaken. A segment’s behavior can change. A constraint can become more or less important.

When the reasoning trail remains legible, the team can compare the current reading with what it believed before and understand why a decision deserves to be revisited.

The quality of that memory matters because product decisions rarely happen at a single moment. They evolve with the evidence available to the team.